DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 22 June 2026 has been entered.
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.
Claim(s) 1,5-8,12-14,18-19 and 22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A computer-implemented method comprising:
receiving an input common data element and one or more associated input values;
generating a radiology passage based on the input common data element and the one or more associated input values using a trained language model; and
outputting the generated radiology passage,
wherein the trained language model is trained by:
receiving text-based radiological data comprising one or more passages;
extracting a concept from each of the one or more passages;
for each respective passage of the one or more passages, mapping the concept extracted from the respective passage to a common data element and one or more associated values, thereby resulting in pairs of 1) the respective passage and 2) the common data element and the one or more associated values;
fine-tuning a pretrained language model via instruction tuning using an instruction tuning dataset defining inputs and expected outputs to result in an instruction tuned language model, the inputs comprising the common data elements and the one or more associated values and the expected outputs comprising the passages, and
training the instruction tuned language model to generate the radiology passage via reinforcement learning by evaluating the common data elements and the one or more associated values against passages generated by the language model and determining a policy of the language model based on the evaluating.
Claim Interpretation:
The claim recites a method comprising receiving data, generating a radiology passage using a trained language model, and output the generated data. The “wherein” clause does not place material limitation on the recited “trained language model” because there appears to be no functional relationship between the training steps and how the model would materially use the training steps to generate a radiology pass.
Applicant’s attention is direct to the “generating” step, which merely recites “using a trained language model”. This recitation places no functional limitation on the proceeding “training” steps.
In the interest of compact prosecution for Applicant, Examiner has considered the “training” steps, as follows, with the full understanding that they are considered non-limiting optional limitations and thereby need not be addressed.
For proper consideration under Section 101, Applicant is advised to positively recite the “training” steps within the context of the method claim, and not merely as part of a “wherein” clause that does not materially limit the scope of the “using a training language model” limitation.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the step of generating a radiology passage based on data is traditionally performed by a physician when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality in a post hoc manner, the generating, extracting, and mapping steps may be performed in the human mind either mentally or with pen and paper, e.g. by looking at the data and thinking about the result).
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 5-7 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
receiving an input common data element and one or more associated input values; and
outputting the generated radiology passage,
wherein the trained language model is trained by:
receiving text-based radiological data comprising one or more passages;
fine-tuning a pretrained language model via instruction tuning using an instruction tuning dataset defining inputs and expected outputs to result in an instruction tuned language model, the inputs comprising the common data elements and the one or more associated values and the expected outputs comprising the passages, and
training the instruction tuned language model to generate the radiology passage via reinforcement learning by evaluating the common data elements and the one or more associated values against passages generated by the language model and determining a policy of the language model based on the evaluating.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the trained language model, this limitation amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea) and provide nothing more than mere instructions to implement an abstract idea on a generic computer. The trained language model is used to generally apply the abstract idea without placing any limits on how the trained language model functions. Rather, these limitations only recite the outcome of “generating a radiology passage” and do not include any details about how the “generating” is accomplished. MPEP 2106.05(f))
Regarding the steps of receiving and outputting data, these steps are mere data gathering and output recited at a high level of generality, and merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Similarly, regarding the training step, this step has been recited with a high level of generality, and merely add(s) insignificant extra-solution activity to the abstract idea.
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim(s) 5-7 reciting using the trained language model in a generic manner (additional limitation(s) which amount(s) to invoking computers as a tool to perform the abstract idea) and receiving/outputting data (additional limitation(s) which add(s) insignificant extra-solution activity to the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
The steps of receiving and outputting data amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Regarding the training step, Lo (20200387635) discloses that a variety of NLP machine training techniques are WURC activity in the pertinent fields (page 8 paragraph 0100).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim(s) 5-7 reciting receiving/outputting data; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim(s) 8,12-14 and 18-19, 22 recite(s) substantially similar limitations as those of claim(s) 1, 2-7 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5-8, 12-14, 18-19, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lo (20200387635) in view of Delbrouck (Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards)
Claim 1: Lo discloses:
A computer-implemented (Abstract illustrating a computer) method (page 1 paragraph 0007 illustrating a method) comprising:
receiving an input common data element (Figure 1 label 12 illustrating entering a medical report in a common format [considered to be forms of “data element” in common because the report contains numerous amounts of data in a standardized format]) and one or more associated input values (Figure 1 label 13-14 illustrating entering a plurality of annotations and user-defined rules [considered to be a form of “input values”]);
generating a radiology passage (page 7 paragraph 0082 illustrating generating an anonymized version of a radiology report [considered to be a form of “passage”]) based on the input common data element and the one or more associated input values (page 7 paragraph 0082, Figure 1 illustrating using the common report format and other entered data, as discussed above and incorporated herein) using a trained language model (page 8 paragraph 0100 illustrating a NLP model); and
outputting the generated radiology passage (page 7 paragraph 0082 illustrating outputting the anonymized report),
wherein the trained language model is trained by:
receiving text-based radiological data comprising one or more passages (page 5 paragraph 0063 illustrating receiving text comprising a plurality of information [considered to be a form of “passages”]);
extracting a concept from each of the one or more passages (page 5 paragraph 0063 illustrating identifying the data as a type of data, e.g. diagnosis, prognosis, test [considered to be a form of “concept”]);
for each respective passage of the one or more passages, mapping the concept extracted from the respective passage to a common data element and one or more associated values, thereby resulting in pairs of 1) the respective passage and 2) the common data element and the one or more associated values (page 5 paragraph 0063 illustrating creating pairs of type of data with the text data [considered to be a form of “mapping”]);
Lo does not disclose:
fine-tuning a pretrained language model via instruction tuning using an instruction tuning dataset defining inputs and expected outputs to result in an instruction tuned language model, the inputs comprising the common data elements and the one or more associated values and the expected outputs comprising the passages, and
training the instruction tuned language model to generate the radiology passage via reinforcement learning by evaluating the common data elements and the one or more associated values against passages generated by the language model and determining a policy of the language model based on the evaluating.
