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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 12 and 13 are rejected under 35 U.S.C. 101 Abstract idea.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. Three categories of subject matter are found to be judicially recognized exceptions to 35 U.S.C. § 101 (i.e. patent ineligible) (1) laws of nature, (2) physical phenomena, and (3) abstract ideas. MPEP 2106(II). To be patent-eligible, a claim directed to a judicial exception must as whole be directed to significantly more than the exception itself. See 2014 Interim Guidance on Patent Subject Matter Eligibility, 79 Fed. Reg. 74618, 74624 (Dec. 16, 2014). Hence, the claim must describe a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception. Id
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Claims 1 and 13 recite limitations capturing endoscopic images using camera of the endoscope of target region and scoring possibility of presence of region of interest in the images and starting a time based on detection of variation and further classifying if preset condition is met or not. The limitations are similar to comparing new and stored information in images and determining change in region of interest and its presence in the images. Therefore, the cited limitations are similar to being performed with pen and paper by a human, and does not recite any significantly more to add to the basic of these steps. (SmartGene, Inc. v. Advanced Biological Laboratories, SA).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4, 6-14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 12 and 13 recite limitations – “upon detecting variation in captured images, set a time after the variation as the start time”, appears to be directed to determining specific changes from one image to other image in region of interest and starting a time. However, it is not clear as to among all the captured images, if only one region of interest is tracked and changes are detected and time is started or if there are multiple changes in captured images can be tracked and what specific changes are tracked among multiple images captured is not specified.
Further, the recited limitations determine score of likelihood of region of interest in images, determine variation in images and classify the images based on score if preset condition is satisfied. However, the features of region of interest and variability in captured images and preset condition do not appear to be interconnected with respect to each other in specific context.
Therefore, Examiner suggests amending claims to explicitly define the above discussed features as disclosed in original specifications in order to render the claims definite.
Further, claim 5 recite limitations – “determine whether or not the score shifts to approach the threshold value within a predetermined time; upon determining that the score shifts to approach the threshold value within the predetermined time, stop to set the time after the variation as the start time; and upon determining that the score does not shift to approach the threshold value within the predetermined time, set the time after the variation as the start time”, appears to be directed to starting time after variation on both instances of if the score shifts to threshold value or not. Further, score value would shift to threshold value within predetermining time is determined, however, it is not clear as to how it is determined that the score value will shift to threshold value or not.
Therefore, Examiner suggests to amending claim in order to explicitly define the features above in order to render the claim definite as disclosed in original specifications.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 6-14/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being as being anticipated by Imaizumi et al. (US Pub No. 20180249900 A1).
Imaizumi discloses An image processing device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (Imaizumi, [0004], discloses An endoscope apparatus according to an aspect of the present invention includes: a detecting section to which an observation image of a subject is sequentially inputted, the detecting section being configured to detect a characteristic region in the observation image based on a predetermined feature value concerning the observation image; and an emphasis processing section configured to apply, when the characteristic region is continuously detected in the detecting section, emphasis processing of a position corresponding to the characteristic region to the observation image of the subject inputted after elapse of a first predetermined time period from a time when the characteristic region is detected and not to apply the emphasis processing of the position corresponding to the characteristic region to the observation image of the subject inputted until the first predetermined time period elapses from the time when the characteristic region is detected)
acquire a captured image obtained by photographing an examination target by a photographing unit provided in an endoscope; (Imaizumi, [0004], [0022], discloses An endoscope apparatus according to an aspect of the present invention includes: a detecting section to which an observation image of a subject is sequentially inputted, the detecting section being configured to detect a characteristic region in the observation image based on a predetermined feature value concerning the observation image; and an emphasis processing section configured to apply, when the characteristic region is continuously detected in the detecting section, emphasis processing of a position corresponding to the characteristic region to the observation image of the subject inputted after elapse of a first predetermined time period from a time when the characteristic region is detected and not to apply the emphasis processing of the position corresponding to the characteristic region to the observation image of the subject inputted until the first predetermined time period elapses from the time when the characteristic region is detected; A schematic configuration of the endoscope system 1, which is an endoscope apparatus, includes a light-source driving section 11, an endoscope 21, a video processor 31, and a display section 41. The light-source driving section 11 is connected to the endoscope 21 and the video processor 31. The endoscope 21 is connected to the video processor 31. The video processor 31 is connected to the display section 41; observation target images is obtained of region with camera sensor of an endoscopic unit)
