Prosecution Insights
Last updated: July 17, 2026
Application No. 18/512,237

MACHINE LEARNING BASED MEDICAL IMAGING ANALYSIS USING FEW SHOT LEARNING WITH TASK INSTRUCTIONS

Non-Final OA §101§102
Filed
Nov 17, 2023
Examiner
THIRUGNANAM, GANDHI
Art Unit
2672
Tech Center
2600 — Communications
Assignee
Siemens Healthineers AG
OA Round
1 (Non-Final)
74%
Grant Probability
Favorable
1-2
OA Rounds
9m
Est. Remaining
86%
With Interview

Examiner Intelligence

Grants 74% — above average
74%
Career Allowance Rate
421 granted / 570 resolved
+11.9% vs TC avg
Moderate +12% lift
Without
With
+11.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
33 currently pending
Career history
606
Total Applications
across all art units

Statute-Specific Performance

§101
2.5%
-37.5% vs TC avg
§103
70.8%
+30.8% vs TC avg
§102
10.9%
-29.1% vs TC avg
§112
14.0%
-26.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 570 resolved cases

Office Action

§101 §102
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 . Election/Restrictions Applicant’s election without traverse of Group I in the reply filed on 4/10/2026 is acknowledged. Claims 18-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 4/10/2026. 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. This application includes one or more claim limitations (10-13) that use the word “means” or “step” and are 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 1-17 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s): “receiving 1) one or more input medical images of a patient and 2) task instructions for performing a medical imaging analysis task;” Is directed to receiving input data (Extra-Solution Activity). See MPEP 2106.05(g) “encoding the one or more input medical images into imaging features using an image encoder network; encoding the task instructions into text features using a text encoder network;”, which is direct to mental process, for example a person could save (encode) a png file as a jpg file using a generic computer as a tool and write/type verbal instructions into text. The “encoder unit” and “text encoder unit” are additional elements which amount to nothing more than “apply it”. See MPEP 2106.05(f). Additionally, these limitation could also be considered as well-understood, routine and conventional in the field. See MPEP 2106.05(d) “performing the medical imaging analysis task based on the imaging features and the text features using a machine learning based task network;” and, which is directed to a mental process of performing a task such as finding a tumor in an image based on the encoded image and task text. A machine learning based task network is an additional element akin to “apply it”. See MPEP 2016.05(f) “outputting results of the medical imaging analysis task.” Is directed to outputting data (Extra-Solution Activity). See MPEP 2106.05(g) This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 10 and 14 are rejected under similar grounds as claim 1. Claims 2 – 9, 11-13 and 15-17 are rejected as dependent upon a rejected claim and do not include any additional elements that amount to significantly more. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticpated by Driess (“Toward Generalist Biomedical AI”), hereafter referred to as Driess. NOTE: Driess “PalM-E : An Embodied Multimodal Language Model” is being used as evidentiary reference showing inherent properties in the system of Driess, Driess discloses 1. (Original) A computer-implemented method comprising: receiving 1) one or more input medical images of a patient and 2) task instructions for performing a medical imaging analysis task; (Driess, pg .7, Fig. 2, PNG media_image1.png 674 1222 media_image1.png Greyscale ) encoding the one or more input medical images into imaging features using an image encoder network; encoding the task instructions into text features using a text encoder network; (Driess, pg. 6, Section 4.2 discloses “Med-PaLM M is developed by finetuning and aligning the PaLM-E model to the biomedical domain using MultiMedBench. “ ; Below shows a picture of PaLM-E from “PaLM-0E: An Embodied Mulimodal Language Model” by Driess et al. PNG media_image2.png 264 540 media_image2.png Greyscale ) performing the medical imaging analysis task based on the imaging features and the text features using a machine learning based task network; and (Driess, pg. 7, Fig. 2 , see above, “A”) outputting results of the medical imaging analysis task (Driess, pg. 7, Fig. 2 , see above, “A”). Driess discloses 2. (Original) The computer-implemented method of claim 1, wherein the task instructions comprise references to image regions in at least one of the one or more input medical images.(Driess, Fig. 2 “lesion”, lung opacity locations) Driess discloses 3. (Original) The computer-implemented method of claim 1, wherein the task instructions comprise anatomical knowledge and task knowledge, the task knowledge comprising at least one of a description of an anatomical abnormality, how the anatomical abnormality is represented, and how the anatomical abnormality can be detected in the one or more input medical images. (Driess, Fig. 2 ) Driess discloses 4. (Original) The computer- implemented method of claim 1, wherein the task instructions are user-defined. (Driess, Fig. 2) Driess discloses 5. (Original) The computer-implemented method of claim 1, wherein the text features and the imaging features are aligned in a same latent space.(See rejection of claim 1) Driess discloses 6. (Original) The computer-implemented method of claim 1, wherein the machine learning based task network is trained with self-supervised learning based on unannotated training medical images and text. (Driess Section I, paragraph 3 states “models are often trained on large-scale data with self-supervised or unsupervised” and page 26, discloses training with the Path-VQA dataset, which are unannotated images ; Note: Driess “PaLM-E : An Embodied Multimodal Lanugage Model” pg. 14 discloses PNG media_image3.png 182 1158 media_image3.png Greyscale ) Driess discloses 7. (Original) The computer-implemented method of claim 1, wherein the machine learning based task network is trained with few shot learning using annotated training medical images and annotated task descriptions. (Driess, Section 5.1 , PNG media_image4.png 248 1278 media_image4.png Greyscale ) Driess discloses 8. (Original) The computer-implemented method of claim 1, wherein the machine learning based task network comprises an LLM (large language model) based task network. (Driess , pg. 6, paragraph 3) Driess discloses 9. (Original) The computer-implemented method of claim 1, wherein: receiving 1) one or more input medical images of a patient and 2) task instructions for performing a medical imaging analysis task comprises receiving text-based medical data of the patient; and encoding the task instructions into text features using a text encoder network comprises encoding the task instructions and the text-based medical data into the text features using the text encoder network. (see claim 1 above) Claim 10 is rejected under similar reasoning to claim 1. Claim 11 is rejected under similar reasoning to claim 2. Claim 12 is rejected under similar reasoning to claim 3. Claim 13 is rejected under similar reasoning to claim 4. Claim 14 is rejected under similar reasoning to claim 1. Claim 15 is rejected under similar reasoning to claim 5. Claim 16 is rejected under similar reasoning to claim 6. Claim 17 is rejected under similar reasoning to claim 7. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM. 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, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
Read full office action

Prosecution Timeline

Nov 17, 2023
Application Filed
Jan 29, 2025
Response after Non-Final Action
Jun 12, 2026
Non-Final Rejection mailed — §101, §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
74%
Grant Probability
86%
With Interview (+11.9%)
3y 5m (~9m remaining)
Median Time to Grant
Low
PTA Risk
Based on 570 resolved cases by this examiner. Grant probability derived from career allowance rate.

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