DETAILED ACTION
The present office action represents final action on the merits.
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 .
Priority
This application claims the priority date of foreign application IN202311034814 of May 18, 2023.
Status of Claims
Claims 1, 3-5, 9, 11-14, 18, and 20 are amended, claims 2, 10, and 19 are cancelled, and claims 1, 3-9, 11-18, and 20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitations are: “means for receiving”, “means for performing”, “means for outputting”, in claims 9 and 11-13.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Examiner has reviewed the as-filed disclosure and has made the following findings: an embodiment of the structure is described at the Specification Paragraphs [0110]-[0115], indicates that the various recited components are components of a general-purpose computer. The Examiner finds that there is sufficient algorithmic description of the claimed functionality such that sufficient structure (computer + algorithm) is disclosed.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 3-9, 11-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 3-8 are drawn to a computer-implemented method, which is within the four statutory categories (i.e., process). Claims 9 and 11-13 are drawn to an apparatus, which is within the four statutory categories (i.e., machine). Claims 14-17 are drawn to a non-transitory computer-readable storage medium, which is within the four statutory categories (i.e., machine). Claims 18 and 20 are drawn to a computer-implemented method, which is within the four statutory categories (i.e., process).
Claims 1-8 recite a computer-implemented method comprising:
receiving input medical data associated with a medical domain;
performing a clinical task based on the input medical data using a trained language model; and
outputting results of the clinical task, wherein the trained language model is trained by:
performing general pretraining of a language model using non- domain-specific training data to provide for a general pretrained language model,
after the performing of the general pretraining, performing instruction tuning on the general pretrained language model using labeled training data to provide for a pretrained, instruction-tuned language model,
receiving domain-specific training data associated with the medical domain, and
after the performing of the instruction tuning, training the pretrained, instruction-tuned language model for the medical domain using the domain-specific training data to provide for the trained language model.
Claim 9 recites an apparatus comprising:
means for receiving input medical data associated with a medical domain;
means for performing a clinical task based on the input medical data using a trained language model; and
means for outputting results of the clinical task,
wherein the trained language model is trained by:
performing general pretraining of a language model using non- domain-specific training data to provide for a general pretrained language model,
after the performing of the general pretraining, performing instruction tuning on the general pretrained language model using labeled training data to provide for a pretrained, instruction-tuned language model,
receiving domain-specific training data associated with the medical domain, and
after the performing of the instruction tuning, training the pretrained, instruction-tuned language model for the medical domain using the domain-specific training data to provide for the trained language model.
Claim 14 recites a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations in addition to the same abstract idea that is recited in claim 1.
Claim 18 recites a computer-implemented method comprising:
performing general pretraining of a language model using non-domain-specific training data to provide for a general pretrained language model;
after the performing of the general pretraining, performing instruction tuning on the general pretrained language model using labeled training data to provide for a pretrained, instruction-tuned language model;
receiving domain-specific training data associated with a medical domain;
after the performing of the instruction tuning, training the pretrained, instruction- tuned language model for the medical domain using the domain-specific training data to provide for a trained language model; and
outputting the trained language model.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and/or mathematical concepts, but for the recitation of generic computer components (e.g., in this case a computer and an apparatus.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 3-8, 11-13, 15-17, and 20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, 14, and 18.
The additional elements from claims 1, 9, 14, and 18 include:
receiving input medical data (insignificant extra-solution activity MPEP 2106.05(g)).
receiving domain-specific training data (insignificant extra-solution activity MPEP 2106.05(g)).
The additional elements from claim 9 include:
an apparatus (apply it, MPEP 2106.05(f)).
The additional elements from claim 14 include:
a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of an apparatus and computer, See Specification paragraphs [0107]-[0109] (See MPEP 2106.05(f)).
amount to insignificant extra-solution activity- for example, the recitation of receiving input medical data See Specification paragraphs [0027] (See MPEP 2106.05(g)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification, paragraphs [0107]-[0109], discloses that the additional elements (i.e., an apparatus and computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e., receiving data) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, domain-adaptive pre-training of instruction-tuned LLMS for radiology report impression generation.);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving input medical data and domain-specific training data, e.g., see Intellectual Ventures v. Symantec.
Dependent claims 3-8, 11-13, 15-17, and 20 include other limitations, but none of these functions are deemed significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or relate to mathematical concepts, using domain-specific training data. The inventive concept is the domain-adaptive pre-training of instruction-tuned LLMS for radiology report impression generation, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1, 3-9, 11-18, and 20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-8, 11-13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (U.S. Pub. No. 2021/0082561 A1) in view of Dinu (U.S. Pub. No. 2024/0354319 A1).
