Notice of Pre-AIA or AIA Status
1. The present application, filed on or after Marc 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
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, 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.
Claim(s) 1-4, 6-9, 11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 12,141,541 B1 to Mishra in view of U.S. Patent Application No. 2019/0350564 A1 to Gajdos et al. (hereinafter Gajdos).
With regards to claim 1, Mishra discloses:
1. A method, comprising:
[acquiring a current image of a patient during a current exam];
generating, with a computer vision-enabled large language model (CV/LLM), a first compressed representation (CR) of the current image (see, Fig. 5B, and detailed description, including, the descriptive text and the prior generated text-based video narrative already generated for the video, may be provided to an edge model, such as a large language model (“LLM”) operating on the edge with instructions to generate a natural language description of the segment of the video, col. 17, lines 24-33);
obtaining a second CR of a similar image, the similar image similar to the current image and acquired in [a prior exam] (see, Fig. 11, and detailed description, including, in another example, a report or summary may be generated any time an alert is triggered, or an incident is detected. Similar to an incident report, any of a variety of reports may be predefined and populated based on the descriptive text and the context of the video as determined herein. In comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37);
generating a text-based comparison of the current image and the similar image using the CV/LLM by entering the first CR and the second CR as input to the CV/LLM (see, as above, and in comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37); and
outputting the text-based comparison (see, as above, and, in some implementations, a summary may be generated for every video. In another example, a report or summary may be generated any time an alert is triggered, or an incident is detected, col. 31, lines 20-37).
Mishra fails to explicitly disclose an examination of a patient.
However, Gajdos discloses:
An examination of a patient, and acquiring a current image of a patient during a current exam (see, detailed description, including, The input is the image with or without other information (e.g., patient and location). When the network is invoked, the current ultrasound image, imaging parameters, patient information, and region information are collected and fed as input into trained network, para. 0063; and
Subsequent Exams, (see, detailed description, Summary, The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation, para. 0004).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, An examination of a patient, and acquiring a current image of a patient during a current exam (see, detailed description, including, The input is the image with or without other information (e.g., patient and location). When the network is invoked, the current ultrasound image, imaging parameters, patient information, and region information are collected and fed as input into trained network, para. 0063. And, for example, subsequent Exams, (see, detailed description, Summary, The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation, para. 0004).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art1.
With regard to claim 2, Gajdos discloses:
2. The method of claim 1, wherein the similar image is of the patient and includes the same anatomical features imaged in the current image (see, detailed description, including, the log data may indicate repetition of the same scan or regeneration of the image from the same image data with one or more different settings, with the interpretation that repetition of the same scan will contain the same anatomy of the patient, para. 0033).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, the similar image is of the patient and includes the same anatomical features imaged in the current image (see, detailed description, including, the log data may indicate repetition of the same scan or regeneration of the image from the same image data with one or more different settings, with the interpretation that repetition of the same scan will contain the same anatomy of the patient, para. 0033).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art2.
With regard to claim 3, Mishra discloses:
3. The method of claim 1, wherein the second CR is generated with the CV/LLM, and wherein outputting the text-based comparison comprises outputting the text-based comparison to a display device (see, as above, and a summary may be generated for every video. In another example, a report or summary may be generated any time an alert is triggered, or an incident is detected. Similar to an incident report, any of a variety of reports may be predefined and populated based on the descriptive text and the context of the video as determined herein. In comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37).
With regards to claim 4, Gajdos discloses:
4. The method of claim 3, further comprising, upon acquiring the similar image during the prior exam, entering the similar image as input to the CV/LLM to generate the second CR, saving the second CR inside a DICOM Structured Reporting (DICOM SR) object, and sending the DICOM SR object and similar image to an image archive for long-term storage (see, detailed description, including, the datasets may be uploaded to a third-party storage (e.g., scanner manufacturer storage) via a web/cloud service. The dataset may be stored as part of DICOM as private tag, so that, when DICOM is exported to PACS, the PACS is queried for the datasets, para. 0044).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, the datasets may be uploaded to a third-party storage (e.g., scanner manufacturer storage) via a web/cloud service. The dataset may be stored as part of DICOM as private tag, so that, when DICOM is exported to PACS, the PACS is queried for the datasets, para. 0044).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art3.
