DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1-20, as filed 04/23/2026, are currently pending and have been considered below.
Foreign priority is generally acknowledged to KR 10-2024-0060559 which was filed 05/08/2024.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Claim Objections
Claims 1, 12, and 20 are objected to for the following reasons. Claims 1, 12, and 20 each recite “a target medical image” and “the target image.” This terminology must be unified but for purposed of compact prosecution, these are interpreted as referring to the same feature. The same applies to the terms “a catalog set” and “the catalog”.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A system for generating a radiology report, the system comprising:
a memory; and
a processor for executing instructions stored in the memory,
wherein the processor is configured to: (additional element – merely applying the abstract idea with a computer)
obtain an analysis result for a target medical image using an artificial intelligence analysis model; (additional element – insignificant extra-solution activity, namely, mere data-gathering)
extract at least one similar image to the target medical image from a catalog set comprising medical image-radiology report pairs wherein extracting the at least one similar image comprises: (abstract idea – mental process because a person can think about similar images and extract an appropriate one from his memory)
generating a feature representation vector of the target image, and (abstract idea – mathematical concepts because generating a feature vector is a mathematical computation)
comparing the feature representation vector of the target image with feature representation vectors of images in the catalog; (abstract idea – mathematical concepts because comparing feature vectors is a mathematical computation)
determine at least one radiology report paired with the at least one similar image as a reference report; (abstract idea – mental process)
generate a radiology report for the target medical image based on the analysis result, using the reference report as a guideline (abstract idea – mental process because a person can create a report based on a reference mentally or with pen and paper);
determine presence of findings corresponding to predetermined finding labels in the radiology report; and generate a finding label set with finding labels extracted from the radiology report (abstract idea – mental process because a person can mentally identify findings in a report).
2. The system of claim 1, wherein the processor is configured to:
wherein the finding label set is provided as a separate report distinct from the radiology report, or included in a designated section of the radiology report (abstract idea – mental process).
3. The system of claim 1, wherein the processor is configured to:
obtain clinical information through user input or interworking with a database of a medical institution; and (additional element – insignificant extra-solution activity, namely, mere data-gathering)
revise the radiology report using the clinical information or an analysis result of the clinical information (abstract idea – mental process).
4. The system of claim 1, wherein the processor is configured to store a final radiology report, edited or confirmed for the radiology report by a user, in a designated location (additional element – insignificant extra-solution activity, namely, mere data output and storing data in memory; merely applying the abstract idea with a computer).
5. The system of claim 1, wherein the processor is configured to:
determine whether to add the radiology report to the catalog set; and (abstract idea – mental process)
add a pair of the radiology report and the target medical image to the catalog set based on the determination (additional element – insignificant extra-solution activity, namely, mere data output and storing data in memory; merely applying the abstract idea with a computer).
6. The system of claim 1, wherein the analysis result comprises lesion information detected in the target medical image (abstract idea – mental process).
7. The system of claim 6, wherein the analysis result further comprises additional information extracted from the target medical image, and
the additional information comprises at least one of detailed information on the detected lesion, information on additional findings other than the detected lesion, quality information on the target medical image, or information on metadata for the target medical image (abstract idea – mental process).
8. The system of claim 7, wherein the processor is configured to generate the additional information through visual question answering process, which extracts answers to questions in the target medical image (abstract idea – mental process).
9. The system of claim 8, wherein the processor is configured to:
select a question set related to the target medical image or an analysis result of the target medical image from a question bank having questions; and
extract an answer to each question included in the question set to generate the additional information (abstract idea – mental process).
10. The system of claim 6, wherein the analysis result further comprises quantitative information on an interest object present in the target medical image (abstract idea – mental process).
11. The system of claim 1, wherein the processor is configured to associate a non-text analysis result for the target medical image with the radiology report (abstract idea – mental process)..
Claims 1-11 are presented as an exemplary claim but the same analysis applies to the other claims. The Examiner notes that claim 20 recites a “computer product” which, according to [0162], “is stored in a non-transitory computer readable storage medium, and instructions cause the processor to execute the operation of the present disclosure” and thus is directed to statutory subject matter (i.e., the scope of claim 20 does not encompass transitory memory or signals per se).
