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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 11/27/2023. It is noted, however, that applicant has not filed a certified copy of the JP 2023-200194 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/25/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
[0031]: As written it reads “For example, the storage unit 24 includes a hard disk drive (HDD), a solid state drive (SSD), various memories (for example, RAM, DRAM, ROM, and the like) […]”. However, this is the first indication of the acronyms “RAM”, “DRAM”, and “ROM”. Therefore, the terms should be spelled out to provide clarity.
Appropriate correction is required.
Claim Objections
Claims 1-3 and 10 are objected to because of the following informalities:
Regarding claims 1 and 10, as written they read “wherein, in a case where a state where the reliability degree of the plurality of candidates having higher reliability degrees satisfies a threshold value condition is continued for a predetermined time, the acquisition unit acquires a second ultrasound image of the subject according to a second imaging condition according to the recognized cross section, the second imaging condition being different from the first imaging condition”. However, the examiner believes the phrase “a state where” (underlined above) is a typo which should be removed because “a case where” means the same thing (i.e. the phrase “a state where” is redundant).
Regarding claim 2, as written it reads “and the case where the state where the reliability degree of the plurality of candidates having higher reliability degrees satisfies the threshold value condition is continued for the predetermined time is a case where a state where the sum is within a threshold value range is continued for a predetermined time”. However, the examiner believes that phrases “the state where” and “a state where” (underlined above) represent typos which should be removed because “a/the case where” means the same thing (i.e. the phrase “the/a state where” is redundant).
Regarding claim 3, as written it reads “wherein the case where the state where the reliability degree of the plurality of candidates having higher reliability degrees satisfies the threshold value condition is continued for the predetermined time is a case where a state where ranks of the plurality of candidates having higher reliability degrees are not changed is continued for a predetermined time and the reliability degree of each of the plurality of candidates having higher reliability degrees is lower than a threshold value”. However, the examiner believes that phrases “the state where” and “a state where” (underlined above) represent typos which should be removed because “a/the case where” means the same thing (i.e. the phrase “the/a state where” is redundant).
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) 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) 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: the acquisition unit in claims 1, 6-10 and the recognition unit in claims 1, 6 and 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. That being said, the acquisition unit is described within the specification when it states “First, the acquisition unit acquires a first ultrasound image of the subject according to a first imaging condition. That is, the transmission/reception unit 14 transmits and receives the ultrasound waves by the ultrasound probe 12 according to the first imaging condition, and the image generation unit 16 generates the ultrasound image based on the reception frame that is output from the transmission/reception unit 14 according to the first imaging condition” [0053]; “The acquisition unit (that is, the transmission/reception unit 14 and the image generation unit 16) acquires the first ultrasound image according to the preset condition that is selected by the user” [0054]; “the acquisition unit acquires a second ultrasound image of the subject according to a second imaging condition. That is, the transmission/reception unit 14 transmits and receives the ultrasound waves by the ultrasound probe 12 according to the second imaging condition, and the image generation unit 16 generates the ultrasound image based on the reception frame that is output from the transmission/reception unit 14 according to the second imaging condition” [0056]. Therefore, it appears that the acquisition unit represents a processor which includes processing circuitry configured to transmit and receive ultrasound waves and generate one or more ultrasound images. Thus, claims 1 and 6-10 are not subject to further rejection under 35 U.S.C. 112 with respect to the acquisition unit.