Regarding claim 1, Chang discloses computer-implemented method comprising:
receiving input medical data associated with a medical domain (Paragraphs [0002], [0013] discuss receiving a set of finding inputs in the radiology field.);
performing a clinical task based on the input medical data using a trained language model (Paragraphs [0009], [0011]-[0013] discuss inputs and outputs in a system/method for the automated generation of impression text in a radiology report using a trained machine learning model.); and
outputting results of the clinical task (Paragraphs [0009], [0011]-[0013] discuss inputs and outputs in a system/method for the automated generation of impression text in a radiology report using a trained machine learning model.),
wherein the trained language model is trained by (Paragraphs [0035]-[0036] discuss training machine learning model.):
performing general pretraining of a language model using non- domain-specific training data to provide for a general pretrained language model (Paragraphs [0035]-[0036] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it.),
after the performing of the general pretraining, performing tuning on the general pretrained language model using labeled training data to provide for a pretrained, model (Paragraphs [0035]-[0036] and [0061] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it, implemented prior to or during the training of the model(s), but can additionally or alternatively be implemented on data serving as input to the trained model.),
receiving domain-specific training data associated with the medical domain (Paragraphs [0029]-[0035] discuss receive training input from clinical reports.), and
after the performing of the tuning, training the pretrained, tuned language model for the medical domain using the domain-specific training data to provide for the trained language model (Paragraphs [0025], [0028]-[0029], [0035]-[0036], [0057], and [0061] discuss training the models can additionally or alternatively include fine tuning one or more models (e.g., pretrained models) then the model is trained, pretrained, fine-tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it, implemented prior to or during the training of the model(s), but can additionally or alternatively be implemented on data serving as input to the trained model.).
Chang does not explicitly disclose:
wherein the model is an instruction-tuned language model;
wherein the tuning is instruction tuning.
Dinu teaches:
wherein the model is an instruction-tuned language model (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.);
wherein the tuning is instruction tuning (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the model is an instruction-tuned language model and wherein the tuning is instruction tuning, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Regarding claims 3, 11, and 20, Chang discloses wherein a same loss function is used for the performing of the general pretraining, the performing of the tuning, and the training (Paragraphs [0062] and [0065] discuss preprocessing can optionally additionally or alternatively include upweighting or up sampling sets of cases in the training data, which can function to help the model(s) properly handle complex and/or particularly important (e.g., critical, life-threatening, etc.) cases and includes implementing a loss function to upweight cases so that model pays closer attention to them, for example, in cancer cases, which are complicated to interpret and determine accurate impressions for, are upweighted through a loss function; Preprocessing can include training separate models based on a set of preferences (e.g., preferred and/or prescribed recommendations, radiology group preferences, radiologist preferences, healthcare facility preferences, preferred follow-up treatments, etc.), for instance, models are trained separately to be able to determine particular recommendations based on the patient's condition.).
Chang does not explicitly disclose:
wherein the tuning is instruction tuning.
Dinu teaches:
wherein the tuning is instruction tuning (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the tuning is instruction tuning, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Regarding claims 4 and 12, Chang discloses wherein training the pretrained, tuned language model for the medical domain using the domain-specific training data to provide for the trained language model comprises:
updating only parameters of certain layers of the pretrained, tuned language model at each iteration (Paragraphs [0032], [0065], and [0106]-[0108] discuss the models can be trained to reflect recommendations which often get updated, for instance, the recommendation data being trained on is historical, wherein the method includes tagging that recommendation and updating it and/or flagging it so that it can be updated. In specific examples, a token is used to tag a portion of the impression (e.g., corresponding to outdated and/or potentially outdated information) when training the model, wherein in post-processing, logic adjusts the language corresponding to the token to reflect the up-to-date language; further the set of models further preferably includes one or more deep learning models configured for natural language processing (NLP) (e.g., models configured to handle sequential data), such as one or more deep learning models with attention mechanisms, such as any or all of: a sequence-to-sequence architecture; one or more attention layers (e.g., in one or more encoders, in one or more decoders, etc.); one or more self-attention layers (e.g., in one or more encoders, in one or more decoders, etc.); and/or any other tools, features, and/or architecture.).
Chang does not explicitly disclose:
wherein the model is an instruction-tuned language model.
Dinu teaches:
wherein the model is an instruction-tuned language model (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the model is an instruction-tuned language model, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Regarding claims 5 and 13, Chang discloses wherein training the pretrained, language model for the medical domain using the domain-specific training data to provide for the trained language model comprises:
adding domain-specific vocabulary for the medical domain to the pretrained, tuned language model (Paragraphs [0061] and [0106] discuss preprocessing can optionally additionally or alternatively include adding and/or removing items from training data (e.g., using a syntheticator), which can include any or all of: training the model(s) to add phrasing and/or use particular language which is recommended and/or prescribed (e.g., based on standards of a radiology society and/or group, based on preferences of a radiology group, based on preferences of a particular radiologist, based on preferences of a healthcare facility for coding and/or billing optimization and/or to satisfy coding and/or billing requirements or standards, etc.).).