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes such as evaluating medical images and reports to generate a finding. For example, but for the memory and processor language, determining at least one radiology report paired with the at least one similar image as a reference report in the context of this claim is analogous to steps a human would perform by thinking about an appropriate report drawn from memory. Nothing in the claims precludes the italicized portions from practically being performed in the mind. Additionally, generating feature representation vectors from images and comparing them are mathematical computations that fall under the mathematical concept abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims as shown above. For example, selecting a question set related to the target medical image and extracting an answer to each question in claim 9 can be performed in the mind.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, the memory and process of claim 1 amount to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, obtain an analysis result for a target medical image using an artificial intelligence analysis model amounts to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims as described in greater detail above. For example, wherein the processor is configured to store a final radiology report, edited or confirmed for the radiology report by a user, in a designated location in claim 4 recites additional limitations which amount to invoking computers as a tool to perform the abstract idea. Claim recites additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering. 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.
The Examiner notes that the specification that the claimed method can reduce hallucination (e.g., “By using the report of the similar image as the guideline, the system 1 may enhance the reliability and accuracy of the generated radiology report, and reduce errors caused by hallucination, a common issue with the generative artificial intelligence model,” [0055]) but this is set forth in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art) and is thus not sufficient to improve the functioning of a computer. See MPEP 2106.04(d)(1).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of 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 generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, obtaining an analysis result for a target medical image using an artificial intelligence model amounts to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2, 6, 7, 11-13, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Syeda-Mahmood (US11244755B1) in view of Eswaran (US20200019617A1).
Regarding claim 1, Syeda-Mahmood discloses: A system for generating a radiology report, the system comprising: a memory; and a processor for executing instructions stored in the memory,
wherein the processor is configured (“The configuring of the computing device(s) may also, or alternatively, comprise the providing of software applications stored in one or more storage devices and loaded into memory of the computing device(s), such as server 1104A, for causing one or more hardware processors of the computing device to execute the software applications that specifically configure the processors to perform the operations and generate the outputs described herein with regard to the illustrative embodiments,” col. 22 line 62 – col. 23 line 8]; col. 2 lines 12-17) to:
--obtain an analysis result for a target medical image using an artificial intelligence analysis model (inputting an image into a trained model, generating a prediction, finding a matching medical imaging report data structure, and generating a medical imaging report in claim 1);
--determine at least one radiology report paired with the at least one similar image as a reference report (“performing, by the automated medical imaging report generator, based on the finding label prediction output vector, a lookup operation in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label,” claim 1; 1420/1430 in FIG. 14A).
--generate a radiology report for the target medical image based on the analysis result, using the reference report as a guideline (“automatically generating, by the automated medical imaging report generator, an output medical imaging report for the input medical image based on natural language content of the matching medical imaging report data structure,” claim 1; the retrieved report is used as a guideline because the system “drops all sentences from the retrieved report whose evidence cannot be found in the FFL label pattern of the query,” col. 35 lines 21-26; 1680-1700 in FIG. 16);
--determine presence of findings corresponding to predetermined finding labels in the radiology report (“As shown in FIG. 13, the operation starts by performing natural language processing and computer textual analysis on a first corpus of medical imaging report data structures to extract core findings and core modifiers used in natural language content or text of medical imaging reports (step 1310). The extracted core findings and core modifiers are evaluated through an automated and/or semi-automated process to identify a subset of core findings and core modifiers to be retrained as part of a core finding lexicon or vocabulary (step 1320). The core finding lexicon/vocabulary may include the core finding and core modifiers/modifier types, as well as other information associated with the core findings, such as finding type or the like,” col 31 lines 3-15; “Of these, 78 were the original core labels identified in the core finding lexicon, and the remaining were finer-grained labels with modifiers extracted automatically using the above processes. FIG. 9 provides an example of some fine-grained finding labels extracted from medical imaging reports and retained as part of a fine grained finding descriptor database using the processes of the illustrative embodiments,” col. 18 lines 30-37); and
--generate a finding label set with finding labels extracted from the radiology report (“fine-grained finding descriptor data structures are generated for defining fine-grain descriptors or labels (FFLs) (step 1370),” col. 31 lines 40-43).