Additionally, the recognition unit is descried in the specification when it states “The recognition unit 28 estimates one or a plurality of candidates of a scanning cross section that is scanned by the ultrasound waves by executing processing (hereinafter, referred to as “cross section recognition processing”) of recognizing a cross section on the ultrasound image. In addition, the recognition unit 28 calculates a reliability degree of the recognition for each of the candidates of the scanning cross section. The reliability degree is a score (that is, certainty or likelihood of the estimation) representing certainty of the estimation” [0039]; “For example, the recognition unit 28 may estimate a candidate of a scanning cross section that is currently being scanned by executing cross section recognition processing on the ultrasound image that is currently acquired. That is, the recognition unit 28 may estimate a candidate of the scanning cross section in real time” [0040]; “For example, the recognition unit 28 estimates one or a plurality of candidates of the scanning cross section by executing the cross section recognition processing using machine learning on the ultrasound image, and calculates a reliability degree representing certainty of the estimation using the machine learning for each of the candidates of the scanning cross section. The recognition unit 28 may estimate one or a plurality of candidates of a portion that is being scanned with the ultrasound waves, and may calculate a reliability degree representing certainty of the estimation for each of the candidates of the portion” [0044]; “The recognition unit 28 may estimate one or a plurality of candidates of the scanning cross section that is being scanned with the ultrasound waves by comparing the ultrasound image (for example, the B-mode image) generated by transmission and reception of the ultrasound waves with a plurality of reference cross section images (for example, the B-mode images), and may calculate a reliability degree representing certainty of the estimation for each of the candidates of the scanning cross section” [0045]. Therefore, it appears that the recognition unit represents a processor which includes processing circuitry configured to estimate one or a plurality of candidates of a scanning cross section, calculate a reliability degree of the recognition for each of the candidates of the scanning cross section and/or comparing the ultrasound image with a plurality of reference cross section images. Therefore, claims 1, 6 and 10 are not subject to further rejection under 35 U.S.C. 112 with respect to the recognition unit.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
Claim Rejections - 35 USC § 112
Claims 1-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 1 and 10, as written the claims read “a recognition unit that executes processing of recognizing a cross section on the first ultrasound image and outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates”.
However, it is unclear what the term “candidates” corresponds to. Under broadest reasonable interpretation “candidates” can be interpreted to correspond to: 1) an anatomical structure present within the first ultrasound image, 2) image frames, or 3) viewing angles of the cross section.
The examiner recommends clarifying what the plurality of candidates represent in the context of claims 1 and 10.
Regarding claims 2-9, due to their dependence on claim 1, either directly or indirectly, these claims are subject to the reasoning provided therein. Thus, these claims inherit the rejection under 35 U.S.C. 112(b) for the reasons stated above.
Regarding claims 2-5, as written the claims read “wherein the reliability degree of the plurality of candidates having higher reliability degrees is a sum of the reliability degrees calculated by adding the reliability degree of each candidate in an order from a candidate having a highest reliability degree” (Claim 2); “wherein the case where the state where the reliability degree of the plurality of candidates having higher reliability degrees satisfies the threshold value condition is continued for the predetermined time is a case where a state where ranks of the plurality of candidates having higher reliability degrees are not changed is continued for a predetermined time and the reliability degree of each of the plurality of candidates having higher reliability degrees is lower than a threshold value” (Claim 3); “wherein the second imaging condition is an imaging condition corresponding to a candidate of which the reliability degree is higher than a threshold value among the plurality of candidates having higher reliability degrees” (Claim 4) and “wherein the second imaging condition is an imaging condition corresponding to a candidate having a highest reliability degree among the plurality of candidates having higher reliability degrees” (Claim 5).
The term “higher reliability degrees” in these claims is a relative term which renders the claim indefinite. The term “higher reliability degrees” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree to which the reliability degree would be considered “higher”. For example, it is unclear what value of reliability degree would cause that reliability degree to be classified as a “higher reliability degree”. Therefore, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The examiner would recommend clarifying the scope of the term “higher reliability degrees”. This can be done, for example, by specifying a range or value at which the reliability degree is considered to be higher.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 3-7, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rafter et al. US 2022/0338845 A1 “Rafter” and further in view of Matsumoto US 2019/0200963 A1 “Matsumoto”.
