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 .
Claim Objections
Claims 3-4, 8, and 16 are objected to because of the following informalities:
In claim 3, line 2, “a light” should read “a light source”.
In claim 4, line 5, the “ringdown range the single ultrasound transducer” should read the “ringdown range of the single ultrasound transducer”.
In claims 8, and 16, the “LMS, an SPS, a GRU, an LSTM, or an RNN” should read the “least mean squares (LMS) filter, a spectrum suppression (SPS), a gated recurrent unit (GRU), a long short-term memory (LSTM), or a recurrent neural network (RNN)”.
Appropriate correction is required.
Claim Interpretation
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:
“an output device configured to provide …” in claim 1, line 1. Interpreted as a display, a microphone, or a haptic feedback device. [0059]; a speaker, a printer, an actuator, or at least one light source [0069].
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1, 4, 7, 11-12, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gris et al (US 11209541), hereinafter Gris, in view of Luijten et al ("Adaptive Ultrasound Beamforming using Deep Learning;" arxiv.org, 1909.10342; pp. 1-10; IEEE Transactions on Medical Imaging; Published 2020), hereinafter Luijten.
Regarding claim 1, Gris teaches an instrument (“The device may be any type of device which utilizes sonic sensing” Col. 3, l. 47-67), comprising:
a probe (100) (“FIG. 4A shows an example external depiction of a device 100 using a sonic transducer such as ultrasonic transducer 150” Col. 10, l. 59-67);
an output device (“display components.” Col. 4, l. 45-47) (“a processor” Col. 21, l. 3-12) configured to provide a feedback signal (“With continued reference to FIG. 9, at procedure 950 of flow diagram 900, … a signal indicating the estimated distance from the ultrasonic transducer to the object is provided…the signal is provided by a processor which is communicatively coupled with the ultrasonic transducer (e.g., host processor 110, sensor processor 130, and/or controller 151)” Col. 21, l. 3-12; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B); and
a single ultrasound transducer (150) disposed at an end of the probe and configured to:
transmit an incident ultrasound signal (401) (“emitted ultrasonic pulse 401” Col. 10, l. 65-67; Fig. 4A); and
detect a received ultrasound signal (402A) at least partially from an object (210) (“In FIG. 4A emitted ultrasonic pulse 401 is reflected from object 210 and received as corresponding returned signals 402A.” Col. 10, l. 65-67) within a range (550) of about 0.5 centimeters to about two centimeters of the single ultrasound transducer (“a transducer may have a much smaller blind spot in the range of a few centimeters” Col. 10, l. 4-7; “The x-axis shows distance represented in centimeters, based on round-trip time of flight,” Col. 16, l. 30-40; Fig. 7. Overlapping ranges)(“The processor is configured to evaluate the returned signals to find a candidate echo, from an object located in a ringdown blind spot area, in a time window between one and two times the ringdown period; …, wherein the ringdown blind spot area is located between the transducer and a closest distance at which objects can be sensed by the transducer.” Abstract);
at least one processor (110) (“A device comprises a processor coupled with an ultrasonic transducer…The processor is configured to evaluate the returned signals to find a candidate echo, from an object located in a ringdown blind spot area, in a time window between one and two times the ringdown period; …, wherein the ringdown blind spot area is located between the transducer and a closest distance at which objects can be sensed by the transducer.” Abstract; “example device 100A comprises a communications interface 105, a host processor 110,” Col. 6, l. 54-59; Fig. 1A; Fig. 1A) configured to:
determine, based on the data, a time-of-flight (400) between the single ultrasound transducer and the object (“An arrow 205 below device 100 and object 210 illustrates the distance between device 100 and object 210. It should be appreciated that any distances may be equated to a time for a roundtrip time-of-flight of an emitted pulse and a corresponding returned signal. Distance D0 is located at a position that begins even with ultrasonic transducer 150.” Col. 9, l. 38-55; Fig. 4A.“FIG. 4B illustrates a time-lapse 400 of the receipt of returned signals, in the form of multiple higher order echoes, by a sonic transducer from an object that is in the ringdown blind spot area of the sonic transducer,” Col. 11, l. 5-58); and
cause the output device to provide the feedback signal based at least in part on the time-of-flight (950) (1040) (1060) (1140) (1160) (“With continued reference to FIG. 9, at procedure 950 of flow diagram 900, in various embodiments, a signal indicating the estimated distance from the ultrasonic transducer to the object is provided…the signal is provided by a processor which is communicatively coupled with the ultrasonic transducer (e.g., host processor 110, sensor processor 130, and/or controller 151)” Col. 21, l. 3-12; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B).
