DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 03/30/2026 has been entered.
Response to Arguments
Applicant's arguments with respect to the rejections of claims 1-19 and 22 have been considered but are moot in view of the new ground(s) of rejection.
Response to Amendment
Claim Rejections - 35 USC § 103
4. The text of those sections of Title 35, U.S. Code not included in this section can be found in a prior Office action.
5. Claims 1, 8, 10-12, 19, and 22 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Akiyama et al. (US Publication 2021/0168411) in view of Despande et al. (US Publication 2014/0064693), and further in view of Takizawa et al. (US publication 2019/0304508).
Regarding claim 1, Akiyama discloses an image processing apparatus comprising:
at least one memory storing instructions, and at least one processor (Akiyama, para’s 0063 and 0069, fig. 1, a video image generating system comprising processor and memory unit) configured to execute the instructions to:
determine one or more feature motions of a target by analyzing a motion of the target, based on capturing data (Akiyama, para’s 0147-0153, detecting motion of a ball and a player during a shooting event includes: detecting the ball 25 from the partial region 20a, the detection unit acquires an image frame 21, which precedes the image frame 20 by one or two frames, and detects the ball 25 from the image frame 21. The detection unit calculates the three-dimensional coordinates of the ball 25 detected from the image frame 21, based on the principle of stereoscopy. Using, as a clue, the position of the ball 25 detected in the image frame 20, the detection unit may detect the ball 25 from the image frame 21. The detection unit estimates a path 25a of the ball 25 from the respective three-dimensional coordinates of the ball 25 detected from the image frames 20 and 21. Using the path 25a, the detection unit estimates a start position 26 of the path 25a and a time point at which the ball 25 is present at the start position 26; the detection unit identifies a player 27 who is present at the three-dimensional coordinates of the ball 25. The detection unit detects the identification information of the player 27 in such a case, as specific identification information, and outputs the specific identification information to the conversion unit 253);
detect a trigger from the capturing data or distribution data for distribution to one or more viewers being generated from the capturing data (Akiyama, para. 0100, the first server detects/recognizes a goal has been scored “a trigger” when a ball has passed through a goal area, the first server then tracks back the path of the ball, after detecting the scored goal, so as to determine which player “target” has been at the position of the ball shooting. The first server 100 thus recognizes that the player who shot the ball has scored the goal. The first server 100 transmits the identification information of the player to the second server 200. It is noted that, in one interpretation, detecting the goal score can also be considered as detecting a trigger that is different from the detected motion of the player who has scored the goal);
extract one or more of the determined one or more feature motions of the target from the capturing data in response to detection of the trigger, and generate different distribution data for distribution to one or more viewers, based on the extracted one or more feature motions (Akiyama, para. 0100, determining the path of the ball after detecting the scored goal so as to track/extract positions of the players before the shooting, and determining which player has been at the position of the ball shooting; para’s 0053-0057, the second server acquires tracking information from the first server and acquires plural pieces of partial video information from the second cameras 5. The second server 200 generates bird's-eye view video information from the plural pieces of partial video information. When accepting the identification information of a specific player among a plurality of players, using the tracking information, the second server sequentially converts the positional information of the specific player when and after the identification information is accepted, to second positional information in the bird's-eye view video information. The second server generates third video information that is a partial area cut out from the bird's-eye view video information, in accordance with the second positional information. Specifically, as illustrated in fig. 2, the second server cuts out a partial area from the bird's-eye view video information 10A, in accordance with the second positional information (x[P1], y[P1]). The second server generates the video information on the cut-out area as the third video information. The second server transmits the generated third video information to the video distribution server 300 for distribution to terminal devices of viewers).
Akiyama does not explicitly disclose:
the detected is different from the one or more feature motions of the target;
the different distribution data being generated from video before a time the trigger was detected, or from video after a time the trigger was detected, depending on a type of the trigger.
Despande discloses the detected is different from the one or more feature motions of the target (Despande et 2014/0064693, para. 0014, detecting events extracted based on processing of the on-screen score bug content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Despande’s teachings into Akiyama-Takizawa’s invention for providing an efficient and low-cost automatic sport analytic.
