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 .
DETAILED ACTION
RESPONSE TO ARGUMENTS
35 USC 101 REJECTION
The examiner acknowledges the amendment of claims 1, 8, 15 & 20 filed 04/30/2026. After carefully reviewing applicant amendments, 35 USC 101 guidance and claim limitations, examiner respectfully disagrees.
Applicant Argument 1
Applicant submits claim 1 is not directed to a mental process or abstract idea because claim requires medical imaging data to be processed by a trained image processing machine learning model to generate an image feature vector representing a difference between first and second medical imaging data. The claim further requires plural text vectors and requires both the image feature vector and text feature vectors to be represented in a common embedding space, with the similarity measure being a distance measure in that common embedding space.
Applicant submits machine learning operations on high dimensional vector representations are not steps practically performable in the human mind.
In response, examiner submits amended claim recites collecting data, generating vectors, comparing vectors, selecting one vector based on a similarity measure and determining data representing a change. Those operations are still mathematical concepts/mental processes at a high level.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 2
Applicant submits claim is rooted in machine learning technology. Applicant submits a human cannot practically generate an image feature vector from medical imaging data using a trained image processing ML model, align the vector with a plurality of text feature vectors in a common embedding space, and calculate distances in that embedding space. Applicant concludes the claim therefore does not merely recite a human judgment or an organizing human activity concept.
In response, examiner submits using AI or a trained ML model does not automatically make a claim eligible. Examiner submits claim uses a trained model as a tool to generate vectors and perform similarity comparison. USPTO AI guidance treats AI claims as eligible only when the claim integrates the AI related limitations into a practical application or technical improvement.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 3
Applicant submits claim integrates any abstract idea into a practical application. Applicant submits claim is directed to determining data representing a change between first and second medical imaging data. The machine learning model generates an image feature vector representative of the medical image difference, compares that vector against natural language text feature vectors in a common embedding space, and determines change data based on the selected text feature vector. Examiner concludes that this is specific medical imaging application.
In response, examiner submits limiting an abstract idea to a medical imaging field of use does not by itself integrate the exception into a practical application. The claims do not improve the medical imaging device, scanner, image acquisition, image reconstruction, image registration or model architecture. The claims use generic machine learning/vector math to label or describe a change.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 4
Applicant submits claim embedding is not generic math. Also, applicant submits that the solution solves a problem. This is analogous to a technical improvement in representation learning, because the claim requires both image and text vectors to exist in the same embedding space and uses a distance measure within that space.
In response, examiner submits claims do not recite how the common embedding space is generated, how the encoders are structured, what training data is used for claim 1, what objective improves the embedding or any measurement improvement in computer or medical imaging technology. Examiner submits claims are viewed as generic computer components to apply mathematical vector comparison.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 5
Applicant submits claim 8 strengthens eligibility now because the trained image processing machine learning model has been trained using a contrastive loss function. Applicant submits this is not a mental process. Applicant submits the claimed distance measure is tied to a trained technical model beyond generic post solution activity.
In response, examiner submits a “contrastive loss function” is itself a mathematical optimization objective. Claim 8 discloses the model has been trained using a contrastive loss function but does not require a specific network architecture, data structure, training pipeline, hardware improvement, image acquisition improvement, or change to the operation of the computer itself. Examiner submits claim limitations are viewed as mathematical training of a generic ML model.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 6
Applicant submits the claim contains significantly more. Applicant submits claim language is not conventional generic computer use; it is a specific cross modal medical imaging pipeline.
In response, examiner submits scope of claim limitations is drawn to obtaining, inputting, determining, selecting and determining steps which examiner views are instructions above via generic components.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
Applicant Argument 7
Applicant submits AI related claims can be eligible when claims uses AI in a way that improves a technology or technical field. Applicant submits claims here improves medical image change detection by using a trained image model and cross modal common embedding space to determine medical imaging change data. Applicant submits claims are not merely presenting information, it improves how the system extracts and maps medical image change information to natural language change description.
In response, the claim does not currently recite enough of applicant claimed technical improvement. Examiner submits claims broadly covers vector generation and vector comparison. Examiner sees scope of claims are selecting text/vector based on similarity and not limited to an AI improvement as applicant submits.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
PRIOR ART REJECTION
The examiner acknowledges the amendment of claims 1, 8, 15 & 20 filed 04/30/2026. After carefully reviewing applicant amendments, prior art guidance and claim limitations, claim limitations are sufficient to overcome grounds of rejection.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to as ineligible under subject eligibility test. In the Subject Matter Eligibility Test for Products and Processes (Federal Register, Vol. 79, No. 241, dated Tuesday, December 16, 2014, page 74621), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional device elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 15 & 20
Step 1
This step inquires “is the claim to a process, article of machine, manufacture or composition of matter?” Yes,
Claim 1 – “Method” is a process.
Claims 15 & 20 - “Systems” or “Non-Transitory CRM” are machines.
Step 2A - Prong 1
This step inquires “does the claim recite an abstract idea, law or natural phenomenon”. This claim appears to directed to an abstract idea.