Gris does not explicitly teach the instrument being a laparoscopic instrument; at least one processor configured to: remove, from data indicative of the received ultrasound signal, a ringdown artifact; and the determining based on removing the ringdown artifact.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches a laparoscopic instrument (“the cathether” p. 2, last para., right col.); and at least one processor (a processor for the “adaptive beamforming by deep learning” p. 2, 3rd para., right col.; pp. 3-6) configured to:
remove, from data indicative of the received ultrasound signal, a ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8. “In this work, we demonstrated how deep learning can be used to improve upon conventional beamforming methods. Specifically, we show that a compact and model-based architecture, which we term ABLE (adaptive beamforming by deep learning), enables the reconstruction of high-quality ultrasound images for multiple imaging systems.” VII. CONCLUSION, p. 9).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to have a laparoscopic instrument; and the at least one processor configured to: remove, from data indicative of the received ultrasound signal, a ringdown artifact, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the invention of Gris modified by Luijten, the determining step is based on removing the ringdown artifact.
Regarding claim 4, Gris teaches at least one device (100), comprising:
a single ultrasound transducer (150) disposed at an end of the probe (“FIG. 4A shows an example external depiction of a device 100 using a sonic transducer such as ultrasonic transducer 150”) and configured to:
transmit an incident ultrasound signal (401) (“emitted ultrasonic pulse 401” Fig. 4A); and
detect a received ultrasound signal (402A) at least partially from a structure (210) (“In FIG. 4A emitted ultrasonic pulse 401 is reflected from object 210 and received as corresponding returned signals 402A.”) within a ringdown range (“in a time window between one and two times the ringdown period” Abstract) of the single ultrasound transducer (“a transducer may have a much smaller blind spot in the range of a few centimeters”; “The x-axis shows distance represented in centimeters, based on round-trip time of flight,” Fig. 7) (“The processor is configured to evaluate the returned signals to find a candidate echo, from an object located in a ringdown blind spot area, in a time window between one and two times the ringdown period; …, wherein the ringdown blind spot area is located between the transducer and a closest distance at which objects can be sensed by the transducer.” Abstract);
at least one processor (110) (“A device comprises a processor coupled with an ultrasonic transducer…The processor is configured to evaluate the returned signals to find a candidate echo, from an object located in a ringdown blind spot area, in a time window between one and two times the ringdown period; …, wherein the ringdown blind spot area is located between the transducer and a closest distance at which objects can be sensed by the transducer.” Abstract; “example device 100A comprises a communications interface 105, a host processor 110,” Col. 6, l. 54-59; Fig. 1A) configured to:
determine, based on the data, a distance to the structure (950) (1040) (1060) (1140) (1160) or a characteristic of the structure (“An arrow 205 below device 100 and object 210 illustrates the distance between device 100 and object 210. It should be appreciated that any distances may be equated to a time for a roundtrip time-of-flight of an emitted pulse and a corresponding returned signal. Distance D0 is located at a position that begins even with ultrasonic transducer 150.” Col. 9, l. 38-55; Fig. 4A.“FIG. 4B illustrates a time-lapse 400 of the receipt of returned signals, in the form of multiple higher order echoes, by a sonic transducer from an object that is in the ringdown blind spot area of the sonic transducer” Col. 11, l. 5-58; “With continued reference to FIG. 9, at procedure 950 of flow diagram 900, in various embodiments, a signal indicating the estimated distance from the ultrasonic transducer to the object is provided.” Col. 21, l. 3-9; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B).
While Gris teaches a ringdown artifact (“a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B), Gris does not explicitly teach the at least one processor configured to: remove, from data indicative of the received ultrasound signal, a ringdown artifact; and the determining based on removing the ringdown artifact.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches the at least one processor (a processor for the “adaptive beamforming by deep learning” p. 2, 3rd para., right col.; pp. 3-6) configured to:
remove, from data indicative of the received ultrasound signal, a ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8. “In this work, we demonstrated how deep learning can be used to improve upon conventional beamforming methods. Specifically, we show that a compact and model-based architecture, which we term ABLE (adaptive beamforming by deep learning), enables the reconstruction of high-quality ultrasound images for multiple imaging systems.” VII. CONCLUSION, p. 9).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to employ the at least one processor configured to: remove, from data indicative of the received ultrasound signal, a ringdown artifact, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the invention of Gris modified by Luijten, the determining step is based on removing the ringdown artifact.
