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 .
Allowable Subject Matter
Claims 22-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments filed on 02/11/2026 with respect to claims 1-21, 25 and 28-29 have been fully considered but they are moot in view of the new ground(s) of rejection necessitated by Applicant’s amendment, and with respect to claims 26-27 are not persuasive.
In re pages 14-15, Applicant states that “[Regarding Independent Claim 26]
Referring to Para [0047] of Onozato, referenced on Page 22 of the Office Action, [0047] The example of FIG. 9 shows how the "face authentication processing" is executed on a captured image captured by the camera C of a "gate camera 1". At this time, the whole captured image may be output in the whole region of the image display region 21. However, in this example, a reduced whole captured image 21 a is displayed in the upper left corner, and a part 21 b of the captured image 21 a is enlarged and displayed in the whole image display region 21. The enlarged image 22 b is display showing the processing result of the face authentication processing executed on the captured image 21 a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication. At this time, in the enlarged image 21 b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner. For example, the person who answers the registered person is blacked and the face portion of this person is boxed. Meanwhile, in a case where the face authentication processing is executed and a person who answers a registered person is not found, the whole captured image is displayed in the image display region 21 as shown in FIG. 10. Onozato discloses that, after face authentication processing is executed on a captured image, a portion of that same captured image is enlarged and displayed in an emphasized manner. However, the emphasized display is still derived from the identical image data, and no additional image data different from the original captured image is displayed. Therefore, Applicant respectfully submits that "perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state; and perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject" as recited in Claim 26 is neither disclosed nor suggested by Onozato. Based on the aforesaid rationales, Claim 26 is thus allowable under 35 U.S.C. 102.”
(1) In response, the Examiner respectfully disagrees. For instance, ONOZATO discloses the following: First, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C as described in figs. 1-3 paragraph 28. Second, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in figs. 1-3 paragraph 28. Third, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in figs. 1-3 paragraph 28. Fourth, the video detection part 11 may detect the game progress status and the weather information mentioned above based on the content of the captured image captured with the camera C and notify the detected information to the processing mode determination part 13, for example, the video detection part 11 detects the number of the persons P on the ground, the presence/absence of a referee, motion, and so on based on the captured image captured with the camera C in the stadium S, and detects whether the progress status of the game is before the start of the game, during the game, or after the end of the game from the detected content as described in figs. 1-3 paragraph 30. Fifth, besides, the video detection part 11 detects whether the person P as a spectator in the captured image is using an umbrella, noise of the image due to rain, and so on, and detects the weather information such as sunny or rainy from the detected content as described in figs. 1-3 paragraph 30. Sixth, the video detection part 11 may detect various kinds of information of the stadium S such as the above-described progress status and weather information from the video image by any method as described in figs. 1-3 paragraph 30. Seventh, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1” as described in fig. 9 paragraph 47. Eighth, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21 as described in fig. 9 paragraph 47. Nineth, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication as described in fig. 9 paragraph 47. Tenth, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner, for example, the person who answers the registered person is blacked and the face portion of this person is boxed as described in fig. 9 paragraph 47. Eleventh, meanwhile, in a case where the face authentication processing is executed and a person who answers a registered person is not found, the whole captured image is displayed in the image display region 21 as shown in FIG. 10 as described in fig. 9 paragraph 47. Also, see paragraphs 45-46 and 50-51. As a result, the Applicant’s statements are unsupported by ONOZATO. Thus, from the above passages, ONOZATO indeed discloses the following claimed limitations of independent claim 26 that recites “perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state; and perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject.” See actual claim rejection below.
