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 .
Notice to Applicant
This communication is in response to the amendment filed 05/02/2026. Claims 21, 34, 38 have been amended. Claims 21-40 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 21-40 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “at the computing system and with a trained machine learning model, automatically: determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on a set of features of impression sections in a historical report previously generated by the radiologist, wherein the radiologist style comprises a preference and a clinical reasoning tendency: determining, with the trained machine learning model, a context based on the set of finding words; combining the context with the radiologist style; generating, with the trained machine learning model, the impression section, wherein the generated impression section is configured to mimic the radiologist style, wherein training the trained machine learning model comprises modifying false positives in a training dataset to minimize machine learning hallucinations.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 21, 34, claims 21, 34 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 22-33, 35-40 incorporate the allowable features of originally numbered independent claims 21, 34 through dependency, respectively.
However, the claims are still rejected under 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,342,055. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are directed toward a method for automatically generating an impression section of a radiology report, the method comprising: receiving, at a computing system: a string of text from the findings section of the radiology report, the string of text comprising a set of finding words; at the computing system and with a trained machine learning model, automatically: determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on a set of features of impression sections in a historical report previously generated by the radiologist: determining, with the trained machine learning model, a context based on the set of finding words; combining the context with the radiologist style; generating, with the trained machine learning model, the impression section, wherein the generated impression section is configured to mimic the radiologist style; and automatically inserting the impression section into the radiology report as a proposed impression section.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 21 is drawn to a method which is within the four statutory categories (i.e., method). Claim 34 is drawn to a system which is within the four statutory categories (i.e., machine).
Independent claim 21 recites…receiving…a string of text from a findings section of the radiology report, the string of text comprising a set of finding words; …determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on a set of features of impression sections in a historical report previously generated by the radiologist, wherein the radiologist style comprises a preference and a clinical reasoning tendency: determining…a context based on the set of finding words; combining the context with the radiologist style; generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style, wherein training the trained machine learning model comprises modifying false positives in a training dataset to minimize machine learning hallucinations; and automatically inserting the impression section into the radiology report as a proposed impression section.
Independent claim 34 recites… receiving a string of text from a findings section of the radiology report, the string of text comprising a set of finding words; and…determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist; and generating…the impression section based on the radiologist style, wherein the impression section is configured to mimic the radiologist style, generation comprising: determining…a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating…the impression section based on the concatenated element; and automatically inserting the impression section into the radiology report as a proposed impression section.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting a “computing system,” the claim encompasses rules or instructions to help a user (i.e., radiologist) write a report, which is described as human activity in ¶ 0003-0004 of the specification If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Claim 21 recites additional elements (i.e., a computing system; a trained machine learning model). Claim 34 recites additional elements (i.e., a computing system; a trained machine learning model). Looking to the specifications, a computing system is described at a high level of generality (¶ 0026; ¶ 0042-0044), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, a “trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “determining…a context based on the set of finding words” and “generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. 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.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computing system) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, a “trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “determining…a context based on the set of finding words” and “generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 22-33, 35-40 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 22, 28-33, 39 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 23, 38 further recite the additional elements of a “dictation device,” which only invokes the dictation device merely as a tool in its ordinary capacity to perform an existing process (i.e., generating text), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only generally links the claimed invention to a particular technological environment or field of use, which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 24, 36 further recite the additional elements of “wherein the trained machine learning model comprises a decoder.” Claim 25 further recites the additional elements of “wherein the trained machine learning model comprises a multi-transformer model.” Claims 26, 35 further recites the additional elements of “wherein the trained machine learning model comprises a natural language processing (NLP) model.” However, a “decoder,” “multi-transformer model,” and “natural language processing (NLP) model” is only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not further include details about how “determining…a context based on the set of finding words” and “generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 27, 40 further recite the additional elements of a “a user interface, wherein the user interface comprises a navigation device,” which only invokes the user interface and navigation device merely as a tool in its ordinary capacity to perform an existing process (i.e., presenting output and receiving input), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only generally links the claimed invention to a particular technological environment or field of use, which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 37 further recites the additional elements of “a database, wherein the database comprises at least one of: a Picture Archiving and Communication System (PACS), an electronic medical record (EMR) database, an electronic health record (EHR) database, or a Radiology Information System (RIS),” which only invokes the database merely as a tool in its ordinary capacity to perform an existing process (i.e., interface with a computing system), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only generally links the claimed invention to a particular technological environment or field of use, which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 05/02/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/02/2026.
