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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/28/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/28/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Response to Amendment
1 This action is in response to the amendment filed on 07/28/2026. Claims 1 and 13-14 have been amended, and claims 2-6 and 18-22 have been cancelled. Claims 1 and 7-14 remain rejected.
Response to Arguments
2 Applicant’s arguments with respect to claims 1 and 13-14 filed on 07/28/2026, with respect to the rejection under 35 U.S.C. § 103 regarding that the prior art does not teach the following but not limited to “…generates point group data of the object by performing three dimensional composition processing on the acquired image group, performs identification of members constituting the object from the point group data extracts regions of a same member of the object from a result of the identification of the members…assigns same identification information to images constituting the extracted regions”. This argument has been considered, but are moot due to new grounds of rejection.
3 Regarding claims 7-12, they directly/indirectly depend on independent claim 1 respectively. Applicant does not argue anything other than independent claims 1 and 13-14. The limitations in those claims, in conjunction with combination, was previously established as explained.
4 Regarding claims 2-6 and 18-22, they have been cancelled as previously mentioned, therefore they will not be reviewed further.
Claim Rejections - 35 USC § 103
5 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.
6 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.
7 Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yumibe et al. (JP 2015001756 A) in view of Dianokov et al. (US 20200148489 A1).
8 Regarding claim 1, Yumibe teaches an information processing apparatus comprising: a processor ([Claim 1] reciting “A state change management system comprising an imaging unit that captures an image of a target object, a position information acquisition unit that measures a position of the imaging unit, a posture information acquisition unit that measures a posture of the imaging unit, one or more processors, and one or more memories connected to the one or more processors…”), wherein the processor
acquires an image group of an object captured with overlapping imaging ranges ([0082] reciting “In addition, the change detection program 311 cuts out only the facility portion 1909 (that is, a portion in which the photographed portion 1904 is photographed) from the image data of the photographed image 1900.”; [0110] reciting “First, the facility image model construction program 310 detects an overlapping region of two images (for example, an overlapping region 2410 of a portion 2408 and a portion 2409 in FIG. 24) from a plurality of images pasted on the omnidirectional image model to be processed…”),
performs identification of members constituting the object ([Abstract] reciting “…specifies a portion of the target object included in an image captured by the image capturing unit as a first captured portion based on a position and a posture of the image capturing unit and the position information of the target object, specifies an image of the first captured portion included in the image model based on the position of the image capturing unit, the posture of the image capturing unit, and the image model…”)
extracts regions of a same member of the object from a result of the identification of the members ([0177] reciting “The change detection program 311 refers to the imaging conditions added to the captured image in step 3505, searches for the past omnidirectional image model of the corresponding facility from the facility image model data downloaded to the memory 206, cuts out a portion corresponding to the facility portion captured in the captured image from the searched omnidirectional image model, and matches the cut-out portion with the captured image to detect the secular change of the target facility 211 (step 3506).”; [0190] reciting “Except for the differences described below, the respective units of the inspection work support system according to the fifth embodiment have the same functions as those of the respective units denoted by the same reference numerals in the first embodiment, and thus the description thereof will be omitted.”),
assigns same identification information to images constituting the extracted regions ([0099] reciting “First, the facility image model construction program 310 refers to the photographing conditions added to the images from the facility image data for all the facilities uploaded to the server system 100, and searches for image data having the same facility ID photographed in the same time zone (step 220 0). Here, the "image data having the same facility ID photographed in the same time zone" is a plurality of image data of one target facility 211 photographed in one patrol inspection.”; [0204] reciting “The portable information terminal 101 specifies the coordinate value on the map of the position designated by the patroller to input the target facility 211, specifies the facility ID of the input target facility 211 by collating the specified coordinate value with the facility information data 400 (step 3604), and transmits the result to the server system 100 (step 3605).”), and
attaches the assigned identification information to image data of each of the images as accessory information ([0030] reciting “In the present embodiment, Exif ( Exchangeable image file format ) is adopted as the format of the image 704. The Exif format is an image format including metadata for photographs, and various types of metadata can be added to photographs. A specific example will be described with reference to FIG. 8.”; [0040] reciting “In the facility image model 402, an omnidirectional image model of a target facility generated from a plurality of images obtained by photographing the target facility from arbitrary positions and directions at the time of patrol and inspection in the power distribution facility is held in time series, and the facility ID1100 and the omnidirectional image models 1101, 1102, and 1104 in time series are stored in association with each other. In the example of FIG. 11, an omnidirectional image model for each year (that is, an omnidirectional image model generated from image data captured in each year) is stored…”; [0050] reciting “The photographing condition addition program 309 calculates photographing conditions (equipment ID, photographing distance, photographing direction, attitude angles (A, B, C) of the portable information terminal 101 at the time of photographing, photographing date and time, photographing position information, and the like) from the acquired information transmitted from the portable information terminal 101, and adds the photographing conditions to the Exif information of the photographed image as metadata (step 1302).”).