In fact, ONOZATO discloses all the claimed limitations of independent claim 26 that recites “a display control device comprising: a processor” (i.e. the computer includes a processor as described in paragraph 98); “and a memory” (i.e. memories as describes in paragraph 98), “wherein the processor is configured to” (i.e. the computer includes a processor as described in paragraph 98): “acquire a plurality of image data” (see ¶s 26-27 for acquire a plurality of image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28). Also, see paragraphs 29-31); “perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state” (see fig. 3 ¶s 30, 45-46 for perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28, furthermore, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51); “and perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject” (see fig. 3 ¶s 28, 30, 45-46 for perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
In re pages 15-16, Applicant states that “[Regarding Independent Claim 27] Referring to Para [0058] of Sakaguchi, referenced on Page 29 of the Office Action, [0058] The face image detection module 221 extracts face images from the still image data items 51 that should be indexed (e.g., new still images stored into a photo folder). The face images can be detected by, for example, first analyzing the characteristics of the still image data items 51 and then searching for regions having characteristic similar to a face-image characteristic sample prepared before. The face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons. In the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected. Sakaguchi merely discloses detecting face regions and obtaining position information of the detected regions (coordinates) within individuals still images based on facial feature analysis. Such position information only indicates where a face appears within an image, and does not constitute information on a position at which the image files themselves are captured. Therefore, Applicant respectfully submits that "acquire position information which is information on a position at which the image files are captured; and perform control of classifying the plurality of image files based on the position information" as recited in Claim 27 is neither disclosed nor suggested by Sakaguchi. Based on the aforesaid rationales, Claim 27 is thus allowable under 35 U.S.C. 102.”
(2) In response, the Examiner respectfully disagrees. For instance, Sakaguchi discloses the following: First, in the process of extracting still images, still images better than others in terms of the smile degree and sharpness of face images may be extracted from the photo folder as described in fig. 3 paragraph 52. Second, further, the person of each face image may be recognized by executing face clustering, and photos each containing the face image of the person selected or photos each containing the face image of another person related to the person selected may be extracted from the photo folder as described in fig. 3 paragraph 52. Third, moreover, an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52. Fourth, in this case, the relevancy between any two events may be inferred from the relation between the persons participating in both events, and the result of inference may be used to extract some photos from the photo folders, for example, events in which the same person has participated may be inferred as relevant to each other as described in fig. 3 paragraph 52. Fifth, further, for example, if Person A and Person B appear together in many photos (if coexistence frequency is high), the event in which Person A has participated can be inferred as relevant to the event in which Person B has participated as described in fig. 3 paragraph 52. Sixth,
the indexing module 22 analyzes a plurality of still image data items 51 stored in the content database 301, and generates index information 302A representing the attributes of the respective still image data items 51 as described in fig. 3 paragraph 55. Seventh, the indexing module 22 starts indexing, triggered by, for example, the storage of one or more still image (photo files) into the content database 301 as described in fig. 3 paragraph 55. Eighth, that is, when one or more new still images are stored into the content database 301, the indexing module 22 generates index information about the new still images as described in fig. 3 paragraph 55. Nineth, the face image detection module 221 extracts face images from the still image data items 51 that should be indexed (e.g., new still images stored into a photo folder) as described in fig. 3 paragraph 58. Tenth, the face images can be detected by, for example, first analyzing the characteristics of the still image data items 51 and then searching for regions having characteristic similar to a face-image characteristic sample prepared before as described in fig. 3 paragraph 58. Eleventh, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons as described in fig. 3 paragraph 58. Twelfth, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58. Also, see fig. 4 paragraphs 49, 60-61, 63, 146-147. As a result, the Applicant’s statements are unsupported by Sakaguchi. Thus, from the above passages, Sakaguchi indeed discloses the following claimed limitations of independent claim 27 that recites “acquire position information which is information on a position at which the image files are captured; and perform control of classifying the plurality of image files based on the position information.” See actual claim rejection below.
In fact, Sakaguchi discloses all the claimed limitations of independent claim 27 that recites “an image file management device comprising: a processor” (i.e. the CPU 101 is the processor as described in fig. 2 paragraph 41); “and a memory” (i.e. memory 103 as described in fig. 2 paragraph 41), “wherein the processor is configured to” (i.e. the CPU 101 is the processor as described in fig. 2 paragraph 41): “acquire a plurality of image files” (see ¶ 55 for acquire a plurality of image files (i.e. a plurality of photo files as described in fig. 3 paragraph 49)); “acquire position information which is information on a position at which the image files are captured” (see ¶ 55 for acquire position information which is information on a position at which the image files are captured (i.e. a plurality of photo files as described in fig. 3 paragraph 49, furthermore, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 63, 146); “and perform control of classifying the plurality of image files based on the position information” (see ¶ 55 for perform control of classifying the plurality of image files based on the position information (i.e. a plurality of photo files as described in fig. 3 paragraph 49, furthermore, an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52, moreover, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 60-61, 63, 146-147)
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.