In the remarks, Applicant argues in substance that:
Regarding the 112(b) rejections, the amendments overcome the rejections.
Regarding the 101 rejections,
“"determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist"…cannot be practically performed in the human mind, and therefore does not constitute a mental process… the limitation cannot be construed as a method of organizing human activity because the "activity itself' (2019 PEG, pg. 5) does not fall into: fundamental economic practices or principles, commercial or legal interactions…or managing personal behavior or relationships or interactions between people… this limitation does not recite a mathematical formula, mathematical relationship between variables or numbers, or a mathematical calculation (e.g., the limitation does not recite a mathematical operation or act of calculating using mathematical methods)”; and
“"generating, with the trained machine learning model, the impression section...generation comprising: determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" and "wherein training the trained machine learning model comprises modifying false positives in a training dataset to minimize machine learning hallucinations" integrate the alleged judicial exception into the practical application of (i) improving the performance of a computing system, and (ii) improving machine learning technology by reducing risk of hallucination when generating an impression section. The claimed limitation reflects a specific technical solution to a specific technical problem. The limitation targets a specific cause of ML hallucination: training data containing "a finding included in an impression section and not included in a findings section of a radiology report." By removing or modifying these false positives at the data level, the claim recites how the training data is corrected, not merely that better results follow… This constitutes an improvement to the field of computing performance and a machine learning system's performance by reducing risk of hallucination. The improvement is particularly significant in the medical context, for which the invention(s) claimed can be applied. In medical radiology, ML hallucinations (outputs unsupported by the underlying imaging data) carry heightened consequences for patient safety and regulatory compliance. The claimed architecture directly reduces such inaccuracies, improving both the reproducibility of generated radiology reports and the adoptability of ML systems in a tightly regulated field. This real-world technical effect further confirms that the limitation is directed to a practical application, not an abstract idea standing alone. For these reasons, the Applicant respectfully submits that the claimed limitation mirrors patent-eligible Example 47, Claim 3 by using an output of an allegedly abstract step ("removing... a false positive") to produce a technical result in a specific system (fewer hallucinations in impression generation)”;
“"determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" integrates the alleged judicial exception into the practical application of improving the functioning of automated clinical report generation systems by enabling a trained ML model to operate on two distinct machine-encoded representations ("a context" and a "radiologist style") through a concatenation operation which constrains the generation process… The claimed invention(s) constitutes an improvement to the field of radiology reporting technology by (i) reducing radiologist documentation burden, which is a recognized inefficiency in clinical workflows, and (ii) producing outputs that are personalized to individual radiologist style, not merely generic text generation, which improves utility of the output in clinical settings.”
Regarding the 103 rejections, the cited prior art reference(s) fails to teach the amended claim limitations.
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 112(b) rejections, the amendments overcome the rejections:
It is respectfully submitted that Examiner withdraws the aforementioned 112(b) rejections of Office Action dated 11/04/2025 because the amendments have rendered the rejections moot.