Syeda-Mahmood teaches that retrieval is performed by representing the target image’s findings as a finding label prediction output vector and computing semantic distance between that vector and stored pattern vectors, but this is not expressly a “feature representation vector”. Syeda-Mahmoud does not expressly disclose but Eswaran teaches:
--extract at least one similar image to the target medical image from a catalog set comprising medical image-radiology report pairs (“the system includes one or more fetchers receiving the query image and retrieving a set of candidate similar radiology images from a data store in the form of a library of ground truth annotated reference radiology images. The fetcher can take the form of a trained deep convolutional neural network, nearest neighbor algorithm based on a feature vector extracted from the image, or classifier,” [0010]; “These attributes of similarity can be represented as coordinate axes in a multidimensional embedding space, see FIG. 11, where feature vectors of the image and associated annotations are used to plot the position of the query image and the candidate set of similar images in this feature space, and distance metrics or other types of modeling techniques described below are then used to generate similarity scores reflecting the similarity,” [0053]);
--wherein extracting the at least one similar image comprises: generating a feature representation vector of the target image, and comparing the feature representation vector of the target image with feature representation vectors of images in the catalog (“feature vectors of the image and associated annotations are used to plot the position of the query image and the candidate set of similar images in this feature space, and distance metrics or other types of modeling techniques described below are then used to generate similarity scores reflecting the similarity,” [0053]; “. The score can be computed for example based on pre-computed embedding and a standard distance metric (e.g., cosine or Euclidean distance) in an embedding space,” [0011]; “Similar medical images to a query image are found by projecting the query image feature vectors into the embedding of FIG. 4 scoring the neighboring images by distance in the multidimensional space,” [0057]).
One of ordinary skill in the art would have been motivated before the effective filing date to expand Syeda-Mahmood’s similar image and reference report retrieval to include the feature representation vector and calculations of Eswaran because this would “[allow] for different aspects of similarity modelling to be combined to generate a set of similar medical images that provide diagnostically useful information to a user and that meet the needs of clinical applications of similar medical image search, particularly in the radiology context,” (Eswaran [0017]).
Claims 12 and 20 are substantially similar to claim 1 and are rejected with the same reasoning.
Regarding claim 2, Syeda-Mahmood discloses: wherein the processor is configured to:
--wherein the finding label set is provided as a separate report distinct from the radiology report, or included in a designated section of the radiology report (fine-grained finding descriptor data structure is stored separately as 1450 in FIG. 14A; FFL vectors are separate from reports in FIG. 14B).
Claim 13 is substantially similar to claim 2 and are rejected with the same reasoning.
Regarding claim 6, Syeda-Mahmood discloses: wherein the analysis result comprises lesion information detected in the target medical image (“labels identifying opacities, masses, and nodules,” col. 3 line 58).
Claim 17 is substantially similar to claim 6 and are rejected with the same reasoning.
Regarding claim 7, Syeda-Mahmood discloses: wherein the analysis result further comprises additional information extracted from the target medical image, and the additional information comprises at least one of detailed information on the detected lesion, information on additional findings other than the detected lesion, quality information on the target medical image, or information on metadata for the target medical image (“The modifiers in the illustrative embodiments described herein may be any clinical attribute that is descriptive of the core finding and thus, indicates a fine-grained specific type of the core finding. For example, the modifiers may specify clinical attributes such as laterality, anatomical location, severity, appearance characteristics, and the like,” col. 13 lines 28-33).
Regarding claim 11, Syeda-Mahmood discloses: wherein the processor is configured to associate a non-text analysis result for the target medical image with the radiology report (“a vector slot for each FFL , whose value is set to either 1 or 0 depending on whether or not the FFL is predicted to be applicable to the extracted image features,” col. 33 lines 23-25; FIGs. 14A and 14B).