Chang does not explicitly disclose:
wherein the model is an instruction-tuned language model.
Dinu teaches:
wherein the model is an instruction-tuned language model (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the model is an instruction-tuned language model, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Regarding claims 6 and 15, Chang discloses wherein the input medical data comprises a findings section of a radiology report and the clinical task comprises generation of an impressions section of the radiology report (Paragraphs [0013] and FIG. 2 discuss a method for automatically generating impression text (and/or any other suitable fields of a radiology report such as comparisons, contrast amounts, specific measurements, etc.) includes: receiving a radiologist identifier (radiologist ID); receiving a set of findings inputs and optionally other inputs; determining a context of each of the set of inputs; determining an impression based on the context and the radiologist style; and inserting the impression text (and/or any other suitable text) into the report.).
Regarding claims 7 and 16, Chang discloses wherein the medical domain is radiology (Paragraphs [0002], [0013] discuss receiving a set of finding inputs in the radiology field.).
Regarding claims 8 and 17, Chang discloses wherein the trained language model is a trained large language model (Paragraphs [0011] and [0027] discuss generating a field (e.g., impression field) of a radiology report includes a set of one or more models includes one or more machine learning models, further preferably one or more deep learning models or a transformer machine learning model. Additionally or alternatively, the set of models can include any or all of: algorithms, equations, rules and/or rulesets, databases, lookup tables, and/or any other suitable tools for generating, checking, editing, and/or otherwise processing language information in a radiology report.).
Regarding claim 18, Chang discloses a computer-implemented method comprising (Paragraph [0013 discusses a method for automatically generating impression text of a radiology report.):
performing general pretraining of a language model using non-domain-specific training data to provide for a general pretrained language model (Paragraphs [0035]-[0036] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it.);
after the performing of the general pretraining, performing tuning on the general pretrained language model using labeled training data to provide for a pretrained, model (Paragraphs [0035]-[0036] and [0061] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it, implemented prior to or during the training of the model(s), but can additionally or alternatively be implemented on data serving as input to the trained model.);
receiving domain-specific training data associated with a medical domain (Paragraphs [0029]-[0035] discuss receive training input from clinical reports.);
after the performing of the tuning, training the pretrained, model for the medical domain using the domain-specific training data to provide for a trained language model (Paragraphs [0025], [0028]-[0029], [0035]-[0036], and [0061] discuss trained, pretrained, fine-tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it, implemented prior to or during the training of the model(s), but can additionally or alternatively be implemented on data serving as input to the trained model.); and
outputting the trained language model (Paragraphs [0064]-[0065] and [0114] discuss trained models are used to output recommendations.).
Chang does not explicitly disclose:
wherein the model is an instruction-tuned language model;
wherein the tuning is instruction tuning.
Dinu teaches:
wherein the model is an instruction-tuned language model (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.);
wherein the tuning is instruction tuning (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the model is an instruction-tuned language model and wherein the tuning is instruction tuning, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chang in view of Dinu and in further view of Rahbar (U.S. Pub. No. 2022/0059200 A1).
Regarding claim 9, Chang discloses:
means for receiving input medical data associated with a medical domain (Paragraphs [0002], [0013] discuss receiving a set of finding inputs in the radiology field.);
means for performing a clinical task based on the input medical data using a trained language model (Paragraphs [0009], [0011]-[0013] discuss inputs and outputs in a system/method for the automated generation of impression text in a radiology report using a trained machine learning model.); and
means for outputting results of the clinical task (Paragraphs [0009], [0011]-[0013] discuss inputs and outputs in a system/method for the automated generation of impression text in a radiology report using a trained machine learning model.),
wherein the trained language model is trained by (Paragraphs [0035]-[0036] discuss training machine learning model.)
performing general pretraining of a language model using non- domain-specific training data to provide for a general pretrained language model (Paragraphs [0035]-[0036] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it.),
after the performing of the general pretraining, performing tuning on the general pretrained language model using labeled training data to provide for a pretrained, model (Paragraphs [0035]-[0036] and [0061] discuss model(s) can be any or all of: trained, pretrained, fine- tuned or using other forms of transfer learning (e.g., based on a pretrained model), combined with one or more ontologies (e.g., radiological or other clinical ontology database), and/or any combination of these; set of models can include and/or interface with a pre-processing module, which functions to clean up and/or otherwise modify data prior to training on and/or processing it, implemented prior to or during the training of the model(s), but can additionally or alternatively be implemented on data serving as input to the trained model.),
receiving domain-specific training data associated with the medical domain, and
after the performing of the tuning, training the pretrained, instruction-tuned language model for the medical domain using the domain-specific training data to provide for the trained language model.
