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 .
Election/Restrictions
Claims 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected method, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 04/01/2026.
Claim Interpretation
Claim 5 & 12 recites the limitation of “ when probability for the first probe guidance is equal to or greater than a predetermined level.” which in an interpretation it may be construed as a conditional limitation where the conditional limitations may not be given a full weight in light of the below decisions as for considering the other case scenario of “when” not being advanced… which the claim would not require this limitation to be a positive recitation.
In the recent Ex parte Gopalan decision, the PTAB addressed a claim where all of the features were recited in a conditional manner. A first step of “identifying … an outlier” was performed if “traffic is outside of a prediction interval.” A second step of “identifying” was performed “only when a count of outliers … is greater than or equal to two, and exceeds an anomaly threshold.” These were the only two elements of the independent claim. Thus, if the traffic is never outside Gopalan’s prediction interval, then the steps of the method are never performed.
However, the PTAB distinguished Schulhauser and noted that this construction “would render the entire claim meaningless.” Gopalan at p. 5. The Board went on to state, “Although each of these steps is conditional, they are integrated into one method or path and do not cause the claim to diverge into two methods or paths, as in Schulhauser. Thus, we conclude that the broadest reasonable interpretation of claim 1 requires the performance of both steps…” Id. at p. 6.”
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “communication unit” in claim 9 (interpreted as being a communication interface as described in applicants spec [0168-0169]; and “an output unit” in claim 12 (interpreted as being a digital assistant client module as described in applicants spec [0154-0156]).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1-15 recite a system and method comprising: receiving a cardiac ultrasound image of a captured subject; and determining probe guidance based on the received cardiac ultrasound image by using a prediction model trained to determine the probe guidance according to movement of an ultrasound probe by inputting the cardiac ultrasound image.; as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but presumable recitation of generic computer components.
That is, other than presumably reciting “processor”, nothing in the claim element precludes the step from practically being performed in the mind. For example “receiving the cardiac ultrasound image of a captured subject” in the context of this claim encompasses the user conducting a generic data gathering step. The user could manually also “determining probe guidance based on the received cardiac ultrasound image by using a prediction model trained to determine the probe guidance according to movement of an ultrasound probe by inputting the cardiac ultrasound image” by using their base knowledge of ultrasound probe movement in a medical setting.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the imitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The judicial exception is not integrated into a practical application. In particular, the claim only recites three additional element – using a processor to perform the above noted steps. The processor is recited at a high-level of generality (i.e., as a generic processing system performing a generic data gathering and processing function of determining guidance) such that it amounts no more than mere instructions to apply the exception using a processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the determining steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
As for the depending claim(s), they are also rejected under 35 USC 101 at least for the similar
reasons noted above as they are directed to abstract ideas and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-11,& 14-15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Nouri et al (US20230117915A1; hereinafter referred to as Nouri).
Regarding Claim 1, Nouri discloses a method for providing a guideline for a cardiac ultrasound image implemented by a processor (“the disclosure provides techniques to guide an operator of an ultrasound device to capture medically relevant ultrasound images” [0005] ”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], PLAX,PSAX,ALAX are known in the art as being cardiac ultrasound images), the method comprising:
receiving the cardiac ultrasound image of a captured subject (”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], “FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101… the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181]);
and determining probe guidance based on the received cardiac ultrasound image by using a prediction model trained to determine the probe guidance according to movement of an ultrasound probe by inputting the cardiac ultrasound image (“the computing device 104 may generate the instruction 108 without performing the intermediate step of determining whether the ultrasound image 110 contains the target anatomical view. For example, the computing device 104 may use a machine learning technique (such as a deep learning technique) to directly map the ultrasound image 110 to an output to provide to the user such as an indication of proper positioning or an instruction to reposition the ultrasound device 102 (e.g., instruction 108).” [0185], “The computing device may identify the initial position 202 by analyzing the ultrasound data received from the ultrasound device using an automated image processing technique (e.g., a deep learning technique)” [0187], “Once the initial position 202 and the target position 204 have been identified, the computing device may identify the guide path 208 that an operator should follow to move the ultrasound device from the initial position 202 to the target position 204.” [0189], “The computing device may store the generated guide path 208 locally and use the guide path to generate a sequence of instructions to provide to the operator. For example, the computing device may use the guide path 208 to generate the sequence of instructions: (1) “MOVE LATERAL,” (2) “MOVE UP,” and (3) “TWIST CLOCKWISE.”” [0190]).
