Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/28/2026 has been entered.
Notice to Applicant
This communication is in response to the amendment filed 04/28/2026. Claims 1, 14 have been amended. Claim 3 has been canceled. Claim 22 has been added. Claims 1-2, 4-10, 12-22 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-2, 4-10, 12-22 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, 22 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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14-19, 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 14 recites “automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action.” However, with regards to ICD codes, the specification only mentions: “The set of models 110 can optionally include and/or interface with a post-processing module 114, which functions to edit and/or otherwise modify one or more outputs produced by the set of models. This can include…checking and/or adjusting language for compliance with recommended and/or required language (e.g., medical classification lists such as the International Classification of Diseases and Related Health Problems [ICD], ICD-10…)” (¶ 0037). The specification does not describe “automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action.” Because no additional information is given, the disclosure fails to sufficiently describe the “automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action” step. As such, it constitutes new matter.
Claim(s) 15-19, 22 is/are rejected as being dependent on claim 14.
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, 4-10, 12-22 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 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 1 (which is representative of independent claim 20) recites… training a machine learning model based on a first subset of a set of historical radiology reports…, and wherein training comprises improving system robustness by minimizing machine learning hallucinations upon removing or modifying, from training data used to train the machine learning model, a false positive pertaining to a finding included in an impression section and not included in a findings section of a radiology report; 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; 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; inserting the impression section into the radiology report; and retraining the machine learning model whenever the style for the radiologist is updated.
Independent claim 14 recites… train a machine learning model based on a first set of historical radiology reports…; 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; 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; insert the impression section into the radiology report; retrain the machine learning model whenever the style for the radiologist is updated; and automatically extract International Classification of Diseases and Related Health Problems (ICD) codes…and write the ICD code…without radiologist action.
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. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary) (which the specification describes as a human activity being performed by a “radiologist” in ¶ 0003) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps as indicated supra. The training and retraining a machine learning model is considered to be part of the abstract idea because they fall under data manipulations that humans can perform and thus, are part of the rules or instructions followed to summarizing patient information for a user; for example, 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). That is, other than reciting generic computer components (discussed infra), the claim amounts to managing personal behavior or relationships or interactions between people following rules or instructions. 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.
Independent claim 1 (which is representative of independent claim 20) further recites… 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 encoding of content of a radiology report with the encoding architecture…
Independent claim 14 further recites…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....
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 generic computer components (discussed infra), the claim recites learning a writing style and encoding content of a report. For example, 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.
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; a clinical ontology database; a Radiology Information System). 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. Also, “a clinical ontology database” and “Radiology Information System” is only invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., storing, providing data), 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), 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. 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. Also, “a clinical ontology database” and “Radiology Information System” is only invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., storing, providing data), 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), 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. 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, 22 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, 22 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 04/28/2026 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 04/28/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“"training a machine learning model based on a first subset of a set of historical radiology reports, wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture"…cannot be practically performed in the human mind, and therefore does not constitute a mental process…this limitation does not recite a method of organizing human activity…while independent claims 1 and 16, as amended, may involve mathematical concepts, they do not recite a mathematical concept (i.e., they do not recite a mathematical relationship, a mathematical formula, a mathematical calculation) according to the evaluation provided on pages 3-4 of the 2019 PEG”;
“the limitations involving improving performance by "minimizing machine learning hallucinations upon removing or modifying, from training data used to train the machine learning model..." of Claims 1 and 20 integrates 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. As stated in the present Specification 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… 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)”;
“"automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action" integrates the alleged judicial exception into the practical application of improving the performance of a computer, specifically improving automated structured data extraction and interoperability between a natural language processing system and a downstream clinical billing system…This constitutes an improvement to the technical field of clinical natural language processing and healthcare information systems interoperability by eliminating the manual step of human-mediated ICD code assignments from radiology impressions, enabling automatic transformation of free-text clinical findings into structured billing records in the Radiology Information System. The amended claim is further analogous to the eligible Claim 3 of Example 47…The present system uses a ML-generated output to automatically trigger a concrete action on a downstream technical system (writing structured ICD codes to the RIS) without radiologist action. In the context of the claimed invention(s), a human would manually assign clinical codes and billing codes after reviewing a radiology report, just as the prior art network systems in Example 47 required a network administrator to manually respond to alerts. The present limitation eliminates that human intermediation in the same way and for the same technical reason: the system acts on its own output to produce a concrete state change in a downstream technical system in real time”; and
“The claimed limitation "further comprising increasing processing speed of the computing system by replacing a string of text surpassing a length threshold, in the radiology report, with a token representing the string of text, and reverting the token to the string of text during post-processing of the impression section" integrates the alleged judicial exception into the practical application of increasing processing speed of a computing system. As discussed in paragraph [0064], "tokenizing one or more recommendations, which functions to increase the processing speed." This constitutes an improvement to the technical field of computing and clinical natural language processing by transforming clinical information for faster processing.”