Regarding claim 7, Gris modified by Luijten teaches the at least one device of claim 4, wherein Gris teaches that the ringdown artifact comprises a residual vibration of the single ultrasound transducer in response to transmitting the incident ultrasound signal (“The physics of the operation of a transducer mean that it is vibrating while emitting a pulse (in the emitting portion of its duty cycle) and perhaps shortly afterward due to the emission of the pulse. This vibration due to the emission of a pulse from a transducer has a very high amplitude and is referred to as “ringdown.””).
Regarding claim 11, Gris modified by Luijten teaches the at least one device of claim 4, wherein Gris teaches an output device (“a processor” Col. 21, l. 3-12) configured to output an indication of the characteristic (950) (1040) (1060) (1140) (1160) (“With continued reference to FIG. 9, at procedure 950 of flow diagram 900, …, a signal indicating the estimated distance from the ultrasonic transducer to the object is provided. In various embodiments, the signal is provided by a processor which is communicatively coupled with the ultrasonic transducer (e.g., host processor 110, sensor processor 130, and/or controller 151). The signal provides information which allows a processor or logic of a device 100 to take a particular action, such as pausing movement or avoiding the object.”; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Figs. 9, 10A-B, and 11A-B), wherein the output device comprises at least one of a light source, a display, a speaker, or a haptic feedback device (“display components.” Col. 4, l. 45-47).
Regarding claim 12, Gris teaches a method (600A-B) (800-1100) (Abstract; Figs. 6A-B and 8-11), comprising:
transmitting, by a single ultrasound transducer (150), an incident ultrasound signal (401) (“emitted ultrasonic pulse 401” Fig. 4A);
detecting, at least partially from an object (210) (“In FIG. 4A emitted ultrasonic pulse 401 is reflected from object 210 and received as corresponding returned signals 402A.”) within a ringdown range (“in a time window between one and two times the ringdown period” Abstract) of the single ultrasound transducer and by the single ultrasound transducer, a received ultrasound signal (402A) (“a transducer may have a much smaller blind spot in the range of a few centimeters”; “The x-axis shows distance represented in centimeters, based on round-trip time of flight,” Fig. 7) (“The processor is configured to evaluate the returned signals to find a candidate echo, from an object located in a ringdown blind spot area, in a time window between one and two times the ringdown period; …, wherein the ringdown blind spot area is located between the transducer and a closest distance at which objects can be sensed by the transducer.” Abstract);
analyzing the object (“indicating the estimated distance” Col. 21, l. 3-9; “the object … being detected” Col. 22, l. 11- 18) based on the data (950) (1040) (1060) (1140) (1160) (“With continued reference to FIG. 9, at procedure 950 of flow diagram 900, … signal indicating the estimated distance from the ultrasonic transducer to the object is provided.” Col. 21, l. 3-9; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B).
While Gris teaches a ringdown artifact (“a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B), Gris does not explicitly teach removing, from data indicative of the received ultrasound signal, a ringdown artifact; and the analyzing based on removing the ringdown artifact.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches removing, from data indicative of the received ultrasound signal, a ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8. “In this work, we demonstrated how deep learning can be used to improve upon conventional beamforming methods. Specifically, we show that a compact and model-based architecture, which we term ABLE (adaptive beamforming by deep learning), enables the reconstruction of high-quality ultrasound images for multiple imaging systems.” VII. CONCLUSION, p. 9).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to employ the step of removing, from data indicative of the received ultrasound signal, a ringdown artifact, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the invention of Gris modified by Luijten, the analyzing is based on removing the ringdown artifact.
Regarding claim 15, Gris modified by Luijten teaches the method of claim 12, wherein Gris teaches that the ringdown artifact comprises a residual vibration of the single ultrasound transducer in response to transmitting the incident ultrasound signal (“The physics of the operation of a transducer mean that it is vibrating while emitting a pulse (in the emitting portion of its duty cycle) and perhaps shortly afterward due to the emission of the pulse. This vibration due to the emission of a pulse from a transducer has a very high amplitude and is referred to as “ringdown.””).
