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 .
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.
DETAILED ACTION
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/31/2025 and 03/17/2025 have been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Drawings
The drawings filed on 03/17/2025 are accepted by the examiner.
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.
1. Claims 1-3 and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Islam et al. (US Publication No. 2023/0081305, hereinafter “Islam”).
Regarding claim 1, Islam does disclose, a method for providing a trained machine learning model for feature extraction from medical data, the method comprising: obtaining a fundamental machine learning model in an untrained or a partially trained state, wherein the fundamental machine learning model has an architecture that is trainable by way of unsupervised or self-supervised training (Islam, (para. [0031]), Self-supervised learning (SSL) has recently garnered attention for its capacity to learn generalizable representations without requiring expert annotation. The idea is to pre-train models on pretext tasks (i.e. partially trained state) and then fine-tune the pre-trained models to the target tasks; (para. [0046-0048]), these architectures are first trained for a pretext task. …), and wherein the fundamental machine learning model encompasses a machine learning model for feature extraction (Islam, (para. [0050-0051]), the features extracted from the models trained for image-level PE classification were utilized and two learning paradigms were explored) and at least one downstream machine learning model to perform at least one corresponding downstream task (Islam, (para. [0052]), … varying images (N) in the exams …); obtaining first medical data (Islam, (para. [0091]), receives a plurality of medical images for processing by the system); training the fundamental machine learning model including the machine learning model for feature extraction and the at least one downstream machine learning model in an unsupervised or a self-supervised manner based on the first medical data; and storing the trained machine learning model for feature extraction (Islam, (para. [0095]), processing logic extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model, in which the pre-trained is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture; (para. [0105]), applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient).
Regarding claim 2, Islam further discloses, the method as claimed in claim 1, wherein the fundamental machine learning model has an architecture that is trainable by way of unsupervised or self-supervised training based on multimodal data, and wherein the first medical data is first multimodal medical data (Islam, (para. [0091]), receives a plurality of medical images for processing by the system; (para. [0095]), extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model).
Regarding claim 3, Islam further discloses, the method as claimed in claim 1, further comprising: performing an imaging method to generate at least some of the first medical data (Islam, (para. [0095]), extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model).
Regarding claim 16, a non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method of claim 1 (this claim is rejected under the same rationale as to claim 1).
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 of this title, 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.
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.
2. Claims 1-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Islam et al. (US Publication No. 2023/0081305, hereinafter “Islam”) in view of Syeda-Mahmood (US Pub No. 11,763,081, hereinafter “Syeda-Mahmood”).
Regarding claim 4, Islam does disclose, a method for anonymizing medical data, the method comprising: performing the method as claimed in claim 1 to provide the trained machine learning model for feature extraction (rejected under the same rationale as to claim 1).
Islam does not explicitly disclose but the analogous art Syeda-Mahmood discloses,
obtaining second medical data encompassing personal data; and anonymizing the second medical data via encoding by applying the trained machine learning model for feature extraction to the second medical data (Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Islam by including anonymizing the second medical data taught by Syeda-Mahmood for the advantage of improving operational efficiencies of hospitals and medical practices (Syeda-Mahmood, (col. 36 lines 44-45)).
Regarding claim 5, the combination of Islam-Syeda-Mahmood does disclose the method as claimed in claim 4, wherein the personal data includes at least one of image data from an imaging method; at least one of text data, tabular data or numerical data relating to a medical assessment or appraisal of a patient; or data relating to an identity of the patient (Syada-Mahmood, (col. 36 lines 16-20), performs the examination of the patient to capture the medical image data and provides the medical image data to the specially configured server computing device 1104 via one or more data networks, for automated medical imaging report generation).
Regarding claim 6, the combination of Islam-Syeda-Mahmood does disclose the method for providing a trained machine learning model for at least one of processing or analyzing medical data, the method comprising: performing the method as claimed in claim 4 to generate encoded second medical data; and training a machine learning model for at least one of processing or analyzing medical data in an unsupervised or a self-supervised manner based on the encoded second medical data (Islam, (para. [0067]), based on rigorous analysis, the optimal architectures for the tasks of image-level and exam-level classification were determined to be SeXception and Xception).