In response to Applicant’s argument that (b) regarding the 101 rejections,
“”determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist"…cannot be practically performed in the human mind, and therefore does not constitute a mental process… the limitation cannot be construed as a method of organizing human activity because the "activity itself' (2019 PEG, pg. 5) does not fall into: fundamental economic practices or principles, commercial or legal interactions…or managing personal behavior or relationships or interactions between people… this limitation does not recite a mathematical formula, mathematical relationship between variables or numbers, or a mathematical calculation (e.g., the limitation does not recite a mathematical operation or act of calculating using mathematical methods)”:
It is respectfully submitted that Applicant argues “”determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist"…cannot be practically performed in the human mind, and therefore does not constitute a mental process… the limitation cannot be construed as a method of organizing human activity because the "activity itself' (2019 PEG, pg. 5) does not fall into: fundamental economic practices or principles, commercial or legal interactions…or managing personal behavior or relationships or interactions between people… this limitation does not recite a mathematical formula, mathematical relationship between variables or numbers, or a mathematical calculation (e.g., the limitation does not recite a mathematical operation or act of calculating using mathematical methods).” However, Applicant fails to specify how “”determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist"…cannot be practically performed in the human mind, and therefore does not constitute a mental process… the limitation cannot be construed as a method of organizing human activity because the "activity itself' (2019 PEG, pg. 5) does not fall into: fundamental economic practices or principles, commercial or legal interactions…or managing personal behavior or relationships or interactions between people… this limitation does not recite a mathematical formula, mathematical relationship between variables or numbers, or a mathematical calculation (e.g., the limitation does not recite a mathematical operation or act of calculating using mathematical methods).” The claim limitations of “determining a radiologist style based on a radiologist identifier of a radiologist, wherein the radiologist style is determined based on features of impression sections in a historical report previously generated by the radiologist” are rules or instructions to help a user (i.e., radiologist) write a report (which is described as human activity in ¶ 0003-0004 of the specification), which covers managing personal behavior or relationships or interactions between people following rules or instructions within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and not the “Mental Processes” or “Mathematical Concepts” grouping, as Applicant now argues.
Thus, the claims recite an abstract idea.
“”generating, with the trained machine learning model, the impression section...generation comprising: determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" and "wherein training the trained machine learning model comprises modifying false positives in a training dataset to minimize machine learning hallucinations" integrate the alleged judicial exception into the practical application of (i) improving the performance of a computing system, and (ii) improving machine learning technology by reducing risk of hallucination when generating an impression section. The claimed limitation reflects a specific technical solution to a specific technical problem. The limitation targets a specific cause of ML hallucination: training data containing "a finding included in an impression section and not included in a findings section of a radiology report." By removing or modifying these false positives at the data level, the claim recites how the training data is corrected, not merely that better results follow… This constitutes an improvement to the field of computing performance and a machine learning system's performance by reducing risk of hallucination. The improvement is particularly significant in the medical context, for which the invention(s) claimed can be applied. In medical radiology, ML hallucinations (outputs unsupported by the underlying imaging data) carry heightened consequences for patient safety and regulatory compliance. The claimed architecture directly reduces such inaccuracies, improving both the reproducibility of generated radiology reports and the adoptability of ML systems in a tightly regulated field. This real-world technical effect further confirms that the limitation is directed to a practical application, not an abstract idea standing alone. For these reasons, the Applicant respectfully submits that the claimed limitation mirrors patent-eligible Example 47, Claim 3 by using an output of an allegedly abstract step ("removing... a false positive") to produce a technical result in a specific system (fewer hallucinations in impression generation)”:
Applicant argues “”generating, with the trained machine learning model, the impression section...generation comprising: determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" and "wherein training the trained machine learning model comprises modifying false positives in a training dataset to minimize machine learning hallucinations" integrate the alleged judicial exception into the practical application of (i) improving the performance of a computing system, and (ii) improving machine learning technology by reducing risk of hallucination when generating an impression section. The claimed limitation reflects a specific technical solution to a specific technical problem. The limitation targets a specific cause of ML hallucination: training data containing "a finding included in an impression section and not included in a findings section of a radiology report." By removing or modifying these false positives at the data level, the claim recites how the training data is corrected, not merely that better results follow… This constitutes an improvement to the field of computing performance and a machine learning system's performance by reducing risk of hallucination.” However, the claim limitations to which Applicant refer as providing the alleged improvements are interpreted as part of the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. For example, “minimize machine learning hallucinations” is achieved by “modifying false positives in a training dataset,” which are rules or instructions to help a user (i.e., radiologist) write a report (which is described as human activity in ¶ 0003-0004 of the specification), which is the abstract idea. Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea. However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement. Also, a “trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “determining…a context based on the set of finding words” and “generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “The improvement is particularly significant in the medical context, for which the invention(s) claimed can be applied. In medical radiology, ML hallucinations (outputs unsupported by the underlying imaging data) carry heightened consequences for patient safety and regulatory compliance. The claimed architecture directly reduces such inaccuracies, improving both the reproducibility of generated radiology reports and the adoptability of ML systems in a tightly regulated field.” However, “patient safety and regulatory compliance” and “the reproducibility of generated radiology reports” addresses administrative problems, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution.