Regarding claim 17, Gris modified by Luijten teaches the method of claim 12, wherein Gris teaches determining a time-of-flight (400) between the single ultrasound transducer and the object (“An arrow 205 below device 100 and object 210 illustrates the distance between device 100 and object 210. It should be appreciated that any distances may be equated to a time for a roundtrip time-of-flight of an emitted pulse and a corresponding returned signal. Distance D0 is located at a position that begins even with ultrasonic transducer 150.” Col. 9, l. 38-55; Fig. 4A.“FIG. 4B illustrates a time-lapse 400 of the receipt of returned signals, in the form of multiple higher order echoes, by a sonic transducer from an object that is in the ringdown blind spot area of the sonic transducer,” Col. 11, l. 5-58).
Regarding claim 19, Gris modified by Luijten teaches the method of claim 12, wherein Gris teaches that analyzing the object based on the data comprises at least one of:
determining a velocity of the object;
determining a distance between the single ultrasound transducer and the object (950) (1040) (1060) (1140) (1160) (“the distance between device 100 and object 210” Col. 9, l. 38-55; Fig. 4A);
determining a size of the object;
determining a thickness of the object;
determining a mechanical characteristic of the object; or identifying the object (“An arrow 205 below device 100 and object 210 illustrates the distance between device 100 and object 210. It should be appreciated that any distances may be equated to a time for a roundtrip time-of-flight of an emitted pulse and a corresponding returned signal. Distance D0 is located at a position that begins even with ultrasonic transducer 150.” Col. 9, l. 38-55; Fig. 4A.“FIG. 4B illustrates a time-lapse 400 of the receipt of returned signals, in the form of multiple higher order echoes, by a sonic transducer from an object that is in the ringdown blind spot area of the sonic transducer” Col. 11, l. 5-58; “With continued reference to FIG. 9, at procedure 950 of flow diagram 900, in various embodiments, a signal indicating the estimated distance from the ultrasonic transducer to the object is provided.” Col. 21, l. 3-9; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B).
Regarding claim 20, Gris modified by Luijten teaches the method of claim 12, wherein Gris teaches transmitting, to an external device (“a processor or logic of a device 100” Col. 21, l. 3-12), a communication signal indicative of the object (“With continued reference to FIG. 9, at procedure 950 of flow diagram 900, … a signal indicating the estimated distance from the ultrasonic transducer to the object is provided… The signal provides information which allows a processor or logic of a device 100 to take a particular action, such as pausing movement or avoiding the object.” Col. 21, l. 3-12; “a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B). Any device connected to the claimed single ultrasound transducer is an external device).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claim 1, and further in view of Matsuura et al (US 20220139006), hereinafter Matsuura, and Jia et al (A high resolution ultrasonic ranging system using laser sensing and a cross-correlation method, Appl. Sci., vol. 9, no. 7, Apr. 2019, Art. no. 1483), hereinafter Jia.
Regarding claim 2, Gris modified by Luijten teaches the laparoscopic instrument of claim 1.
While Gris teaches a ringdown artifact (“a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B), Gris does not explicitly teach removing the ringdown artifact that comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches that removing the ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that
the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8) comprises applying a neural network to the data indicative of the received ultrasound signal (“Rather than relying on a large general-purpose network in conjuncture with abundant training data, we aim to train a network that is specifically designed for a certain task, thereby limiting its degrees of freedom, and allowing for a more compact architecture that is data efficient in training. We here propose Adaptive Beamforming by deep Learning (ABLE), a method that exploits the algorithmic structure of adaptive beamforming, with a neural network adaptively computing a set of optimal image reconstruction parameters given the received RF data (see Fig. 1)”; p. 2; Right col., 2nd complete para.).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to employ the step of removing the ringdown artifact that comprises applying a neural network to the data indicative of the received ultrasound signal, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves.
Gris as modified by Luijten does not teach that the neural network is a recurrent neural network (RNN).
However, in the medical imaging field of endeavor, Matsuura discloses information processing method, medical image diagnostic apparatus, and information processing system, which is analogous art. Matsuura teaches that removing the artifact comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal (“the noise reduction processing function 144e may configure the noise reduction processing model by another type of neural network such as a fully connected neural network and a recurrent neural network (RNN).” [0163]; “ultrasonic imaging" [0164]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ the step of removing the artifact that comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal, as taught by Matsuura, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the combined invention of Gris and Luijten further modified Matsuura, the artifact is the ringdown artifact.