Claims 1-5, 7-21, 25, 28 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over ONOZATO et al. (US 2022/0101632 A1)(hereinafter ONOZATO), and further in view of Sakaguchi et al. (US 2011/0305437 A1)(hereinafter Sakaguchi).
Re claim 1, ONOZATO discloses a display control device comprising: a processor (i.e. the computer includes a processor as described in paragraph 98); and a memory (i.e. memories as describes in paragraph 98), wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98): acquire a plurality of image data (see ¶s 26-27 for acquire a plurality of image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28). Also, see paragraphs 29-31); acquire area information which is information on an area related to the image data (see ¶ 33 for acquire area information which is information on an area related to the image data (i.e. the processing mode determination part 13 determines a processing mode for each area (camera), however, the processing mode determination part 13 may determine a processing mode for each of the division regions obtained by dividing an area, namely, an image captured by the camera C into a plurality of regions as described in fig. 3 paragraph 42). Also, see paragraphs 29-31); select first image data based on a first criterion from the first image data group (see ¶ 33 for select first image data based on a first criterion from the first image data group (i.e. in this example, a case where a determination criterion No. 1 shown in FIG. 3B is applied will be described, when receiving information representing the number or density of the persons P shown in a captured image as the content of the captured image from the camera C installed in the vicinity of the entrance/exit gate G, the processing mode determination part 13 checks whether or not the number or density of the persons exceeds a threshold for determining as a crowd, when the number or density of the persons exceeds the threshold as shown in FIG. 4, the processing mode determination part 13 determines a processing mode for a captured image captured by the camera C in the vicinity of the entrance/exit gate G to be analysis processing of “crowd detection processing” (Yes at step S4, step S5 in FIG. 8) as described in paragraph 34). Also, see paragraph 35); and perform control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (see fig. 3 ¶s 45-46 for perform control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
ONOZATO fails to explicitly teach classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area. However, the reference of Sakaguchi explicitly teaches classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (see ¶ 55 for classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (i.e. an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52, furthermore, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 60-61, 63, 146-147)
Therefore, taking the combined teachings of ONOZATO and Sakaguchi as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (classify) into the system of ONOZATO as taught by Sakaguchi.
One would have been motivated to incorporate the above feature as taught by Sakaguchi into the system of ONOZATO for the benefit of having an event detection module 223 which detects an event associated with the still image data items 51 to be indexed, more specifically, in accordance with the dates and times (photographing dates and times) when the still images were acquired, the event detection module 223 classifies these still image data items 51 into groups, each consisting of still images acquired within a period (e.g., one day) and therefore regarded as photographed at an event, then, the event detection module 223 assigns event identification data items (event IDs) to the still image data items 51 to be indexed, wherein the event IDs, each assigned to still images acquire at the same event, are output from the event detection module 223 to the index information generation module 224, wherein the index information generation module 224 generates index information 302A from the data coming from the face image detection module 221, clustering module 222 and event detection module 223 in order to ease the processing time when classifying still image data items 51 into groups (see fig. 3 ¶s 61-62)
Re claim 2, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select third image data based on a second criterion from the plurality of image data, and the image data group is a set of the third image data corresponding to the same area information (see ¶ 33 for select third image data based on a second criterion from the plurality of image data, and the image data group is a set of the third image data corresponding to the same area information (i.e. further, as an example, a case where a determination criterion No. 2 shown in FIG. 3B is applied will be described, when the number or density of the persons P shown in a captured image received from the camera C installed in the vicinity of the entrance/exit gate G is equal to or less than a threshold as shown in FIG. 5 in contrast with the abovementioned case, the processing mode determination part 13 determines a processing mode for the camera C to be analysis processing of “face authentication processing”, face authentication processing is authentication processing for extracting a feature value from a face image of a person in a captured image and checking whether the face image agrees with a previously registered face image, and is executed by the analysis engine E connected to the monitoring device 10 as described in fig. 3 paragraph 35). Also, see paragraph 34)
Re claim 3, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a capturing time point of the first image data (see ¶s 26-27 for select the second image data based on a capturing time point of the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28))
