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
Claim 10 is added, and claims 1-10 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 07/30/2025.
Response to Arguments
Applicant’s arguments filed 10/30/2025 with respect to claim 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 Naidu is being relied upon to teach dependent claims 2, 4-5 and 10 more-consistently with the instant claim language, as shown below.
Given the amendments to claim 1, reference to Holmes is being relied upon to teach dependent claims 2 and 4-5 more-consistently with the instant claim language, as shown below.
Claim Objections
Claim 2 is objected to because of the following informalities: In claim 2, line 3, “wherein the processor an analysis target image…” should be “wherein the processor is further configured to save an analysis target image…” for clarity. Appropriate correction is required.
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.
Claims 1-2, 4-5, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasa Naidu et al. (US 20210353260 A1, published November 18, 2021) in view of Holmes et al. (US 20220199229 A1, published December 23, 2022), hereinafter referred to as Naidu and Holmes, respectively.
Regarding claim 1 and similarly for claims 8 and 9, Naidu teaches an ultrasound diagnostic apparatus (Fig. 1) comprising:
a processor having an image analysis model which has been trained through machine learning to analyze an ultrasound image (see para. 0050 – “As shown in FIG. 3, processor 300 [includes anatomy classifier 310 and settings prediction model 320 as image analysis model] receives, as input, an ultrasound image 304, which may be coupled to processor 300 in real-time (e.g., during live imaging).”);
wherein the processor is configured to create a log consisting of a plurality of operation records by recording a plurality of operations including an operation of the image analysis model in time series order (Fig. 1, log repository 117 as a plurality of operation records; see para. 0034 – “…system logs produced by a number of ultrasound imaging systems (e.g., ultrasound scanners 101-1, 101-2, through 101-m, which may include the ultrasound scanner 101-j).”; Fig. 8; see para. 0061 – “To prepare the training data, a time series plots may be constructed from the data extracted from the system logs.”);
save detailed information on each operation of the image analysis model for supporting model operation verification of the image analysis model (Fig. 7A-7B, saved detail information for each Event TimeStamp; Fig. 9; see para. 0061 – “In some examples, a portion of the tabulated data (e.g., up to 5%, up to 10%, or more) may be reserved as validation data and/or test data, to be used to validate and/or test the performance [operation verification] of the model prior to deployment.”),
wherein the detailed information is saved while being associated with a corresponding operation record among the plurality of the operation records in the log (Fig. 7A-7B, detailed information saved at each Event Timestamp as a corresponding operation record among a plurality of operation records (different Event Timestamps) in the log; see para. 0061 – “The system log may track any adjustments made to the system such as to change imaging settings. The portion of the log 700 shows recorded information 710, 712 associated with a given user action, for example recording the type of user action (e.g., preset_activated 710 to indicate the selection of a preset) and the specific preset selected (e.g., Adult Echo 712).”).
Naidu teaches detailed information associated with an operation record of the plurality of operation records in the log (Fig. 7A-7B, recorded information), and it is inherent to recall and display saved information, but does not explicitly teach creating a report including the detailed information associated with an operation record selected in the log.
Whereas, Holmes, in the same field of endeavor, teaches creating, in a case where a specific operation record of the image analysis model among the plurality of operation records in the log is selected, a model operation report including the detailed information associated with the specific operation record (see para. 0067 – “In a preferred embodiment, the visualization generation component 158 is configured within the controller 120 adapted to visualize the output onto the reporting unit 126 as illustrated in FIG. 4A.”; see para. 0086 – “The images, processed output, and/or decisions are collated together in the form of a report, which may either be deleted, saved for later, modified and/or stored within the data repository 148 of the back-end server 140 or otherwise within the controller's inbuilt storage 127 and/or any Picture Archive and communication system.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified saving detailed information, as disclosed in Naidu, by also creating a report including the detailed information associated with an operation record selected in the log, as disclosed in Holmes. One of ordinary skill in the art would have been motivated to make this modification in order to generate a visualization of the probe orientation guidelines/diagnosis/remedial plans, as taught in Holmes (see para. 0067).
Furthermore, regarding claim 2, Naidu further teaches wherein the processor saves an analysis target image as the detailed information for each operation of the image analysis model (Fig. 7A, ImagingMode of analysis target image included as detailed information; Fig. 2; see para. 0043 – “Alternatively, the images 232 may be generated from previously acquired image data stored in memory [saved image] (e.g., local or external memory) associated with system 200.”),
the analysis target image is an ultrasound image input to the image analysis model or an image corresponding to the ultrasound image (see para. 0050 – “As shown in FIG. 3, processor 300 [includes anatomy classifier 310 and settings prediction model 320 as image analysis model] receives, as input, an ultrasound image 304, which may be coupled to processor 300 in real-time (e.g., during live imaging).”), and
Holmes further teaches the model operation report includes the analysis target image (Fig. 4A, ultrasound image in report; see para. 0086 – “The images, processed output, and/or decisions are collated together in the form of a report, which may either be deleted, saved for later, modified and/or stored within the data repository 148 of the back-end server 140 or otherwise within the controller's inbuilt storage 127 and/or any Picture Archive and communication system.”).