Regarding Claim 2, Nouri discloses further comprising receiving a cross- sectional view of the cardiac ultrasound image, wherein the determining the probe guidance further includes determining the probe guidance based on the received cardiac ultrasound image and the received cross-sectional view using the prediction model (”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], “the computing device 104 may generate the instruction 108 without performing the intermediate step of determining whether the ultrasound image 110 contains the target anatomical view. For example, the computing device 104 may use a machine learning technique (such as a deep learning technique) to directly map the ultrasound image 110 to an output to provide to the user such as an indication of proper positioning or an instruction to reposition the ultrasound device 102 (e.g., instruction 108).” [0185], “The computing device may identify the initial position 202 by analyzing the ultrasound data received from the ultrasound device using an automated image processing technique (e.g., a deep learning technique)” [0187], “Once the initial position 202 and the target position 204 have been identified, the computing device may identify the guide path 208 that an operator should follow to move the ultrasound device from the initial position 202 to the target position 204.” [0189], “The computing device may store the generated guide path 208 locally and use the guide path to generate a sequence of instructions to provide to the operator. For example, the computing device may use the guide path 208 to generate the sequence of instructions: (1) “MOVE LATERAL,” (2) “MOVE UP,” and (3) “TWIST CLOCKWISE.”” [0190], PLAX is a cross sectional ultrasound view).
Regarding Claim 3, Nouri discloses that the determining the probe guidance includes determining first probe guidance and second probe guidance according to the movement of the probe based on the received cardiac ultrasound image using the prediction model (“the computing device may initially provide the operator a coarse instruction to move the ultrasound device to a general area of the subject 201 (such as an upper torso of the subject 201) and subsequently provide one or more fine instructions to move the ultrasound device in particular directions (such as “MOVE UP”).” [0191].)
Regarding Claim 4, Nouri discloses that the second probe guidance is defined as guidance with greater movement of the probe than the first probe guidance, the first probe guidance includes at least one probe operation guidance of Hold, Probe Head Tilt Down, Probe Head Tilt Up, Probe Head Rock Right, Probe Head Rock Left, Probe Head Tilt Right, Probe Head Tilt Left, Probe Head Rock Down, Probe Head Rock Up, Probe Rotate Clockwise, and Probe Rotate Counter-clockwise, and the second probe guidance includes at least one probe operation guidance of Slide Up, Slide Down, Slide Left, and Slide Right (“an operator may initially position the ultrasound device on a leg of the subject 201 and the computing device may provide a coarse instruction that instructs the operator to move the ultrasound device to an upper torso (e.g., the predetermined area 206) of the subject 201. Once the operator has positioned the ultrasound device on the upper torso of the subject 201 (and thereby within the predetermined area 206), the computing device may provide a fine instruction including an indication of a particular direction to move the ultrasound device towards the target position 204” [0192], “The fine instruction 312 may also comprise a message 316 that compliments the symbol 314 such as the message “TURN CLOCKWISE.” The symbol 314 and/or the message 316 may be overlaid onto a background image 311. The background image 311 may be, for example, an ultrasound image generated using ultrasound data received from the ultrasound device.” [0195], “FIG. 3C shows an example confirmation 318 that may be provided to an operator via the display 306 on the computing device 304. The confirmation 318 may be provided when the ultrasound device is properly positioned on the subject to capture an ultrasound image containing the target anatomical view. As shown, the confirmation 318 includes a symbol 320 (e.g., a checkmark) indicating that the ultrasound device is properly positioned. The confirmation 318 may also comprise a message 322 that compliments the symbol 320 such as the message “HOLD.”” [0196]).
Regarding Claim 7, Nouri discloses that the prediction model is further configured to classify a cross-sectional view of the input cardiac ultrasound image by inputting the cardiac ultrasound image, and the determining the probe guidance further includes classifying the cross-sectional view of the received cardiac ultrasound image using the prediction model, and determining the probe guidance corresponding to the classified cross- sectional view using the prediction model (“FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101. As shown, the ultrasound system 100 comprises an ultrasound device 102 that is communicatively coupled to the computing device 104 by a communication link 112. The computing device 104 may be configured to receive ultrasound data from the ultrasound device 102 and use the received ultrasound data to generate an ultrasound image 110. The computing device 104 may analyze the ultrasound image 110 to provide guidance to an operator of the ultrasound device 102 regarding how to reposition the ultrasound device 102 to capture an ultrasound image containing a target anatomical view. For example, the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181]).