Re claim 4, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select at least one of the image data captured before a capturing time point of the first image data or the image data captured after the capturing time point of the first image data as the second image data (see ¶s 26-27 for select at least one of the image data captured before a capturing time point of the first image data or the image data captured after the capturing time point of the first image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28))
Re claim 5, the combination of ONOZATO and Sakaguchi as discussed above in claim 4 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the image data captured at a first time point before the capturing time point of the first image data and the image data captured at a second time point before the first time point as the second image data (see ¶s 26-27 for select the image data captured at a first time point before the capturing time point of the first image data and the image data captured at a second time point before the first time point as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28))
Re claim 6, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98)
ONOZATO fails to explicitly teach select the image data captured on a day different from a day including a capturing time point of the first image data as the second image data. However, the reference of Sakaguchi explicitly teaches select the image data captured on a day different from a day including a capturing time point of the first image data as the second image data (see ¶ 61 for select the image data captured on a day different from a day including a capturing time point of the first image data as the second image data (i.e. the date/time relevant image select module 233A further selects (extracts), on the basis of the index information 302A, the still images generated during a period (day) different from the period the key image was generated (for example, on the same day or in the same month exactly one year before or after) as described in fig. 3 paragraph 70)
Therefore, taking the combined teachings of ONOZATO and Sakaguchi as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (day different) into the system of ONOZATO as taught by Sakaguchi.
One would have been motivated to incorporate the above feature as taught by Sakaguchi into the system of ONOZATO for the benefit of having a date/time relevant image select module 233A that selects (extracts) the still images relevant to the data and time the key image was generated, from the still image data items 51 stored in the content database 301, wherein the date/time relevant image select module 233A further selects (extracts), on the basis of the index information 302A, the still images generated during a period (day, month, season or year) different from the period the key image was generated (for example, on the same day or in the same month exactly one year before or after) in order to ease the processing time when selecting, on the basis of the index information 302A, the still images generated during a period (day) different from the period the key image was generated (for example, on the same day or in the same month exactly one year before or after) (see fig. 3 ¶ 70)
Re claim 7, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a subject included in the first image data (see ¶ 33 for select the second image data based on a subject included in the first image data (i.e. further, as an example, a case where a determination criterion No. 2 shown in FIG. 3B is applied will be described, when the number or density of the persons P shown in a captured image received from the camera C installed in the vicinity of the entrance/exit gate G is equal to or less than a threshold as shown in FIG. 5 in contrast with the abovementioned case, the processing mode determination part 13 determines a processing mode for the camera C to be analysis processing of “face authentication processing”, face authentication processing is authentication processing for extracting a feature value from a face image of a person in a captured image and checking whether the face image agrees with a previously registered face image, and is executed by the analysis engine E connected to the monitoring device 10 as described in fig. 3 paragraph 35). Also, see paragraphs 28, 30, 31, 34)
Re claim 8, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a state of a subject included in the first image data (see ¶s 26-27 for select the second image data based on a state of a subject included in the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 30-31)
Re claim 9, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a state of a person among subjects included in the first image data (see ¶s 26-27 for select the second image data based on a state of a person among subjects included in the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 30-31)
Re claim 10, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a state of an object other than a person among subjects included in the first image data (see ¶s 26-27 for select the second image data based on a state of an object other than a person among subjects included in the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, for example, the video detection part 11 detects the number of the persons P on the ground, the presence/absence of a referee, motion, and so on based on the captured image captured with the camera C in the stadium S, and detects whether the progress status of the game is before the start of the game, during the game, or after the end of the game from the detected content, besides, the video detection part 11 detects whether the person P as a spectator in the captured image is using an umbrella, noise of the image due to rain, and so on, and detects the weather information such as sunny or rainy from the detected content as described in fig. 3 paragraph 30). Also, see paragraph 31)