Akiyama-Despande does not explicitly disclose but Takizawa discloses the different distribution data being generated from video before a time the trigger was detected, or from video after a time the trigger was detected, depending on a type of the trigger (Takizawa, para’s 0035-0036, FIG. 2 describes analyzing video data, detecting an event (trigger) that has occurred, displaying a video from before the preview time when the event has occurred, and adjusting the preview time according to the event type; para’s 0049 and 0057, the event table 144 holds various information with regard to the event detected from the video data. FIG. 7 illustrates an example of a data structure of the event table which associates a serial number, an event type, the tracking ID, the player color, the uniform number, a time, and coordinates with one another. The event type indicates a type of the event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Takizawa’s teachings into Akiyama’s invention for enhancing user’s viewing experience by providing video edit that includes video portion before the occurrence of an event type.
Regarding claim 8, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1, wherein the at least one processor is configured to execute the instructions to detect, as the trigger, a predetermined trigger motion of a target in the distribution data (Akiyama, para. 0100, detecting the scored goal when a ball has passed through a goal area).
Regarding claim 10, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1, wherein the at least one processor is configured to execute the instructions to generate a different distribution video for a different predetermined period of time according to a kind of the trigger (Akiyama, para. 0146, detecting a predetermined event among a plurality of events, for example, para. 0100, detecting a scored goal. Therefore, a different distribution video may be generated for a different predetermined period of time according to a kind of the trigger).
Regarding claim 11, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1, wherein the at least one processor is configured to execute the instructions to determine a desired target among one or more targets included in the capturing data (Akiyama, para. 0133, indicates that a subject identification unit can identify, i.e., specify specific identification information, a player whose selection is accepted, i.e., a desired subject, among one or more subjects/players included in the image-capture data).
Claims 12 and 22 are rejected for the same reasons set forth in claim 1, Akiyama further discloses computer readable medium (see Akiyama, para. 0010).
Claim 19 is rejected the same reasons set forth in claim 8.
6. Claims 2-3, and 13-14 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Akiyama-Despande-Takizawa, as applied to claims 1 and 12 above, in view of Song et al. (English Translation of Korean Publication KR20210010191 01-2021).
Regarding claims 2 and 3, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1.
Akiyama-Despande-Takizawa does not explicitly disclose but Song discloses wherein the at least one processor is configured to execute the instructions to determine a feature point and a pseudo skeleton of a body of the target, based on the capturing data; and wherein the at least one processor is configured to execute the instructions to determine a motion of a body along a time series of the target, based on a plurality of continuous frames of the capturing data or the distribution data (Song, para’s 0030-0032, recognizing facial features of players; para. 0060, detecting/recognizing players in a sports broadcasting video, and then form a skeleton for the recognized objects; the posture of each player can be tracked based on the skeletal information of each recognized object to identify and recognize a specific position, for example, a pitcher's posture. Thereafter, only the recognized pitcher can be tracked and the corresponding image can be extracted. A skeletal analysis can be performed, and the process of the skeleton changing when a pitcher throws a ball can be applied to machine learning so that when a skeletal change occurs, it can be programmed to recognize that the pitcher has thrown the ball).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Song’s teachings into Akiyama-Despande-Takizawa’s invention for enhancing user’s viewing experience by providing video edit that identifies specific motions of detected objects in the video.
Claims 13-14 are rejected the same reasons set forth in claim 2-3.
7. Claims 4, 7, 15 and 18 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Akiyama-Despande-Takizawa, as applied to claims 1 and 12 above, in view of Marty et al. (US Publication 2008/0312010).
Regarding claims 4 and 7, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1.
Akiyama-Despande-Takizawa does not explicitly disclose but Marty discloses wherein the at least one processor is configured to execute the instructions to store a reference motion associated for each target, and to detect a feature motion by using a reference motion of each target; and wherein the at least one processor is configured to execute the instructions to detect a predetermined motion of a referee of a game in the distribution data or the capturing data (Marty, para. 0108, after the success criterion is defined, a make/miss zone consistent with the success criterion may be generated and one or more actual shots may be compared to some graphical representation of the make/miss zone. For example, the outcome of one or more basketball trajectories, i.e., motion of an object in a timed sequence of images, may be predicted and compared to a make/miss zone in accord with a defined success criterion. Next, in 278, the predicted shot outcome may be output in some format, such as but not limited to a graphic format. A few examples of an output format are shown in FIGS. 5A and 5B. In 280, information related to the basketball trajectory performance may be stored to a database, such as but not limited to captured frame data, primary trajectory determination, second trajectory determination, make/miss zone and shot outcome; see also Lucey, US 2014/0058992 for disclosure about storing a reference motion associated for each target, and detecting a feature motion by using a reference motion of each target; para. 0127, detecting referee's hand motions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Marty’s teachings into Akiyama-Despande-Takizawa’s invention for enhancing user’s viewing experience by effectively detecting feature motions of an object or target based on reference motions.
