Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
The status of claims 1-17 is:
Claims 1-17 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. Three categories of subject matter are found to be judicially recognized exceptions to 35 U.S.C. § 101 (i.e. patent ineligible) (1) laws of nature, (2) physical phenomena, and (3) abstract ideas. MPEP 2106(II). To be patent-eligible, a claim directed to a judicial exception must as whole be integrated into a practical application or directed to significantly more than the exception itself (MPEP 2106). Hence, the claim must describe a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In the analysis below, the method of independent claim 1 is considered representative of independent claims 16 and 17. Each of the independent claims 1 and 16-17 are directed to one of the four statutory categories of eligible subject matter; thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, Prong 1 Analysis
Independent claims 1 and 16-17 are directed to extracting a plurality of key points related to golf swing motion of a human subject from each of a plurality of images included in a video, the key points including a key point representing a body part of the human subject and a key point representing a golf club; acquiring data representing motion of both the human subject and the golf club in each of the image, based on the plurality of key points, the data including data related to at least one of positions, or velocities, of both the body part of the human subject and the golf club; and estimating a timing of at least one predetermined event in the video, based on a similarity between the data acquired for each of the images and reference data representing motion of a person and a golf club, upon occurrence of a condition in which the predetermined event in golf swing motion occurs. An individual can acquire a plurality of key points related to golf swing motion of a human subject from each of a plurality of images included in a video, the key points including a key point representing a body part of the human subject and a key point representing a golf club, and acquiring data representing motion of both the human subject and the golf club in each of the image, based on the plurality of key points, the data including data related to at least one of positions, or velocities, of both the body part of the human subject and the golf club as it is insignificant extra-solution activity (mere data gathering), and can estimate a timing of at least one predetermined event in the video, based on a similarity between the data acquired for each of the images and reference data representing motion of a person and a golf club, upon occurrence of a condition in which the predetermined event in golf swing motion occurs. Accordingly, the analysis under prong one of Step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements
Independent claim 1 does not have any additional elements. Independent claim 16 claims a non-transitory computer readable medium. Independent claim 17 claims a device comprising: a memory and circuitry.
Step 2A, Prong 2 Analysis
The above-identified elements do not integrate the judicial into a practical application nor do they suggest an improvement.
The additional elements of a non-transitory computer readable medium and a device comprising: a memory; and a circuitry amounts to merely using generic computer hardware or components as a tool to perform the claimed mental process.
Using a general purpose computer to apply a judicial exception does not qualify as a particular machine and therefore, does not integrate a judicial exception into a practical application (See MPEP 2106.05(b)). Furthermore, implementing an abstract idea on a computer does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or another technology or technical field, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)).
Further, the act of acquiring data is mere data gathering which amounts to insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claims 1 and 16-17, as noted above, the additional elements are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves and other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all the foregoing reasons, independent claims 1 and 16-17 do not recite eligible subject matter under 35 USC 101.
