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
Claims 1-16 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Final Office Action mailed 04/08/2026.
Response to Arguments
Applicant’s arguments, see pg. 2-6, filed 07/01/2026, with respect to the rejection(s) of claim(s) 1 and 7 under 35 U.S.C. 102(a) (Nakamura) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lou in view of Lyman, 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 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Lou et al. (US 20180173852 A1, published June 21, 2018) in view of Lyman et al. (US 20220051771 A1, published February 17, 2022), hereinafter referred to as Lou and Lyman, respectively.
Regarding claim 1, and similarly for claims 10 and 12, Lou teaches a method for controlling an ultrasound scanning system, comprising:
acquiring an ultrasound scanning-related document, comprising an ultrasound scanning guidance for at least one tissue (Fig. 1; see para. 0001 – “The disclosed methods, systems, and apparatuses may be applied to scanners for any imaging modality.” Including ultrasound scanning tissue; see para. 0025 – “Using the NLP [natural language processing] module 105B, semantic information relative to scan requirements [acquired scanning guidance] should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types.” inherent and known in the art to process a document using an NLP model to extract the information);
processing the ultrasound scanning-related document using a natural language processing model, comprising processing the ultrasound scanning guidance (Fig. 1; see para. 0001 – “The disclosed methods, systems, and apparatuses may be applied to scanners for any imaging modality.”; see para. 0025 – “Using the NLP [natural language processing] module 105B, semantic information relative to scan requirements [acquired scanning guidance] should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types.” inherent and known in the art to process a document using an NLP model to extract the information);
generating an ultrasound scanning protocol for ultrasound scanning of the at least one tissue to be scanned on the basis of the processing the ultrasound scanning guidance (Fig. 1; see para. 0001 – “The disclosed methods, systems, and apparatuses may be applied to scanners for any imaging modality.”; see para. 0025 – “Using the NLP [natural language processing] module 105B, semantic information relative to scan requirements [acquired scanning guidance] should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types. The Intelligent Medical Imaging Scanner 105 can use the NLP Module 105B to interpret this information and select from the pre-defined Protocols 105C to determine a set of parameters for performing the scan [scanning protol].”); and
configuring the ultrasound scanning protocol for ultrasound scanning of the at least one tissue in the ultrasound scanning system (Fig. 1 and 2A; see para. 0001 – “The disclosed methods, systems, and apparatuses may be applied to scanners for any imaging modality.”; see para. 0025 – “Using the NLP [natural language processing] module 105B, semantic information relative to scan requirements [acquired scanning guidance] should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types. The Intelligent Medical Imaging Scanner 105 can use the NLP Module 105B to interpret this information and select from the pre-defined Protocols 105C to determine a set of parameters for performing the scan [configuring scanning protocol of system].”).
Lou teaches acquiring information (ultrasound scanning guidance) such as the type of scan being performed and the desired output image types (see para. 0025), and it is inherent and known in the art to process a document using an NLP model to extract the information, but does not explicitly teach acquiring an ultrasound scanning-related document.
Whereas, Lyman, in an analogous field of endeavor, teaches acquiring an ultrasound scanning-related document, comprising an ultrasound scanning guidance for at least one tissue (Fig. 12; see para. 0315 – “…the medical scan viewing system can further operate, via processing system 3106 to retrieve from the resource database 3112a set of guideline recitations and/or one or more publications associated with the at least one abnormality… For each abnormality, links for accessing the appropriate guideline recitations/treatment plans and/or related scholarly article summary/link can be presented as pop-up text, thumbnails or other indicators that, when selected via the interactive interface 3110, result in the corresponding guideline recitations or publication being displayed.”).
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 acquiring ultrasound scanning guidance, as disclosed in Lou, by acquiring an ultrasound scanning-related document, as disclosed in Lyman. One of ordinary skill in the art would have been motivated to make this modification in order to utilize useful information about treatment trends and standards for particular abnormalities from scholarly articles, literature, journal papers and/or other publications, as taught in Lyman (see para. 0298).