Re claim 11, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a numerical value related to a subject included in the first image data (see ¶ 33 for select the second image data based on a numerical value related to a subject included in the first image data (i.e. further, as an example, a case where a determination criterion No. 2 shown in FIG. 3B is applied will be described, when the number or density of the persons P shown in a captured image received from the camera C installed in the vicinity of the entrance/exit gate G is equal to or less than a threshold as shown in FIG. 5 in contrast with the abovementioned case, the processing mode determination part 13 determines a processing mode for the camera C to be analysis processing of “face authentication processing”, face authentication processing is authentication processing for extracting a feature value from a face image of a person in a captured image and checking whether the face image agrees with a previously registered face image, and is executed by the analysis engine E connected to the monitoring device 10 as described in fig. 3 paragraph 35). Also, see paragraphs 28, 34)
Re claim 12, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the image data including the same number of subjects as the number of subjects included in the first image data as the second image data (see ¶s 26-27 for select the image data including the same number of subjects as the number of subjects included in the first image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28))
Re claim 13, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the image data including the number of subjects different from the number of subjects included in the first image data as the second image data (see ¶s 26-27 for select the image data including the same number of subjects as the number of subjects included in the first image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, for example, the video detection part 11 detects the number of the persons P on the ground, the presence/absence of a referee, motion, and so on based on the captured image captured with the camera C in the stadium S, and detects whether the progress status of the game is before the start of the game, during the game, or after the end of the game from the detected content, besides, the video detection part 11 detects whether the person P as a spectator in the captured image is using an umbrella, noise of the image due to rain, and so on, and detects the weather information such as sunny or rainy from the detected content as described in fig. 3 paragraph 30). Also, see paragraph 31)
Re claim 14, the combination of ONOZATO and Sakaguchi as discussed above in claim 11 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the plurality of image data are generated by an imaging apparatus (see ¶s 26-27 for the plurality of image data are generated by an imaging apparatus (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, determination criteria for determining a processing mode based on the result of detection of the content of the captured image and the information received from the information acquisition part 12 are stored in the determination criteria storage part 15 of the monitoring device 10 as described in fig. 3 paragraph 31). Also, see paragraphs 29-30), and the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) perform control such that the imaging apparatus generates image data including subjects whose number is based on the number of subjects included in the first image data, based on the number of the subjects included in the first image data (see ¶s 26-27 for perform control such that the imaging apparatus generates image data including subjects whose number is based on the number of subjects included in the first image data, based on the number of the subjects included in the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28))
Re claim 15, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on a position of a subject included in the first image data (see ¶s 26-27 for perform control such that the imaging apparatus generates image data including subjects whose number is based on the number of subjects included in the first image data, based on the number of the subjects included in the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 34-35)
Re claim 16, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the image data including a subject of which a position is different from a position of a subject included in the first image data as the second image data (see ¶s 26-27 for select the image data including a subject of which a position is different from a position of a subject included in the first image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 34-35)
Re claim 17, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the plurality of image data are generated by a plurality of imaging apparatuses, and the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the second image data based on an imaging apparatus that has captured the first image data (see ¶s 26-27 for select the second image data based on an imaging apparatus that has captured the first image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 29-31)
Re claim 18, the combination of ONOZATO and Sakaguchi as discussed above in claim 17 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select the image data captured by an imaging apparatus different from the imaging apparatus that has captured the first image data as the second image data (see ¶s 26-27 for select the image data captured by an imaging apparatus different from the imaging apparatus that has captured the first image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 29-31)