Claim 2 recites wherein the key points include at least one key point of a golf ball. The features of claim 2 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 2 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3 recites wherein the acquired data includes data representing motion of a plurality of body parts of the human subject. The features of claim 3 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 3 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4 recites wherein the predetermined event includes at least one of address, takeaway, backswing, top-of-swing, halfway down, impact, follow-through, or finish. The features of claim 4 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 4 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5 recites wherein the reference data includes data representing an average of motions of two or more persons at the timing of the predetermined event in golf swing motion. The features of claim 5 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 5 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 6 recites wherein the estimating includes estimating the timing of the predetermined event within a period preceding or following a predetermined timing in the video. The features of claim 6 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 6 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 7 recites wherein the at least one predetermined event includes a first event and a second event, and wherein the estimating includes estimating a timing of the second event within a period preceding or following the timing of the first event in the video, after estimating a timing of the first event as the predetermined timing. The features of claim 7 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 7 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 8 recites wherein the predetermined timing includes a timing at which speed of the golf club is maximum or minimum. The features of claim 8 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 8 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 9 recites wherein the predetermined timing is a timing at which the similarity takes a minimum value. The features of claim 9 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 9 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 10 recites wherein the estimating includes estimating the timing of the predetermined event within a predetermined period preceding or following a period in which the similarity is smaller than a predetermined threshold value. The features of claim 10 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 10 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 11 recites wherein the predetermined timing includes a timing designated by a user. The features of claim 11 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 11 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 12 recites wherein the acquiring includes acquiring data normalized by a height or a length of the body part of the human subject, and wherein the reference data includes data normalized in a same manner as in the acquiring. The features of claim 12 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 12 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 13 recites wherein the acquiring includes acquiring data normalized by (i) a distance corresponding to a height of the human subject on an image, (ii) a distance corresponding to a length of a predetermined body part of the human subject on the image, or (iii) a distance corresponding to a length of the golf club on the image, and wherein the reference data includes data normalized in a same manner as in the acquiring. The features of claim 13 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 13 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 14 recites wherein the acquiring includes a plurality of types of data, and wherein the estimating includes estimating the timing of the predetermined event in the video, based on a similarity between the types of the data and a plurality of types of the reference data corresponding to the types of the data, in consideration of relative weighting between the types of data. The features of claim 14 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 14 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 15 recites wherein the at least one predetermined event includes a plurality of events, and wherein a pattern of the relative weighting between the types of the data is different for each event of the plurality of events. The features of claim 15 are directed to the mental process since they do not preclude the data from being acquired or mentally analyzed as recited in claim 1. Accordingly, claim 15 does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-7, 10, 12-13, and 16-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ishimura (U.S. Patent Publication No 2024/0394897, hereinafter “Ishimura”).
Regarding claim 1, Ishimura discloses an information processing method executed by an information processing device (Ishimura [0147]: “The processing device 130 is, for example, a computer including a processor and a memory”), the information processing method comprising:
extracting a plurality of key points related to golf swing motion of a human subject from each of a plurality of images included in a video (Ishimura [0112]: “For example, the motion analysis server 200 extracts a plurality of key points KP (a plurality of feature points indicating a shoulder, an elbow, a wrist, a waist, a knee, an ankle, and the like: refer to FIG. 10) from the image of the target TG using a deep learning method. The motion analysis server 200 estimates the posture of the target TG based on a relative position between the extracted key points KP”), the key points including a key point representing a body part of the human subject (Ishimura [0112]: “For example, the motion analysis server 200 extracts a plurality of key points KP (a plurality of feature points indicating a shoulder, an elbow, a wrist, a waist, a knee, an ankle, and the like: refer to FIG. 10) from the image of the target TG using a deep learning method. The motion analysis server 200 estimates the posture of the target TG based on a relative position between the extracted key points KP”) and a key point representing a golf club (Ishimura [0114]: “In the motion analysis algorithm AL, definition information on how to define the posture of each phase is defined. The posture is defined based on, for example, a positional relationship between the key points KP (an angle, a distance, and the like) and a mode of movement of a specific key point KP (a state of a change in moving direction, moving speed, moving speed, and the like). The posture may be defined based on a positional relationship with a specific object OB (such as a ball) used by the target TG”; Ishimura [0100]: “The specific object OB is, for example, a soccer ball in the case of soccer, and is a golf club and a golf ball in the case of golf”, the object is acting as a feature point);
acquiring data representing motion of both the human subject and the golf club in each of the image, based on the plurality of key points (Ishimura [0113]: “The motion analysis server 200 extracts the posture information HPI of the target TG from each frame image included in the specific scene. The posture information HPI means information indicating the position (coordinates) of each key point KP and a positional relationship (a joint angle or the like) between the key points KP”), the data including data related to at least one of positions, or velocities, of both the body part of the human subject and the golf club (Ishimura [0114]: “In the motion analysis algorithm AL, definition information on how to define the posture of each phase is defined. The posture is defined based on, for example, a positional relationship between the key points KP (an angle, a distance, and the like) and a mode of movement of a specific key point KP (a state of a change in moving direction, moving speed, moving speed, and the like). The posture may be defined based on a positional relationship with a specific object OB (such as a ball) used by the target TG”; Ishimura [0100]: “The specific object OB is, for example, a soccer ball in the case of soccer, and is a golf club and a golf ball in the case of golf”); and
estimating a timing of at least one predetermined event in the video, based on a similarity between the data acquired for each of the images and reference data representing motion of a person and a golf club (Ishimura [0165]: “The first analysis information MAIL includes, for example, skeleton information SI of the target TG and reference skeleton information RSI (skeleton information on the specific person RM) serving as a reference of comparison in each phase”), upon occurrence of a condition in which the predetermined event in golf swing motion occurs (Ishimura [0101]: “A phase determination condition is defined based on, for example, an angle of a specific joint and a relative position between a ball, which is the object OB, and a feature point (a key point) of a specific body”; Ishimura [0127]: “In the scene information 142, a plurality of specific scenes corresponding to the respective phases and a determination condition for determining each specific scene are defined in association with each other”).