Regarding claim 7, the combination of Islam-Syeda-Mahmood does disclose the method as claimed in claim 6, further comprising: predicting at least one of a processing result or an analytical result by applying the machine learning model for at least one of processing or analyzing medical data to the encoded second medical data; evaluating a loss function as a function of the predicted at least one of the processing result or the analytical result; and updating the machine learning model for at least one of processing or analyzing medical data as a function of a result of the evaluating of the loss function (Islam, (para. [0096]), processing logic applies the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of a Pulmonary Embolism within the new medical images; (para. [0097]), processing logic outputs the prediction as a PE diagnosis for a medical patient).
Regarding claim 8, the combination of Islam-Syeda-Mahmood does disclose the method as claimed in claim 6, wherein the machine learning model for feature extraction remains unchanged when training the machine learning model for at least one of processing or analyzing medical data based on the encoded second medical data (Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Regarding claim 9, the combination of Islam-Syeda-Mahmood does disclose the method as claimed in claim 6, wherein the training of the machine learning model for at least one of processing or analyzing medical data is carried out based on the encoded second medical data via a data processing system that has no access to the second medical data (Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Regarding claim 10, the combination of Islam-Syeda-Mahmood does disclose a method for at least one of processing or analyzing medical data, the method comprising: performing the method as claimed in claim 6 to provide a trained machine learning model for at least one of processing or analyzing medical data; obtaining third medical data and generating encoded third medical data by applying the trained machine learning model for feature extraction to the third medical data; and producing at least one of a processing result or an analytical result applying the trained machine learning model for at least one of processing or analyzing medical data to the encoded third medical data (rejected under the same rationale as to claim 6).
Regarding claim 11, the combination of Islam-Syeda-Mahmood does disclose the method as claimed in claim 10, wherein the machine learning model for at least one of processing or analyzing medical data is a machine learning model for at least one of anatomical image classification; identifying a disease or an anomaly; medical image segmentation; or image preparation (Islam, (para. [0105]), wherein the pre-trained AI model is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture to extract informative features from the plurality of medical images by fusing spatial and channel-wise information; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient).
Regarding claim 12, the combination of Islam-Syeda-Mahmood does disclose an infrastructure system for performing the method as claimed in claim 6, the infrastructure system comprising: a first data processing system configured to provide a trained machine learning model for feature extraction; and a second data processing system configured to train a machine learning model for at least one of processing or analyzing medical data based on the encoded second medical data (Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Regarding claim 13, the combination of Islam-Syeda-Mahmood does disclose the infrastructure system as claimed in claim 12, further comprising: an imaging modality configured to perform an imaging method to generate image data, and transfer the image data to the first data processing system, wherein the first data processing system is configured to at least one of use the image data to train the fundamental machine learning model, or after training of the fundamental machine learning model, use the image data to further train the fundamental machine learning model (Islam, (para. [0095]), processing logic extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model, in which the pre-trained is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture; (para. [0105]), applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient).
Regarding claim 14, the combination of Islam-Syeda-Mahmood does disclose the infrastructure system as claimed in claim 12, wherein the first data processing system is configured to obtain third medical data, and generate encoded third medical data by applying the trained machine learning model for feature extraction to the third medical data, and wherein the first data processing system is configured to produce at least one of a processing result or an analytical result by applying the trained machine learning model for at least one of processing or analyzing medical data to the encoded third medical data, or the infrastructure system includes a third data processing system that is configured to produce at least one of a processing result or an analytical result by applying the trained machine learning model for at least one of processing or analyzing medical data to the encoded third medical data (Islam, (para. [0095]), processing logic extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model, in which the pre-trained is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture; (para. [0105]), applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient; Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Regarding claim 15, the combination of Islam-Syeda-Mahmood does disclose the infrastructure system as claimed in claim 12, further comprising at least one of: a hospital that contains the first data processing system; or at least one of a research facility or a development facility that contains the second data processing system and is spatially separate from the hospital (Syada-Mahmood, (col. 28 lines 4-15), the trained ML/DL computer model 1170 may also be trained for various other operations, such as patient medical condition synopsis or summary generation, for example. That is, the ML/DL computer model 1170 may be trained using the database 1160 to identify instances of the FFLs defined by the descriptor data structures present in the database 1160 in patient electronic medical records, which may include medical imaging reports as well as other electronically stored medical information from various source computing systems, e.g., pharmacies, doctor offices, hospitals, medical laboratories, medical imaging companies, medical supply stores, etc).