Gris as modified by Luijten and Matsuura does not teach that determining, based on the data, the time-of-flight between the single ultrasound transducer and the object comprises estimating the time-of-flight by performing cross-correlation on the data.
However, in the medical devices field of endeavor, Jia discloses a high resolution ultrasonic ranging system using laser sensing and a cross-correlation method, which is analogous art. Jia teaches that determining, based on the data, the time-of-flight between the single ultrasound transducer (“Ultrasonic transducer” Fig. 1) and the object (“Target” Fig. 1) comprises estimating the time-of-flight (“TOF”) by performing cross-correlation on the data (“For ranging or thickness detection, the pulse-echo type is more suitable. It is common to measure the time-of-flight (TOF) to determine the distance [15]. In this case, the TOF is the time that a transmitted pulse takes to reflect back to the receiver. We can calculate the distance in cases where the sound velocity is known… A more suitable TOF estimation technique is cross-correlation… Here, the transmitted and received signals are cross-correlated. The time at which the correlation result reaches its maximum is an estimation of the TOF.”; p. 2; Fig. 2. (b) a result of the pulse-echo signal and the time-of-flight (TOF). “5.1. TOF by the Cross-Correlation Method”; p. 8; Fig. 6 (b).“Second, stochastic noise is the main noise in the proposed method. Two ways to reduce stochastic noise in the experiment are bandwidth limitation and average sampling. The central frequency of the transducer is 1 MHz. As such, the limiting frequency of the filter is set at 20 MHz.” p. 10).
Therefore, based on Jia’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris, Luijten, and Matsuura to employ the step of determining, based on the data, the time-of-flight between the single ultrasound transducer and the object that comprises estimating the time-of-flight by performing cross-correlation on the data, as taught by Jia, in order to facilitate the estimation of the time-of-flight (TOF).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claim 1, and further in view of Xu et al (US20190150889), hereinafter Xu.
Regarding claim 3, Gris modified by Luijten teaches the laparoscopic instrument of claim 1 the output device comprises at least one of a light, a display, a haptic feedback device, or a speaker (“display components.” Col. 4, l. 45-47).
Gris as modified by Luijten does not teach that the feedback signal comprises at least one of a visual signal, a tactile signal, or an audio signal.
However, in the medical devices field of endeavor, Xu discloses an ultrasound system and method, which is analogous art. Xu teaches that the feedback signal comprises at least one of a visual signal, a tactile signal, or an audio signal (“The user interface 107 aims to present an indication IDA to the operator in order to guide the operator for the next step. For example, the operator is guided to move the ultrasound probe 101 to the next region of interest. The indication IDA presented by the user interface 107 may be visible or audible to the operator. One example is a light indicator, which uses different colours of the light to indicate different types of feedback signals. Another example is an icon on a display, which visualizes the statuses to represent different types of feedback signals. An alternative example is a voice reminder, which provides audio instructions to guide the operator based on the different types of feedback signals. The embodiments of the user interface 107 are not limited to the examples mentioned above." [0060]).
Therefore, based on Xu’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to have the feedback signal that comprises at least one of a visual signal, a tactile signal, or an audio signal, as taught by Xu, in order to facilitate presenting indications to the operator in order to guide the operator for the next step.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claims 4 and 12, and further in view of Alleman et al (US 20120083717), hereinafter Alleman.
Regarding claim 5, Gris modified by Luijten teaches the at least one device of claim 4.
Gris as modified by Luijten does not teach that a frequency of the incident ultrasound signal is in a range of about 2.5 MHz to about 3.5 MHz.
However, in the ultrasound systems field of endeavor, Alleman discloses non-invasive transcranial ultrasound apparatus, which is analogous art. Alleman teaches that a frequency of the incident ultrasound signal is in a range of about 2.5 MHz to about 3.5 MHz (“a primary frequency signal (PULSE GEN, 332) may be selected between 500 KHz to 3.5 MHz” [0132]).
Therefore, based on Alleman’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ a frequency of the incident ultrasound signal that is in a range of about 2.5 MHz to about 3.5 MHz, as taught by Alleman, in order to facilitate ultrasound imaging using series of microsecond pulses (Alleman: [0139]).
Regarding claim 13, Gris modified by Luijten teaches the method of claim 12.
Gris as modified by Luijten does not teach that a frequency of the incident ultrasound signal is in a range of about 2.5 MHz to about 3.5 MHz.