Regarding claim 16, it is rejected under the same analysis as claim 1 above along with Ishimura’s disclosure of a non-transitory computer readable medium storing a program (Ishimura [0146]: “The program 144 is a program that causes a computer to execute information processing of the client terminal 100. The processing device 130 performs various types of processing in accordance with the program 144. The storage device 140 may be used as a work area for temporarily storing a processing result of the processing device 130. The storage device 140 includes any non-transitory storage medium, such as a semiconductor storage medium and a magnetic storage medium. The storage device 140 includes, for example, an optical disk, a magneto-optical disk, or a flash memory. The program 144 is stored in, for example, a non-transitory computer-readable storage medium”).
Regarding claim 17, it is rejected under the same analysis as claim 1 above along with Ishimura’s disclosure of an information device comprising a memory and circuitry (Ishimura [0147]: “The processing device 130 is, for example, a computer including a processor and a memory”).
Regarding claim 2, Ishimura discloses the method, wherein the key points include at least one key point of a golf ball (Ishimura [0100]: “The specific object OB is, for example, a soccer ball in the case of soccer, and is a golf club and a golf ball in the case of golf”).
Regarding claim 3, Ishimura discloses the method, wherein the acquired data includes data representing motion of a plurality of body parts of the human subject (Ishimura [0114]: “In the motion analysis algorithm AL, definition information on how to define the posture of each phase is defined. The posture is defined based on, for example, a positional relationship between the key points KP (an angle, a distance, and the like) and a mode of movement of a specific key point KP (a state of a change in moving direction, moving speed, moving speed, and the like)”).
Regarding claim 4, Ishimura discloses the method, wherein the predetermined event includes at least one of address, takeaway, backswing, top-of-swing, halfway down, impact, follow-through, or finish (Ishimura [0101]: “A phase determination condition is defined based on, for example, an angle of a specific joint and a relative position between a ball, which is the object OB, and a feature point (a key point) of a specific body”; Ishimura [0127]: “In the scene information 142, a plurality of specific scenes corresponding to the respective phases and a determination condition for determining each specific scene are defined in association with each other”; Ishimura [0183]: “FIG. 12 illustrates an example in which six phases are set. For example, a timing of backswing, a timing of downswing, a timing immediately before an impact, a timing of the impact, a timing immediately after the impact, and a timing of a follow-through are set as phases respectively serving as targets to be analyzed”).
Regarding claim 6, Ishimura discloses the method, wherein the estimating includes estimating the timing of the predetermined event within a period preceding or following a predetermined timing in the video (Ishimura [0137]: “The determination of the specific scene corresponding to the (iii) is performed on the moving image data MD after the specific scene corresponding to the (ii). Considering the preceding and subsequent contexts in the flow of the motion, it is considered that the specific scene corresponding to the (iii) occurs immediately after the specific scene corresponding to the (ii). Therefore, in a case where the above-described change in distance occurs within a predetermined time immediately after the specific scene corresponding to the (ii), there is a high possibility that the scene is the specific scene corresponding to the (iii). Therefore, the scene extraction unit 133 determines the scene as the specific scene corresponding to the (iii), and extracts one or more frame images FI indicating the specific scene from the moving image data MD”).