Regarding claims 1 and 10, Rafter teaches “An ultrasound diagnostic apparatus comprising:” (Claim 1) (“FIG. 1 shows a block diagram of an ultrasound imaging system 100 constructed in accordance with the principles of the present disclosure. An ultrasound imaging system 100 according to the present disclosure may include a transducer array 114” [0027]. The system shown in FIG. 1 represents an ultrasound diagnostic apparatus.);
“A non-transitory computer-readable storage medium storing a program causing a computer to function as:” (Claim 10) (“The system 100 may include local memory 142. Local memory 142 may be implemented as any suitable non-transitory computer readable medium (e.g., flash drive, disk drive). Local memory 142 may store data generated by the system 100 including ultrasound images, executable instructions, imaging parameters, training data sets, or any other information necessary for the operation of the system 100” [0050]. Therefore, Rafter discloses a non-transitory computer readable storage medium storing a program causing a computer to function.);
“an acquisition unit that acquires a first ultrasound image of a subject according to a first imaging condition” (Claims 1 and 10) (“The transducer array 114 is configured to transmit ultrasound signals (e.g., beams, waves) and receive echoes responsive to the ultrasound signals” [0027]; “In some embodiments, the partially beamformed signals produced by the microbeamformer 116 may be coupled to a main beamformer 122 where partially beamformed signals from individual patches of transducer elements may be combined into a fully beamformed signal” [0031]; “The signal processor 126 may be configured to process the received beamformed RF data in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. he signal processor 126 may also perform additional signal enhancement such as speckle reduction, signal compounding, and noise elimination. The processed signals (also referred to as I and Q components or IQ signals) may be coupled to additional downstream signal processing circuits for image generation” [0032].
Therefore, the transducer array 114 transmits ultrasound signals and receives (i.e. acquires) echoes responsive to the ultrasound signals. After receiving the echoes, the microbeamformer 116/main beamformer 122 perform beamforming on the signal such that it can be processed by the signal processor 126 and additional downstream signal processing circuits for image generation (See [0032]). Therefore, the transducer array 114 in combination with the and signal processor 126/additional downstream signal processing circuits represents an acquisition unit that acquires a first ultrasound image.
Additionally, FIG. 4 shows methods steps carried out by the system of FIG. 1. Specifically, step 402 which involves acquiring an ultrasound image, step 408 involves determining one or more view-specific imaging parameter, and step 412 involves reacquiring the ultrasound image with the one or more view specific imaging parameters. Therefore, the imaging parameters utilized in the first image acquisition (i.e. step 402) are different than the imaging parameters used in the second image acquisition (i.e. step 412). Therefore, in step 402, the acquisition unit (i.e. transducer array 114 in combination with the signal processor 126/additional downstream signal processing circuits) acquires a first ultrasound image of a subject according to a first imaging condition.); and
“a recognition unit that executes processing of recognizing a cross section on the first ultrasound image and outputs a […] candidate[s] for the cross section and a reliability degree of the recognition for […] the candidate[s]” (Claims 1 and 10) (“According to principles of the present disclosure, view-specific optimization may be implemented by a view recognition processor, acquisition parameters that are optimized for imaging the views identified by the view recognition processor, and an optimization state controller that monitors outputs of the view recognition processor and applies the imaging parameters (e.g., view-specific system settings) in a manner that improves system responsiveness while reducing erratic transitions between imaging parameters that may be distracting to a user” [0023]; “The view recognition processor may provide an output that includes an indication of the standard view acquired” [0025]; “Based on the determination that the specific view has been acquired, the view recognition processor 170 may generate an output (e.g., signal). The output may include one or more signals or data that identify the specific view from the plurality of views analyzed by the processor 170 and/or the physiological state of the anatomy in the ultrasound image […] In some embodiments, the output may further include a signal or data that represents a confidence score. The confidence score may be a measure of the accuracy of the view identification by the view recognition processor 170. That is, the confidence score may represent a likelihood or probability that the view identified as the specific view by the processor 170 does in fact correspond to the desired specific view and/or physiological state” [0037]; “In some embodiments, the view recognition processor 170 may utilize a neural network, for example a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), an autoencoder neural network, or the like, to recognize specific views” [0038].
