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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/27/2024, 04/09/2025, and 12/14/2025 have been made record of and considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: In [0036], “Policy module 406 receives image features from pretrained image encoder 402 and text features from pretrained image encoder 402 and generates…” is recited. Based on FIG. 4, the examiner believes this is a typo and should recite: “Policy module 406 receives image features from pretrained image encoder 402 and text features from pretrained text encoder 404 and generates…”
In addition, [0062] recites “image encoding 501, text encoding 504, and fine-tuning policy 506 of Figure 5.” Based on FIG. 5, the examiner believes this is a typo and should recite: “image encoding 502, text encoding 504, and fine-tuning policy 506 of Figure 5…”
Appropriate correction is required.
The abstract of the disclosure is objected to because it contains minor grammatical errors. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Appropriate correction is required.
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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4 and 11-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 11 recite the limitation "the one or more actions" in the last limitation. There is no previous recitation of “actions.” The examiner is unsure if the “actions” correspond to the previously recited instructions or response. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claims 3-4 and 12-13 depend from claims 2 and 11, and so are similarly rejected.
Claim Objections
Claims 3 and 12 are objected to because of the following informality: a minor typo in the limitation “performing the one or more instructions the one or more medical applications.” Appropriate correction is required.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-7, 9-11, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (‘VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks’), and in further view of Kohli (US 2019/0156921 A1).
Consider claims 1, 10, and 15, Wu discloses an apparatus comprising:
[claim 15: a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising (4.1 Model Details; GPU):]
[claim 1: a computer-implemented method comprising (4.1 Model Details; GPU):]
receiving text-based instructions (FIG. 2 input text prompts; 3.1 Model Design, Tokenization; (1) For text prompts…);
encoding the text-based instructions into text features using a machine learning based text encoder network (3.1; “employ the text tokenizer to tokenize them into distinct vocabulary indices, which can be further processed by LLM and result in the text features Ftext”; text encoder);
determining one or more instructions for performing by one or more medical applications (Table A10; FIGs. A3, A5; “We comprehensively evaluate the proposed model on a wide range of vision and vision-language tasks, from visual perception … to long-tailed domains (e.g., medical, remote-sensing…”) using a policy module based on the text features (3.2 Super Link Technique, (1) Routing Token, (2) Super-Link Queries, Visual Perception and Visualization);
performing the one or more instructions by the one or more medical applications (FIGs. A3, A5; “We comprehensively evaluate the proposed model on a wide range of vision and vision-language tasks, from visual perception … to long-tailed domains (e.g., medical, remote-sensing…”) to generate a response to the text-based instructions (3.1 Task-Specific Decoders; 3.2 Super-Link Queries; FIG. 2 “The central LLM parses the user instructions and generates the textual responses… LLM can also output the special routing token such as [DET] when needed. The super-link queries would be automatically appended after the routing token embeddings and further processed by LLM. They play as the bridge for connecting LLM and task-specific decoders. In this way, our generalist model can support hundreds of visual tasks.”); and
outputting the response to the text-based instructions (3.1 Task-Specific Decoders; 3.2 Super-Link Queries; FIG. 2 “The central LLM parses the user instructions and generates the textual responses”).
In related art, Kohli more explicitly discloses determining/performing the one or more instructions by the one or more medical applications based on the text features (Kohli ¶31-33; ¶65; “The example system 100 can be configured to implement a variety of systems and processes including image storage…, image processing and/or analysis, radiology reporting and/or review (e.g., radiology information system (RIS), etc.), computerized provider order entry (CPOE) system, clinical decision support…”; ¶107; “data presentation 416 can be generated for display, input into another program (e.g., an image viewer, reporting tool, patient library, comparison engine, etc.) via IRCC APIs 414, for example”; ¶136-143, FIG. 11; “the IRCC 404 can process the changing context 1020 to further analyze questions being posed in various reviews by various users. Using AI to identify questions/RFEs posed, the IRCC 404 can provide relevant, real-time selection and analysis of documents and/or other data for automated and/or user review. ... For example, questions 1110 - 1114 such as whether or not to administer a contrast agent to a patient, protocol selection for a patient, reason for performing an imaging exam, etc., can be identified and analyzed to drive selection of relevant data 1120 for further processing, organization, and display”).
Wu, while stating that “our generalist model can support hundreds of visual tasks,” only provides a couple explicit medical examples. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the medical application options of Kohli into the task-specific decoding of Wu to support hundreds of visual tasks (Wu FIG. 2), including medical visual tasks (Kohli ¶31, 136-143). Kohli further states that “automatically presenting patient information to the radiologist in a reading workflow helps improve interpretation and adds value to a chain of care within a radiology department (Kohli ¶36).”