Regarding claim 7, Ishimura discloses the method, wherein the at least one predetermined event includes a first event and a second event (Ishimura [0137]: “The determination of the specific scene corresponding to the (iii) is performed on the moving image data MD after the specific scene corresponding to the (ii). Considering the preceding and subsequent contexts in the flow of the motion, it is considered that the specific scene corresponding to the (iii) occurs immediately after the specific scene corresponding to the (ii)”), and
wherein the estimating includes estimating a timing of the second event within a period preceding or following the timing of the first event in the video, after estimating a timing of the first event as the predetermined timing (Ishimura [0137]: “Therefore, in a case where the above-described change in distance occurs within a predetermined time immediately after the specific scene corresponding to the (ii), there is a high possibility that the scene is the specific scene corresponding to the (iii). Therefore, the scene extraction unit 133 determines the scene as the specific scene corresponding to the (iii), and extracts one or more frame images FI indicating the specific scene from the moving image data MD”).
Regarding claim 10, Ishimura discloses the method, wherein the estimating includes estimating the timing of the predetermined event within a predetermined period preceding or following a period in which the similarity is smaller than a predetermined threshold value (Ishimura [0132]: “The scene extraction unit 133 extracts a skeleton motion area in which all the N skeleton areas are included. When the size of the skeleton motion area is within a threshold value and the skeleton motion area is included in the unique area, the scene extraction unit 133 determines that the pivot foot is stepped on. The scene extraction unit 133 extracts one or more frame images FI indicating the timing at which the pivot foot is stepped on from the moving image data MD”).
Regarding claim 12, Ishimura discloses the method, wherein the acquiring includes acquiring data normalized by a height or a length of the body part of the human subject (Ishimura [0166]: “The motion analysis unit 222 calculates a ratio of the sum of the lengths of the spine and the foot bones as a ratio of the body sizes of the specific person RM and the target TG, and changes the scale of the skeleton of the specific person RM based on the ratio. This facilitates comparison with the specific person RM, thereby making it easy to understand how the target TG should motion”), and
wherein the reference data includes data normalized in a same manner as in the acquiring (Ishimura [0166]: “For each of the specific person RM and the target TG, the motion analysis unit 222 detects the lengths of the spine and the foot bones at the timing when the posture is aligned. The motion analysis unit 222 calculates a ratio of the sum of the lengths of the spine and the foot bones as a ratio of the body sizes of the specific person RM and the target TG, and changes the scale of the skeleton of the specific person RM based on the ratio. This facilitates comparison with the specific person RM, thereby making it easy to understand how the target TG should motion”).
Regarding claim 13, Ishimura discloses the method, wherein the acquiring includes acquiring data normalized by (i) a distance corresponding to a height of the human subject on an image (Ishimura [0166]: “For each of the specific person RM and the target TG, the motion analysis unit 222 detects the lengths of the spine and the foot bones at the timing when the posture is aligned. The motion analysis unit 222 calculates a ratio of the sum of the lengths of the spine and the foot bones as a ratio of the body sizes of the specific person RM and the target TG, and changes the scale of the skeleton of the specific person RM based on the ratio. This facilitates comparison with the specific person RM, thereby making it easy to understand how the target TG should motion”), (ii) a distance corresponding to a length of a predetermined body part of the human subject on the image, or (iii) a distance corresponding to a length of the golf club on the image, and
wherein the reference data includes data normalized in a same manner as in the acquiring (Ishimura [0166]: “For each of the specific person RM and the target TG, the motion analysis unit 222 detects the lengths of the spine and the foot bones at the timing when the posture is aligned. The motion analysis unit 222 calculates a ratio of the sum of the lengths of the spine and the foot bones as a ratio of the body sizes of the specific person RM and the target TG, and changes the scale of the skeleton of the specific person RM based on the ratio. This facilitates comparison with the specific person RM, thereby making it easy to understand how the target TG should motion”).
Claim Rejections - 35 USC § 103
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.
Claim(s) 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ishimura in view of Oku et al. (U.S. Patent Publication No 2024/0355223, hereinafter “Oku”).