However, in the ultrasound systems field of endeavor, Alleman discloses non-invasive transcranial ultrasound apparatus, which is analogous art. Alleman teaches that a frequency of the incident ultrasound signal is in a range of about 2.5 MHz to about 3.5 MHz (“a primary frequency signal (PULSE GEN, 332) may be selected between 500 KHz to 3.5 MHz” [0132]).
Therefore, based on Alleman’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ a frequency of the incident ultrasound signal that is in a range of about 2.5 MHz to about 3.5 MHz, as taught by Alleman, in order to facilitate ultrasound imaging using series of microsecond pulses (Alleman: [0139]).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claims 4 and 12, and further in view of Matthias (US20250049418), hereinafter Matthias.
Regarding claim 6, Gris modified by Luijten teaches the at least one device of claim 4.
Gris as modified by Luijten does not teach that the object comprises a soft tissue, a bone, a foreign body, or a tumor.
However, in the ultrasound systems field of endeavor, Matthias discloses technologies for ultrasound asynchronous resonance imaging (ARI) for needle tip localization, which is analogous art. Matthias teaches that the object comprises a soft tissue, a bone, a foreign body, or a tumor (“In FIG. 12B, the time delay was 40 microseconds. In FIG. 12C the time delay was 45 microseconds. The narrowest point of the double ringdown artifact remains at the same depth in FIG. 12A, FIG. 12B, and FIG. 12C, while soft tissue echoes, appearing as horizontal lines, decrease in depth as the time delay is increased.” [0088]).
Therefore, based on Matthias’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to have the object that comprises a soft tissue, a bone, a foreign body, or a tumor, as taught by Matthias, in order to facilitate ultrasound imaging of a human body.
Regarding claim 14, Gris modified by Luijten teaches the method of claim 12.
Gris as modified by Luijten does not teach that the object comprises a soft tissue, a bone, a foreign body, or a tumor.
However, in the ultrasound systems field of endeavor, Matthias discloses technologies for ultrasound asynchronous resonance imaging (ARI) for needle tip localization, which is analogous art. Matthias teaches that the object comprises a soft tissue, a bone, a foreign body, or a tumor (“In FIG. 12B, the time delay was 40 microseconds. In FIG. 12C the time delay was 45 microseconds. The narrowest point of the double ringdown artifact remains at the same depth in FIG. 12A, FIG. 12B, and FIG. 12C, while soft tissue echoes, appearing as horizontal lines, decrease in depth as the time delay is increased.” [0088]).
Therefore, based on Matthias’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to have the object that comprises a soft tissue, a bone, a foreign body, or a tumor, as taught by Matthias, in order to facilitate ultrasound imaging of a human body.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claims 4 and 12, and further in view of Matsuura et al (US 20220139006), hereinafter Matsuura.
Regarding claim 8, Gris modified by Luijten teaches the at least one device of claim 4.
While Gris teaches a ringdown artifact (“a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B), Gris does not explicitly teach removing the ringdown artifact that comprises applying, to the data indicative of the received ultrasound signal, at least one of:
a bandpass filter,
an adaptive LMS filter,
an SPS,
a GRU,
an LSTM, or
an RNN.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches that removing the ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that
the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8) comprises applying, to the data indicative of the received ultrasound signal, at least one of:
a bandpass filter,
an adaptive LMS filter,
an SPS,
a GRU,
an LSTM, or
a neural network (“Rather than relying on a large general-purpose network in conjuncture with abundant training data, we aim to train a network that is specifically designed for a certain task, thereby limiting its degrees of freedom, and allowing for a more compact architecture that is data efficient in training. We here propose Adaptive Beamforming by deep Learning (ABLE), a method that exploits the algorithmic structure of adaptive beamforming, with a neural network adaptively computing a set of optimal image reconstruction parameters given the received RF data (see Fig. 1)”; p. 2; Right col., 2nd complete para.).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to employ the step of removing the ringdown artifact that comprises applying, to the data indicative of the received ultrasound signal, a neural network, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves.
Gris as modified by Luijten does not teach that the neural network is a recurrent neural network (RNN).
However, in the medical imaging field of endeavor, Matsuura discloses information processing method, medical image diagnostic apparatus, and information processing system, which is analogous art. Matsuura teaches that removing the artifact comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal (“the noise reduction processing function 144e may configure the noise reduction processing model by another type of neural network such as a fully connected neural network and a recurrent neural network (RNN).” [0163]; “ultrasonic imaging" [0164]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ the step of removing the artifact that comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal, as taught by Matsuura, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the combined invention of Gris and Luijten further modified Matsuura, the artifact is the ringdown artifact.
