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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed November 13, 2025 and December 11, 2025 is being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on February 21, 2023. It is noted, however, that applicant has not filed a certified copy of the JP2023-025534 application as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 102
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 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 17 and 18 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Hayami et al. (US2021/0149182).
Regarding claim 17, Hayami discloses a medical support method comprising: recognizing, using a medical image, an observation target region appearing in the medical image (image processing apparatus 32 includes image input section 33 and lesion detecting section 34 [0030], lesion detecting section 34 configured to detect a lesion region in the observation image G1 [0032-0033]); measuring a size corresponding to a characteristic of the observation target region, based on the medical image; and outputting the size (image processing apparatus for an endoscope analyzes visibility of at least one of a distance from the endoscope, an occupied area, a shape, a size, a position in each of the observation images, a color, a luminance or an organ part relating to each of the detected lesioned parts [0007]; lesion-size estimating section estimates the size of the lesioned part and outputs the size as an analysis result [0093]).
Regarding claim 18, Hayami discloses the non-transitory computer-readable storage medium storing a program executable by a computer to execute a medical support process (image processing apparatus 32, a computer-readable non-transitory recording medium [0031], or software to be executed by the processor), the medical support process comprising: recognizing, using a medical image, an observation target region appearing in the medical image (image processing apparatus 32 includes image input section 33 and lesion detecting section 34 [0030], lesion detecting section 34 configured to detect a lesion region in the observation image G1 [0032-0033]); measuring a size corresponding to a characteristic of the observation target region, based on the medical image; and outputting the size (image processing apparatus for an endoscope analyzes visibility of at least one of a distance from the endoscope, an occupied area, a shape, a size, a position in each of the observation images, a color, a luminance or an organ part relating to each of the detected lesioned parts [0007]; lesion-size estimating section estimates the size of the lesioned part and outputs the size as an analysis result [0093]).
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.
Claims 1-9 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Saikou (US2024/0161283) in view of Hayami et al. (US2021/0149182).
Regarding claim 1, Saikou discloses a medical support device (endoscopic examination system 100 [0034]) comprising: a processor (image processing device 1 [0034]), the processor being configured to: recognize, using a medical image, an observation target region appearing in the medical image (endoscope 3 captures an endoscopic image Ia, the captured image Ia is analyzed by processing device 1 to detect presence of a lesion region in the endoscopic image [0035]). Saikou fails to explicitly disclose wherein the size corresponding to a characteristic of the observation target region is measured and output. In the same field of endeavor, Hayami teaches a substantially similar medical support device comprising: a processor (endoscope system 1 comprising image processing apparatus 32 [0024]), the processor being configured to: recognized, using a medical image, an observation target region appearing in the medical image (image processing apparatus 32 includes image input section 33 and lesion detecting section 34 [0030], lesion detecting section 34 configured to detect a lesion region in the observation image G1 [0032-0033]), further teaching the processor configured to measure a size corresponding to a characteristic of the observation target region, based on the medical image; and output the size (image processing apparatus for an endoscope analyzes visibility of at least one of a distance from the endoscope, an occupied area, a shape, a size, a position in each of the observation images, a color, a luminance or an organ part relating to each of the detected lesioned parts [0007]).
In view of Hayami, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included size as one of the desired characteristics of observation target region, as it is well-known in the endoscope art that when detecting observation target regions or lesions in a procedure, size of the lesion is one of the desired characteristics to be determined in order to accurately diagnose conditions and diseases.
Regarding claim 2, Saikou, modified by Hayami, discloses the medical support device according to claim 1. Hayami further teaches wherein the characteristic includes a shape of the observation target region, a category of the observation target region, a type of the observation target region, clarity of a contour of the observation target region, and/or an overlap between the observation target region and a peripheral region (image processing apparatus for an endoscope analyzes visibility of at least one of a distance from the endoscope, an occupied area, a shape, a size, a position in each of the observation images, a color, a luminance or an organ part relating to each of the detected lesioned parts [0007], which are all characteristics of the observation target region).
Regarding claim 3, Saikou, modified by Hayami, discloses the medical support device according to claim 1. Hayami further teaches wherein the processor is configured to recognize the characteristic, based on the medical image (processor receives observation images from the endoscope, and detects one or more lesioned parts from each of the observation images [0007]; lesion-state analyzing section 36b in the image processing apparatus 32, Fig. 2, performs analysis of visibility [0066]).
