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 Amendment
The amendment filed 07/01/2026 has been entered. Claims 1, 4, 6, 8, 10, 13-14, and 17-20 remain pending, with claims 10 and 17-20 withdrawn, in the application. Applicant’s amendments to the Claims have overcome each and every 101 rejections previously set forth in the Non-Final Office Action mailed 04/21/2026
Response to Arguments
Applicant’s arguments filed 07/01/2026 with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Given the amendments to claim 1, reference to Raju is being relied upon to teach dependent claims 4, 6, 8, and 13-14 more-consistently with the instant claim language, as shown below.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 4, 6, 8, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Raju et al. (US 20200352547 A1, published November 12, 2020), hereinafter referred to as Raju.
Regarding claim 1, Raju teaches an ultrasound diagnostic system comprising:
an ultrasound probe having a transducer array (Fig. 2; see para. 0023 – “The ultrasound data acquisition unit 210 can include an ultrasound probe which includes an ultrasound sensor array 212 [transducer array]…”);
a monitor or a speaker (see para. 0024 – “Determinations made by the data processor 228 can be communicated to a display processor 232 coupled with a graphical user interface 234 [monitor].”);
a data memory configured to store information of an artifact which occurs due to a medical condition of a subject (see para. 0021 – “For example, a software-based neural network may be implemented using a processor (e.g., single or multi-core CPU, a single GPU or GPU cluster, or multiple processors arranged for parallel processing) configured to execute instructions, which may be stored in computer readable medium [data memory], and which when executed cause the processor to perform a trained algorithm for assessing B-lines present [information of an artifact which occurs due to a medical condition] within an ultrasound image.”; see para. 0023 – “…neural network 230, trained to assess B-line patterns and determine whether the assessed patterns indicate cardiogenic or non-cardiogenic etiology.”);
a processor (Fig. 2, processor includes ultrasound data acquisition unit 210 and data processor 228) configured to:
transmit and receive an ultrasound beam to and from the subject with the transducer array (Fig. 2; see para. 0023 – “The ultrasound data acquisition unit 210 can include an ultrasound probe which includes an ultrasound sensor array 212 configured to transmit ultrasound pulses 214 into a target region 216 of a patient, which may include one or both lungs, and receive ultrasound echoes 218 responsive to the transmitted pulses.”);
acquire an ultrasound image based on a received signal output from the transducer array (Fig. 2; see para. 0023 – “As further shown, the ultrasound data acquisition unit 210 can include a beamformer 220 and a signal processor 222, which can be configured to generate a stream of discrete ultrasound image frames 224 from the ultrasound echoes 218 received at the array 212.”);
determine whether or not the artifact has occurred in the ultrasound image, using a trained model which has trained a plurality of ultrasound images including the artifact (Fig. 2; see para. 0023 – “…the data processor 228 may be configured to implement at least one neural network, such as neural network 230, trained to assess B-line patterns [presence of artifacts] and determine whether the assessed patterns indicate cardiogenic or non-cardiogenic etiology.”; see para. 0033 – “According to such examples, the neural network 230 may be a feed-forward neural network trained using a plurality, e.g., thousands, of ultrasound images containing various numbers and spatial distributions of B-lines [trained a plurality of ultrasound images including the artifact].”); and
give a notification for a user of that the artifact is useful for diagnosing the subject by displaying the notification on the monitor or outputting a voice through the speaker (Fig. 4A-4B; see para. 0024 – “In addition to the displayed ultrasound images 236, the user interface 234 [monitor] can be configured to generate one or more additional outputs 240 [notification], which can include an assortment of graphics displayed concurrently with, e.g., overlaid on, the ultrasound images 236. The graphics may label certain anatomical features and measurements identified by the system, such as the presence, number, location and/or spatial distribution of B-lines, an etiology notification based on the B-line determination(s) [notification for a user of that the artifact is useful for diagnosing]…”).
Furthermore, regarding claim 4, Raju further teaches a diagnostic apparatus including the processor and the data memory (see para. 0021 – “In some embodiments, the ultrasound images and associated measurements may be provided to a storage and/or memory device, such as a picture archiving and communication system (PACS) [known in the art to include processor and memory] for reporting purposes or future training (e.g., to continue to enhance the performance of the neural network).”).