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 101 rejections,
“"training a machine learning model based on a first subset of a set of historical radiology reports, wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture"…cannot be practically performed in the human mind, and therefore does not constitute a mental process…this limitation does not recite a method of organizing human activity…while independent claims 1 and 16, as amended, may involve mathematical concepts, they do not recite a mathematical concept (i.e., they do not recite a mathematical relationship, a mathematical formula, a mathematical calculation) according to the evaluation provided on pages 3-4 of the 2019 PEG”:
It is respectfully submitted that Applicant argues “”training a machine learning model based on a first subset of a set of historical radiology reports, wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture"…cannot be practically performed in the human mind, and therefore does not constitute a mental process…this limitation does not recite a method of organizing human activity.” However, Applicant fails to specify how “this limitation does not recite a method of organizing human activity.” The claim limitations of “training a machine learning model based on a first subset of a set of historical radiology reports” fall under data manipulations that humans can perform and thus, are part of the rules or instructions followed to summarizing patient information for a user and is considered to be part of the abstract idea of 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” “Mathematical Concepts” grouping, as Applicant now argues. Furthermore, the claim limitations of “wherein the machine learning model comprises a transformer model comprising encoding architecture and decoding architecture” is not interpreted as part of the abstract idea, but as an additional element, which 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 “while independent claims 1 and 16, as amended, may involve mathematical concepts, they do not recite a mathematical concept (i.e., they do not recite a mathematical relationship, a mathematical formula, a mathematical calculation) according to the evaluation provided on pages 3-4 of the 2019 PEG.” However, Applicant fails to specify how “independent claims 1 and 16, as amended…do not recite a mathematical concept (i.e., they do not recite a mathematical relationship, a mathematical formula, a mathematical calculation) according to the evaluation provided on pages 3-4 of the 2019 PEG.” As stated previously in Office Action dated 11/20/2025 and above: the claim recites learning a writing style and encoding content of a report. For example, 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.
Thus, the claims are directed to an abstract idea.
“the limitations involving improving performance by "minimizing machine learning hallucinations upon removing or modifying, from training data used to train the machine learning model..." of Claims 1 and 20 integrates 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. As stated in the present Specification 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… 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 “the limitations involving improving performance by "minimizing machine learning hallucinations upon removing or modifying, from training data used to train the machine learning model..." of Claims 1 and 20 integrates 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. As stated in the present Specification 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 are interpreted as part of the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. For example, “improving system robustness by minimizing machine learning hallucinations” is achieved by “removing or modifying, from training data used to train the machine learning model, a false positive pertaining to a finding included in an impression section and not included in a findings section of a radiology report,” which are rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary) in the manner described in the identified abstract idea, supra. 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.
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 for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary), 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.