Furthermore, regarding claim 4, Naidu further teaches wherein the processor saves an analysis result of the image analysis model as the detailed information for each operation of the image analysis model (see para. 0061 – “FIG. 7B shows a portion of another system log 720, e.g., a workflow log generated by the EPIQ system. The portion of log 720 shows the changes to the depth parameter, e.g., depth change events 722-1 and 722-2 [saved analysis result], with corresponding depth values 724-1 and 724-2 being set to 1 and 2, respectively.”), and
Holmes further teaches the model operation report includes the analysis result (Fig. 4A, Scan settings information in report; see para. 0086 – “The images, processed output, and/or decisions are collated together in the form of a report, which may either be deleted, saved for later, modified and/or stored within the data repository 148 of the back-end server 140 or otherwise within the controller's inbuilt storage 127 and/or any Picture Archive and communication system.”).
Furthermore, regarding claim 5, Naidu further teaches wherein the analysis result includes a plurality of scores corresponding to a plurality of classes, the report creation unit creates a graph based on the plurality of scores (Fig. 5; see para. 0057 – “…a fully connected output layer 512 configured to output a classification of the image into one of a plurality of possible categories 518 or the probabilities of the image falling in each of the possible categories [plurality of scores].”), and
Holmes further teaches the model operation report includes the graph (see para. 0084 – “In a preferred embodiment, the step of analysis includes processing the images and/or frames thereof to perform a classification to define the target area, perform identification in comparison with the normal and/or historical data and other data sets 167 followed by diagnosing using CAD functions to prepare an output of the analysis.”).
Furthermore, regarding claim 10, Naidu further teaches wherein the operation of the image analysis model includes event occurrence and processing execution (see para. 0050 – “As shown in FIG. 3, processor 300 [includes anatomy classifier 310 and settings prediction model 320 as image analysis model] receives, as input, an ultrasound image 304, which may be coupled to processor 300 in real-time (e.g., during live imaging).”), and
the detailed information includes an input image and an analysis result corresponding to the operation of the image analysis (see para. 0061 – “FIG. 7B shows a portion of another system log 720, e.g., a workflow log generated by the EPIQ system. The portion of log 720 shows the changes to the depth parameter, e.g., depth change events 722-1 and 722-2 [analysis result], with corresponding depth values 724-1 and 724-2 being set to 1 and 2, respectively.”).
The motivation for claims 2 and 4-5 was shown previously in claim 1.
Claims 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Naidu in view of Holmes, as applied to claim 2 above, and in further view of Lee (US 20220061816 A1, published March 3, 2022), hereinafter referred to as Lee.
Regarding claim 3, Naidu in view of Holmes teaches all of the elements disclosed in claim 2 above.
Naidu in view of Holmes teaches inputting an ultrasound image to an image analysis model, but does not explicitly teach where the ultrasound image is a low-resolution ultrasound image.
Whereas, Lee, in an analogous field of endeavor, teaches wherein the processor is further configured to convert the ultrasound image input to the image analysis model into a low-resolution image, wherein the analysis target image is the low-resolution image (Fig. 7; see para. 0102 — “At 706, method 700 includes acquiring first scan data with the 1D transducer, wherein the first scan data is obtained by scanning a first volume of a given volume of interest.”; see para. 0104 — “At 708, method 700 includes generating a first ultrasound image using the first scan data. The first ultrasound image has a lower resolution profile.”; see para. 0105 — “Next, method 700 proceeds to 710 at which the method includes providing, as input to the trained resolution mapping algorithm, the first ultrasound image with a lower resolution profile.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified inputting an ultrasound image to an image analysis model, as disclosed in Naidu in view of Holmes, by inputting a low-resolution ultrasound image to the image analysis model, as disclosed in Lee. One of ordinary skill in the art would have been motivated to make this modification in order to reduce complexity of scanning and without bulkiness of additional mechanical controls, as taught in Lee (see para. 0103).