Re claim 19, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the plurality of image data include moving image data (see ¶s 26-27 for the plurality of image data include moving image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 29-31), the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) select third image data based on a second criterion from the plurality of image data, the image data group is a set of the third image data corresponding to the same area information (see ¶ 33 for select third image data based on a second criterion from the plurality of image data, the image data group is a set of the third image data corresponding to the same area information (i.e. further, as an example, a case where a determination criterion No. 2 shown in FIG. 3B is applied will be described, when the number or density of the persons P shown in a captured image received from the camera C installed in the vicinity of the entrance/exit gate G is equal to or less than a threshold as shown in FIG. 5 in contrast with the abovementioned case, the processing mode determination part 13 determines a processing mode for the camera C to be analysis processing of “face authentication processing”, face authentication processing is authentication processing for extracting a feature value from a face image of a person in a captured image and checking whether the face image agrees with a previously registered face image, and is executed by the analysis engine E connected to the monitoring device 10 as described in fig. 3 paragraph 35). Also, see paragraph 34), and the processor is further configured to (i.e. the computer includes a processor as described in paragraph 98) select, in a case where the third image data composing the moving image data is selected as the first image data, image data included in the moving image data as the second image data (see ¶s 26-27 for select, in a case where the third image data composing the moving image data is selected as the first image data, image data included in the moving image data as the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28). Also, see paragraphs 29-31)
Re claim 20, the combination of ONOZATO and Sakaguchi as discussed above in claim 19 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) perform control of reproducing moving image data including the first image data and moving image data including the second image data (see ¶s 45-46 for perform control of reproducing moving image data including the first image data and moving image data including the second image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 29-31, 50-51)
Re claim 21, the combination of ONOZATO and Sakaguchi as discussed above in claim 20 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) perform control of reproducing a portion of the moving image data from a capturing time point of the second image data in a case where the second image data is selected (see ¶s 45-46 for perform control of reproducing a portion of the moving image data from a capturing time point of the second image data in a case where the second image data is selected (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 29-31, 50-51), and reproducing the moving image data from a beginning in a case where the first image data is selected (see ¶s 45-46 for the plurality of image data include moving image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image, then, the video detection part 11 notifies the detected condition of the persons P to the processing mode determination part 13 as described in fig. 3 paragraph 28, furthermore, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 29-31, 50-51)
Re claim 25, the combination of ONOZATO and Sakaguchi as discussed above in claim 1 discloses all the claim limitations with additional claimed feature taught by ONOZATO wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98) perform control of displaying the first image data on the display (see fig. 3 ¶s 45-46 for perform control of displaying the first image data on the display (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
Re claim 28, ONOZATO discloses a display control method comprising: acquiring a plurality of image data (see ¶s 26-27 for acquiring a plurality of image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28). Also, see paragraphs 29-31); acquiring are information which is information on an area related to the image data (see ¶ 33 for acquiring area information which is information on an area related to the image data (i.e. the processing mode determination part 13 determines a processing mode for each area (camera), however, the processing mode determination part 13 may determine a processing mode for each of the division regions obtained by dividing an area, namely, an image captured by the camera C into a plurality of regions as described in fig. 3 paragraph 42). Also, see paragraphs 29-31); selecting first image data based on a first criterion from the first image data group (see ¶ 33 for selecting first image data based on a first criterion from the first image data group (i.e. in this example, a case where a determination criterion No. 1 shown in FIG. 3B is applied will be described, when receiving information representing the number or density of the persons P shown in a captured image as the content of the captured image from the camera C installed in the vicinity of the entrance/exit gate G, the processing mode determination part 13 checks whether or not the number or density of the persons exceeds a threshold for determining as a crowd, when the number or density of the persons exceeds the threshold as shown in FIG. 4, the processing mode determination part 13 determines a processing mode for a captured image captured by the camera C in the vicinity of the entrance/exit gate G to be analysis processing of “crowd detection processing” (Yes at step S4, step S5 in FIG. 8) as described in paragraph 34). Also, see paragraph 35); and performing control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (see fig. 3 ¶s 45-46 for performing control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
ONOZATO fails to explicitly teach classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area. However, the reference of Sakaguchi explicitly teaches classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (see ¶ 55 for classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (i.e. an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52, furthermore, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 60-61, 63, 146-147)
Therefore, taking the combined teachings of ONOZATO and Sakaguchi as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (classify) into the system of ONOZATO as taught by Sakaguchi.
