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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/17/2026 has been entered. Claims 1-10 remain pending in the application.
Response to Amendment
Claims 1-10 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Final Office Action mailed 12/03/2025.
Response to Arguments
Applicant’s arguments filed 04/02/2026 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 Bricker 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 Holvey is being relied upon to teach dependent claim 7 more-consistently with the instant claim language, as shown below.
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 Bricker et al. (US 20220133279 A1, published May 5, 2022) in view of Kikuchi et al. (JP 2023062448 A, published May 8, 2023), hereinafter referred to as Bricker and Kikuchi, respectively.
Regarding claim 1, and similarly for claims 8 and 9, Bricker teaches an ultrasound diagnostic system (Fig. 1A, pressure elastography system 100a) comprising:
a processor is configured to analyze ultrasound images using an image analysis model trained through machine learning (see para. 0009 – “…a second data set associated with an ultrasound scan of a second breast mass detection procedure contemporaneously conducted on the subject following the pressure elastography measurement (e.g., acquired from a portable ultrasound device)…wherein the first data set and the second data set are…ii) subsequently analyzed via machine learning or deep learning operations (e.g., in a remote/cloud server or the device) to provide an indication associated with the assessment of the breast mass.”);
create a log including a plurality of operation records by recording a plurality of operations including operations of the image analysis model in time series order (Fig. 6; see para. 0104 – “During operation, the local controller 108 may be configured to generate an event log comprising a timestamped list of key events during an exam, e.g., screen change, start/stop recording, mass finding identified, patient data edited, among others. The event log can be provided to the local cloud infrastructure 102 a, e.g., as 154 a. Sensor log is a timestamped list of exam forces measured by the sensor throughout the exam, along with other diagnostic information.”);
save, for each operation of the image analysis model, detailed information associated with a corresponding operation record in the log, the detailed information including output generated by the image analysis model (see para. 0144 – “In essence, the local controller (e.g., 108) can log [save] any input made to the interface 600, the sensor device (e.g., the buttons on the devices 104, 106) as detected by the controller, as well as any functions or monitoring operations, and their outputs, being executed on the controller (e.g., 108).”); and
display the plurality of operation records included in the log (see para. 0144 – “FIG. 6B shows an example display of panel 608 (shown as 608 a). The panel 608 a includes the exam log 616, which can include events relating to the start of the exam (616 a), the timestamp and location of a detected mass (616 b), the start of a recording (616 c), the current displayed views of the interface (616 d), edits to the appointment or patient information (616 e), and the end of the exam (6160.” Information of Fig. 6B as plurality of operation records on display).
Bricker teaches displaying the plurality of operation records, but does not explicitly teach selecting a specific operation record among the displayed operation records.
Whereas, Kikuchi, in an analogous field of endeavor, teaches
in response to selection of a specific operation record among the displayed operation records that corresponds to an operation of the image analysis model, retrieve the detailed information associated with the specific operation record (Fig. 3; see pg. 6, para. 4 – “The first display area G10 is a display area of a user interface that receives input of an operation for selecting a log to be displayed. The second display area G20 is an area where the content of the log selected as the display target is displayed. The third display area G30 is a display area of a user interface that accepts input of a keyword (hereinafter referred to as "search key") for searching the selected log. Note that when a search key is entered, search results of each selected log are displayed in the second display area G20. The fourth display area G40 is an area in which an ultrasound image selected as a display target is displayed [retrieved detailed information].”); and
create a model operation report including the retrieved detailed information (Fig. 3; see pg. 6, para. 4 – “The fourth display area G40 is an area in which an ultrasound image selected as a display target is displayed [retrieved detailed information, report].”).
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 displaying the plurality of operation records, as disclosed in Bricker, by also selecting a specific operation record among the displayed operation records, as disclosed in Kikuchi. One of ordinary skill in the art would have been motivated to make this modification in order for the maintenance worker to refer to the corresponding ultrasonic image simply by selecting the entry of the target log displayed on the investigation screen, and an abnormality occurring in the ultrasonic diagnostic apparatus can be investigated efficiently, as taught in Kikuchi (see pg. 13, para. 4).
Furthermore, regarding claim 2, Bricker further teaches wherein the processor is further configured to save an analysis target image as the detailed information for each operation of the image analysis model, the analysis target image is an ultrasound image input to the image analysis model or an image corresponding to the ultrasound image, and the model operation report includes the analysis target image (see para. 0134 – “The local controller (e.g., 108) can maintain the scan or recordings as time-series data or convert the data to a video or image file (e.g., healthcare video or image file format) [save images]…At the conclusion of the exam, the cloud infrastructure 103′ or local controller 108′ may initiate (524) the generation of a report that includes the pressure elastography results.”).
Furthermore, regarding claim 4, Bricker 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, and the model operation report includes the analysis result (see para. 0134 – “The local controller (e.g., 108) can maintain the scan or recordings as time-series data or convert the data to a video or image file (e.g., healthcare video or image file format)…At the conclusion of the exam, the cloud infrastructure 103′ or local controller 108′ may initiate (524) the generation of a report that includes the pressure elastography results.”).