Regarding claim 16, Gris modified by Luijten teaches the method of claim 12.
While Gris teaches a ringdown artifact (“a signal is provided to indicate presence of the object as being detected within the ringdown blind spot area associated with the ultrasonic transducer.” Col. 22, l. 11- 18; Figs. 9, 10A-B, and 11A-B), Gris does not explicitly teach removing the ringdown artifact that comprises applying, to the data indicative of the received ultrasound signal, at least one of:
a bandpass filter,
an adaptive LMS filter,
an SPS,
a GRU,
an LSTM, or
an RNN.
However, in the medical imaging field of endeavor, Luijten discloses adaptive ultrasound beamforming using deep learning, which is analogous art. Luijten teaches that removing the ringdown artifact (“Additionally, for IVUS imaging, adaptive beamforming yields a strong suppression of typical ringdown artifacts caused by residual vibrations after transmission. ABLE learns this behavior from the MV beamformer, in which these artifacts, caused by high-intensity yet mostly incoherent signal components, are minimized by optimizing the weights such that
the signals destructively interfere with each other. Strong scatterers that were previously obscured by the ringdown become visible due to the highly correlated nature of their echoes.”; p. 8) comprises applying, to the data indicative of the received ultrasound signal, at least one of:
a bandpass filter,
an adaptive LMS filter,
an SPS,
a GRU,
an LSTM, or
a neural network (“Rather than relying on a large general-purpose network in conjuncture with abundant training data, we aim to train a network that is specifically designed for a certain task, thereby limiting its degrees of freedom, and allowing for a more compact architecture that is data efficient in training. We here propose Adaptive Beamforming by deep Learning (ABLE), a method that exploits the algorithmic structure of adaptive beamforming, with a neural network adaptively computing a set of optimal image reconstruction parameters given the received RF data (see Fig. 1)”; p. 2; Right col., 2nd complete para.).
Therefore, based on Luijten’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Gris to employ the step of removing the ringdown artifact that comprises applying, to the data indicative of the received ultrasound signal, a neural network, as taught by Luijten, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves.
Gris as modified by Luijten does not teach that the neural network is a recurrent neural network (RNN).
However, in the medical imaging field of endeavor, Matsuura discloses information processing method, medical image diagnostic apparatus, and information processing system, which is analogous art. Matsuura teaches that removing the artifact comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal (“the noise reduction processing function 144e may configure the noise reduction processing model by another type of neural network such as a fully connected neural network and a recurrent neural network (RNN).” [0163]; “ultrasonic imaging" [0164]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ the step of removing the artifact that comprises applying a recurrent neural network (RNN) to the data indicative of the received ultrasound signal, as taught by Matsuura, in order to improve the quality of ultrasound images that are reconstructed from the reflected ultrasound waves. In the combined invention of Gris and Luijten further modified Matsuura, the artifact is the ringdown artifact.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claim 4, and further in view of Steininger et al (US 20170119356), hereinafter Steininger.
Regarding claim 9, Gris modified by Luijten teaches the at least one device of claim 4.
Gris as modified by Luijten does not teach that the characteristic of the structure comprises at least one of:
a velocity of blood in the structure;
a velocity of a wall of the structure;
a size of the structure;
a thickness of the structure;
a mechanical characteristic of the structure; or
an identification of the structure.
However, in the medical ultrasound imaging field of endeavor, Steininger discloses methods and systems for a velocity threshold ultrasound image, which is analogous art. Steininger teaches that the characteristic of the structure comprises at least one of: a velocity of blood in the structure (“a flow velocity… blood flow” [0060]); a velocity of a wall of the structure; a size of the structure; a thickness of the structure; a mechanical characteristic of the structure; or an identification of the structure (“The color flow circuit 252 calculates a flow velocity (e.g., movement of the tissue with respect to the ultrasound probe 126, blood flow) along the one more scan planes of the ultrasound probe 126 for multiple vector positions and multiple range gates within the tissue based on phase shifts of the digitized signals with respect to the transmitted ultrasound pulses. The flow velocity may be based on the variables N and D calculated by the color flow circuit 252 as shown below in Equations 2 and 3 to determine a phase shift as shown in Equation 4. The variable T corresponding to the pulse repetition time between each pulse in the pulse sequence corresponding to the color flow imaging.” [0060]).