Therefore, the view recognition processor 170 utilizes a neural network (i.e. DNN, CNN, or RNN) to recognize the specific view acquired in the first ultrasound image and outputs one or more signals or data that identify the specific view from the plurality of views (i.e. candidates) analyzed by the processor 170 and a confidence score (i.e. representing a reliability degree). Therefore, the view recognition processor 170 represents a recognition unit that executes processing of recognizing a cross section (i.e. specific view) on the first ultrasound image and outputs a candidate (i.e. specific view) for the cross section and a reliability degree (i.e. confidence score) of the recognition for the candidate.);
“wherein, in a case where a state where the reliability degree of the […] candidate[s] having higher reliability degree[s] satisfies a threshold value condition is continued for a predetermined time, the acquisition unit acquires a second ultrasound image of the subject according to a second imaging condition according to the recognized cross section, the second imaging condition being different from the first imaging condition” (Claims 1 and 10) (See step 412 in FIG. 4 and “If the optimization state controller 172 responds too quickly to certain view recognition processor 170 outputs, the incorrect imaging parameters could be chosen and/or the system 100 could change imaging parameters so quickly that the display 138 becomes erratic and the image unusable. In either case, the user may lose confidence in the ability of the system 100 to provide reliable diagnostic imaging. Thus, in some embodiments, the optimization state controller 172 may wait for one or more conditions prior to determining or providing determined imaging parameters. For example, the optimization state controller 172 may wait until the indication provided by the view recognition processor 170 is stable for a certain period of time (e.g., 0.5 s, 1 s, 2 s) or a certain number of image frames (e.g., 5, 10, 30). In some embodiments, the optimization state controller 172 may analyze confidence scores provided by the view recognition processor 170, possibly over multiple image frames, and determine if and when the view recognition processor 170 is sufficiently confident prior to determining or providing the imaging parameters, for example, when the confidence scores are above a threshold value (e.g., 70%, 90%) for one or more frames. In some embodiments, the threshold value for the confidence score may be preset. In other embodiments, the threshold value may be set by a user input” [0046].
Therefore, the optimization state controller 172 waits until the indication provided by the view recognition processor is stable for a certain period of time (e.g. 0.5 s, 1 s, 2 s, see [0046]) and may analyze confidence scores provided by the view recognition processor 170 (i.e. sufficient confidence being a confidence score above a threshold value) before determining and providing one or more view specific imaging parameters (i.e. determined imaging parameters, see steps 408-410 in FIG. 4) for the acquisition unit (i.e. transducer array 114 in combination with signal processor 126 and additional downstream processing circuitry) to use when acquiring a second ultrasound image with view specific imaging parameters (see step 412 in FIG. 4).
Thus, in a case where a state where the reliability degree (i.e. confidence score) of the candidate having higher reliability degree satisfies a threshold value condition is continued for a predetermined time, the acquisition unit acquires a second ultrasound image of the subject according to a second imaging condition according to the recognized cross section, the second imaging condition (i.e. view specific imaging parameters used in step 412 of FIG. 4) being different from the first imaging condition (i.e. imaging parameters used in step 402 of FIG. 4).).
Although Rafter discloses “The output may include one or more signals or data that identify the specific view from the plurality of views analyzed by the processor 170 and/or the physiological state of the anatomy in the ultrasound image” [0037], Rafter does not teach that the recognition unit outputs “a plurality of candidates for a cross section” and a reliability degree of the recognition “for each of the candidates”.
Matsumoto is within the same field of endeavor as the claimed invention because it involves an ultrasound diagnostic apparatus which has an image acquiring unit that transmits/receives an ultrasound beam and a part probability calculating unit that calculates a probability that a part included in the ultrasound image is a specific part based on an analysis of the ultrasound image (see [Abstract]).