Regarding Claim 8, Nouri discloses that the classifying the cross-sectional view includes classifying the received cardiac ultrasound image into at least one cross-sectional view of PLAX, PSAX-AV, PSAX MV, PSAX PM, PSAX APEX, A4C, A3C, and A2C using the prediction model, andthe determining the corresponding probe guidance includes determining the probe guidance for the at least one cross-sectional view (“FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101. As shown, the ultrasound system 100 comprises an ultrasound device 102 that is communicatively coupled to the computing device 104 by a communication link 112. The computing device 104 may be configured to receive ultrasound data from the ultrasound device 102 and use the received ultrasound data to generate an ultrasound image 110. The computing device 104 may analyze the ultrasound image 110 to provide guidance to an operator of the ultrasound device 102 regarding how to reposition the ultrasound device 102 to capture an ultrasound image containing a target anatomical view. For example, the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181]).
Regarding Claim 9, Nouri discloses A device for providing a guideline for a cardiac ultrasound image (“the disclosure provides techniques to guide an operator of an ultrasound device to capture medically relevant ultrasound images” [0005] ”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], PLAX,PSAX,ALAX are known in the art as being cardiac ultrasound images), the device comprising
a communication unit configured to receive the cardiac ultrasound image of a captured subject (”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], “FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101… the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a "target anatomical view, such as a PLAX anatomical view.” [0181]);
and a processor functionally connected to the communication unit (“FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101… the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181], “The computing device 104 may comprise one or more processing elements (such as a processor) to, for example, process ultrasound data received from the ultrasound device 102.” [0184]),
wherein the processor is configured to determine probe guidance based on the received cardiac ultrasound image by using a prediction model trained to determine the probe guidance according to movement of an ultrasound probe by inputting the cardiac ultrasound image (“the computing device 104 may generate the instruction 108 without performing the intermediate step of determining whether the ultrasound image 110 contains the target anatomical view. For example, the computing device 104 may use a machine learning technique (such as a deep learning technique) to directly map the ultrasound image 110 to an output to provide to the user such as an indication of proper positioning or an instruction to reposition the ultrasound device 102 (e.g., instruction 108).” [0185], “The computing device may identify the initial position 202 by analyzing the ultrasound data received from the ultrasound device using an automated image processing technique (e.g., a deep learning technique)” [0187], “Once the initial position 202 and the target position 204 have been identified, the computing device may identify the guide path 208 that an operator should follow to move the ultrasound device from the initial position 202 to the target position 204.” [0189], “The computing device may store the generated guide path 208 locally and use the guide path to generate a sequence of instructions to provide to the operator. For example, the computing device may use the guide path 208 to generate the sequence of instructions: (1) “MOVE LATERAL,” (2) “MOVE UP,” and (3) “TWIST CLOCKWISE.”” [0190]).
Regarding Claim 10, Nouri discloses the prediction model is a model trained to determine the probe guidance by inputting the cardiac ultrasound image and a cross-sectional view of the cardiac ultrasound image (”the ultrasound image contains an anatomical view selected from the group consisting of: a parasternal long axis (PLAX) anatomical view, a parasternal short-axis (PSAX) anatomical view, an apical four-chamber (A4C) anatomical view, and apical long axis (ALAX) anatomical view.” [0082], “the computing device 104 may generate the instruction 108 without performing the intermediate step of determining whether the ultrasound image 110 contains the target anatomical view. For example, the computing device 104 may use a machine learning technique (such as a deep learning technique) to directly map the ultrasound image 110 to an output to provide to the user such as an indication of proper positioning or an instruction to reposition the ultrasound device 102 (e.g., instruction 108).” [0185], “The computing device may identify the initial position 202 by analyzing the ultrasound data received from the ultrasound device using an automated image processing technique (e.g., a deep learning technique)” [0187], “Once the initial position 202 and the target position 204 have been identified, the computing device may identify the guide path 208 that an operator should follow to move the ultrasound device from the initial position 202 to the target position 204.” [0189], “The computing device may store the generated guide path 208 locally and use the guide path to generate a sequence of instructions to provide to the operator. For example, the computing device may use the guide path 208 to generate the sequence of instructions: (1) “MOVE LATERAL,” (2) “MOVE UP,” and (3) “TWIST CLOCKWISE.”” [0190], PLAX is a cross sectional ultrasound view).
