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 10/17/2025. Claims 1, 3, 14, 20-21 have been amended. Claim 11 has been canceled. Claims 1–10, 12-21 are presented for examination.
Terminal Disclaimer
The terminal disclaimer filed on 10/17/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11,915,809, 11,810,654, 11,342,055 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Subject Matter Free of Prior Art
Claim(s) 1–10, 12-21 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: “tuning the machine learning model based on a second subset of the set of historical radiology reports, wherein the second subset of the set of historical radiology reports are associated with a radiologist, wherein tuning the machine learning model comprises learning a style for the radiologist, the style reflecting at least one of: a writing style and a dictation style,” “using the machine learning model, generating an impression section of the radiology report upon decoding the encoding with the decoding architecture, wherein the impression section is configured to mimic the style for the radiologist.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 14, 20, claims 1, 14, 20 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-10, 12-13, 15-19, 21 incorporate the allowable features of originally numbered independent claims 1, 14, 20, through dependency, respectively.
However, the claims are still rejected under 101.
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 1–10, 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to 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, wherein the judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 14 is drawn to a system which is within the four statutory categories (i.e., machine). Claim 20 is drawn to a method which is within the four statutory categories (i.e., method).
Independent claim 14 (which is representative of independent claims 1, 20) recites… train a machine learning model based on a first set of historical radiology reports…; tune the machine learning model based on a second set of historical radiology reports, wherein the second set of historical radiology reports are associated with a radiologist, wherein tuning the machine learning model comprises learning a style for the radiologist, the style reflecting at least one of: a writing style and a dictation style; use the encoding architecture of the tuned machine learning model to encode content of a radiology report...; retrain the machine learning model based on an edit made to the impression section of the radiology report, to determine a retrained machine learning model…; and retrain the machine learning model whenever the style for the radiologist is updated.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. That is, other than reciting “computing system” (claim 14), the claim recite training and retraining a machine learning model, learning a writing style, and encoding content of a report. For example, with regards to training and retraining a machine learning model, the specification mentions: “The set of models can be trained with any or all of: supervised learning, semi-supervised learning, unsupervised learning, and/or any other suitable training processes. Training the models can additionally or alternatively include fine tuning one or more models (e.g., pretrained models) with radiology report data” (¶ 0057). With regards to learning a writing style for the radiologist, the specification mentions: “The radiologist style is preferably in the form of a mapping (e.g., matrix, vector, auxiliary field of another matrix such as a set of word embeddings, etc.) including a set of weights…The radiologist style is preferably determined through deep learning, such as through any or all of: a set of trained models, a set of algorithms (e.g., machine learning algorithms), a set of neural networks, and/or any other suitable deep learning infrastructure” (¶ 0074). With regards to encoding content of a radiology report, the specification mentions: “The context is preferably determined with a set of models 110 as described above, further preferably with a model including one or more attention processes (e.g., self-attention process, during an encoding process, with a self-attention layer in an encoder, with a self-attention layer in a decoder, during an encoder-decoder attention process, etc.), such as, but not limited to, any or all of the attention processes described above. One or more of the attention processes preferably assesses (e.g., quantifies, calculates, etc.) the relationship of each of a set of finding inputs, such as each word of the text of the radiology reports findings section, to each of the other finding inputs” (¶ 0092-0093); “determining a context of each of the set of finding inputs includes, at self-attention layers of the encoders, calculating a set of context values for each finding input, wherein set of context values quantifies how much the finding input depends on each of the surrounding finding inputs” (¶ 00102). In light of the disclosure, the claims encompass the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 14 further recites…generate an impression section of the radiology report upon decoding encoded content of a radiology report, wherein the impression section is configured to mimic the style for the radiologist…; insert the impression section into the radiology report.