Delbrouck discloses:
fine-tuning a pretrained language model via instruction tuning using an instruction tuning dataset defining inputs and expected outputs to result in an instruction tuned language model, the inputs comprising the common data elements and the one or more associated values and the expected outputs comprising the passages (page 4350 column 1 paragraph 4 illustrating fine-tuning a model with radiographic data to improve accuracy of the model), and
training the instruction tuned language model to generate the radiology passage via reinforcement learning by evaluating the common data elements and the one or more associated values against passages generated by the language model and determining a policy of the language model based on the evaluating (page 4355 column 2 paragraph 1-2 illustrating reinforcement learning to generate accurate outputs from the data inputs).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the machine learning models of Delbrouck within the radiology system of Lo with the motivation of improving patient care by leveraging known machine learning techniques (Delbrouck; page 4348 Abstract).
Claim 5: Lo discloses:
The computer-implemented method of claim 1, as discussed above and incorporated herein.
Lo further discloses:
wherein mapping the concept extracted from the respective passage to a common data element and one or more associated values comprises:
in response to determining that the common data element is not available for mapping, mapping the concept extracted from the respective passage to an entity of an ontology (page 1 paragraph 0003 illustrating identifying name-entity recognition [considered to be a form of “ontology”] for data not known by the system [considered to be a form of “common data element not available”]).
Claim 6: Lo discloses:
The computer-implemented method of claim 5, as discussed above and incorporated herein.
Lo further discloses:
wherein mapping the concept extracted from the respective passage to an entity of an ontology comprises:
determining a ranking of entities for the ontology from each of a plurality of entity linking models based on the concept extracted from the respective passage (page 6 paragraph 0073 illustrating determining that a word pair would rank higher than a single word in the pair as being a technical concept and not a name); and
determining the entity of the ontology based on the ranking of the entities (page 6 paragraph 0073 illustrating identifying the pair of words as belonging to a different context rather than each single word belonging to separate contexts).
Claim 7: Lo discloses:
The computer-implemented method of claim 1, as discussed above and incorporated herein.
Lo further discloses:
wherein:
extracting a concept from each of the one or more passages comprises encoding each of the one or more passages into features using a machine learning based encoder network (page 6 paragraph 0073 illustrating the NLP tagging each text data [considered to be a form of “encoding”]); and
mapping the concept extracted from the respective passage to a common data element and one or more associated values comprises classifying the concept to a common data element and the one or more associated values using a machine learning based classifier model (page 6 paragraph 0073 illustrating an NLP classifier model).
Claim(s) 8, 12, 13, 14, 18, 22 recite(s) substantially similar limitations as those of claim(s) 1, 5, 6, 1, 7, 1 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Response to Arguments
In the Remarks filed on 22 June 2026, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 8-9 Applicant asserts that the claims comply with Section 112(a).
Applicant’s arguments have been carefully considered and are persuasive. The applicable rejection of the claim under Section 112(a) has been withdrawn in view of Applicant’s amendment to the corresponding claim(s).
On page 9-10 Applicant argues that the fine-tuning step is not directed towards an abstract idea.
Applicant’s arguments have been carefully considered, and are found persuasive because the argued limitation(s) are not part of the abstract idea; however, the claims are still not found eligible for the reasons stated in the section above.
On page 10-11 Applicant argues that the claims provide practical application.
Applicant’s arguments have been carefully considered, but are found not persuasive for the reasons stated in the section above (the argued additional elements fail to provide beyond mere insignificant extrasolution activities because they are nonfunctional).
On page 12-13 Applicant argues that the claims provide significantly more.
Applicant’s arguments have been carefully considered, but are found not persuasive for the reasons stated in the section above (the argued additional elements fail to provide beyond what is WURC in the pertinent arts, and because they are nonfunctional).
Applicant’s arguments with respect to claim(s) 1 on page 14-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Based on the evidence presented above, Applicant’s arguments are not found persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goettges (20160217269) discloses processing radiology reports to identify particular text items of interest (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Xu (20160267226) discloses processing a radiology report to identify misdiagnoses (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
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/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