Furthermore, regarding claim 2, Lyman further teaches wherein the ultrasound scanning-related document comprises at least one of a user manual, an ultrasound scanning-related guideline, and an ultrasound scanning-related paper (Fig. 12E; see para. 0315 the medical scan viewing system can further operate, via processing system3106 to retrieve from the resource database 3112a set of guideline recitations and/or one or more publications associated with the at least one abnormality.. For each abnormality, links for accessing the appropriate guideline recitations/treatment plans and/or related scholarly article summary/link can be presented as pop-up text, thumbnails or other indicators that, when selected via the interactive interface 3110, result in the corresponding guideline recitations or publication being displayed.").
Furthermore, regarding claim 3, Lou further teaches wherein processing the ultrasound scanning guidance comprises: performing extraction on the content of the ultrasound scanning guidance at least once, and generating several steps for ultrasound scanning of the at least one tissue to be scanned (Fig. 1; see para. 0001 – “The disclosed methods, systems, and apparatuses may be applied to scanners for any imaging modality.”; see para. 0025 – “Using the NLP [natural language processing] module 105B, semantic information relative to scan requirements [acquired scanning guidance] should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types. The Intelligent Medical Imaging Scanner 105 can use the NLP Module 105B to interpret this information and select from the pre-defined Protocols 105C to determine a set of parameters for performing the scan.”).
Furthermore, regarding claim 4, Lou further teaches wherein processing the ultrasound scanning guidance further comprises: in response to being operated, further extracting, augmenting, or modifying the several steps (see para. 0039 – “For example, in one embodiment, the feedback comprises a modification to the workflow automatically detected by the intelligent medical imaging scanner system while an operator is performing the workflow.”).
Furthermore, regarding claim 5, Lou further teaches wherein processing the ultrasound scanning guidance further comprises: extracting keywords in each of the steps; and categorizing the keywords or each of the steps among preset categories (see para. 0025 – “Using the NLP module 105B, semantic information relative to scan requirements should be extracted and processed…For example, the Operator 110 may provide information such as the type of scan being performed and the desired output image types. The Intelligent Medical Imaging Scanner 105 can use the NLP Module 105B to interpret this information and select from the pre-defined Protocols 105C to determine a set of parameters for performing the scan.” extracting and categorizing keywords are NLP steps that are inherent and known in the art).
Furthermore, regarding claim 6, Lou further teaches wherein the preset categories comprise at least one of a target tissue, probe movement, and configuring an ultrasound scanning parameter (see para. 0023 – “For example, suppose the Intelligent Medical Imaging Scanner 105 has a set of pre-defined Protocols 105C that may be utilized, parameter settings and post-processing algorithms. For an incoming scanning requirement from the Operator 110 to be achieved, the Medical Imaging Scanner 115 is able to fit the requirement into the current framework and select the most appropriate protocols, parameters, algorithms, workflows, etc.”).
Furthermore, regarding claim 7, Lou further teaches wherein the ultrasound scanning protocol comprises several steps for ultrasound scanning of the at least one tissue to be scanned, and the method further comprises: determining a step in the ultrasound scanning protocol to which a current ultrasound scan corresponds; and providing scanning assistance for the current ultrasound scan (see para. 0039 – “For example, in one embodiment, the feedback comprises a modification to the workflow automatically detected by the intelligent medical imaging scanner system while an operator is performing the workflow.”).
Furthermore, regarding claim 8, Lou further teaches wherein providing scanning assistance for the current ultrasound scan comprises at least one of:
in response to the corresponding step being related to configuring ultrasound scanning parameter, automatically configuring the ultrasound scanning system (see para. 0025 – “The Intelligent Medical Imaging Scanner 105 can use the NLP Module 105B to interpret this information and select from the pre-defined Protocols 105C to determine a set of parameters for performing the scan.”; see para. 0053 – “An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity.”);
in response to the corresponding step being related to probe movement, providing and displaying a guide for the probe movement; and
in response to the corresponding step being related to a target tissue, automatically identifying and evaluating an image of the current ultrasound scan.
Furthermore, regarding claim 9, Lou further teaches wherein the automatically identifying and evaluating an image of the current ultrasound scan is performed using an artificial intelligence model (see para. 0029 – “In the example of FIG. 1, the Intelligent Medical Imaging Scanner 105 has an Outcome Prediction 105E module which operates in conjunction with the Learning Module 105E [artificial intelligence model] to analyze the Bio-Feedback 115 and provide disease prediction…For example, in some embodiments, the system can analyze the image just acquired [current ultrasound image] to provide disease predictions or identify abnormality in the image (e.g., abnormal volume of an organ, deformation of an organ, etc.).”).