Claims 15 and 18 are rejected the same reasons set forth in claims 4 and 7.
8. Claims 5-6, 9, and 16-17 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Akiyama-Despande-Takizawa, as applied to claims 1 and 12 above, in view of Konishi (English Translation of Japanese Publication JP2020058000 04-2020).
Regarding claims 5-6 and 9, Akiyama-Despande-Takizawa discloses the image processing apparatus according to claim 1, wherein the at least one processor is configured to execute the instructions to generate the different distribution data from video before the time the trigger was detected in a case where changes in match score data are detected as the trigger in the distribution data (see Takizawa, para’s 0056-0060, the image analysis unit 152 performs various event detections based on the coordinates of the player and combinations of areas through which the ball passes illustrated in the tracking table 142. For example, the image analysis unit 152 detects an occurrence of an event type “3-point shot success”, i.e., change in game score, in a case where the ball away from the player 2 at a position, whose distance from a basket goal is greater than or equal to a threshold, passes through a predetermined area (basket goal), and a video from before the time when the 3-point shot success event has occurred is displayed).
/* generate the different distribution data from video before the time the trigger was detected in a case where changes in match score data are detected as the trigger in the distribution data; and generate the different distribution data from video after the time the trigger was detected in a case where a volume of a shout emitted from an audience is equal to or more than a threshold value in the distribution data is detected as the trigger. */
Akiyama-Despande-Takizawa does not explicitly disclose but Konishi discloses wherein the at least one processor is configured to execute the instructions to generate the different distribution data from video after the time the trigger was detected in a case where a volume of a shout emitted from an audience is equal to or more than a threshold value in the distribution data is detected as the trigger (Konishi, para’s 0055 and 0075, the occurrence of loud cheers may be determined; the start time information of the scene with loud cheers may be automatically recorded based on the volume of cheers in the stadium, indicating that after the volume of cheers has reached a threshold, the start time information of the scene may be recorded); and wherein the at least one processor is configured to execute the instructions to detect that a comment of a viewer or the number of favorites in the distribution data exceeds a threshold value (Konishi, 0075, 0080, and 0136, the occurrence of an event wherein the number of comments per unit time is equal to or greater than the predetermined number; after the distribution control information is stored, distribution target users are determined and appropriate distribution processing is performed. Specifically, as shown in FIG. 20, steps S1002 to S1007 are executed in response to a loud cheer (S1001), an increase in the number of comments per unit time (S1009), a change in score (S1010), or the like.); wherein the at least one processor is configured to execute the instructions to generate the different distribution data having different times depending on the type of the trigger (see Takizawa, para’s 0056-0060, the image analysis unit 152 detects an occurrence of an event type “3-point shot success”, i.e., change in game score, in a case where the ball away from the player 2 at a position, whose distance from a basket goal is greater than or equal to a threshold, passes through a predetermined area (basket goal), and a video from before the time when the 3-point shot success event has occurred is displayed; Konishi, para’s 0055 and 0075, the occurrence of loud cheers may be determined; the start time information of the scene with loud cheers may be automatically recorded based on the volume of cheers in the stadium, indicating that after the volume of cheers has reached a threshold, the start time information of the scene may be recorded, it is also noted that determining the number of likes of the live streaming video data in a time period exceeds a preset threshold is well known in the art as is evidenced by Geng et al., US Publication 2021/0258658, para. 0093).
The goal detection disclosed in Akiyama, the detection of a score change, disclosed in Takizawa (see para’s 0056-0060, change of score due to success of a 3-pont shot) and in Konishi (see para’s 0075 and 0080, change of score), a loud cheer occurrence, and the number of comments being at least a predetermined threshold value disclosed in Konishi share a common feature in terms of the function of triggering the automatic generation of video images, and belong to similar technical fields; therefore, instead of the goal detection disclosed in Akiyama, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Konishi’s events of detecting a score change, a loud cheer occurrence, and number of comments being at least a predetermined threshold value into Akiyama-Despande-Takizawa’s invention for enhancing user’s viewing experience by providing video edit according to specific featured events.
Claims 16-17 are rejected the same reasons set forth in claims 5-6.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOI H TRAN whose telephone number is (571)270-5645. The examiner can normally be reached 8:00AM-5:00PM PST FIRST FRIDAY OF BIWEEK OFF.
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/LOI H TRAN/ Primary Examiner, Art Unit 2484
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