Regarding Claim 11, Nouri discloses that the processor is further configured to determine first probe guidance and second probe guidance according to the movement of the probe based on the received cardiac ultrasound image using the prediction model (“the computing device may initially provide the operator a coarse instruction to move the ultrasound device to a general area of the subject 201 (such as an upper torso of the subject 201) and subsequently provide one or more fine instructions to move the ultrasound device in particular directions (such as “MOVE UP”).” [0191].)
Regarding Claim 14, Nouri discloses that the prediction model is further configured to classify a cross-sectional view of the input cardiac ultrasound image by inputting the cardiac ultrasound image, and the determining the probe guidance further includes classifying the cross-sectional view of the received cardiac ultrasound image using the prediction model, and determining the probe guidance corresponding to the classified cross- sectional view using the prediction model (“FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101. As shown, the ultrasound system 100 comprises an ultrasound device 102 that is communicatively coupled to the computing device 104 by a communication link 112. The computing device 104 may be configured to receive ultrasound data from the ultrasound device 102 and use the received ultrasound data to generate an ultrasound image 110. The computing device 104 may analyze the ultrasound image 110 to provide guidance to an operator of the ultrasound device 102 regarding how to reposition the ultrasound device 102 to capture an ultrasound image containing a target anatomical view. For example, the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181]).
Regarding Claim 15, Nouri discloses that the processor is further configured to classify the received cardiac ultrasound image into at least one cross-sectional view of PLAX, PSAX-AV, PSAX MV, PSAX PM, PSAX APEX, A4C, A3C, and A2C using the prediction model, andthe determining the corresponding probe guidance includes determining the probe guidance for the at least one cross-sectional view (“FIG. 1 shows an example ultrasound system 100 that is configured to guide an operator of an ultrasound device 102 to obtain an ultrasound image of a target anatomical view of a subject 101. As shown, the ultrasound system 100 comprises an ultrasound device 102 that is communicatively coupled to the computing device 104 by a communication link 112. The computing device 104 may be configured to receive ultrasound data from the ultrasound device 102 and use the received ultrasound data to generate an ultrasound image 110. The computing device 104 may analyze the ultrasound image 110 to provide guidance to an operator of the ultrasound device 102 regarding how to reposition the ultrasound device 102 to capture an ultrasound image containing a target anatomical view. For example, the computing device 104 may analyze the ultrasound image 110 to determine whether the ultrasound image 110 contains a target anatomical view, such as a PLAX anatomical view.” [0181]).
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 5 & 11 are rejected under 35 U.S.C. 103 as being unpatentable over Nouri in view of Cadieu et al (US20200245970A1; hereinafter referred to as Cadieu).
Regarding Claim 5, Nouri discloses that the method further includes providing the first probe guidance, and selectively providing the second probe guidance (“an operator may initially position the ultrasound device on a leg of the subject 201 and the computing device may provide a coarse instruction that instructs the operator to move the ultrasound device to an upper torso (e.g., the predetermined area 206) of the subject 201. Once the operator has positioned the ultrasound device on the upper torso of the subject 201 (and thereby within the predetermined area 206), the computing device may provide a fine instruction including an indication of a particular direction to move the ultrasound device towards the target position 204” [0192]).
Nouri does not specifically disclose the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance, and selectively providing the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level.
However, in a similar field of endeavor, Cadieu teaches a method for prescriptively guiding an operator of an ultrasound imaging probe includes acquiring a sequence of image clips of a target organ utilizing an ultrasound imaging probe [Abstract].