9 Yumibe does not explicitly teach generates point group data of the object by performing three dimensional composition processing on the acquired image group, performs identification of members constituting the object from the point group data…
10 Dianokov teaches generates point group data of the object by performing three dimensional composition processing on the acquired image group, performs identification of members constituting the object from the point group data ([0043] reciting “As an example, first, the loading/unloading system 122 identifies the shape and current position of the package 20 transported thereto by the conveyor 270. For example, the loading/unloading system 122 obtains an image captured by the three-dimensional camera 280, and identifies the shape and current position of the package 20 based on this image. The three-dimensional camera 280 may acquire point group data or a depth image of the package 20.”; [0072] reciting “The composite map generating section 630 may generate a three-dimensional map showing a portion of the surface shapes of the plurality of packages 20 or a positon of the surface shapes inside the container 22, based on (i) the plurality of sets of point group data or (ii) the composite point group data generated using the plurality of sets of point group data.”)…
11 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Yumibe) to incorporate the teachings of Dianokov to provide a method that includes point cloud data utilizing a type of 3D composition utilizing the method of identifying members based on the teachings of Yumibe. Doing so would utilize the point group data respectively from different locations ([0068] recited).
12 Regarding claim 7, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 1 (see claim 1 rejection above), wherein the processor further
acquires information on a result of image analysis for the image (Yumibe; [0021] reciting “At the same time, the change detection program 311 refers to the imaging conditions of the captured image transmitted from the portable information terminal 101, searches the database for a past omnidirectional image model of the corresponding facility, cuts out a portion corresponding to the facility portion captured in the captured image from the omnidirectional image model, matches the cut-out portion with the captured image to detect a secular change, and transmits the detection result to the portable information terminal 101. The portable information terminal 101 presents the detection result received from the server system 100 to the patroller by displaying the detection result on the display unit 201.”), and
adds the acquired information on the result of the image analysis to the accessory information to be attached to the image ([0021] reciting “In addition, the facility image model construction program 310 generates an omnidirectional image model of the facility from a plurality of captured images to which the imaging conditions are added and which are accumulated in the database 303, and manages the omnidirectional image model in time series (that is, in association with the imaging time of the captured image).”).
13 Regarding claim 8, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 7 (see claims 1 and 7 rejections above),
wherein the information on the result of the image analysis includes at least one of information on a detection result by the image analysis (Yumibe; [0051] reciting “The change detection program 311 refers to the imaging condition added to the captured image in step 1302, searches the past omnidirectional image model of the corresponding facility from the facility image model data 402 of the database 303, cuts out a portion corresponding to the facility portion captured in the captured image from the omnidirectional image model, matches the cut-out portion with the captured image to detect a secular change of the target facility 211, and transmits a detection result to the portable information terminal (step 1303).”), information on a type determination result by the image analysis, or information on a measurement result by the image analysis.
14 Regarding claim 12, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 1 (see claim 1 rejections above) wherein the accessory information is used for searching for the image (Yumibe; [0075] reciting “The change detection program 311 reads the facility ID from the photographed image to which the photographing condition is added by the photographing condition addition program 309, and searches the facility image model data 402 of the database 303 for a time-series omnidirectional image model corresponding to the facility ID (step 1700).”).
15 Claims 13 and 14 has similar limitations as of claim 1, therefore it is rejected under the same rationale as claim 1.
16 Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yumibe et al. (JP 2015001756 A) in view of Dianokov et al. (US 20200148489 A1) as of claim 1 and 7-8, further in view of Liu et al. (US 20200293830 A1).
17 Regarding claim 9, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 8 (see claims 1 and 7-8 rejections above), but does not explicitly teach wherein the information on the detection result by the image analysis includes at least one of information on a detection result of a defect or information on a detection result of a damage.
