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 .
Response to Arguments
Applicant's arguments filed 9/10/2026 have been fully considered but they are not persuasive.
Applicant argues :
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Examiner’s response :
The claims recite “an image encoder network”, “text encoder” and a “machine learning based task network”. These recite no structure, algorithm, or architecture. These element are recited at a high level of generality and are equivalent to a generic processor/computer, where the steps which are performed is nothing more that “apply it”. See MPEP 2106.05(g). At the level of generality claimed, the claimed invention is nothing more than :
Receive image and instruction;
Forming a representation of the image;
Forming a representation of the instruction;
Evaluating the representation of the image with the representation of the instruction; and
Outputting a result of the evaluation.
As another Example, a doctor receives an X-ray and a long story about how their child fell. The doctor takes keywords from the story such as (fell on arm broken) and looks at the X-ray and looks for discontinuities in the bone, using the key concepts of the story and discontinuities in the bone makes a diagnosis. And says verbally “It is only a sprain, not a break”.
Applicant argues:
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Examiner’s response:
The Examiner agrees that the claim does not explicitly recite saving a PNG file as a JPG file. The PNG/JPG example was offered as an example of converting one data to another representation using a generic computer as a tool, which is simplified characterization of what the scope of the claim covers.
Applicant argues:
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Examiner’s response:
In the independent claims there is no recitation of any specific training steps. Additionally there is no parameter adjustment, nor network architecture, no specific type of encoding. The claims merely recite generic networks which perform their normal functions.
The examiner agrees the spec discusses an improvement of not needing a large amount of annotated medical images. How does the claimed invention solve that problem? Possibly, withdrawn claim 18 meets that goal, under the assumption that any type of encoding solves that problem.
Applicant argues:
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Examiner’s response :
101 and 102/103 are separate are separate statutes. Applicant’s additional elements are generic networks performing ordinary functions.
Applicant argues
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Examiners response:
As an initial point, The claims are rejected using Med-PaLM M, not PaLM E. Yes, Med-PaLM M is built on PaLM-E.
Second, Driess PALM-E never says it is “single purpose”. Even if it did, single purpose doesn’t mean single component or single neural network as applicant argues.
Finally, the claim says nothing about separate networks. Even if it did require separate networks, Applicant’s arguments(pg. 12 Fig.1 top middle) shows a ViT vision encoder which maps images into fixed number of tokens embeddings and the vocabulary is encoded into a word token space.
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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,
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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.
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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
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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 ,
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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
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 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 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.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
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