Examiner cannot find any problem caused by the technological environment to which the claims are confined, which per broadest reasonable interpretation of the claim in light of the specification, is a well-known, general purpose computer. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Applicant argues “the claimed limitation mirrors patent-eligible Example 47, Claim 3 by using an output of an allegedly abstract step ("removing... a false positive") to produce a technical result in a specific system (fewer hallucinations in impression generation).” However, Applicant fails to specify how “the claimed limitation mirrors patent-eligible Example 47, Claim 3 by using an output of an allegedly abstract step ("removing... a false positive") to produce a technical result in a specific system (fewer hallucinations in impression generation).” Regardless, the claim limitations of the present invention are different from the claim limitations of Example 47, Claim 3. Even if the claim limitations of the present invention are similar to that of the claims found eligible (and they are not similar), the claimed inventions are fundamentally different in scope and examples should be interpreted based on the asserted fact patterns; as previously stated above, other fact patterns may have different eligibility outcomes, as is the case with the claims of the present invention. Unlike the claims found eligible in Example 47, the claims of the present invention do not recite additional elements that improve the functioning of a computer or technical field. The claim limitations to which Applicant refer (i.e., “removing... a false positive”) are interpreted as rules or instructions to help a user (i.e., radiologist) write a report (which is described as human activity in ¶ 0003-0004 of the specification), which is the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two.
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“”determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" integrates the alleged judicial exception into the practical application of improving the functioning of automated clinical report generation systems by enabling a trained ML model to operate on two distinct machine-encoded representations ("a context" and a "radiologist style") through a concatenation operation which constrains the generation process… The claimed invention(s) constitutes an improvement to the field of radiology reporting technology by (i) reducing radiologist documentation burden, which is a recognized inefficiency in clinical workflows, and (ii) producing outputs that are personalized to individual radiologist style, not merely generic text generation, which improves utility of the output in clinical settings”:
Applicant argues “”determining, with the trained machine learning model, a context based on the set of finding words, concatenating the context element with the radiologist style to produce a concatenated element, and generating, with the trained machine learning model, the impression section based on the concatenated element" integrates the alleged judicial exception into the practical application of improving the functioning of automated clinical report generation systems by enabling a trained ML model to operate on two distinct machine-encoded representations ("a context" and a "radiologist style") through a concatenation operation which constrains the generation process… The claimed invention(s) constitutes an improvement to the field of radiology reporting technology by (i) reducing radiologist documentation burden, which is a recognized inefficiency in clinical workflows, and (ii) producing outputs that are personalized to individual radiologist style, not merely generic text generation, which improves utility of the output in clinical settings.” However, the claim limitations to which Applicant refer as providing the alleged improvements are interpreted as rules or instructions to help a user (i.e., radiologist) write a report (which is described as human activity in ¶ 0003-0004 of the specification), which is the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. Also, a “trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions and only recite the outcome of the abstract idea and does not include details about how “determining…a context based on the set of finding words” and “generating…the impression section, wherein the generated impression section is configured to mimic the radiologist style” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Furthermore, the claims of the present invention do not improve any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution; “reducing radiologist documentation burden" and " producing outputs that are personalized to individual radiologist style” addresses administrative problems, and not a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. Furthermore, the courts have indicated that “Mere automation of manual processes” may not be sufficient to show an improvement in computer functionality. See: MPEP § 2106.05(a)(I).
Examiner cannot find any problem caused by the technological environment to which the claims are confined, which per broadest reasonable interpretation of the claim in light of the specification, is a well-known, general purpose computer. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 21-40, which have been updated to address Applicant’s amendments and remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance in the above Office Action and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
In response to Applicant’s argument that (c) regarding the 103 rejections, the cited prior art reference(s) fails to teach the amended claim limitations:
It is respectfully submitted that Examiner withdraws the aforementioned 103 rejections of Office Action dated 11/04/2025 because the amendments have rendered the rejections moot.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272-6773.The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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