One would have been motivated to incorporate the above feature as taught by Sakaguchi into the system of ONOZATO for the benefit of having an event detection module 223 which detects an event associated with the still image data items 51 to be indexed, more specifically, in accordance with the dates and times (photographing dates and times) when the still images were acquired, the event detection module 223 classifies these still image data items 51 into groups, each consisting of still images acquired within a period (e.g., one day) and therefore regarded as photographed at an event, then, the event detection module 223 assigns event identification data items (event IDs) to the still image data items 51 to be indexed, wherein the event IDs, each assigned to still images acquire at the same event, are output from the event detection module 223 to the index information generation module 224, wherein the index information generation module 224 generates index information 302A from the data coming from the face image detection module 221, clustering module 222 and event detection module 223 in order to ease the processing time when classifying still image data items 51 into groups (see fig. 3 ¶s 61-62)
Re claim 29, ONOZATO discloses a non-transitory computer-readable storage medium that stores a display control program for causing a processor to execute steps comprising: acquiring a plurality of image data (see ¶s 26-27 for acquiring a plurality of image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28). Also, see paragraphs 29-31); acquiring area information which is information on an area related to the image data (see ¶ 33 for acquiring area information which is information on an area related to the image data (i.e. the processing mode determination part 13 determines a processing mode for each area (camera), however, the processing mode determination part 13 may determine a processing mode for each of the division regions obtained by dividing an area, namely, an image captured by the camera C into a plurality of regions as described in fig. 3 paragraph 42). Also, see paragraphs 29-31); selecting first image data based on a first criterion from the first image data group (see ¶ 33 for selecting first image data based on a first criterion from the first image data group (i.e. in this example, a case where a determination criterion No. 1 shown in FIG. 3B is applied will be described, when receiving information representing the number or density of the persons P shown in a captured image as the content of the captured image from the camera C installed in the vicinity of the entrance/exit gate G, the processing mode determination part 13 checks whether or not the number or density of the persons exceeds a threshold for determining as a crowd, when the number or density of the persons exceeds the threshold as shown in FIG. 4, the processing mode determination part 13 determines a processing mode for a captured image captured by the camera C in the vicinity of the entrance/exit gate G to be analysis processing of “crowd detection processing” (Yes at step S4, step S5 in FIG. 8) as described in paragraph 34). Also, see paragraph 35); and performing control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (see fig. 3 ¶s 45-46 for performing control of displaying, on a display, at least one image data in the first image data group as second image data based on the first image data (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
ONOZATO fails to explicitly teach classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area. However, the reference of Sakaguchi explicitly teaches classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (see ¶ 55 for classify the plurality of image data into a plurality of image data groups according to the area information, wherein the plurality of image data groups comprise a first image data group corresponding to the area information related to a first area and a second image data group corresponding to the area information related to a second area different from the first area (i.e. an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52, furthermore, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 60-61, 63, 146-147)
Therefore, taking the combined teachings of ONOZATO and Sakaguchi as a whole, it would have been obvious before the effective filing date of the claimed invention to incorporate this feature (classify) into the system of ONOZATO as taught by Sakaguchi.
One would have been motivated to incorporate the above feature as taught by Sakaguchi into the system of ONOZATO for the benefit of having an event detection module 223 which detects an event associated with the still image data items 51 to be indexed, more specifically, in accordance with the dates and times (photographing dates and times) when the still images were acquired, the event detection module 223 classifies these still image data items 51 into groups, each consisting of still images acquired within a period (e.g., one day) and therefore regarded as photographed at an event, then, the event detection module 223 assigns event identification data items (event IDs) to the still image data items 51 to be indexed, wherein the event IDs, each assigned to still images acquire at the same event, are output from the event detection module 223 to the index information generation module 224, wherein the index information generation module 224 generates index information 302A from the data coming from the face image detection module 221, clustering module 222 and event detection module 223 in order to ease the processing time when classifying still image data items 51 into groups (see fig. 3 ¶s 61-62)
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.