Consider claims 2, 11, and 16, Wu, as modified by Kohli, discloses the claimed invention further comprising: receiving one or more medical images (Wu FIGs. 2, A3, A5, Section 3; Kohli ¶31-46, 141-143); and
encoding the one or more medical images into image features using a machine learning based image encoder network (Wu FIGs. 2, A3, A5, Section 3; Kohli ¶31-46, 141-143), wherein
determining one or more instructions for performing by one or more medical applications using a policy module based on the text features comprises determining the one or more actions further based on the image features (Wu FIGs. 2, A3, A5, Section 3; Kohli ¶31-46, 124, 136-143).
Consider claims 5 and 14, Wu, as modified by Kohli, discloses the claimed invention further comprising: adapting the policy module based on user feedback to the response to the text-based instructions (Wu 5 Conclusion; Kohli ¶107, 158).
Consider claims 6 and 17, Wu, as modified by Kohli, discloses the claimed invention wherein the one or more instructions comprise at least one of: one or more medical image analysis tasks performed on one or more medical images, functions to derive findings from the one or more medical images, functions to apply transformations on the one or more medical images, functions to derive information from the one or more medical images, or outputting text to a machine learning based model (Wu FIGs. 2, A3, A5, Sections 3, 4; Kohli ¶31-46, 124, 136-143).
Consider claims 7 and 18, Wu, as modified by Kohli, discloses the claimed invention wherein the one or more instructions comprise one or more APIs (application programming interfaces) (Wu Section 3; Kohli ¶31-46, 107, 124, 136-143).
Consider claims 9 and 20, Wu, as modified by Kohli, discloses the claimed invention wherein the machine learning based text encoder network comprises a language model (Wu FIG. 2; Sections 3, 4; Kohli ¶143).
Claims 3-4 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, in view of Kohli, as applied to claims 1-2, 5-7, 9-11, 14-18, and 20 above, and further in view of Zhang (Large-Scale Domain-Specific Pretraining For Biomedical Vision-Language Processing).
Consider claims 3 and 12, Wu, as modified by Kohli, discloses the claimed invention wherein: performing the one or more instructions by the one or more medical applications to generate a response to the text-based instructions comprises performing the one or more instructions the one or more medical applications to modify the one or more medical images (Wu FIGs. 2, A3, A5, Sections 3, 4; Kohli ¶31-46, 124, 136-143); and
outputting the response to the text-based instructions comprises outputting the one or more modified medical images (Wu FIGs. 2, A3, A5, Sections 3, 4; Kohli ¶31-46, 124, 136-143).
In related art, Zhang further supports generating a response to the text-based instructions by the one or more medical applications (Zhang FIGs. 6, 7, Section 2.2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the biomedical domain adapted encoder of Zhang for the image encoder of Wu, as modified by Kohli, to predictably apply known instruction-based image editing (Wu Section 3, FIG. 2) to medical images.
Consider claims 4 and 13, Wu, as modified by Kohli, discloses the claimed invention wherein the machine learning based text encoder network and the machine learning based image encoder network are trained to generate similar features for associated text-based instructions and medical images (Wu Sections 3 and 4; Kohli ¶127, 134).
In related art, Zhang explicitly discloses wherein the machine learning based text encoder network and the machine learning based image encoder network are trained to generate similar features for associated text-based instructions and medical images (Zhang Section 2, 2.2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the explicit similarity scoring of Zhang into the comparisons of Wu, as modified by Kohli, to predictably apply known instruction-based image editing (Wu Section 3, FIG. 2) to medical images.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, in view of Kohli, as applied to claims 1-2, 5-7, 9-11, 14-18, and 20 above, and further in view of Das (“Voice Recognition System: Speech-to-Text”).
Consider claims 8 and 19, Wu, as modified by Kohli, discloses the claimed invention wherein receiving text-based instructions comprises: receiving spoken instructions from a user (Kohli ¶184); and
Wu, as modified by Kohli, fails to explicitly disclose converting the spoken instructions to the text-based instructions.
In related art, Das discloses converting the spoken instructions to the text-based instructions (Das page 194).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to apply the speech-to-text conversion of Das to the voice command instruction of Wu, as modified by Kohli, to predictably convert speech to text for input.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hoopes (‘VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis’).
Shah (‘Mutex: Learning unified policies from multimodal task specifications’).
Shen (‘HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face’).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY HYTREK whose telephone number is (703)756-4562. The examiner can normally be reached M-F 9:00-5:00.
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, Steve Koziol can be reached at (408)918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY HYTREK/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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