Furthermore, regarding claim 5, Bricker 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, and the model operation report includes the graph (Fig. 6; see para. 0146 – “The aggregated visualization can include…findings of masses [graph of scores/classes] (646) (e.g., by site location/office, examiner, time period).”).
Furthermore, regarding claim 10, Bricker further teaches wherein the operation of the image analysis model includes event occurrence and processing execution (see para. 0144 – “FIG. 6B shows an example display of panel 608 (shown as 608 a). The panel 608 a includes the exam log 616, which can include events relating to the start of the exam (616 a), the timestamp and location of a detected mass (616 b), the start of a recording (616 c), the current displayed views of the interface (616 d), edits to the appointment or patient information (616 e), and the end of the exam (6160.”), and
the detailed information includes an input image and an analysis result corresponding to the operation of the image analysis (see para. 0009 – “…a second data set associated with an ultrasound scan of a second breast mass detection procedure contemporaneously conducted on the subject following the pressure elastography measurement (e.g., acquired from a portable ultrasound device)…wherein the first data set and the second data set are…ii) subsequently analyzed via machine learning or deep learning operations (e.g., in a remote/cloud server or the device) to provide an indication associated with the assessment of the breast mass.”).
Claims 3 and 6 are rejected under35 U.S.C. 103 as being unpatentable over Bricker in view of Kikuchi, 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, Bricker in view of Kikuchi teaches all of the elements disclosed in claim 2 above.
Bricker in view of Kikuchi teaches inputting an ultrasound image into 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 into an image analysis model, as disclosed in Bricker in view of Kikuchi, 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, Bricker further teaches wherein the processor includes
an image saving unit that saves an analysis target image for each operation of the image analysis model, 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. 0134 – “The local controller (e.g., 108) can maintain the scan or recordings as time-series data or convert the data to a video or image file (e.g., healthcare video or image file format) [save images]…At the conclusion of the exam, the cloud infrastructure 103′ or local controller 108′ may initiate (524) the generation of a report that includes the pressure elastography results.”),
the detailed information in the model operation report includes the analysis target image, which is input information of the image analysis model, and the analysis result, which is output information of the image analysis model (see para. 0144 – “In essence, the local controller (e.g., 108) can log [save] any input made to the interface 600, the sensor device (e.g., the buttons on the devices 104, 106) as detected by the controller, as well as any functions or monitoring operations, and their outputs, being executed on the controller (e.g., 108).”), 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 claim 3.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bricker in view of Kikuchi, as applied to claim 1 above, and in further view of Holvey et al. (US 20040054935 A1, published March 18, 2004), hereinafter referred to as Holvey.
Regarding claim 7, Bricker in view of Kikuchi teaches all of the elements disclosed in claim 1 above.
Bricker in view of Kikuchi teaches selecting an operation record in a log, but does not explicitly teach selecting a hyperlink in a log.
Whereas, Holvey, 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 (Fig. 4-6; see para. 0032 – “At step 432 the authorized user uses the subset, for example, selects a hyperlink in the document log, to access the rest of the set of user information stored in the user information database 142 or 242, for example the scanned medical record page associated with the hyperlink.”; see para. 0035 – “Column 554 gives the document ID for each document of a patient's medical records stored in user information DB 142. Cell 570 has document ID 457, which is a hyperlink to the document image. When link “457” is selected a separate window (FIG. 6) opens with the document's image.”).
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 selecting an operation record in a log, as disclosed in Bricker in view of Kikuchi, by also selecting a hyperlink in a log, as disclosed in Holvey. One of ordinary skill in the art would have been motivated to make this modification in order to provide indirect access to a user's information in databases reduces the exposure to hackers compared to the conventional Web server which has the user's information available directly on Web server database, as taught in Holvey (see para. 0032).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Patti et al. (US 20230061007 A1, published March 2, 2023) discloses the system may identify the type of operation to be performed by the user and provide a link to allow the user to view or upload the usage log for the component. The system identifies a location of the usage log in memory. The system displays a user-selectable interface element to allow the user to upload the usage log data from the location specified in memory. The system analyzes the operation to identify whether logged usage is abnormal.
Kunz et al. (US 20230368899 A1, published November 16, 2023 with a priority date of May 13, 2022) discloses if a new protocol and/or guideline is determined, the system may save and/or output the new protocol and/or guideline to an external user or a digital storage device.
Kawai et al. (US 20160055044 A1, published February 25, 2016) discloses among the log information recorded in this log file, the change point display program extracts only the log information of the log that was acquired in the period selected by the system administrator, from among the log files acquired.
Kitamura et al. (US 20180275631 A1, published September 27, 2018) discloses when an event log is output in the abnormality detecting process of a machine learning engine, details of the abnormality are displayed on the display device. The detailed details of the abnormality include an abnormality occurrence time, an abnormality occurrence location, and feature quantities in which the abnormality is detected.
Lou et al. (EP 3979148 A1, published April 6, 2022) discloses displaying log information in the machine learning automatic modeling process to the users, which may enable the users to perceive that the modeling process is in progress, and may satisfy the users' demands for reviewing and debugging error information at any time.
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
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