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 .
Drawings
Regarding FIGS. 2-4, 37 CFR 1.84(a)(1), stated in part, normally requires black and white drawings. India ink, or its equivalent that secures solid black lines, must be used for drawings. In the present case, the drawing has a plurality of very faint lines in the legend describing the waveform that are not of sufficient quality so that all details are reproducible in the printed patent and are thereby undistinguishable. Therefore, the failure to use solid black lines renders FIGS. 2-4 from complying with 37 CFR 1.84(a)(1).
Regarding FIG. 2, 37 CFR 1.84(p)(5), stated in part, requires reference characters that are not mentioned in the description shall not appear in the drawings. In the present case, reference character 2/29 is in the middle of the figure and is not mentioned in the specification as originally filed. Therefore, the meaning is unclear and FIG. 2 fails to comply with 37 CFR 1.84(p)(5).
Regarding FIGS. 3-5, 37 CFR 1.84(p)(4), stated in part, requires the same reference character to never be used to designate different parts. In the present case, there is a lack of agreement between the drawing and the specification as originally filed in regards to reference character 306. In other words, the specification as originally filed clearly describes reference character 306 as follows: “[0053] As shown in FIG. 3, each of the arrows 306 may coincide with a shot being fired.” Presently, it appears that four (i.e. 4) shots have been fired in FIG.3 and five (i.e. 5) shots have been fired in FIG.5. However, there are a plurality of similar shaped arrows without a 306 reference character in FIGS. 3 and 5. Likewise, the same style of arrow is in FIG. 4 with a 416 reference character and an arrow without a reference character. Therefore, the meaning is unclear and FIGS. 3-5 fails to comply with 37 CFR 1.84(p)(4).
Regarding FIG. 9, 37 CFR 1.84(p)(5), stated in part, requires reference characters that are not mentioned in the description shall not appear in the drawings. In the present case, reference character 906c touches and concerns different parts of the waveform. Therefore, the meaning is unclear and FIG. 9 fails to comply with 37 CFR 1.84(p)(5).
Regarding FIG. 10A, 37 CFR 1.84(p)(4), stated in part, requires the same reference character to never be used to designate different parts. In the present case, FIG. 10A is missing the reference characters as provided in FIG. 10B. Therefore, the meaning is unclear and FIG. 10A fails to comply with 37 CFR 1.84(p)(4).
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-17 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-17 of copending Application No. 19/111,297 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – “Statutory Category Identification”
Claims 1 and 12 are directed to “a method” (i.e. a process), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to the abstract idea of “improving shooting performance,” in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“receiving video data of a shooter;
determining one or more body landmarks of the shooter;
tracking the one or more body landmarks during a shot to generate shot motion data;
determining a score of the shot;
associating the shot motion data with the score; and
generating recommendations for altering the motion data on a subsequent shot.”
Per claim 12:
“receiving video data of a body motion;
determining one or more body landmarks viewable in the video data of the body motion;
tracking the one or more body landmarks during an action;
generating, based at least in part on the tracking the one or more body landmarks, motion data;
determining a score associated with the motion data;
associating the motion data with the score; and
generating recommendations for altering the motion data on a subsequent action.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a machine learning model,” “a display screen,” “a mobile phone,” and “a mobile computing device,” are claimed, as these are merely claimed to generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “improving shooting performance,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a machine learning model,” “a display screen,” “a mobile phone,” and “a mobile computing device,” are claimed, these are generic, well-known, and conventional elements. As evidence that these are generic, well-known, and conventional elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Moreover, the element of “a machine learning model,” is best described in paras. [0000] and [0275] as follows:
“[0008] Implementations may include one or more of the following features. The method where receiving the video data includes capturing the video data by a mobile computing device. The method may include executing a machine learning model to correlate the motion data with the score. The machine learning model is configured to determine the motion data that results in a reduced score. The method may include predicting, by the machine learning model, a predicted score based on the motion data. The method may include comparing the predicted score with the score. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.”
“[0275] The system may utilize one or more machine learning models for synchronization, prediction, verification, and may further be trained to analyze scoring data and associate the scoring data with the motion data to determine correlations between specific motion data (e.g., behaviors) and scoring trends. As an example, the system may correlate that a shooter's wrist pivots downwardly before shots that typically score outside and below the 10 ring and determine that the shooter is anticipating the firearm recoil before the shot. The system may then provide feedback to the user with not only information related to the motion/score correlation but may also provide one or more exercises or drills to allow the user to recognize and address the behavior resulting in the reduced score.”