Matsumoto teaches that the recognition unit outputs “a plurality of candidates for a cross section” and a reliability degree of the recognition “for each of the candidates” (“In step S21, the image analyzing unit 13 performs image analysis on ultrasound images of a plurality of frames that are output from the image generating unit 6 of the image acquiring unit 3. As in the image analysis of the ultrasound image of a single frame in step S19, the image analysis of the ultrasound images of a plurality of frames performed in step S21 is performed in order to narrow down candidate parts of a subject included in the ultrasound images from a plurality of diagnostic parts” [0097]; “In the subsequent step S22, on the basis of a result of the image analysis of the ultrasound images of a plurality of frames in step S21, the part probability calculating unit 23 calculates the part probability. […] In this case, by using the target number of vectors for each part of the subject, the part probability calculating unit 23 calculates the probabilities that the part included in the ultrasound images is each specific part” [0098]; “On the other hand, if it is determined in step S23 that at least one of the plurality of part probabilities calculated on the basis of the result of image analysis of the ultrasound images of a plurality of frames is greater than or equal to the threshold value, the process proceeds to step S7” [0103]; “As described above, according to the ultrasound diagnostic apparatus 22 in the second embodiment illustrated in FIG. 5, the part probability is calculated on the basis of the analysis result of the ultrasound image(s), the first imaging condition is changed to the second imaging condition on the basis of the part probability, and the ultrasound image is further acquired by using the second imaging condition” [0104].
These steps S21, S22 and S23 are shown in FIG. 6 of Matsumoto for example. In this case, the part probability calculating unit 23 of Matsumoto calculates the part probability for a plurality of frames (i.e. from the image analyzing unit) in order to narrow down candidate parts of a subject included in the ultrasound images, and calculates the probabilities (i.e. reliability degree) that the part included in the ultrasound image is each specific part (i.e. of a plurality of candidate parts). In order to determine whether the plurality part probabilities are greater than a threshold value (See step S22 in FIG. 6), the part probability calculating unit 23 had to have output a plurality of candidates for a cross section (i.e. ultrasound frame) and a reliability degree (i.e. part probability) of the recognition for each of the candidates (i.e. candidate parts of a subject). Therefore, since the part probability calculating unit calculates the probabilities that the part included in the ultrasound images is each specific part (i.e. of the plurality of candidate parts) such that the part probabilities can be compared to a threshold value and allow for adjustment of imaging parameters (i.e. changing from first imaging conditions to second imaging conditions) when the part probabilities are greater than or equal to the threshold value (See [0103]-[0104]), the part probability calculating unit represents a recognition unit that output a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recognition unit (i.e. view recognition processor) of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates as disclosed by Matsumoto (i.e. performed by the part probability calculating unit) in order to effectively narrow down candidate parts of a subject included in the ultrasound images from a plurality of diagnostic parts (See Matsumoto: [0097]). Calculating a probability that a part included in an ultrasound image is each specific part of a plurality of candidate parts is one of a finite number of techniques which can be used to effectively identify what part(s) is/are included within an ultrasound image with a reasonable expectation of success. Thus, modifying the recognition unit (i.e. view recognition processor) of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates as disclosed by Matsumoto (i.e. performed by the part probability calculating unit) would yield the predictable result of narrowing down which candidate/part is included within the ultrasound image, such that imaging conditions specific to that candidate/part can be determined, provided and utilized when performing updated imaging of that candidate/part.
Regarding claim 3, Rafter in view of Matsumoto discloses all features of the claimed invention as discussed with respect to claim 1 above, and Rafter further teaches “wherein the case where the state where the reliability degree of the plurality of candidates having higher reliability degrees satisfies the threshold value condition is continued for the predetermined time is a case where a state where ranks of the plurality of candidates having higher reliability degrees are not changed is continued for a predetermined time and the reliability degree of each of the plurality of candidates having higher reliability degrees is lower than a threshold value” (See step 412 in FIG. 4 and [0046] as discussed with respect to claim 1 above. Therefore, the optimization state controller 172 waits until the indication provided by the view recognition processor is stable for a certain period of time (e.g. 0.5 s, 1 s, 2 s, see [0046]) and may analyze confidence scores (i.e. over multiple image frames/candidates) provided by the view recognition processor 170 (i.e. sufficient confidence being a confidence score above a threshold value) before determining and providing one or more view specific imaging parameters (i.e. determined imaging parameters, see steps 408-410 in FIG. 4) for the acquisition unit (i.e. transducer array 114 in combination with signal processor 126 and additional downstream processing circuitry) to use when acquiring a second ultrasound image with view specific imaging parameters (see step 412 in FIG. 4).