Furthermore, regarding claim 6, Raju further teaches a diagnostic apparatus including the processor; and a server being connected to the diagnostic apparatus through a network and including the data memory (see para. 0021 – “In some embodiments, the ultrasound images and associated measurements may be provided to a storage and/or memory device, such as a picture archiving and communication system (PACS) [known in the art to include processors, memory, a network, and a server] for reporting purposes or future training (e.g., to continue to enhance the performance of the neural network).”).
Furthermore, regarding claim 8, Raju further teaches wherein the processor is composed of a first processor configured to generate the ultrasound image and determine whether or not the artifact has occurred in the ultrasound image (Fig. 2; see para. 0023 – “The image frames 224 generated by the signal processor 222 can be communicated to a data processor 228 [first processor], e.g., a computational module or circuitry, configured to …determine the presence and/or severity of B-lines [determine whether or not the artifact has occurred] present within one or more image frames 224.”), and
a second processor configured to give the notification for the user (Fig. 2; see para. 0024 – “In addition to the displayed ultrasound images 236, the user interface 234 [second processor] can be configured to generate one or more additional outputs 240 [notification], which can include an assortment of graphics displayed concurrently with, e.g., overlaid on, the ultrasound images 236. The graphics may label certain anatomical features and measurements identified by the system, such as the presence, number, location and/or spatial distribution of B-lines, an etiology notification based on the B-line determination(s) [notification for a user of that the artifact is useful for diagnosing]…”),
the ultrasound diagnostic system further comprises: a diagnostic apparatus including the second processor; and a server being connected to the diagnostic apparatus through a network and including the first processor and the data memory (see para. 0021 – “In some embodiments, the ultrasound images and associated measurements may be provided to a storage and/or memory device, such as a picture archiving and communication system (PACS) [known in the art to include processors, memory, a network, and a server] for reporting purposes or future training (e.g., to continue to enhance the performance of the neural network).”).
Furthermore, regarding claim 13, Raju further teaches an input device configured to designate a region of interest in response to an input operation of the user on the ultrasound image, wherein the processor is further configured to determine whether or not the artifact has occurred in the region of interest (see para. 0024 – “The user interface 234 [input device] can be configured to receive user input 238 at any time before, during or after an ultrasound procedure.”; see para. 0029 – “The data processor 228 can be configured to characterize B-lines [artifact] appearing in one or more image frames 224 in accordance with various methodologies. In some examples, the data processor 228 can be configured to identify B-lines by first locating the pleural line, then defining a region of interest below the pleural line and identifying B-lines [determine whether or not the artifact has occurred in ROI]…”).
Furthermore, regarding claim 14, Raju further teaches an input device configured to designate a region of interest in response to an input operation of the user on the ultrasound image, wherein the processor is further configured to determine whether or not the artifact has occurred in the region of interest (see para. 0024 – “The user interface 234 [input device] can be configured to receive user input 238 at any time before, during or after an ultrasound procedure.”; see para. 0029 – “The data processor 228 can be configured to characterize B-lines [artifact] appearing in one or more image frames 224 in accordance with various methodologies. In some examples, the data processor 228 can be configured to identify B-lines by first locating the pleural line, then defining a region of interest below the pleural line and identifying B-lines [determine whether or not the artifact has occurred in ROI]…”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Arntfield et al. (US 20230148996 A1, published May 18, 2023 with a priority date of July 19, 2021) discloses processing data to distinguish between a plurality of conditions in lung ultrasound images and, in particular, lung ultrasound images containing B lines. Neural network systems and methods, in which the processor is trained using lung ultrasound images to distinguish between acute respiratory distress syndrome due to COVID-19, acute respiratory distress syndrome due to non-COVID-19 causes, and hydrostatic pulmonary edema.
Kruecker et al. (US 20240285259 A1, published August 29, 2024 with a priority date of June 23, 2021) discloses a trained CNN can be configured or designed to analyze the image and determine the number and/or appearance of B-lines in the updated ultrasound image and determine a risk score for a potential ventilator-associated lung injury.
Mehanian et al. (US 20200054306 A1, published February 20, 2020) discloses the ultrasound system yields a likely diagnosis (e.g., likely pneumonia, likely pneumothorax), based on the features, classifications, and severities (of lung sliding, A-lines, B-lines, pleural line, consolidation, and pleural effusion) yielded as the outputs (Fig. 9).
Martin et al. (US 20200008709 A1, published January 9, 2020) discloses the statistical model may determine whether inputted ultrasound image indicates apnea by determining whether the ultrasound data indicates an absence of lung sliding or an absence of movement of internal abdominal organs.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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/N.C./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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