“”automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action" integrates the alleged judicial exception into the practical application of improving the performance of a computer, specifically improving automated structured data extraction and interoperability between a natural language processing system and a downstream clinical billing system…This constitutes an improvement to the technical field of clinical natural language processing and healthcare information systems interoperability by eliminating the manual step of human-mediated ICD code assignments from radiology impressions, enabling automatic transformation of free-text clinical findings into structured billing records in the Radiology Information System. The amended claim is further analogous to the eligible Claim 3 of Example 47…The present system uses a ML-generated output to automatically trigger a concrete action on a downstream technical system (writing structured ICD codes to the RIS) without radiologist action. In the context of the claimed invention(s), a human would manually assign clinical codes and billing codes after reviewing a radiology report, just as the prior art network systems in Example 47 required a network administrator to manually respond to alerts. The present limitation eliminates that human intermediation in the same way and for the same technical reason: the system acts on its own output to produce a concrete state change in a downstream technical system in real time”:
Applicant argues “”automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action" integrates the alleged judicial exception into the practical application of improving the performance of a computer, specifically improving automated structured data extraction and interoperability between a natural language processing system and a downstream clinical billing system.” However, the claim limitations to which Applicant refer (i.e., “automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action”) are interpreted as rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary), which is the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. Although the claims further recite the additional elements of a “clinical ontology database” and “Radiology Information System,” they are only invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., storing, providing data), 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), 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. 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 (i.e., natural language processing and healthcare information systems interoperability), or computers for that matter, and thus, the claims do not provide a technical solution; “partially or fully standardizing any or all of the formatting and/or language of reports generated for a particular radiology group and/or healthcare facility, and of improving report recommendation adherence to consensus guidelines, billing and coding requirements, and/or quality metrics standards" and "optimize a radiology report for healthcare facility billing and/or reimbursements” 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 “This constitutes an improvement to the technical field of clinical natural language processing and healthcare information systems interoperability by eliminating the manual step of human-mediated ICD code assignments from radiology impressions, enabling automatic transformation of free-text clinical findings into structured billing records in the Radiology Information System.” However, 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).
Applicant argues “The amended claim is further analogous to the eligible Claim 3 of Example 47…The present system uses a ML-generated output to automatically trigger a concrete action on a downstream technical system (writing structured ICD codes to the RIS) without radiologist action. In the context of the claimed invention(s), a human would manually assign clinical codes and billing codes after reviewing a radiology report, just as the prior art network systems in Example 47 required a network administrator to manually respond to alerts. The present limitation eliminates that human intermediation in the same way and for the same technical reason: the system acts on its own output to produce a concrete state change in a downstream technical system in real time.” However, 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. Claim 3 of Example 47 was not found eligible simply because it “eliminates that human intermediation” or “the system acts on its own output to produce a concrete state change in a downstream technical system in real time,” as Applicant now argues, but because it recites additional elements that improve the functioning of a computer or technical field, unlike the claims of the present invention. As stated previously above, the claim limitations to which Applicant refer (i.e., “automatically extract International Classification of Diseases and Related Health Problems (ICD) codes using a clinical ontology database and write the ICD code to a Radiology Information System without radiologist action”) are interpreted as rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary), which is the abstract idea, and not as additional elements to be interpreted in Step 2A, Prong Two. Although the claims further recite the additional elements of a “clinical ontology database” and “Radiology Information System,” they are only invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., storing, providing data), 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), 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. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Furthermore, as stated previously above, 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).
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“The claimed limitation "further comprising increasing processing speed of the computing system by replacing a string of text surpassing a length threshold, in the radiology report, with a token representing the string of text, and reverting the token to the string of text during post-processing of the impression section" integrates the alleged judicial exception into the practical application of increasing processing speed of a computing system. As discussed in paragraph [0064], "tokenizing one or more recommendations, which functions to increase the processing speed." This constitutes an improvement to the technical field of computing and clinical natural language processing by transforming clinical information for faster processing”:
Applicant argues “The claimed limitation "further comprising increasing processing speed of the computing system by replacing a string of text surpassing a length threshold, in the radiology report, with a token representing the string of text, and reverting the token to the string of text during post-processing of the impression section" integrates the alleged judicial exception into the practical application of increasing processing speed of a computing system. As discussed in paragraph [0064], "tokenizing one or more recommendations, which functions to increase the processing speed." This constitutes an improvement to the technical field of computing and clinical natural language processing by transforming clinical information for faster processing.” 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. For example, “increasing processing speed of the computing system” is achieved by “replacing a string of text surpassing a length threshold, in the radiology report, with a token representing the string of text, and reverting the token to the string of text during post-processing of the impression section,” which are rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and provide relevant data accordingly (i.e., patient summary) in the manner described in the identified abstract idea, supra. 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.
Thus, Examiner maintains the 101 rejections of claims 1-2, 4-10, 12-22, 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
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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