Regarding claim 4, Saikou, modified by Hayami, discloses the medical support device according to claim 1. Hayami further teaches wherein the size is a long side, a short side, a radius, and/or a diameter of the observation target region (lesion-size is estimated by calculating a ratio occupied by length of the lesion with respect to a circumferential length of the detected lumen [0087-0093], while not explicitly stated whether a long side, short side a radius or a diameter is measured, a length of the lesion is measured to compared to the circumferential length of the lumen to calculate a ratio [0092], therefore the measured length of the lesion must be at least a short side or a long side).
Regarding claim 5, Saikou, modified by Hayami, discloses the medical support device according to claim 1, further teaching wherein the observation target region is recognized by a method using Al (lesion region inference model is a machine learning model [0050]), and the size is measured based on a probability map obtained from the Al (Saikou teaches in Fig. 11: endoscopic image is input in a lesion region inference model with four setting conditions a-d, which outputs four reliability maps [0124-0125], that are weighted and integrated into a combined lesion reliability map that may be used for size measurement, Hayami teaches wherein size of a lesion is typically measured and analyzed during a medical procedure).
Regarding claim 6, Saikou, modified by Hayami, discloses the medical support device according to claim 5, further disclosing wherein the size is measured based on a closed region obtained by dividing the probability map according to a threshold value (Saikou teaches wherein the observation target region/lesion is detected by inputting the endoscopic image into the lesion region inference model A: setting condition a, which outputs a lesion reliability map comprising a closed region, appearing as a white oval shape, obtained by dividing the lesion reliability map according to a setting condition a, which may be a threshold parameter for determining whether or not each pixel falls under a lesion region according to the lesion reliability score of the each pixel [0124-0125]; Hayami teaches wherein the size of the lesion is one of the analysis items typically calculated by a medical image processor when detecting lesions).
Regarding claim 7, Saikou, modified by Hayami, discloses the medical support device according to claim 5, further disclosing wherein the size is measured based on a plurality of segment regions obtained by dividing the probability map according to a plurality of threshold values (Saikou teaches wherein the observation target region/lesion is detected by inputting the endoscopic image into four lesion region inference models A: setting condition a-d, which outputs four lesion reliability maps comprising a plurality of segments region, appearing as white oval shapes in the four lesion reliability maps, obtained by dividing the lesion reliability map according to a setting conditions a-d, which may be threshold parameters for determining whether or not each pixel falls under a lesion region according to the lesion reliability score of the each pixel [0124-125], Hayami teaches wherein the size of the lesion is one of the analysis items typically calculated by a medical image processor when detecting lesions).
Regarding claim 8, Saikou, modified by Hayami, discloses the medical support device according to claim 7, further disclosing wherein the size has a range, and the range is identified based on the plurality of segment regions (Saikou’s Fig. 11: threshold parameters or setting conditions a-d form four reliability maps, an integrated map which is incorrectly labeled as “lesion reliability map”, and a lesion detection result, each having various sizes of the detected lesion region, indicated by the white oval shapes, forming the size range of the measured lesion region).
Regarding claim 9, Saikou, modified by Hayami, discloses the medical support device according to claim 8, further disclosing wherein a lower limit value of the range is measured based on a first segment region that is smallest among the plurality of segment regions (Saikou: lesion detection result, at the bottom right corner of Fig. 11, illustrates the lower limit value of the size range of the lesion region, and is smallest among the plurality of white oval shapes in the integrated image, which is incorrectly labeled as lesion reliability map, because the lesion detection result indicates the pixel values of the integrated image multiplied by the weighing factors to determine the presence of the lesion region that is equal to or larger than a predetermined degree [0125-0127]), and an upper limit value of the range is measured based on a second segment region that is an outer region with respect to the first segment region among the plurality of segment regions (Saikou: integrated image, incorrectly labeled as lesion reliability map in Fig. 11, illustrates the overlapping grey-white ovals from the four lesion reliability maps, which form the second segment region that is an outer region with respect to the white oval appearing in the lesion detection result [0125-0127], the second segment region that is the outer region is less than the predetermined degree, the integrated image illustrates an upper limit value of the size range of the lesion region).
Regarding claim 14, Saikou, modified by Hayami, discloses the medical support device according to claim 1, wherein the medical image is an endoscopic image obtained by imaging with an endoscope (Saikou: endoscope 3 comprising shaft 37 is configured to be inserted into an organ [0037] to acquire the medical image; Hayami: endoscope 21 comprises insertion section 22 with image pickup section 24 for acquiring medical images [0026]).