Thus, in a case where a state where the reliability degree (i.e. confidence score) of the candidate having higher reliability degree satisfies the threshold value condition is continued for a predetermined time (i.e. period of time e.g. 0.5 s, 1 s, 2 s, see [0046]), is a case where a state where ranks of the plurality of candidates (i.e. corresponding to different frames, for example) having higher reliability degrees are not changed is continued for a predetermined time (i.e. period of time e.g. 0.5 s, 1 s, 2 s, see [0046]) and the reliability degree of each of the plurality of candidates (i.e. corresponding to different frames, for example) having higher reliability degrees is lower than a threshold value (see FIG. 5, steps 508 and 512).).
Matsumoto further teaches “the plurality of candidates” (See [0097], [0098], [0103], [0104] as discussed in claim 1 above. In this case, the method carried out by the system of Matsumoto determines that at least one of the plurality of part probabilities (i.e. corresponding to the plurality of candidate parts, see [0097]) calculated on the basis of the result of image analysis of the ultrasound images of a plurality of frames is greater than or equal to the threshold value, the process proceeds to step S7, in which the first imaging condition is changed to the second imaging condition. Therefore, the plurality of part probabilities are compared to a threshold value such that it can be determined which probability values are lower than the threshold value, and which are greater than or equal to the threshold value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recognition unit (i.e. view recognition processor) of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates as disclosed by Matsumoto (i.e. performed by the part probability calculating unit) in order to effectively narrow down candidate parts of a subject included in the ultrasound images from a plurality of diagnostic parts (See Matsumoto: [0097]). Calculating a probability that a part included in an ultrasound image is each specific part of a plurality of candidate parts is one of a finite number of techniques which can be used to effectively identify what part(s) is/are included within an ultrasound image with a reasonable expectation of success. Thus, modifying the recognition unit (i.e. view recognition processor) of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates as disclosed by Matsumoto (i.e. performed by the part probability calculating unit) would yield the predictable result of narrowing down which candidate/part is included within the ultrasound image, such that imaging conditions specific to that candidate/part can be determined, provided and utilized when performing updated imaging of that candidate/part.
Regarding claim 4, Rafter in view of Matsumoto discloses all features of the claimed invention as discussed with respect to claim 1 above, and Rafter further teaches “wherein the second imaging condition is an imaging condition corresponding to a candidate of which the reliability degree is higher than a threshold value […]” (“At block 506, a step of “retrieving imaging parameters for the specific view” may be performed. The imaging parameters retrieved may be based on the lookup table. In some embodiments, the imaging parameters may be retrieved from local memory 142. In some embodiments, based on the specific view determined, one or more algorithms may be retrieved (e.g., from local memory)” [0070]; “At block 508, a step of “comparing the output signal to a threshold value” may be performed. In some embodiments, the threshold value may correspond to a threshold value of the confidence score. In some embodiments, the threshold value may be a number of ultrasound image frames or a time period for which the output signal remains stable, for example, the specific view indicated by the output signal remains constant […] In some embodiments, the threshold value may be a combination of factors and/or multiple threshold values corresponding to different factors are analyzed (e.g., a confidence score above a threshold for a given number of frames). If the output signal meets or exceeds the threshold value or values, at block 510, a step of “providing the retrieved imaging parameters” may be performed. If the output signal is below the threshold, at block 512, a step of “providing existing imaging parameters” may be performed” [0071].
Therefore, when the output signal meets of exceeds (i.e. is higher than) a threshold value, the threshold value corresponding to a confidence score (i.e. reliability degree) or combination of factors (i.e. confidence score above a threshold for a given number of frames), the retrieved imaging parameters are provided. These retrieved imaging parameters represent imaging parameters corresponding to the specific view based on a lookup table and/or local memory. Therefore, the second imaging condition is an imaging condition corresponding to a candidate of which the reliability degree is higher than a threshold value.).