Due to the broad description of typical features (i.e. “executing,” “determining,” “predicting,” etc.), this element is reasonably interpreted as a generic, well-known, and conventional element which provides no details of anything beyond ubiquitous machine learning software.
Likewise, the elements of ““a display screen,” “a mobile phone,” and “a mobile computing device,” are best described in paras. [0058], [0072], and [0154] as follows:
“[0058] The motion capture may be performed by one or more cameras aimed generally at the participant, and one or more cameras aimed at a target. In some cases, one or more of the cameras are associated with a mobile computing device, such as, for example, a smartphone, a tablet, a laptop, a digital personal assistant, and a wearable device (e.g., watch, glasses, body cam, smart hat, etc.).
“[0072]… In some cases, the system will provide information on a display screen associated with a mobile computing device. For example, the system may be implemented on a mobile computing device associated with a shooter, and a display screen on the mobile computing device may provide information, instructions, or practice drills to the shooter to improve the drift and the accuracy issues resulting therefrom.”
“[0154] The user interface may be provided on any suitable display, such as a television, a touch-screen display, a tablet screen, a smart phone screen, or any other visual computer interface.”
Due to the list of examples, this element is reasonably interpreted as a generic, well-known, and as a commercially available product which provides no details of anything beyond ubiquitous standard off-the-shelf equipment.
Therefore, the Applicant’s own specification discloses ubiquitous standard equipment that is (1) generic, routine, conventional, and/or commercially available; and (2) does not provide anything significantly more. Thus, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-11 and 13-17 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-11 and 13-17 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1 or 12. Therefore, claims 1-17 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
Claims rejected under 35 U.S.C. § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
Claims 3, 10, 11, and 14-16, recite “a machine learning model.” When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.
In the present case, the specification does not provide a disclosure of the algorithm (i.e. “a machine learning model”) in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention. Therefore, claims 3, 10, 11, and 14-16 are rejected under 35 U.S.C. § 112(a), as failing to comply with the written description requirement. Claims 11, 15 and 16 are also rejected under 35 U.S.C. § 112(a), based on their respective dependencies to claim 10 or 14.
Claims rejected under 35 U.S.C. § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites the limitation “the motion data.” The limitation “shot motion data,” is originally introduced in claim 1. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “the shot motion data”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1. Therefore, claim 1 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-11 are also rejected 35 U.S.C. § 112(b), based on their respective dependencies to claim 1.
Claim 2 recites the limitations “one or more body landmarks” and “the body landmarks.” The limitations are originally introduced in claim 1. As such, the subsequent limitations are either (1) not following antecedent basis (i.e. “the one or more body landmarks” and “the one or more body landmarks”); or (2) are intended to be new limitations which ambiguously conflict with the previous limitation of claim 1. Therefore, claim 2 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 6 recites the limitation “the received target video.” The limitation is not originally introduced in claim 6. As such, the limitation lacks antecedent basis. The Examiner recommends amending the claim to read as follows: “the .” Therefore, claim 6 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 6 recites the limitation “the target.” The limitation is not originally introduced in claim 6. As such, the limitation lacks antecedent basis. Therefore, claim 6 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 6 recites the limitation “a score.” The limitation is originally introduced in claim 1. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “[[a]] the score”); or (2) is intended to be a new limitation (i.e. “a hit score”) which ambiguously conflicts with the previous limitation of claim 1. Therefore, claim 6 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 recites the limitation “video data.” The limitation is originally introduced in claim 1. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “the video data”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1. Therefore, claim 7 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 recites the limitation “one or more body landmarks.” The limitation is originally introduced in claim 1. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “the one or more body landmarks”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1. Therefore, claim 8 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 recites the limitation “the motion data.” The limitation “shot motion data,” is originally introduced in claim 1. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “the shot motion data”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1. Therefore, claim 11 is rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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.
Claims 1, 2, 4-6, 8 and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ghani (US 2020/0072578).