Cadieu also teaches that the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance, and selectively providing the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level (“The program code yet further is enabled to correlate to determined deviation with prescriptive guidance stored in guidance data store 250, required to correct the deviation through the use of neural network 270. Specifically, the prescriptive guidance can include, by way of example, the rotation of the ultrasound imaging probe 230 either in a clockwise or counter-clockwise direction, the movement of the ultrasound imaging probe 230 laterally away from the sternum, or medially towards the sternum, and the aiming of the imaging beam downwards or upwards by tiling the tail of the ultrasound imaging probe 230 upwards or downwards, respectively, to name only a few examples.” [0023], “In block 340, prescriptive guidance is then computed that correlates to the determined quality including a determined deviation of the pose of the ultrasound imaging probe from ideal and a probability that a particular movement of the ultrasound imaging probe will improve the resultant video clip image towards an ideal form. In this regard, the prescriptive guidance includes one or more movements known to improve quality of an image of the specified view from the determined quality. In decision block 350, if the prescriptive guidance has associated therewith a threshold probability of improving the resultant video clip image towards an ideal form, the prescriptive guidance in block 360 is added to a first in first out queue of fixed size such that a least recently inserted entry in the queue is ejected from the queue as the prescriptive guidance is added to the queue.” [0025])
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Nouri as outlined above with the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance, and selectively providing the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level as taught by Cadieu, because it makes the navigation of ultrasound more optimal [0005].
Regarding Claim 12, Nouri discloses all limitations noted above except that the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance,the processor provides the first probe guidance, and further includes an output unit configured to selectively provide the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level.
However, in a similar field of endeavor, Cadieu teaches a method for prescriptively guiding an operator of an ultrasound imaging probe includes acquiring a sequence of image clips of a target organ utilizing an ultrasound imaging probe [Abstract].
Cadieu also teaches that the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance, the processor provides the first probe guidance, and further includes an output unit configured to selectively provide the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level (“The program code yet further is enabled to correlate to determined deviation with prescriptive guidance stored in guidance data store 250, required to correct the deviation through the use of neural network 270. Specifically, the prescriptive guidance can include, by way of example, the rotation of the ultrasound imaging probe 230 either in a clockwise or counter-clockwise direction, the movement of the ultrasound imaging probe 230 laterally away from the sternum, or medially towards the sternum, and the aiming of the imaging beam downwards or upwards by tiling the tail of the ultrasound imaging probe 230 upwards or downwards, respectively, to name only a few examples.” [0023], “In block 340, prescriptive guidance is then computed that correlates to the determined quality including a determined deviation of the pose of the ultrasound imaging probe from ideal and a probability that a particular movement of the ultrasound imaging probe will improve the resultant video clip image towards an ideal form. In this regard, the prescriptive guidance includes one or more movements known to improve quality of an image of the specified view from the determined quality. In decision block 350, if the prescriptive guidance has associated therewith a threshold probability of improving the resultant video clip image towards an ideal form, the prescriptive guidance in block 360 is added to a first in first out queue of fixed size such that a least recently inserted entry in the queue is ejected from the queue as the prescriptive guidance is added to the queue.” [0025])
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Nouri as outlined above with the prediction model is configured to probabilistically predict each of the first probe guidance and the second probe guidance,the processor provides the first probe guidance, and further includes an output unit configured to selectively provide the second probe guidance when probability for the first probe guidance is equal to or greater than a predetermined level as taught by Cadieu, because it makes the navigation of ultrasound more optimal [0005].
Claims 6 & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Nouri in view of Rouet et al (US20210338203A1; hereinafter referred to as Rouet).
Regarding Claim 6, Nouri discloses that the prediction model is further configured to segment an anatomical structure of a heart by inputting the cardiac ultrasound image (“The computing device may identify these medical parameters by, for example, identifying an anatomical feature in the ultrasound image (such as a heart ventricle, a heart valve, a heart septum, a heart papillary muscle, a heart atrium, an aorta, and a lung) and analyzing the identified anatomical feature. The computing device may identify the anatomical feature using an automated imaging processing technique (such as a deep learning technique). For example, the computing device may provide the captured ultrasound image to a neural network that is configured (e.g., trained) to provide as an output an indication of which pixels in the ultrasound image are associated with a particular anatomical feature.” [0197]),
Nouri does not specifically disclose that the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model.
However, in a similar field of endeavor, Rouet teaches a method for guiding the acquisition of an ultrasound image [Abstract].