With regard to claim 6, Gajdos discloses:
6. The method of claim 1, further comprising entering the similar image and the current image as inputs to one or more artificial intelligence models each trained to generate a respective output related to anatomical features in the similar image and the current image (see, detailed description, including, a sample that is the image, patient information, location information, and/or user information. This ground truth is used to train or retrain a machine-learned network to be applied by the medical scanner or other scanner for examination of other patients. The machine learning is used to optimize and personalize the system to a specific patient type, anatomy type, view type, user preference, and/or regional preference, para. 0019).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, a sample that is the image, patient information, location information, and/or user information. This ground truth is used to train or retrain a machine-learned network to be applied by the medical scanner or other scanner for examination of other patients. The machine learning is used to optimize and personalize the system to a specific patient type, anatomy type, view type, user preference, and/or regional preference, para. 0019).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art4.
With regard to claim 7, Mishra discloses:
7. The method of claim 6, wherein generating the text-based comparison further comprises including one or more respective outputs from one or more of the one or more artificial intelligence models in the text-based comparison (see, detailed description, including, the edge computing unit 330-i may also be used to perform inference or generate predictions locally, e.g., by executing one or more of the trained or pretrained machine learning or artificial intelligence models 335-1 . . . 335-n that may be received from any external computing systems 350 or any other edge computing units, col. 13, lines 10-18).
With regard to claim 8, Gajdos discloses:
8. The method of claim 6, wherein generating the text-based comparison further comprises determining that one or more statements of the text-based comparison does not match respective one or more outputs of the one or more artificial intelligence models, and adjusting the one or more statements of the text-based comparison to match the respective one or more outputs of the one or more artificial intelligence models (see, detailed description, including, for instance, the edge computing unit 330-i may include computational hardware components configured to perform inference for one or more trained machine learning or artificial intelligence models, that is interpreted to operate when there are non-matching, but otherwise close enough for the inference, col. 11, lines 3-9.
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, for instance, the edge computing unit 330-i may include computational hardware components configured to perform inference for one or more trained machine learning or artificial intelligence models, that is interpreted to operate when there are non-matching, but otherwise close enough for the inference, col. 11, lines 3-9.
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art5.
With regard to claim 9, Mishra discloses:
9. An image processing system, comprising a processor (see, Fig. 1, and detailed description, including, computing unit 130 may be a computer system that includes any number of servers, processors, data stores, transceivers, switches, or other computer components or systems, as well as any number of power units, environmental control systems, isolation systems, or systems, col. 3, lines 61-63) and a non-transitory memory storing instructions, see as above, col. 3, lines 61-63) that when executed, cause the processor to:
[acquire a current ultrasound image of a patient during a current exam];
generate a first compressed representation (CR) of the current ultrasound image using a computer vision-enabled large language model (CV/LLM) trained on a dataset [of patient exams] (see, Fig. 5B, and detailed description, including, the descriptive text and the prior generated text-based video narrative already generated for the video, may be provided to an edge model, such as a large language model (“LLM”) operating on the edge with instructions to generate a natural language description of the segment of the video, col. 17, lines 24-33);
identify a similar ultrasound image of [the patient], the similar ultrasound image similar to the current ultrasound image and acquired [in a prior exam of the patient] (see, Fig. 11, and detailed description, including, in another example, a report or summary may be generated any time an alert is triggered, or an incident is detected. Similar to an incident report, any of a variety of reports may be predefined and populated based on the descriptive text and the context of the video as determined herein. In comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37);
obtain a second CR of the similar ultrasound image (see, Fig. 11, and detailed description, including, in another example, a report or summary may be generated any time an alert is triggered, or an incident is detected. Similar to an incident report, any of a variety of reports may be predefined and populated based on the descriptive text and the context of the video as determined herein. In comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37);
generate a natural language comparison of the current ultrasound image and the similar ultrasound image by entering the first CR and the second CR as input to the CV/LLM (see, as above, and in comparison, a summary may be an overall distillation or overview of the video based on the text-based video narrative generated for the video, col. 31, lines 20-37); and
output the natural language comparison (see, as above, and, in some implementations, a summary may be generated for every video. In another example, a report or summary may be generated any time an alert is triggered, or an incident is detected, col. 31, lines 20-37).