Furthermore, regarding claim 11, Lyman further teaches wherein the ultrasound scanning-related document is from at least one of a removable storage medium, a local area network, and a cloud server (Fig. 12E; see para. 0298 – “The medical scan artifact detection system 3100 stores a database of guidelines and publications in resource database 3112.”; see para. 0434 – “As may further be used herein, a computer readable memory includes one or more memory elements…Furthermore, the memory device may be in a form of a solid-state memory, a hard drive memory or other disk storage, cloud memory, thumb drive, server memory, computing device memory, and/or other non-transitory medium for storing data.”).
Furthermore, regarding claim 13, Lyman further teaches wherein the ultrasound scanning-related document is a user manual (Fig. 12E; see para. 0315 the medical scan viewing system can further operate, via processing system 3106 to retrieve from the resource database3112 a set of guideline recitations and/or one or more publications associated with the at least one abnormality.. For each abnormality, links for accessing the appropriate guideline recitations/treatment plans and/or related scholarly article summary/link can be presented as pop-up text, thumbnails or other indicators that, when selected via the interactive interface 3110, result in the corresponding guideline recitations or publication being displayed.").
Furthermore, regarding claim 14, Lyman further teaches wherein the ultrasound scanning-related document is an ultrasound scanning-related paper (Fig. 12E; see para. 0315 the medical scan viewing system can further operate, via processing system 3106 to retrieve from the resource database3112 a set of guideline recitations and/or one or more publications associated with the at least one abnormality.. For each abnormality, links for accessing the appropriate guideline recitations/treatment plans and/or related scholarly article summary/link can be presented as pop-up text, thumbnails or other indicators that, when selected via the interactive interface 3110, result in the corresponding guideline recitations or publication being displayed.").
Furthermore, regarding claim 15, Lyman further teaches wherein the ultrasound scanning-related document is a research paper that includes a published experiment (Fig. 12E; see para. 0315 " the medical scan viewing system can further operate, via processing system 3106 to retrieve from the resource database 3112 a set of guideline recitations and/or one or more publications associated with the at least one abnormality.. For each abnormality, links for accessing the appropriate guideline recitations/treatment plans and/or related scholarly article summary/link can be presented as pop-up text, thumbnails or other indicators that, when selected via the interactive interface 3110, result in the corresponding guideline recitations or publication being displayed.").
Furthermore, regarding claim 16, Lyman further teaches wherein the ultrasound scanning-related document is imported into the ultrasound scanning system by a user (Fig. 12E; see para. 0315 – “…retrieve [import] from the resource database 3112 a set of guideline recitations and/or one or more publications associated with the at least one abnormality.”).
The motivation for claims 2 and 11-16 was shown previously in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Canfield et al. (US 20240221913 A1, published July 4, 2024 with a priority date of April 28, 2021) discloses the chat bot may allow the user to obtain various information (e.g., patient medical records, configuration settings of the imaging system, standard exam protocols, information on an image currently being viewed) and/or allow the user to cause the imaging system to perform various tasks (e.g., call tech support, change image acquisition settings, open an application such as a measurement toolset, etc.).
Dobrean (US 20170068780 A1, published March 9, 2017) discloses NLP module can analyze the free text content of each portion of a medical imaging record and identify a condition marker associated with that medical imaging record.
Steigauf et al. (US 20190279363 A1, published September 12, 2019) discloses the properties of the electronic workflow are further defined based on results of natural language processing, the results of natural language processing being produced from analysis of data associated with the medical imaging procedure or data associated with a prior medical imaging procedure of the human subject.
Velichkovich et al. (US 20240071586 A1, published February 29, 2024 with a priority date of August 31, 2022) discloses receiving text from a prior radiology report prepared for a patient in a prior radiology study; processing the text with a trained NLP engine to classify discrete prior findings in the prior radiology study that identify observed condition(s) and characteristics of the observed condition(s); and outputting or presenting the prior findings, such as in a radiology review user interface.