calculate, based on the captured images in time series obtained from a start time, a score regarding a likelihood of a presence of a region of interest in the captured images; (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; emphasis of detection of region of interest (lesion) is determined based on marker (color classification of lesion accuracy score) within a time period of starting time from when image have been started capturing))
upon detecting a variation in the captured images, set a time after the variation as the start time; (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first varification) and
classify, based on the score, the captured images in time series if a predetermined condition is satisfied. (Imaizumi, [0004], [0039-0040], [0108], discloses continuous-detection determining section 35 is a circuit that determines whether the lesion candidate region L is continuously detected. The continuous-detection determining section 35 includes a RAM 35a to be capable of storing lesion candidate information of an immediately preceding frame. The continuous-detection determining section 35 is connected to the detection-result output section 36; continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; detected region of interest of lesion is classified according to its color as its predetermined condition)
Regarding Claim 2,
Imaizumi further discloses wherein the at least one processor is configured to execute the instructions to detect the variation, based on a degree of similarity between the captured image obtained at a current processing time and the captured image obtained at a processing time immediately preceding the current processing time. (Imaizumi, [0040], discloses continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; lesion is tracked from first time period to second time period and similarity is determined).
Regarding Claim 3,
Imaizumi further discloses wherein the at least one processor is configured to execute the instructions to detect the variation, based on a confidence level regarding the presence of the region of interest in the captured image obtained at a current processing time and a confidence level regarding the presence of the region of interest in the captured image obtained at a processing time immediately preceding the current processing time. (Imaizumi, [0040], discloses continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; lesion is tracked from first time period to second time period and similarity is determined).
Regarding Claim 4,
Imaizumi further discloses wherein, even in a case where the variation in the captured images is detected, if the score is close within a predetermined value to a threshold value for executing the classification, the at least one processor is configured to execute the instructions to stop to set the time after the variation as the start time. (Imaizumi, [0040], discloses continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; lesion is tracked from first time period to second time period and similarity is determined).
Regarding Claim 6,
Imaizumi further discloses wherein the at least one processor is configured to execute the instructions to calculate the score, based on a likelihood ratio regarding the presence and an absence of the region of interest in the captured images in time series, and wherein the at least one processor is configured to execute the instructions to perform the classification regarding the presence and the absence of the region of interest in the captured images in time series. (Imaizumi, [0004], [0039-0040], [0108], discloses continuous-detection determining section 35 is a circuit that determines whether the lesion candidate region L is continuously detected. The continuous-detection determining section 35 includes a RAM 35a to be capable of storing lesion candidate information of an immediately preceding frame. The continuous-detection determining section 35 is connected to the detection-result output section 36; continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; detected region of interest of lesion is classified according to its color as its predetermined condition).
Regarding Claim 7,
wherein if the score reaches a predetermined threshold value, the at least one processor is configured to execute the instructions to determine that the predetermined condition is satisfied and perform the classification. (Imaizumi, [0004], [0039-0040], [0108], discloses continuous-detection determining section 35 is a circuit that determines whether the lesion candidate region L is continuously detected. The continuous-detection determining section 35 includes a RAM 35a to be capable of storing lesion candidate information of an immediately preceding frame. The continuous-detection determining section 35 is connected to the detection-result output section 36; continuous-detection determining section 35 determines whether a first lesion candidate region on a first observation image and a second lesion candidate region on a second observation image inputted before the first observation image are the same lesion candidate region L such that the lesion candidate region L can be tracked, for example, even when a position of the lesion candidate region L deviates on the observation image G1. When the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images G1, the continuous-detection determining section 35 determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section 36; the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; detected region of interest of lesion is classified according to its color as its predetermined condition).
Regarding Claim 8,
Imaizumi further discloses wherein, if the predetermined condition is satisfied, or, if the variation is detected, the at least one processor is configured to execute the instructions to update the start time and sequentially calculate the score based on the updated start time. (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first verification).