Regarding claim 5, Ishimura discloses the method, wherein the reference data includes data representing the motions of two or more persons at the timing of the predetermined event in golf swing motion (Ishimura [0165]: “The first analysis information MAIL includes, for example, skeleton information SI of the target TG and reference skeleton information RSI (skeleton information on the specific person RM) serving as a reference of comparison in each phase”).
Ishimura does not explicitly disclose the method, wherein the reference data includes data representing an average of motions of two or more persons at the timing of the predetermined event in golf swing motion.
However, Oku teaches the method, wherein the reference data includes data representing an average of motions of two or more persons at the timing of the predetermined event in golf swing motion (Oku [0190]: “The expression (13) is satisfied in a case where a relative value of the Euclidean distance of the feature amount with respect to the average value of the reference data 23c is large”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the averages in reference data as taught by Oku with the method of Ishimura because it would improve the method as averaging the reference data allows the method to only need to make one comparison when evaluating a swing, and it can also eliminate outlier reference data that may affect a comparison (Oku [0188]). This motivation for the combination of Ishimura and Oku is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 9, Ishimura does not explicitly disclose the method, wherein the predetermined timing is a timing at which the similarity takes a minimum value.
However, Oku teaches the method, wherein the predetermined timing is a timing at which the similarity takes a minimum value (Oku [0250]: “As described with reference to FIG. 34 and FIG. 35 and the like, the user interface unit 21 may present the comparison result between the data 23b and the reference data 23c. The user interface unit 21 displays an alert indicating a portion in which the similarity between the data 23b and the reference data 23c is distant. The data 23b can also be used for such comparison and similarity determination. By utilization of the data 23b in which the feature amounts are organized and described for each of the plurality of pieces of information, comparison with the reference data 23c and determination of similarity can be easily performed”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate setting the predetermined event as a timing when the similarity takes a minimum value as taught by Oku with the method of Ishimura because it would improve the method as the timing of the swing when the similarity takes a minimum is the most incorrect aspect of the swing and as such, focusing on that timing would most improve the user’s swing. This motivation for the combination of Ishimura and Oku is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ishimura in view of Omid-Zohoor et al. (U.S. Patent Publication No 2024/0382806, hereinafter “Omid”).
Regarding claim 8, Ishimura does not explicitly disclose the method, wherein the predetermined timing includes a timing at which speed of the golf club is maximum or minimum.
However, Omid teaches the method, wherein the predetermined timing includes a timing at which speed of the golf club is maximum or minimum (Omid [0125]: “In a golf swing motion analysis system in particular, rate and position motion data are typically processed by the application software into performance or diagnostic parameters relating to the golfer's body segment performance, including: hip velocity (degrees per second); hip rotation (degrees negative and positive); shoulder velocity (degrees per second); shoulder rotation (degrees negative and positive); club release (degrees per second); club speed (miles per hour); club face rotation (degrees open/closed); club path (degrees inside or outside of club's address position); hip linear movement (centimeters left or right of neutral address); hip and shoulder separation (time difference between maximum hip, shoulder, and club velocity)”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate setting the predetermined event as when the speed of the golf club is at a maximum or a minimum as taught by Omid with the method of Ishimura because it would improve the method by allowing it to analyze that specific aspect of the golf swing. This motivation for the combination of Ishimura and Omid is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ishimura in view of Kimura et al. (U.S. Patent Publication No 2015/0201150, hereinafter “Kimura”).
Regarding claim 11, Ishimura does not explicitly disclose the method, wherein the predetermined timing includes a timing designated by a user.
However, Kimura teaches the method, wherein the predetermined timing includes a timing designated by a user (Kimura [0037]: “It is noted, however, that the search range is set such that the search is performed for, such as, the predetermined number of the frames or the number of the frames involved in a predetermined period of time. More preferably, a user can designate the way for setting the search range, through the use of controller 3”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate having the user designate the predetermined timing as taught by Kimura with the method of Ishimura because it would improve the method by allowing the user to determine which part of their swing that they would like the method to focus on. This motivation for the combination of Ishimura and Kimura is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Claim(s) 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ishimura in view of Nogami et al. (JP 2016081121 A using the translation provided herein, hereinafter “Nogami”).