18 Liu teaches wherein the information on the detection result by the image analysis includes at least one of information on a detection result of a defect or information on a detection result of a damage ([0017] reciting “A detection model is built by using a first sub-model and a second sub-model that are cascaded; the first sub-model uses images of a detected article that are obtained at different angles and generated in time order as inputs, to obtain feature processing results of the images, and outputs the feature processing results to the second sub-model; and the second sub-model performs time series analysis on the feature processing results of the images to determine a damage detection result. As such, damage on the detected article can be found more comprehensively by using the images at different angles, and damage found in the images can be combined into a uniform detection result through time series analysis, thereby greatly improving damage detection accuracy.”).
19 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Yumibe in view of Dianokov) to incorporate the teachings of Liu to provide a method to have the information results taught by Yumibe in view of Dianokov to be specifically related to damage that was detected. Doing so would allow more accurate price estimation depending on the item as stated by Liu ([0061] recited).
20 Regarding claim 10, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 8 (see claims 1 and 7-8 rejections above), but does not explicitly teach wherein the information on the type determination result by the image analysis includes at least one of information on a defect type determination result or information on a damage type determination result.
21 Liu teaches wherein the information on the type determination result by the image analysis includes at least one of information on a defect type determination result or information on a damage type determination result ([0017] reciting “A detection model is built by using a first sub-model and a second sub-model that are cascaded; the first sub-model uses images of a detected article that are obtained at different angles and generated in time order as inputs, to obtain feature processing results of the images, and outputs the feature processing results to the second sub-model; and the second sub-model performs time series analysis on the feature processing results of the images to determine a damage detection result. As such, damage on the detected article can be found more comprehensively by using the images at different angles, and damage found in the images can be combined into a uniform detection result through time series analysis, thereby greatly improving damage detection accuracy.”; [0022] reciting “For example, the damage detection result can be a classification result indicating whether there is damage on the detected article, can be a degree of a certain type of damage on the detected article, can be a classification result indicating whether there are two or more types of damage on the detected article, or can be degrees of two or more types of damage on the detected article. Types of damage can include scratches, damage, stains, adhesives, etc. Sample data can be labeled based on a determined form of the damage detection result, and the damage detection result in this form can be obtained by using the trained detection model.”).
22 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Yumibe in view of Dianokov) to incorporate the teachings of Liu to provide a method to have the information results taught by Yumibe in view of Dianokov to be specifically related to certain types of damage that was detected by a specific detector. Doing so would allow more accurate price estimation depending on the item as stated by Liu ([0061] recited).
23 Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yumibe et al. (JP 2015001756 A) in view of Dianokov et al. (US 20200148489 A1) as of claims 1 and 7-8, further in view of Do et al. (US 20210390677 A1).
24 Regarding claim 11, Yumibe in view of Dianokov teaches the information processing apparatus according to claim 8 (see claims 1 and 7-8 rejections above), but does not explicitly teach wherein the information on the measurement result by the image analysis includes at least one of information on a measurement result related to a size of a defect, information on a measurement result related to a size of a damage, information on a measurement result related to a shape of the defect, or information on a measurement result related to a shape of the damage.
25 Do teaches wherein the information on the measurement result by the image analysis includes at least one of information on a measurement result related to a size of a defect ([0086] reciting “The visualization of the object along with the overlay is sometimes referred to herein as a composite object image 170. The complementary information can take varying forms including, for example, position information (e.g., location of barcodes, location of text, locations of features, locations of components, etc.), defect information (e.g. the location, size, severity, etc. of imperfections identified by the image analysis inspection tools)”), information on a measurement result related to a size of a damage, information on a measurement result related to a shape of the defect, or information on a measurement result related to a shape of the damage.
26 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Yumibe in view of Dianokov) to incorporate the teachings of Do to provide a method that can get the information of a defect which can be the size that is related to the image, where the image is obtain by the teachings of Yumibe in view of Dianokov. Doing so would allow the inspection modules to be utilized and the graphical user interfaces can be rendered on various local and remote computing devices either in real-time/near-real time as well as on-demand as stated by Do ([0086] recited).
Conclusion
27 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Higashiyama et al. (US 20150358546 A1) teaches an image processing method that can detect blurs, and can obtain information related to brightness and change of color for example.
28 Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY TRAN LE whose telephone number is (571)272-5680. The examiner can normally be reached Mon-Thu: 7:30am-5pm; First Fridays Off; Second Fridays: 7:30am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHNNY T LE/ Examiner, Art Unit 2614
/KENT W CHANG/ Supervisory Patent Examiner, Art Unit 2614
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