Matsumoto teaches “among the plurality of candidates having higher reliability degrees” (See [0097], [0098], [0103] and [0104] as described with respect to claim 1 above. Therefore, the method carried out by the system of Matsumoto obtains part probabilities for each specific part (i.e. of the plurality of candidate parts, see [0097]) and compares them to a threshold value, the method determines which among the plurality of candidates has higher reliability degrees (i.e. high part probability, corresponding to probability greater than or equal to threshold value).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasound diagnostic apparatus of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates as disclosed by Matsumoto (i.e. performed by the part probability calculating unit) in order to effectively narrow down candidate parts of a subject included in the ultrasound images from a plurality of diagnostic parts (See Matsumoto: [0097]) and select the second imaging condition which has a reliability degree higher than a threshold value. Calculating a probability that a part included in an ultrasound image is each specific part of a plurality of candidate parts is one of a finite number of techniques which can be used to effectively identify what part(s) is/are included within an ultrasound image with a reasonable expectation of success. Furthermore, comparing these probabilities (i.e. reliability degrees) to a threshold value is one of a finite number of techniques which can be used to identify an ideal imaging condition with a reasonable expectation of success. Thus, modifying the ultrasound diagnostic apparatus of Rafter such that it outputs a plurality of candidates for the cross section and a reliability degree of the recognition for each of the candidates (i.e. performed by the part probability calculating unit), such that each probability (i.e. reliability degree) can be compared to a threshold value in order to select a second imaging condition as disclosed by Matsumoto would yield the predictable result of narrowing down which candidate/part is included within the ultrasound image, such that imaging conditions specific to that candidate/part can be determined, provided and utilized when performing updated imaging of that candidate/part.
Regarding claim 5, Rafter in view of Matsumoto discloses all features of the claimed invention as discussed with respect to claim 4 above, and Matsumoto further teaches “wherein the second imaging condition is an imaging condition corresponding to a candidate having a highest reliability degree among the plurality of candidates having higher reliability degrees” (See [0103] and [0104] as discussed with respect to claim 1. In this case, when it is determined that at least one of the plurality of part probabilities calculated on the basis of the result of image analysis of the ultrasound images of a plurality of frames is greater than or equal to a threshold value (see [0103]), the process proceeds to step S7 in which the first imaging condition is changed to the second imaging condition on the basis of the part probability determined in step S23 (see [0104]). Therefore, the at least one of the plurality of part probabilities which is greater than or equal to the threshold value represents the candidate having a highest reliability degree among the plurality of candidates having higher reliability degrees. Thus, the second imaging condition is an imaging condition corresponding to a candidate having a highest reliability degree (i.e. part probability) among the plurality of candidates having higher reliability degrees.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasound diagnostic apparatus of Rafter such that the second imaging condition is an imaging condition corresponding to a candidate having a highest reliability degree among the plurality of candidates having higher reliability degrees as disclosed by Matsumoto in order to effectively narrow down candidate parts of a subject included in the ultrasound images from a plurality of diagnostic parts (See Matsumoto: [0097]) and select the second imaging condition which has a reliability degree higher than a threshold value. Calculating a probability that a part included in an ultrasound image is each specific part of a plurality of candidate parts is one of a finite number of techniques which can be used to effectively identify what part(s) is/are included within an ultrasound image with a reasonable expectation of success. Furthermore, comparing these probabilities (i.e. reliability degrees) to a threshold value is one of a finite number of techniques which can be used to identify an ideal imaging condition with a reasonable expectation of success. Thus, modifying the ultrasound diagnostic apparatus of Rafter such that such that the second imaging condition is an imaging condition corresponding to a candidate having a highest reliability degree among the plurality of candidates having higher reliability degrees as disclosed by Matsumoto would yield the predictable result of narrowing down which candidate/part is included within the ultrasound image, such that imaging conditions specific to that candidate/part can be determined, provided and utilized when performing updated imaging of that candidate/part.