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 “computing system” (claim 14), the claim encompasses rules or instructions followed to summarizing patient information for a user. 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.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture). Claim 14 recites additional elements (i.e., a computing system; wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture). Claim 20 recites additional elements (i.e., wherein the machine learning model comprises a transformer model comprising decoding architecture). Looking to the specifications, a computing system is described at a high level of generality (¶ 0043), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a transformer model comprising encoding architecture and decoding architecture” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a transformer model comprising encoding architecture and decoding architecture amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), 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. 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 transformer model comprising encoding architecture and decoding architecture” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a transformer model comprising encoding architecture and decoding architecture amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), 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. 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 2-10, 12-13, 15-19, 21 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 3-4, 6-10, 17 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 2, 18 further recites the additional elements of “wherein the machine learning model comprises sequence-to-sequence architecture,” which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using sequence-to-sequence architecture amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 5, 19 further recites the additional elements of “wherein decoding the encoding comprises using non-autoregressive decoding,” which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using non-autoregressive decoding amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 12 further recites the additional elements of “determining a clinical recommendation from at least one of the machine learning model and a second model trained to provide the clinical recommendation.” Claim 15 further recites the additional elements of “determine a clinical recommendation from at least one of the tuned machine learning model and a second model trained to provide the clinical recommendation.” Claim 21 further recites the additional elements of “wherein the machine learning model is a pre-trained machine learning model.” The “machine learning” models are described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), 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. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 13 further recites the additional elements of “inserting the impression section with a zero-click insertion process,“ which amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, 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. 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 10/17/2025 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 10/17/2025.
In the remarks, Applicant argues in substance that:
Regarding the 112(a) rejections, the amendments overcome the rejections.
Regarding the 101 rejections,
“the features of the claims, as amended, involve non-generic computer architecture (e.g., with respect to specialized computing architecture structured to improve the performance of radiology report generation devices) and any supposed judicial exception is integrated into a practical application as described below. In particular, the Applicant has amended each of Claims 1 and 20 (and analogously, claim 14) to recite "retraining the machine learning model based on an edit made to the impression section of the radiology report, to determine a retrained machine learning model" which the Applicant also respectfully asserts cannot be construed as a method of organizing human activity. In particular, the features of the claims involve interactions, within a digital environment, solely between devices, without human activity or involvement”; “the amended independent claims recite trained and retrained machine learning models, removing the claims from being directed to an abstract idea (e.g., in a manner similar to eligible claims of U.S. App. No. 17/020,593 and 17/725,031)”; “the limitations of the claims are integrated into a practical application, citing at least the following exemplary limitations of Claim 1: "A method... learning a style for the radiologist, the style reflecting at least one of: a writing style and a dictation style...generating the impression section ... automatically inserting the impression section into the radiology report"… Current radiology workflows are typically long and inefficient, requiring the radiologist to spend time and effort generating numerous fields within a radiology report… the system and/or method confers the benefit of mimicking a radiologist's writing style (e.g., word choice, grammar, consolidation and summary of findings, style of conclusions drawn from summarized findings, preferred follow-up recommendations, etc.) in the automated generation of one or more sections of a radiology report.”
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(a) rejections, the amendments overcome the rejections:
It is respectfully submitted that Examiner withdraws the aforementioned 112(a) rejections of Office Action dated 07/29/2025 because the amendments and remarks have rendered the rejections moot.