Regarding claim 14, Ishimura discloses the method, wherein the acquiring includes a plurality of types of data (Ishimura [0051]: “The client terminal 100 transmits the vital data, the exercise data, and the inquiry data of the target TG to the motion analysis server 200. The motion analysis server 200 performs the motion analysis of the target TG based on various pieces of data acquired from the client terminal 100”).
Ishimura does not explicitly disclose the method, wherein the estimating includes estimating the timing of the predetermined event in the video, based on a similarity between the types of the data and a plurality of types of the reference data corresponding to the types of the data, in consideration of relative weighting between the types of data.
However, Nogami teaches the method, wherein the estimating includes estimating the timing of the predetermined event in the video, based on a similarity between the types of the data and a plurality of types of the reference data corresponding to the types of the data, in consideration of relative weighting between the types of data (Nogami Page 8: “For example, the combining unit 207 may perform combining after weighting the adjustment data. For example, the synthesizing unit 207 performs the reliability evaluation described in the second modification, and weights each adjustment data based on the reliability of the original unit data of each adjustment data. The weights can be used for outlier removal or data integration by moving average (weighted moving average) or the like. As a weight setting method, the synthesizer 207 may add weight to unit data that is used as reference data”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate weighting the data as taught by Nogami with the method of Ishimura because it would improve the method by improving the accuracy of the analysis as the weighting would cause the method to focus on the data that is most important when comparing golf swings. This motivation for the combination of Ishimura and Nogami is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 15, Ishimura discloses the method, wherein the at least one predetermined event includes a plurality of events (Ishimura [0134]: “The determination of the specific scene corresponding to the (ii) is performed on the moving image data MD after the specific scene corresponding to the (i). Considering the preceding and subsequent contexts in the flow of the motion, it is considered that the specific scene corresponding to the (ii) occurs immediately after the specific scene corresponding to the (i). Therefore, if there is a scene in which the extension line of the foot detected as the pivot foot passes through the ball within a predetermined time immediately after the specific scene corresponding to the (i), there is a high possibility that the scene is the specific scene corresponding to the (ii). Therefore, the scene extraction unit 133 determines the scene as the specific scene corresponding to the (ii), and extracts one or more frame images FI indicating the specific scene from the moving image data MD”; Ishimura [0137]: “The determination of the specific scene corresponding to the (iii) is performed on the moving image data MD after the specific scene corresponding to the (ii). Considering the preceding and subsequent contexts in the flow of the motion, it is considered that the specific scene corresponding to the (iii) occurs immediately after the specific scene corresponding to the (ii). Therefore, in a case where the above-described change in distance occurs within a predetermined time immediately after the specific scene corresponding to the (ii), there is a high possibility that the scene is the specific scene corresponding to the (iii). Therefore, the scene extraction unit 133 determines the scene as the specific scene corresponding to the (iii), and extracts one or more frame images FI indicating the specific scene from the moving image data MD”).
Ishimura does not explicitly disclose the method, wherein a pattern of the relative weighting between the types of the data is different for each event of the plurality of events.
However, Nogami teaches the method, wherein a pattern of the relative weighting between the types of the data is different for each event of the plurality of events (Nogami Page 8: “For example, the combining unit 207 may perform combining after weighting the adjustment data. For example, the synthesizing unit 207 performs the reliability evaluation described in the second modification, and weights each adjustment data based on the reliability of the original unit data of each adjustment data. The weights can be used for outlier removal or data integration by moving average (weighted moving average) or the like. As a weight setting method, the synthesizer 207 may add weight to unit data that is used as reference data”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate weighting the data as taught by Nogami with the method of Ishimura because it would improve the method by improving the accuracy of the analysis as the weighting would cause the method to focus on the data that is most important when comparing golf swings. This motivation for the combination of Ishimura and Nogami is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Conclusion
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/AIDAN KEUP/ Examiner, Art Unit 2666 /Molly Wilburn/ Primary Examiner, Art Unit 2666