Regarding claim 17, the combination of Islam-Syeda-Mahmood does disclose a non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method of claim 4 (this claim is rejected under the same rationale as to claim 4).
Regarding claim 18, the combination of Islam-Syeda-Mahmood does disclose a non-transitory computer-readable medium storing computer-executable instructions including first commands that, when executed by at least one processor, cause the at least one processor to perform the method of claim 6 (this claim is rejected under the same rationale as to claim 6).
Regarding claim 19, the combination of Islam-Syeda-Mahmood does disclose the non-transitory computer-readable medium of claim 18, wherein the computer-executable instructions include second commands that, when executed by a second data processing system, cause the second data processing system to train the machine learning model for at least one of processing or analyzing medical data based on the encoded second medical data (this claim is rejected under the same rationale as to claim 6).
Regarding claim 20, the combination of Islam-Syeda-Mahmood does disclose the non-transitory computer-readable medium of claim 19, wherein the computer-executable instructions include third commands that, when executed by a third data processing system, cause the third data processing system to obtain third medical data and generate encoded third medical data by applying the trained machine learning model for feature extraction to the third medical data, and produce at least one of a processing result or an analytical result applying the trained machine learning model for at least one of processing or analyzing medical data to the encoded third medical data (Islam, (para. [0095]), processing logic extracts informative features from the plurality of medical images by fusing spatial and channel-wise information via the pre-trained AI model, in which the pre-trained is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture; (para. [0105]), applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient; Syada-Mahmood, (col. 36 lines 30-34), appropriate privacy protection mechanisms, such as encryption and the like, can be used to ensure the privacy of the patient's personally identifiable information in any data exchanged, such as in the automatically generated medical imaging report).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Publication No. 2023/0135706, “a system for modifying an output of a trained machine learning (ML) computer model based on label co-occurrence statistics to provide an improved ML computer model output. The method comprises generating, for each source knowledge data structure in a corpus comprising a plurality of source knowledge data structures, a label vector representation of the source knowledge data structure to thereby generate a plurality of label vector representations. The method further comprises determining co-occurrence scores for each pairing of labels in a plurality of labels, by generating statistical measures of the co-occurrence of labels in the pairings of labels across the plurality of label vector representations, to thereby generate a label co-occurrence data structure. The method also comprises receiving an output of the ML computer model, wherein the output is a vector output specifying probability values associated with labels in the plurality of labels. Moreover, the method comprises configuring a knowledge driven reasoning (KDR) computer model with at least one threshold and at least one delta value. The at least one threshold specifies a condition of a co-occurrence of a first label in the output of the ML computer model with a second label in the plurality of labels which, if present, causes the at least one delta value to be applied to modify a probability value associated with the second label in the output of the ML computer model. In addition, the method comprises executing the KDR computer model on the output of the ML computer model to modify one or more probability values in the output of the ML computer model and generate a modified output of the ML computer model, and outputting the modified output to a downstream computing system.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORSHED MEHEDI whose telephone number is (571) 270-7640. The examiner can normally be reached on M - F, 8:00 am to 4:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Linglan Edwards can be reach on (571) 270-5440. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MORSHED MEHEDI/Primary Examiner, Art Unit 2408
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