Therefore, based on Steininger’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to have the characteristic of the structure comprises at least one of: a velocity of blood in the structure; a velocity of a wall of the structure;
a size of the structure; a thickness of the structure; a mechanical characteristic of the structure; or
an identification of the structure, as taught by Steininger, in order to provide diagnostic information about the characteristic of the structure.
Regarding claim 10, Gris modified by Luijten and Steininger teaches the at least one device of claim 9.
Gris as modified by Luijten does not teach that analyzing the data comprises:
in response to removing the ringdown artifact from the data indicative of the received ultrasound signal, determining a frequency shift and/or a phase shift of the received ultrasound signal with respect to the incident ultrasound signal by analyzing the data; and
determining a velocity of a fluid in the structure based on the frequency shift and/or the phase shift.
However, in the medical ultrasound imaging field of endeavor, Steininger discloses methods and systems for a velocity threshold ultrasound image, which is analogous art. Steininger teaches determining a frequency shift and/or a phase shift of the received ultrasound signal with respect to the incident ultrasound signal by analyzing the data (“to determine a phase shift” [0060]); and
determining a velocity of a fluid in the structure based on the frequency shift and/or the phase shift (“The color flow circuit 252 calculates a flow velocity (e.g., movement of the tissue with respect to the ultrasound probe 126, blood flow) along the one more scan planes of the ultrasound probe 126 for multiple vector positions and multiple range gates within the tissue based on phase shifts of the digitized signals with respect to the transmitted ultrasound pulses. The flow velocity may be based on the variables N and D calculated by the color flow circuit 252 as shown below in Equations 2 and 3 to determine a phase shift as shown in Equation 4. The variable T corresponding to the pulse repetition time between each pulse in the pulse sequence corresponding to the color flow imaging.” [0060]).
Therefore, based on Steininger’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ the step of analyzing the data comprises: determining a frequency shift and/or a phase shift of the received ultrasound signal with respect to the incident ultrasound signal by analyzing the data; and determining a velocity of a fluid in the structure based on the frequency shift and/or the phase shift, as taught by Steininger, in order to provide diagnostic information about the characteristic of the structure. In the combined invention of Gris, Luijten, and Steininger, the determining is in response to removing the ringdown artifact from the data indicative of the received ultrasound signal,
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Gris and Luijten as applied to claim 17, and further in view of Jia et al (A high resolution ultrasonic ranging system using laser sensing and a cross-correlation method, Appl. Sci., vol. 9, no. 7, Apr. 2019, Art. no. 1483), hereinafter Jia.
Regarding claim 18, Gris modified by Luijten teaches the method of claim 17.
Gris as modified by Luijten does not teach that determining the time-of-flight comprises performing a cross-correlation method or a minimum threshold method on the data.
However, in the medical devices field of endeavor, Jia discloses a high resolution ultrasonic ranging system using laser sensing and a cross-correlation method, which is analogous art. Jia teaches that determining the time-of-flight comprises performing cross-correlation method on the data (“For ranging or thickness detection, the pulse-echo type is more suitable. It is common to measure the time-of-flight (TOF) to determine the distance [15]. In this case, the TOF is the time that a transmitted pulse takes to reflect back to the receiver. We can calculate the distance in cases where the sound velocity is known… A more suitable TOF estimation technique is cross-correlation… Here, the transmitted and received signals are cross-correlated. The time at which the correlation result reaches its maximum is an estimation of the TOF.”; p. 2; Fig. 2. (b) a result of the pulse-echo signal and the time-of-flight (TOF). “5.1. TOF by the Cross-Correlation Method”; p. 8; Fig. 6 (b).“Second, stochastic noise is the main noise in the proposed method. Two ways to reduce stochastic noise in the experiment are bandwidth limitation and average sampling. The central frequency of the transducer is 1 MHz. As such, the limiting frequency of the filter is set at 20 MHz.” p. 10).
Therefore, based on Jia’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Gris and Luijten to employ the step of determining the time-of-flight that comprises performing a cross-correlation method or a minimum threshold method on the data, as taught by Jia, in order to facilitate the estimation of the time-of-flight (TOF).
Conclusion
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/ALEXEI BYKHOVSKI/
Primary Examiner, Art Unit 3798
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