In response to Applicant’s argument that (b) regarding the 101 rejections,
“the features of the claims, as amended, involve non-generic computer architecture (e.g., with respect to specialized computing architecture structured to improve the performance of radiology report generation devices) and any supposed judicial exception is integrated into a practical application as described below. In particular, the Applicant has amended each of Claims 1 and 20 (and analogously, claim 14) to recite "retraining the machine learning model based on an edit made to the impression section of the radiology report, to determine a retrained machine learning model" which the Applicant also respectfully asserts cannot be construed as a method of organizing human activity. In particular, the features of the claims involve interactions, within a digital environment, solely between devices, without human activity or involvement”; “the amended independent claims recite trained and retrained machine learning models, removing the claims from being directed to an abstract idea (e.g., in a manner similar to eligible claims of U.S. App. No. 17/020,593 and 17/725,031)”; “the limitations of the claims are integrated into a practical application, citing at least the following exemplary limitations of Claim 1: "A method... learning a style for the radiologist, the style reflecting at least one of: a writing style and a dictation style...generating the impression section ... automatically inserting the impression section into the radiology report"… Current radiology workflows are typically long and inefficient, requiring the radiologist to spend time and effort generating numerous fields within a radiology report… the system and/or method confers the benefit of mimicking a radiologist's writing style (e.g., word choice, grammar, consolidation and summary of findings, style of conclusions drawn from summarized findings, preferred follow-up recommendations, etc.) in the automated generation of one or more sections of a radiology report”:
It is respectfully submitted that Applicant argues “the features of the claims, as amended, involve non-generic computer architecture (e.g., with respect to specialized computing architecture structured to improve the performance of radiology report generation devices).” However, Applicant fails to specify the “non-generic computer architecture” and “specialized computing architecture” to which Applicant refers. Regardless, a computing system is described at a high level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a transformer model comprising encoding architecture and decoding architecture” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using a transformer model comprising encoding architecture and decoding architecture amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), 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.
Applicant argues “"retraining the machine learning model based on an edit made to the impression section of the radiology report, to determine a retrained machine learning model"…cannot be construed as a method of organizing human activity. In particular, the features of the claims involve interactions, within a digital environment, solely between devices, without human activity or involvement.” However, the amended claim limitations of “retraining the machine learning model based on an edit made to the impression section of the radiology report, to determine a retrained machine learning model” encompass the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas, and not within the “Certain Methods of Organizing Human Activity” grouping, as Applicant now argues.
Applicant argues “the amended independent claims recite trained and retrained machine learning models, removing the claims from being directed to an abstract idea (e.g., in a manner similar to eligible claims of U.S. App. No. 17/020,593 and 17/725,031).” However, reciting “trained and retrained machine learning models” is not a standalone test for determining eligibility. Furthermore, the claim limitations of the present invention are different from the claim limitations of those found eligible in U.S. App. No. 17/020,593 and 17/725,031. Even if the claim limitations of the present invention are similar to that of the claims found eligible, the claimed inventions may be different in scope and should be interpreted based on the asserted fact patterns; other fact patterns may have different eligibility outcomes, as is the case with the claims of the present invention.
Applicant argues “the limitations of the claims are integrated into a practical application, citing at least the following exemplary limitations of Claim 1: "A method... learning a style for the radiologist, the style reflecting at least one of: a writing style and a dictation style...generating the impression section ... automatically inserting the impression section into the radiology report"… Current radiology workflows are typically long and inefficient, requiring the radiologist to spend time and effort generating numerous fields within a radiology report… the system and/or method confers the benefit of mimicking a radiologist's writing style (e.g., word choice, grammar, consolidation and summary of findings, style of conclusions drawn from summarized findings, preferred follow-up recommendations, etc.) in the automated generation of one or more sections of a radiology report.” However, the claim limitations to which Applicant refer are interpreted as part of the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. Furthermore, “Current radiology workflows are typically long and inefficient, requiring the radiologist to spend time and effort generating numerous fields within a radiology report” is an administrative problem, 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. Even if the claims provide the alleged improvements (i.e., “mimicking a radiologist's writing style (e.g., word choice, grammar, consolidation and summary of findings, style of conclusions drawn from summarized findings, preferred follow-up recommendations, etc.) in the automated generation of one or more sections of a radiology report”), 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. 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 claims are directed to an abstract idea.
Furthermore, the additional elements do not impose any meaningful limits on practicing 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 claim as a whole does not integrate the recited judicial exception into a practical application.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1–10, 12-21, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
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.
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/EMILY HUYNH/Examiner, Art Unit 3683