Cheng et al. (US 20250213220 A1, published July 3, 2025 with a priority date of March 24, 2022) discloses NLP module is configured to execute one or more NLP algorithms using word embedding technology to identify button pushes extracted from button pushing sequences entered by the user.
Kalafut et al. (US 20150100572 A1, published April 9, 2015) discloses the NLP engine can also be configured to include data processing rules that are used to perform some action in the event that certain phrases are determined to be present (or absent) in a particular report, recording, image, etc.
Ferrando et al. (US 20240006039 A1, published January 4, 2024 with a priority date of June 27, 2023) discloses NLP techniques can help to pre-process the data in order to structure information from free text and help the following process of classification and categorization.
Holmes et al. (US 20220199229 A1, published June 23, 2022) discloses imaging processing model is further configured to upgrade each of the predetermined analysis algorithm so as to improvise the image acquisition and/or diagnosis using the ultrasound devices, on the basis of deep learning developed by various datasets present within the data-repository, including NLP.
Mankovich et al. (US 20230368893 A1, published November 16, 2023 with a priority date of October 30, 2015) discloses prior findings can be obtained from natural language processing (NLP) of reports generated previously for the patient using NLP techniques known in the art.
Kohli et al. (US 20190156921 A1, May 23, 2019) discloses the processing engine processes input text documents and metadata by data mining and applying NLP techniques to process the data based on one or more vocabularies, ontologies, etc.
Yerebakan et al. (US 20180196873 A1, published July 12, 2018) discloses a natural language processing system may be used to extract information (e.g., pathology, anatomy, symptom, negation information) and tag the free text documents. Data to be fed to the trained machine learning model may be created using only tags or whole documents.
Sorenson et al. (US 20180060512 A1, published March 1, 2018) discloses An NLP system or an NLP module may be utilized to parse the text within a report to match the extracted image features. A set of NLP rules may be define to determine which processing engines to run the NLP algorithm. For example, if a hand x-ray states “no acute fracture or traumatic subluxation,” a hand x-ray fracture algorithm is initiated via the image processing server to conform the report accuracy.
P. Bobba et al, “Natural language processing in radiology: Clinical applications and future directions”, Clinical Imaging, vol. 97, pp. 55-61, Dec. 2022 discloses NLP can identify based on the text of a chest radiograph report whether a patient has findings relating to acute bacterial pneumonia, thus supplementing institutional protocols in quality assurance and decision support systems.
X. Wu et al, “Identification of patients with carotid stenosis using natural language processing”, European Radiology, vol. 30, pp. 4125-4133, Oct. 2019 discloses developing linear, CNN, and RNN models to predict history and presence of CS from ultrasound reports.
N. Raju et al, “A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging”, June 2022 discloses interpreting a clinical radiology referral and selecting the appropriate imaging technique.
A. Brown et al, “A Natural Language Processing based Model to Automate MRI Brain Protocol Selection and Prioritization”, Academic Radiology, vol. 24, no. 2, pp. 160-166, Feb. 2017 discloses an NLP system could be used to process the unstructured, free-text data from requisitions prepared by referring physicians and basic demographic information about patients in order to construct a model with the capacity to assign appropriate imaging protocols.
B. Xavier et al, “Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data”, Journal of Digital Imaging, vol. 35, pp. 1120-1130, June 2021 discloses text data associated with each study including the ordering provider generated free text study indication and ICD codes were used for NLP analysis and protocol class prediction.
Y. Chillakuru et al, “Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing”, BMC Medical Informatics and Decision Making, vol. 21, no. 213, pp. 1-10, 2021 discloses develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text.
A. Nencka et al, “Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams”, arXiv.org, pp. 1-15, May 2021 discloses automated order-based protocol assignment for magnetic resonance imaging (MRI) exams using natural language processing (NLP) and deep learning (DL).
P. Lopez-Ubeda et al, “Automatic medical protocol classification using machine learning approaches”, Computer Methods and Programs in Biomedicine, vol. 200, pp. 1-11, July 2020 discloses machine learning classification models based on NLP have been developed using patient’s data present in radiological reports and radiological imaging protocols.
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/N.C./Examiner, Art Unit 3798
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