Regarding claim 15, Saikou, modified by Hayami, discloses the medical support device according to claim 1, further disclosing wherein the observation target region is a lesion (Saikou: detect a lesion region [0035]; Hayami: lesion detection section 24 indicates that area of interest is a lesion [0033]).
Regarding claim 16, Saikou, modified by Hayami, discloses an endoscope (Saikou: endoscope 3 [0034]; Hayami: endoscope 21 [0024]) comprising: the medical support device according to claim 1; and a module to be inserted into a body including the observation target region to acquire the medical image by imaging the observation target region (Saikou: endoscope 3 comprising shaft 37 is configured to be inserted into an organ [0037] to acquire the medical image; Hayami: endoscope 21 comprises insertion section 22 with image pickup section 24 for acquiring medical images [0026]).
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Saikou in view of Hayami and Makino (US2021/0259515).
Regarding claim 10, Saikou, modified by Hayami, discloses the medical support device according to claim 1, but fails to disclose wherein the processor is configured to measure a plurality of first sizes of the observation target region, based on the medical image, and the size is a representative value of the plurality of first sizes.
In the same field of endeavor, Makino teaches an endoscope system comprising a processor, the processor configured to recognize, using medical images, observation target regions appearing in the medical images (image processing unit 220 processes a plurality of captured images to evaluate a degree of a lesion [0076]) and evaluate degrees of the observation target regions appearing in the medical images, wherein the degree is a representative value of the plurality of degrees (evaluation values, representing the degrees of the lesions detected in the medical images, contribute to a representative value [0122]). While Makino does not teach the sizes of the lesions as the characteristic of the lesions to construct the representative value, rather teaching the degrees of the lesions forming the representative value, the concept of the representative value as a statistic to be used as an index for handling the intensity of lesions is known in the endoscope art. In view of Makino, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the statistical concept of representative value to the device of Saikou and Hayami, as it is known in the endoscope art to highlight data of the observation target region, or lesions, such as a maximum degree of lesions or a maximum size of a lesion.
Regarding claim 11, Saikou, modified by Hayami and Makino, discloses the medical support device according to claim 10. Makino further teaches wherein the representative value includes a maximum value, a minimum value, a mean value, a median value, and/or a variance value (representative value is a statistic regarding the image evaluation values of a plurality measurements, and is, for example, a maximum value, a median value, or an average value [0122]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Saikou in view of Hayami and Hussain et al. (US2020/0279373).
Regarding claim 12, Saikou, modified by Hayami, discloses the medical support device according to claim 1. Saikou, modified by Hayami, discloses wherein the size is a size of the observation target region in a case where the overlap is not included (Hayami teaches wherein the size of the lesion is measured when the lesion is not overlapped or obscured), but fails to disclose wherein the characteristic includes the overlap between the observation target region and a peripheral region. In the same field of endeavor, Hussain teaches an artificial intelligence platform for detecting and sizing lesions during procedures in real time ([0026]), further teaching wherein lesions are partially obscured, overlapped with something else or halfway in the picture during an image acquiring process ([0028]). In view of Hussain, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have recognized that partially obscured observation target regions are a common occurrence when imaging to detect and size lesions.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Saikou in view of Hayami and Kamon (US2021/0082568).
Regarding claim 13, Saikou, modified by Hayami, discloses the medical support device according to claim 1, but does not explicitly disclose wherein the size is output by displaying the size on a screen. In the same field of endeavor, Kamon teaches a substantially similar medical support device comprising a processor, the processor (processor device 200 [0080) being configured to: recognize, using a medical image, an observation target region appearing in the medical image, calculate a size of the observation target region (processor device 200 detects the region of interest and conducts measurement processing of the region of interest [0117]), and wherein the size is output by displaying the size on a screen (Fig. 7: monitor 400 may display the recognition result of the endoscope, including the measurement result of the region of interest [0156]). In view of Kamon, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have displayed the size of the observation target region on the display, as it informs the operator of the device the details of the detected observation target region.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See references cited in PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LI-TING SONG whose telephone number is (571)272-5771. The examiner can normally be reached 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anhtuan Nguyen can be reached at 571-272-4963. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LI-TING SONG/Examiner, Art Unit 3795
/TIMOTHY J NEAL/Primary Examiner, Art Unit 3795
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