Claim 19 is substantially similar to claim 11 and are rejected with the same reasoning.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Syeda-Mahmood (US11244755B1) in view of Eswaran (US20200019617A1) and Chung (US20220084200A1).
Regarding claim 3, Syed-Mahmood does not expressly disclose but Chung teaches: obtain clinical information through user input or interworking with a database of a medical institution; and revise the radiology report using the clinical information or an analysis result of the clinical information (“the processor 120 may input the clinical information to the readout generation model 222 of the diagnosis result generation unit 220 and generate a readout in the form of a sentence as an output value. For example, the processor 120 may generate a readout, such as “a nodule is spread in the middle lobe of the left lung of the lung region, and there is a high possibility in that the nodule is spread to the upper lobe and the lower lobes, which is dangerous,” based on the clinical information that the plurality of nodules is detected for the lung region, the confidence score is 80%, and the nodules are spread in the middle lobe of the left lung, and the degree of risk is high by using the readout generation model 222,” [0119]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Syeda-Mahmood and Eswaran’s image analysis to include Chung’s structured report generation because using standardized language in a radiology report would make the output more likely to be clinically accurate to understandable by others in the field. See Chung [0098].
Additionally, it can be seen that each element is taught by either Syeda-Mahmood, Eswaran, or Chung. The report generating features of Chung do not affect the normal functioning of the elements of the claim which are taught by Syeda-Mahmood and Eswaran. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Chung with the teachings of Syeda-Mahmood and Eswaran since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claim 14 is substantially similar to claim 3 and are rejected with the same reasoning.
Claims 4, 5, 10, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Syeda-Mahmood (US11244755B1) in view of Eswaran (US20200019617A1) and Sorenson (US20180137244A1).
Regarding claim 4, Syed-Mahmood does not expressly disclose but Sorenson teaches: wherein the processor is configured to store a final radiology report, edited or confirmed for the radiology report by a user, in a designated location (“When the report is finalized, it can be stored with the resultant labelled images, along with a record of what was looked at and what was not, and which findings were validated by observation and which were not. This report can have the option to show all results, only selected results, no results, or only physician validated results from the system,” [0171]; [0152]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Syeda-Mahmood and Eswaran’s image analysis to include Sorenson’s finalized report storage because this would preserve a verifiable, traceable record of the confirmed report and the basis for its validated findings and thus make the system more informative for people investigating historical reports. Additionally, these finalized reports could be part of the machine learning algorithms to “improve the performance of the medical data review system itself.” Sorenson [0045].
Additionally, it can be seen that each element is taught by either Syeda-Mahmood, Eswaran, or Sorenson. The report storage features of Sorenson do not affect the normal functioning of the elements of the claim which are taught by Syeda-Mahmood and Eswaran. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Sorenson with the teachings of Syeda-Mahmood and Eswaran since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claim 15 is substantially similar to claim 4 and are rejected with the same reasoning.
Regarding claim 5, Sorenson discloses: wherein the processor is configured to: determine whether to add the radiology report to the catalog set; and add a pair of the radiology report and the target medical image to the catalog set based on the determination (“The medical data review system detects agreement or disagreement in the results and findings and sends alerts for further adjudication given the discordant results, or it records the differences and provides these to the owner of the algorithm/engine allowing them to govern whether this feedback is accepted (i.e. whether or not the physician input should be accepted as truth, and whether this study should be included in a new or updated cohort.)” [0047]).
The motivation to combine is the same as in claim 4.
Claim 16 is substantially similar to claim 5 and are rejected with the same reasoning.
Regarding claim 10, Sorenson discloses: wherein the analysis result further comprises quantitative information on an interest object present in the target medical image (“The image quantitative data may be used to manually or semi-automatically determine or measure the size and/or characteristics of a particular body part of the medical image. The image quantitative data may be compared with a corresponding benchmark associated with the type of the image to determine whether a particular medical condition, medical issue, or disease is present or suspected,” [0062]).
The motivation to combine is the same as in claim 4.
The Examiner further notes that this feature of Sorenson would improve the performance of the processing engine (Sorenson [0052]-[0062]).