The limitation of “obtaining imaging data representative of the first medical imaging data and the second medical imaging data, or of a difference between the first medical imaging data and the second medical imaging data; inputting the imaging data into a trained image processing machine learning model to generate an image feature vector representative of the difference between the first medical imaging data and the second medical imaging data; obtaining a plurality of text feature vectors, each text feature vector being representative of natural language text describing a respective change in medical imaging data, wherein the image feature vector and the plurality of text feature vectors are represented in a common embedding space; determining, for each of the plurality of text feature vectors, a similarity measure indicating a degree of similarity between the image feature vector and the text feature vector, wherein the similarity measure is a distance measure in the common embedding space between the image feature vector and the text feature vector; selecting a text feature vector from among the plurality of text feature vectors based on the determined similarity measures; and determining, based on the selected text feature vector, the data representing the change between the first medical imaging data and the second medical imaging data.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity) but for the recitation of generic computer components. That is, other than reciting “processor, non-transitory memory device” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processor, non-transitory memory device” language, “obtaining, inputting, determining, selecting, determining” in the context of this claim encompasses covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity).
STEP 2A – PRONG 1 - CONCLUSION
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2
This step inquires “does the claim recite additional elements that integrate the judicial exception into a practical application”. This judicial exception is not integrated into a practical application. In particular, the claim recites two additional element – using a “processor, non-transitory memory device” to perform “obtaining, inputting, determining, selecting, determining” steps. The “processor, non-transitory memory device” are recited at a high-level of generality (i.e., as a generic processor) “obtaining imaging data representative of the first medical imaging data and the second medical imaging data, or of a difference between the first medical imaging data and the second medical imaging data; inputting the imaging data into a trained image processing machine learning model to generate an image feature vector representative of the difference between the first medical imaging data and the second medical imaging data; obtaining a plurality of text feature vectors, each text feature vector being representative of natural language text describing a respective change in medical imaging data, wherein the image feature vector and the plurality of text feature vectors are represented in a common embedding space; determining, for each of the plurality of text feature vectors, a similarity measure indicating a degree of similarity between the image feature vector and the text feature vector, wherein the similarity measure is a distance measure in the common embedding space between the image feature vector and the text feature vector; selecting a text feature vector from among the plurality of text feature vectors based on the determined similarity measures; and determining, based on the selected text feature vector, the data representing the change between the first medical imaging data and the second medical imaging data.” such that it amounts no more than mere instructions to apply the exception using a generic computer component.
STEP 2A – PRONG 2 - CONCLUSION
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The critical inquiry here is does the claim recite additional elements that amount to “significantly more” than the judicial exception? 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 element of using a “processor, non-transitory memory device” to “obtaining, inputting, determining, selecting, determining” steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As to claims 2 & 16, this claim is directed to generic computer components (“generic processor/computer system; generic memory/display; generic “text feature vector representation”), mental process (“describes a change is human judgment/description task”) and insignificant extra-solution activity (“Framing the output as “natural language text” is presentation/reporting of results (post solution activity)”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 3 & 17, this claim is directed to generic computer components (“generic processor; generic classifier/rules; generic UI/flag/indicator output”), mental process (“determining “significance” from text is classic evaluation/judgment”) and insignificant extra-solution activity (“indicator is result evaluation + reporting, not technical improvement by itself.”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 4 & 18, this claim is directed to generic computer components (“generic processor; generic display/output device; generic image data generation/output”), mental process (“output image data representing a change is abstract reporting/visualization”) and insignificant extra-solution activity (“outputting visualization is post solution activity”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 5 & 19, this claim is directed to generic computer components (“generic processor; generic machine learning model/encoder; generic vector store; similarity computation; generic selection logic”), mental process (“comparing similarities and picking closest is matching/selection mental process”) and insignificant extra-solution activity (“to select which image to output is presentation/selection of results not tied to a technical improvement”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 6, this claim is directed to generic computer components (“generic processor; generic image memory; generic pixel comparison/subtraction routine; optional image registration component”), mental process (“compare two values and compute a difference is a mental process”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 7, this claim is directed to generic computer components (“generic processor arithmetic unit; generic image processing routine”), mental process (“subtraction is mathematical operation – mental process”). Thus, his claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 8, this claim is directed to generic computer components (“generic processor; generic vector math library; generic embedding store”), mental process (“Mathematical Optimization Objective ”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 9, this claim is directed to generic computer components (“generic processor; trained large language model; trained text encoder; generic memory/storage”), mental process (“information processing/mental steps”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 10, this claim is directed to generic computer components (“generic storage of a corpus dictionary; generic processor; generic vector store/index”), mental process (“information representation/organizing information”) and insignificant extra-solution activity (“data gathering/extra solution activity”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 11, this claim is directed to generic computer components (“generic processor; LLM; text encoder; storage for reports/vector”), mental process (“comparing reports and describing changes is a human mental task”) and insignificant extra-solution activity (“extra data preparation”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 12, this claim is directed to generic computer components (“generic processor/GPUs; training pipeline; storage; optimization routine”), mental process (“minimizing a loss function between vectors is math/optimization”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 13, this claim is directed to generic computer components (“generic memory/database of reports; training system”), mental process (“organizing information”) and insignificant extra-solution activity (“yes, data gathering”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 14, this claim is directed to generic computer components (“generic storage; training data pipeline; LLM/text generation system; training system”), mental process (“NL text describing change”) and insignificant extra-solution activity (“yes, data gathering/labeling”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
CONCLUSION
No prior art has been found for claims 1-20 in their current form.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen P Coleman whose telephone number is (571)270-5931. The examiner can normally be reached Monday-Thursday 8AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675
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