Regarding Claim 9,
Imaizumi further discloses wherein the at least one processor is configured to execute the instructions to set the start time at predetermined time intervals and calculate the scores based on the respective set start times in parallel. (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first verification).
Regarding Claim 10,
Imaizumi further discloses wherein the region of interest is a lesion part suspected of a lesion, and wherein the at least one processor is configured to execute the instructions calculate the score regarding the likelihood of the presence of the lesion part. (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first verification).
Regarding Claim 11,
Imaizumi further discloses wherein the at least one processor is configured to further execute the instructionsa adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first verification).
Regarding Claim 14,
Imaizumi further discloses wherein the at least one processor is configured to calculate the likelihood ratio using a likelihood ratio calculation model, and wherein the likelihood ratio calculation model is trained through a machine learning to output, when captured images in time series are inputted thereto, output the likelihood ratio regarding the inputted captured images. (Imaizumi, [0004], [0108-0110], discloses the emphasis processing section 36a adds the marker image G2 to the lesion candidate region L. However, the marker image G2 may be classified by color and displayed according to likelihood of the detected lesion candidate region L. In this case, the lesion-candidate detecting section 34b outputs lesion candidate information including likelihood information of the lesion candidate region L to the emphasis processing section 36a. The emphasis processing section 36a performs the emphasis processing according to the color classification based on the likelihood information of the lesion candidate region L. With this configuration, when the surgeon observes the lesion candidate region L, the surgeon is capable of estimating a level of possibility of false positive (error detection) according to a color of the marker image G2; In the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notification processing may be performed until a start of the emphasis processing after the lesion candidate region L is detected. With this configuration, until the marker image G2 is displayed after the lesion candidate region L is detected, the surgeon is capable of recognizing that the lesion candidate region L is detected somewhere on the observation image G1. A lesioned part is easily found. The notification processing may be performed in parallel to the emphasis processing. The notification processing may be continuously performed until the lesion candidate region L is not detected by the detecting section 34 after the lesion candidate region L is detected; in the first embodiment, the modification of the first embodiment, and the second embodiment, the notification processing is performed until the lesion candidate region L is not detected by the detecting section 34 after the elapse of the second predetermined time period. However, the notifying section 36b may start the notification processing after the emphasis processing is started. With this configuration, the notification processing is performed in addition to the display of the marker image G2. The surgeon is capable of more surely recognizing that the lesion candidate region L is detected; the notification processing is performed until as start of the emphasis processing after the lesion candidate region is detected therefore, after a variation is detected emphasis processing is started with a new or second predetermined start time period where likely hood of lesion is first time period and emphasis of lesion detection is second time period after the first verification).
Claims 12 and 13 recite method with steps and storage medium with
instructions corresponding to the device elements recited in Claim 1. Therefore, the
recited steps of the method claim 12 and instructions of storage medium claim 13 are
mapped to the proposed combination in the same manner as the corresponding elements of Claim 1.
Furthermore, Imaizumi further discloses A non-transitory computer readable storage medium storing a program executed by a computer, the program causing the computer (Imaizumi, [0113], discloses the detection supporting section 33 is configured by a circuit. However, the respective functions of the detection supporting section 33 may be configured by processing programs for realizing the functions according to processing of a CPU).
Allowable Subject Matter
Claim 5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kamon et al. (US-20230394780-A1, In a medical image processing apparatus including a
processor, the processor sequentially acquires time-series medical images and causes a display unit to sequentially display the acquired medical images. Further, the processor performs a process of acquiring, based on the acquired medical images, information related to a position of a region of interest in the medical images and classifying the region of interest into a class among a plurality of classes, and displays class information indicating the class of the classified region of interest such that the class information is superimposed at a position of the region of interest in a medical image displayed on the display unit. Further, the processor changes a relative position of the superimposed class information with respect to the region of interest, in accordance with an elapsed time from recognition of the region of interest)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PINALBEN V PATEL whose telephone number is (571)270-5872. The examiner can normally be reached M-F: 10am - 8pm.
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/Pinalben Patel/Examiner, Art Unit 2673
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