Claim 26 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by ONOZATO et al. (US 2022/0101632 A1)(hereinafter ONOZATO).
Re claim 26, ONOZATO discloses a display control device comprising: a processor (i.e. the computer includes a processor as described in paragraph 98); and a memory (i.e. memories as describes in paragraph 98), wherein the processor is configured to (i.e. the computer includes a processor as described in paragraph 98): acquire a plurality of image data (see ¶s 26-27 for acquire a plurality of image data (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28). Also, see paragraphs 29-31); perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state (see fig. 3 ¶s 30, 45-46 for perform control of displaying first image data among the plurality of image data on a display, wherein the first image data comprises a subject presenting in a first state (i.e. first, the video detection part 11 (detection means) receives captured images captured by the camera C at predetermined time intervals, for example, as shown in FIG. 2, the video detection part 11 receives a captured image captured by the camera C installed in the vicinity of the entrance/exit gate G from this camera C, then, from the shape, color, motion and so on of an object shown in the captured image, the video detection part 11 detects the content of the captured image, specifically in this example embodiment, detects the condition of persons Pin the captured image (step S1 in FIG. 8), for example, the video detection part 11 detects the number, density and movement direction of the persons P detected from the captured image as described in fig. 3 paragraph 28, furthermore, the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51); and perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject (see fig. 3 ¶s 45-46 for perform control of displaying at least one image data among the plurality of image data as second image data on the display based on the first state of the subject (i.e. the example of FIG. 9 shows how the “face authentication processing” is executed on a captured image captured by the camera C of a “gate camera 1”, at this time, the whole captured image may be output in the whole region of the image display region 21, however, in this example, a reduced whole captured image 21a is displayed in the upper left corner, and a part 21b of the captured image 21a is enlarged and displayed in the whole image display region 21, the enlarged image 22b is display showing the processing result of the face authentication processing executed on the captured image 21a, specifically, an enlarged image of a region where a person who answers a previously registered person is found by face authentication, at this time, in the enlarged image 21b, the face portion of the person who answers the registered person as a result of the face authentication processing is displayed in an emphasized manner as described in fig. 9 paragraph 47). Also, see fig. 10 paragraphs 50-51)
Claim 27 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sakaguchi et al. (US 2011/0305437 A1)(hereinafter Sakaguchi).
Re claim 27, Sakaguchi discloses an image file management device comprising: a processor (i.e. the CPU 101 is the processor as described in fig. 2 paragraph 41); and a memory (i.e. memory 103 as described in fig. 2 paragraph 41), wherein the processor is configured to (i.e. the CPU 101 is the processor as described in fig. 2 paragraph 41): acquire a plurality of image files (see ¶ 55 for acquire a plurality of image files (i.e. a plurality of photo files as described in fig. 3 paragraph 49)); acquire position information which is information on a position at which the image files are captured (see ¶ 55 for acquire position information which is information on a position at which the image files are captured (i.e. a plurality of photo files as described in fig. 3 paragraph 49, furthermore, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 63, 146); and perform control of classifying the plurality of image files based on the position information (see ¶ 55 for perform control of classifying the plurality of image files based on the position information (i.e. a plurality of photo files as described in fig. 3 paragraph 49, furthermore, an event-grouping technique may be utilized to classify the photos into groups each related to an event as described in fig. 3 paragraph 52, moreover, the face-image characteristic sample is characteristic information that has been acquired by statistically processing the facial characteristics of many persons, in the process of extracting face characteristics, the regions corresponding to the face images contained in the still image data items 51 are detected, and the positions (coordinates) and sizes of these regions are also detected as described in fig. 3 paragraph 58). Also, see fig. 4 paragraphs 60-61, 63, 146-147)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE M MESA whose telephone number is (571)270-1706. The examiner can normally be reached Monday-Friday 8:30AM-6:00PM ET.
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4/29/2026
/JOSE M. MESA/
Examiner
Art Unit 2484
/THAI Q TRAN/Supervisory Patent Examiner, Art Unit 2484
Prosecution Insights