Regarding claim 6, Rafter in view of Matsumoto discloses all features of the claimed invention as discussed with respect to claim 1 above, and Rafter further teaches “wherein the recognition unit further executes processing of recognizing a cross section on the second ultrasound image, and calculates a reliability degree of the recognition” (See [0037] as discussed with respect to claim 1 above. “In some embodiments, the method 400 may involve repeating blocks 402 and 404, as indicated by dashed arrow 414, either until an output is generated at block 406 and/or until a confidence score of at least 50%, or in some cases at least 65% is output at block 404” [0067]. In this case, since the method 400 may involve repeating block 402 and 404 until a confidence score of at least 50% or 65% is output (i.e. acquiring second ultrasound images), the recognition unit (i.e. view recognition processor 170) further executes processing of recognizing a cross section on the second ultrasound image and calculates a reliability degree of the recognition.);
“the acquisition unit further acquires the second ultrasound image according to the second imaging condition in a case where the reliability degree calculated from the second ultrasound image is higher than the reliability degree calculated from the first ultrasound image” (“In some embodiments, the method 500 may be performed by the optimization state controller 172. At block 502, a step of “receiving an output signal” may be performed. In some embodiments, the output signal may be provided by view recognition processor 170 […] In some embodiments, the imaging parameters may be retrieved from local memory 142. In some embodiments, based on the specific view determined, one or more algorithms may be retrieved (e.g., from local memory). The one or more algorithms may be adaptive and may be used to provide different imaging parameters based, at least in part, on the specific view. [0070]; “At block 508, a step of “comparing the output signal to a threshold value” may be performed. In some embodiments, the threshold value may correspond to a threshold value of the confidence score. […] In some embodiments, the threshold value may be a combination of factors and/or multiple threshold values corresponding to different factors are analyzed (e.g., a confidence score above a threshold for a given number of frames). If the output signal meets or exceeds the threshold value or values, at block 510, a step of “providing the retrieved imaging parameters” may be performed. If the output signal is below the threshold, at block 512, a step of “providing existing imaging parameters” may be performed. Alternatively, at block 512, a step of “providing default imaging parameters” may be performed” [0071].
In this case, the threshold value may correspond to the confidence score (i.e. reliability degree), and/or a threshold for the given number of frames. When the output signal meets or exceeds the threshold value or values, then the optimization state controller 172 provides the retrieved imaging parameters (i.e. second/view-specific imaging parameters), such that they can be used to reacquire the ultrasound image with the one or more view specific imaging parameters (see FIG. 4, 412). Conversely, when the output signal is below the threshold value, the optimization state controller 172 provides existing/default imaging parameters (i.e. first imaging parameters), such that they can be used to reacquire the ultrasound image.
Therefore, the acquisition unit (i.e. transducer array 114 in combination with the signal processor 126 and additional downstream signal processing circuits) further acquires the second ultrasound image according to the second imaging condition (i.e. view-specific imaging parameters) in a case where the reliability degree calculated from the second ultrasound image is higher than the reliability degree (i.e. confidence score is greater than the threshold value) calculated from the first ultrasound image.).
Regarding claim 7, Rafter in view of Matsumoto discloses all features of the claimed invention as discussed with respect to claim 6 above, and Rafter further teaches “wherein the acquisition unit further continues ultrasonography according to the first imaging condition in a case where the reliability degree calculated from the second ultrasound image is equal to or lower than the reliability degree calculated from the first ultrasound image” (See [0037] as discussed in claim 1, and [0070], [0071] in claim 6 above.
In this case, the threshold value may correspond to the confidence score (i.e. reliability degree), and/or a threshold for the given number of frames. When the output signal meets or exceeds the threshold value or values, then the optimization state controller 172 provides the retrieved imaging parameters (i.e. second/view-specific imaging parameters), such that they can be used to reacquire the ultrasound image with the one or more view specific imaging parameters (see FIG. 4, 412). Conversely, when the output signal is below the threshold value, the optimization state controller 172 provides existing/default imaging parameters (i.e. first imaging parameters), such that they can be used to reacquire the ultrasound image.
Therefore, the acquisition unit (i.e. transducer array 114 in combination with the signal processor 126 and additional downstream signal processing circuits) further continues ultrasonography according to the first imaging condition in a case where the reliability degree (i.e. confidence score) calculated from the second ultrasound image is equal to or lower than the reliability degree (i.e. below threshold, see step 512 in FIG. 5) calculated from the first ultrasound image).).
Rega