Rouet also teaches that the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model (“that the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model” [0040], “a segmentation approach for the identifying the structure may be based on template deformation, using a template model of the shape of interest. Alternatively, another appropriate approach may make use of a UNET-based algorithm, based on deep learning and providing a mask of the organ to segment.” [0128],” identifying an anatomical structure within the 3D ultrasound image; estimating a target imaging plane based on the identified anatomical structure; determining if the target imaging plane is present within the 3D ultrasound image; if the target imaging plane is present, determining a displacement between a central plane of the 3D ultrasound image and the target plane; and if the displacement is below a predetermined threshold, extracting the target imaging plane from the 3D ultrasound image; or if the displacement is above the predetermined threshold, generating an instruction to acquire a 3D ultrasound image with the ultrasound probe at a second position, different from the first position, based on the displacement.” [0011-0016], ”The user instruction may present a proposed probe movement to obtain the target imaging plane in the center of the subsequently acquired 3D ultrasound image. In the example of a visual instruction, the user may be guided by way of a schematic based instruction or a 3D volume based instruction. The schematic based instruction may include a schematic drawing showing the current position of the ultrasound probe. The schematic drawing may also include a proposed motion of the probe in the form of arrows for translation and rotation in order to acquire a 3D ultrasound image including the target imaging plane.” [0144-0145]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Nouri as outlined above with the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model as taught by Rouet, because it makes the navigation of ultrasound more optimal [0005].
Regarding Claim 13, Nouri discloses that the prediction model is further configured to segment an anatomical structure of a heart by inputting the cardiac ultrasound image (“The computing device may identify these medical parameters by, for example, identifying an anatomical feature in the ultrasound image (such as a heart ventricle, a heart valve, a heart septum, a heart papillary muscle, a heart atrium, an aorta, and a lung) and analyzing the identified anatomical feature. The computing device may identify the anatomical feature using an automated imaging processing technique (such as a deep learning technique). For example, the computing device may provide the captured ultrasound image to a neural network that is configured (e.g., trained) to provide as an output an indication of which pixels in the ultrasound image are associated with a particular anatomical feature.” [0197]),
Nouri does not specifically disclose that the processor is further configured to segment the anatomical structure in the received cardiac ultrasound image using the prediction model, and determine the probe guidance based on the segmentation result using the prediction model.
However, in a similar field of endeavor, Rouet teaches a method for guiding the acquisition of an ultrasound image [Abstract].
Rouet also teaches that the processor is further configured to segment the anatomical structure in the received cardiac ultrasound image using the prediction model, and determine the probe guidance based on the segmentation result using the prediction model (“that the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model” [0040], “a segmentation approach for the identifying the structure may be based on template deformation, using a template model of the shape of interest. Alternatively, another appropriate approach may make use of a UNET-based algorithm, based on deep learning and providing a mask of the organ to segment.” [0128],” identifying an anatomical structure within the 3D ultrasound image; estimating a target imaging plane based on the identified anatomical structure; determining if the target imaging plane is present within the 3D ultrasound image; if the target imaging plane is present, determining a displacement between a central plane of the 3D ultrasound image and the target plane; and if the displacement is below a predetermined threshold, extracting the target imaging plane from the 3D ultrasound image; or if the displacement is above the predetermined threshold, generating an instruction to acquire a 3D ultrasound image with the ultrasound probe at a second position, different from the first position, based on the displacement.” [0011-0016], ”The user instruction may present a proposed probe movement to obtain the target imaging plane in the center of the subsequently acquired 3D ultrasound image. In the example of a visual instruction, the user may be guided by way of a schematic based instruction or a 3D volume based instruction. The schematic based instruction may include a schematic drawing showing the current position of the ultrasound probe. The schematic drawing may also include a proposed motion of the probe in the form of arrows for translation and rotation in order to acquire a 3D ultrasound image including the target imaging plane.” [0144-0145]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Nouri as outlined above with the determining the probe guidance further includes segmenting the anatomical structure in the received cardiac ultrasound image using the prediction model, and determining the probe guidance based on the segmentation result using the prediction model as taught by Rouet, because it makes the navigation of ultrasound more optimal [0005].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure (US 20250213212 A1; US20250331822A1; CN116549020A; US20240173007A1).
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/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
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