Claims 8, 9, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Syeda-Mahmood (US11244755B1) in view of Eswaran (US20200019617A1) and Bazi (Bazi Y, Rahhal MMA, Bashmal L, Zuain M. Vision Language Model for Visual Question Answering in Medical Imagery. Bioengineering. 2023; 10(3):380. https://doi.org/10.3390/bioengineering10030380 - cited in PTO-892 dated 01/23/2026).
Regarding claim 8, Syed-Mahmood does not expressly disclose, but Bazi teaches discloses: wherein the processor is configured to generate the additional information through visual question answering process, which extracts answers to questions in the target medical image (“a mature medical visual question answering system (VQA)…we extract image features using the vision transformer (ViT) model, and we embed the question using a textual encoder transformer. Then, we concatenate the resulting visual and textual representations and feed them into a multi-modal decoder for generating the answer in an autoregressive way,” Abstract; ).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Syed-Mahmood and Eswaran’s AI image analysis with structured report generation to include Bazi’s visual question answering because it would “improve diagnosis by answering clinical questions presented with a medical image” (Abstract).
Additionally, it can be seen that each element is taught by either Syed-Mahmood, Eswaran, or Bazi. The VQA features of Bazi do not affect the normal functioning of the elements of the claim which are taught by Syed-Mahmood or Eswaran. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Bazi with the teachings of Syed-Mahmood and Eswaran since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 9, Syed-Mahmood does not expressly disclose, but Bazi teaches:
--wherein the processor is configured to: select a question set related to the target medical image or an analysis result of the target medical image from a question bank having questions (question dataset on pages 9-10; “VQA-RAD… The questions are divided into a training set and a test set which contain 3064 and 451 question–answer pairs, respectively. Questions are categorized into 11 categories: abnormality, attribute, modality, organ system, color, counting, object/condition presence, size, plane, positional reasoning, and other. Half of the answers are closed-ended (i.e., yes/no type), while the rest are open-ended with either one-word or short phrase answers,” page 9); and
--extract an answer to each question included in the question set to generate the additional information (“Figure 8 shows four samples of questions answered by our model for images from the PathVQA dataset. The first sample shows that the model correctly predicts the answer and the attention span across the relevant regions in the image. In the second example, although the model cannot provide the correct answer, it can still highlight related regions in the image. The third sample asks about the condition of the “mitral valve”. The question is correctly answered by our model and the corresponding region in the image is highlighted. Finally, the question asked in the fourth example is an open-ended question, regarding the “lumen” present in the image. It can be seen that the model could not obtain the correct answer because open-ended questions are more challenging, and require further developments.” Page 14).
The motivation to combine is the same as in claim 8.
Claim 18 is substantially similar to claim 9 and is rejected with the same reasoning.
Response to arguments
Applicant's arguments filed 04/23/2026 have been fully considered and are discussed below.
Regarding the subject matter ineligibility rejections, Applicant argues that the claimed invention recites a technical improvement that reduces errors caused by hallucination, Remarks page 10, but fails to respond to the Examiner’s rebuttal of this argument on page 7 of the Non-Final Rejection from 01/23/2026. As stated before and reiterated above, this benefit of reduced hallucination is set forth in a conclusory manner that amounts to a general statement that the outputted data is more accurate without any particular technical detail necessary to be apparent to a POSITA that the claim amounts to a technical improvement. Regarding Ex Parte Desjardins, that invention involved an improvement to how a machine learning model was trained which is distinct from the instant claims which recite no technical details describing this alleged improvement. The generic benefit of reduced hallucinations is also not even tied to the claims because the claimed artificial intelligence model is recited at such a high level. Applicant is invited to amend the claims to include inventive model architecture or training regimes tied to the claimed feature vector extraction, generation, and comparison.
The 101 rejections are maintained.
Regarding the prior art rejections, Applicant’s arguments are moot in light of the new grounds of rejection set forth above including Syed-Mahmood and Eswaran.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office Action (See MPEP 706.07(a)). Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624
Prosecution Insights