Mishra fails to explicitly disclose an examination of a patient.
However, Gajdos discloses:
An examination of a patient, and acquiring a current image of a patient during a current exam (see, detailed description, including, The input is the image with or without other information (e.g., patient and location). When the network is invoked, the current ultrasound image, imaging parameters, patient information, and region information are collected and fed as input into trained network, para. 0063; and
Subsequent Exams, (see, detailed description, Summary, The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation, para. 0004).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, An examination of a patient, and acquiring a current image of a patient during a current exam (see, detailed description, including, The input is the image with or without other information (e.g., patient and location). When the network is invoked, the current ultrasound image, imaging parameters, patient information, and region information are collected and fed as input into trained network, para. 0063. And, for example, subsequent Exams, (see, detailed description, Summary, The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation, para. 0004).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art6.
With regard to claim 11, claim 11 (a system claim) recites substantially similar limitations to claim 4 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 14, Gajdos discloses:
14. The image processing system of claim 9, wherein the image processing system is operably coupled to an ultrasound probe and wherein acquiring the current ultrasound image of the patient during the current exam comprises receiving ultrasound data from the ultrasound probe and processing the ultrasound data to form the current ultrasound image (see, detailed description, including, the transducer probe is positioned on the patient and moved to generate an image of the region of interest in the patient. For example, the region of interest is a given organ, so the probe is moved relative to the patient to image the organ with the current settings, para. 0039)
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, An examination of a patient, and acquiring a current image of a patient during a current exam (see, detailed description, including, is operably coupled to an ultrasound probe and wherein acquiring the current ultrasound image of the patient during the current exam comprises receiving ultrasound data from the ultrasound probe and processing the ultrasound data to form the current ultrasound image (see, detailed description, including, the transducer probe is positioned on the patient and moved to generate an image of the region of interest in the patient. For example, the region of interest is a given organ, so the probe is moved relative to the patient to image the organ with the current settings, para. 0039).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art7.
With regard to claim 15, Gajdos discloses:
15. The image processing system of claim 9, wherein outputting the natural language comparison comprises displaying the natural language comparison on a display device and wherein during the current exam, the similar ultrasound image is not displayed on the display device (see, detailed description, including, an image of the heart may not show the desired chamber or chambers with sufficient contrast and/or resolution. Where the image is not sufficient, the user changes one or more settings to generate a better image in act 26. Based on this change, the poor quality image and corresponding settings are stored or collected in act 27 as a negative example. Where the image may be diagnostically useful, the user captures the image in act 28. By depressing a capture button, the image is recorded for later use to diagnose the patient, para. 0040).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Mishra with Gajdos before her, to be motivated to combine the features from Gajdos, with Mishra, including, an image of the heart may not show the desired chamber or chambers with sufficient contrast and/or resolution. Where the image is not sufficient, the user changes one or more settings to generate a better image in act 26. Based on this change, the poor quality image and corresponding settings are stored or collected in act 27 as a negative example. Where the image may be diagnostically useful, the user captures the image in act 28. By depressing a capture button, the image is recorded for later use to diagnose the patient, para. 0040).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art8.
Allowable Subject Matter
Claims 5, 10, 12-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 16-20 are allowed.
A sampling of the prior art made of record and not relied upon and considered
pertinent to Applicants’ disclosure includes: US Patent Application Publication No. US
2023/0352127 A1 to Sivan et al. that discloses: a method and a system for automatic electronic health record documentation. It uses knowledge engineering to convert recordings or diarized texts of interactions between a medical practitioner and a patient into a narrative and entering the data into the desired electronic health record. The present invention is trained at the voice level to identify medical concepts spoken in different accent and auto identify the medical context in speech. The present invention learns the electronic health record (EHR) workflow of the medical practitioner and mimics the clinical workflow and protocol to ensure all elements as per the practitioner are in place and logs into the EHR and enter the data into a structured format.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen C. Hong can be reached at 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 12-18-25
1 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
2 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
3 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
4 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
5 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
6 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
7 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
8 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).