Furthermore, regarding claim 6, Naidu further teaches
wherein the processor includes an image saving unit that saves an analysis target image for each operation of the image analysis model (Fig. 7A, ImagingMode of analysis target image included as detailed information; Fig. 2; see para. 0043 – “Alternatively, the images 232 may be generated from previously acquired image data stored in memory [saved image] (e.g., local or external memory) associated with system 200.”), and
a record saving unit that saves a detailed record including an analysis result of the image analysis model for each operation of the image analysis model (see para. 0061 – “FIG. 7B shows a portion of another system log 720, e.g., a workflow log generated by the EPIQ system. The portion of log 720 shows the changes to the depth parameter, e.g., depth change events 722-1 and 722-2 [saved analysis result], with corresponding depth values 724-1 and 724-2 being set to 1 and 2, respectively.”),
the detailed information includes the analysis target image, which is input information of the image analysis model (Fig. 7A, ImagingMode of analysis target image included as detailed information; see para. 0050 – “As shown in FIG. 3, processor 300 [includes anatomy classifier 310 and settings prediction model 320 as image analysis model] receives, as input, an ultrasound image 304, which may be coupled to processor 300 in real-time (e.g., during live imaging).”), and
the detailed information includes the analysis result, which is output information of the image analysis mode (see para. 0061 – “FIG. 7B shows a portion of another system log 720, e.g., a workflow log generated by the EPIQ system. The portion of log 720 shows the changes to the depth parameter, e.g., depth change events 722-1 and 722-2, with corresponding depth values 724-1 and 724-2 being set to 1 and 2, respectively.”),
Holmes further teaches the detailed information in the model operation report includes the analysis target image (Fig. 4A, ultrasound image in report; see para. 0086 – “The images, processed output, and/or decisions are collated together in the form of a report, which may either be deleted, saved for later, modified and/or stored within the data repository 148 of the back-end server 140 or otherwise within the controller's inbuilt storage 127 and/or any Picture Archive and communication system.”), and
the detailed information in the model operation report includes the analysis result, which is output information of the image analysis model (Fig. 4A, Scan settings information in report; see para. 0086 – “The images, processed output, and/or decisions are collated together in the form of a report, which may either be deleted, saved for later, modified and/or stored within the data repository 148 of the back-end server 140 or otherwise within the controller's inbuilt storage 127 and/or any Picture Archive and communication system.”), and
Lee further teaches the analysis target image is a low-resolution image generated from an ultrasound image input to the image analysis model (Fig. 7; see para. 0102 — “At 706, method 700 includes acquiring first scan data with the 1D transducer, wherein the first scan data is obtained by scanning a first volume of a given volume of interest.”; see para. 0104 — “At 708, method 700 includes generating a first ultrasound image using the first scan data. The first ultrasound image has a lower resolution profile.”; see para. 0105 — “Next, method 700 proceeds to 710 at which the method includes providing, as input to the trained resolution mapping algorithm, the first ultrasound image with a lower resolution profile.”).
The motivation for claim 6 was shown previously in claims 1 and 3.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Naidu in view of Holmes, as applied to claim1 above, and in further view of Vincent et al. (US 20200311938 A1, published October 1, 2020), hereinafter referred to as Vincent.
Regarding claim 7, Naidu in view of Holmes teaches all of the elements disclosed in claim 1 above.
Naidu in view of Holmes teaches a plurality of operation records in a log, but does not explicitly teach each operation record includes a portion embedded with a hyperlink.
Whereas, Vincent, in an analogous field of endeavor, teaches wherein each operation record included in the log includes a portion embedded with a hyperlink for specifying a location of the detailed information associated with the operation record, and the model operation report is displayed upon selection of the portion (see para. 003±1 — “Databases 230 may contain images or other study data (e.g., parameter values (e.g., measurements)) that are linked to a patient's electronic medical record (EMR), such that images and/or study data may be selected from within the EMR and displayed within a viewer via display component 222...”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a plurality of operation records in a log, as disclosed in Naidu in view of Holmes, by having each operation record include a portion embedded with a hyperlink, as disclosed in Vincent. One of ordinary skill in the art would have been motivated to make this modification in order for the user to easily see the studies that have been received and the results of the image analysis that has been performed, as taught in Vincent (see para. 0050).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rao et al. (US 20220253592 A1, published August 11, 2022) discloses where a medical report analysis system can generate report analysis data by performing a report processing function and/or can generate correction requirement notification data based on the report analysis data.
Bricker et al. (US 20220133279 A1, published May 5, 2022) discloses a display panel that includes the exam log, which can include events relating to the start of the exam, the timestamp and location of a detected mass, the start of a recording, the current displayed views of the interface, edits to the appointment or patient information, and the end of the exam (Fig. 6B).
Takahashi et al. (US 20220005584 A1, published January 6, 2022) discloses a storage unit configured to store information individually set for each of a plurality of different types of imaging as transmission settings for a plurality of pieces of imaging data obtained by the plurality of different types of imaging.
Samset et al (US 20210280298 A1, published September 9, 2021) discloses displaying the ultra sound image(s) with the generated report, where the generated report may include options or commands for performing the suggested action for verifying the potential abnormality.
Sorenson et al. (US 20180137244 A1, published May 17, 2018) discloses storing user interactions related to the workflow to the processing server in individual user preference file or in a group preference file or in a log file.
Sorenson et al. (US 20210098113 A1, published April 1, 2021) discloses create a log file that can include the image data, user interactions, image information, or any combination thereof, and can track and update the log file based on user interactions and/or changes in the image information.
Simons et al. (US 20170032296 A1, published February 2, 2017) discloses receiving, from at least one ultrasound machine, at least one data log comprising at least one data record based on the operation of the ultrasound machine.
Uehara (US 20230197252 A1, published June 22, 2023) discloses an apparatus that collects an operating log, calculates priority of each of the plurality of types of image processing based on the collected operating log, and updates and manages priority information.
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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/Nyrobi Celestine/Examiner, Art Unit 3798
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