Chang does not explicitly disclose:
an apparatus comprising;
wherein the model is an instruction-tuned language model;
wherein the tuning is instruction tuning.
Dinu teaches:
wherein the model is an instruction-tuned language model (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.);
wherein the tuning is instruction tuning (Paragraphs [0004] and [0040] discuss one or more dialog flows can be used to provide natural language instructions to a language model, such as to provide natural language instructions to an instruction-tuned LLM.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, wherein the model is an instruction-tuned language model and wherein the tuning is instruction tuning, as taught by Dinu, in order to provide more effective techniques constraining language models to generate desired outputs. (Dinu Paragraph [0005]).
Rahbar teaches:
an apparatus comprising (Paragraph [0084] discuss an apparatus.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, an apparatus comprising, as taught by Rahbar, in order to provide detailed descriptions of the imaging findings, anatomical considerations that may impact operative planning, and recommendations for additional work-up. (Rahbar Paragraph [0007]).
Regarding claim 14, Chang discloses the same limitations discussed above in Claim 1 with the addition of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising and is similarly rejected.
Chang does not explicitly disclose:
a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising.
Rahbar teaches:
a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising (Paragraph [0081] discusses the computer systems may include or be implemented via computer-executable instructions stored on non-transitory computer-readable media.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Chang to include, a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising, as taught by Rahbar, in order to provide detailed descriptions of the imaging findings, anatomical considerations that may impact operative planning, and recommendations for additional work-up. (Rahbar Paragraph [0007]).
Response to Arguments
Applicant’s arguments filed 5/29/2026 have been fully considered.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the 35 U.S.C. 101 rejection, Applicant’s arguments fail to overcome the previous rejection. Claim 1 recites an abstract idea, a method of organizing human activity or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People and MPEP 2106.04(a)(2)(I)(A) Mathematical Concepts.
Step 2A, Prong One
Applicant states, “The claims generally relate to an innovative general-pretrain-prompt-tune-and-special- pretrain approach for training a language model for performing a clinical task. The claims have been amended to further define the training of the trained language model. The amended claims are not directed to a fundamental economical principle or practice, a commercial or legal interaction, or managing personal behavior or relationships or interactions between people.” (Remarks, pages 7-8). Examiner respectfully disagrees. The Application recites the abstract idea of domain-adaptive pre-training of instruction-tuned LLMS for radiology report impression generation. The claimed invention does not provide an improvement in technology. The training model is not defined as a particular model and there is no improvement to the training model. Generating the impressions section of a radiology repot from the findings section of the radiology report, is not a technical problem rooted in the technology. Here, there is no improvement to the apparatus or any other device – the apparatus is used to receive input medical data and output results of the clinical task, which itself is not an improvement. Applicant’s claims are directed to gathering information, organizing the information, comparing the information, and presenting the information.
Step 2A, Prong Two
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, here, claim 1 fails to integrate the recited judicial exception into a practical application. Applicant states, the “claims are integrated into the practical application of an improvement in the functioning of a computer or other technology. Specifically, the claims are integrated into the practical application of an improvement to how a language model is trained for performing a clinical task.” (Remarks, page 9). Examiner respectfully disagrees. The "novel three-stage approach for training a pretrained language model for performing domain-specific tasks: 1) general pretraining, 2) prompt-tuning, and 3) domain-specialized pretraining”, does not result in a practical application as it is recited as part of the abstract idea, as stated above. All components in the claims are being used for their intended purpose and as written do not result in a practical application. Here, the improvement is to the abstract idea. For the reasons stated above, claims 9, 14, and 18 similarly fail to overcome the 35 U.S.C. 101 rejection.
Step 2B
All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. Applicant states, “One example of an element that the courts have found to qualify as significantly more is an element adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application. See MPEP 2106.05(d). The claims recite elements that amount to significantly more than the alleged abstract idea itself.” (Remarks, page 11). Examiner respectfully disagrees. Here, claim 1 is, “receiving domain-specific training data associated with the medical domain.” The computer functions are well‐understood, routine, and conventional functions claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Rejections under 35 U.S.C. 103:
With respect to claim 1 and the 35 U.S.C. 102 rejection, Examiner withdraws the rejection and Applicant’s arguments with respect to claim 1 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim 1. Examiner has similarly amended the rejection for claims 9, 14, and 18.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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