Regarding claim 1, and substantially similar limitations in claim 12, Ghani discloses a method for improving shooting performance (see para. [0004] This invention is directed to training aids for individuals who desire to improve their ability to shoot accurately and quickly; see para. [0015] The goal of the training session is to provide important self-instruction by facilitating views that aid the trainee when shooting at a target with live rounds. The trainee can compare their hands and arms position when looking at the accuracy of shot placement on the target. The trainee can then learn what position works the best for stability. Additionally, an instructor can view the recorded shot session, and offer insight as to what the trainee needs to address based on the recorded four view video. An important goal is to aid the trainee in quickly advancing in shooting expertise, without long hours of unproductive shooting practice), comprising: receiving video data of a shooter (see para. [0016] FIG. 1 shows the equipment set up for a training session. A side view camera 101 is connected to a computer 103 by wireless or by a suitable connection such as USB or ethernet. The side view camera records a side view of the trainee during a shooting session. An infrared sensor 102 detects the position of the trainee's arms and hands. It does this by an infrared sensor that utilizes two infrared cameras and a few illuminating infrared LEDs which illuminate the arms/hands of the trainees. The infrared cameras are spaced apart so as to provide depth (i.e. distance above the sensor) perspective. The infrared sensor 102 and the side view camera 101 are both connected to the computer; see para. [0018] The sensor 102 utilizes dual infrared cameras to track the position of the hands and arms. The cameras are separated to provide for depth perception utilizing the differences between the two camera images, similar to what human eyes do. The sensors look only at the infrared wavelength, and the arms-hands are illuminated by three infrared LEDs in line with the cameras. To keep power within the minimum amount that a USB power supply can provide, the LED's are pulsed and matched to the camera's frame rate. The sensor communicates with the training computer which identifies more than 21 points for each hand. Individual fingers are recognized including finger digits. One point represents the Palm of the hand, one point for the wrist, and 4 points for each arm. A rectangular projection of the arms approximates the Ulna and Radius bones with straight lines);
determining one or more body landmarks of the shooter; tracking the one or more body landmarks during a shot to generate shot motion data (see para. [0018] The sensor 102 utilizes dual infrared cameras to track the position of the hands and arms. The cameras are separated to provide for depth perception utilizing the differences between the two camera images, similar to what human eyes do. The sensors look only at the infrared wavelength, and the arms-hands are illuminated by three infrared LEDs in line with the cameras. To keep power within the minimum amount that a USB power supply can provide, the LED's are pulsed and matched to the camera's frame rate. The sensor communicates with the training computer which identifies more than 21 points for each hand. Individual fingers are recognized including finger digits. One point represents the Palm of the hand, one point for the wrist, and 4 points for each arm. A rectangular projection of the arms approximates the Ulna and Radius bones with straight lines);
determining a score of the shot; associating the shot motion data with the score (see para. [0039] FIG. 3 illustrates how a score for the player is identified when a satisfactory shot is made (within a certain distance from target center 301) and within an acceptable hand position (i.e. hand position within hand calibration bubble 302 or shooting width). For example, the score would be 50% for 6 out of 12 shots when both criteria are met. Flexibility to establish an acceptable shot on target region and the size of the hand calibration bubble is part of the training session design. The target camera 107 is used as part of scoring an acceptable shot); and
generating recommendations for altering the motion data on a subsequent shot
(see FIGS. 2-3; see para. [0021] The 2 infrared cameras see depth and are able to find or interpret that position of the hands in a 3D area above the sensor. The goal of the software is to provide feedback during a live fire at a target situation. Also, a laser gun or pellet gun could be used.
see para. [0022] FIG. 2 is an illustration of 4 views that are combined on a single monitor display:
see para. [0023] 1. The upper left screen display 201 shows a hand calibration bubble 206 in crosshairs and a view update target as seen by the trainee with skeletal hands-arms 208.
see para. [0034] Every five seconds, the position of the hands holding the gun is marked 205 on the upper left screen 201 as illustrated in FIG. 2. This provides a record of hand position motion during a trainee multi shot sequence. A number is attached to each circular marking to aid in understanding how the hands moved.
see para.[0035] An oval or circular hand calibration bubble 206 is projected on the screen when the trainee presses a foot calibrate pedal 109, which also starts the trainee shot session. The bubble then projects on the screen based on the hands of the trainee position, as measured by the infrared sensor 102.
see para. [0036] The hand calibration bubble 206 is used to establish an initial hand position for differing heights of the trainee, and different holding heights. Some trainees are short (or tall) and have short arms (or long); and the hand calibration bubble aids in allowing the training session to establish a circle of acceptable hand positions. The bubble is not to scale on the screen and is an additional visual aid for hand position tracking. The size of the bubble is fixed and is not calibrated to the trainee's height, arm length, or body position. The hand calibration bubble will be visible but not locked into position until the trainee presents his hands within the sensor range and is not moving.
see para. [0037] As seen in FIG. 2, a shooting width 207 is marked on the screen with two vertical lines when the foot calibrate pedal 109 is pressed. The shooting width provides a recommended window of acceptable hand positions based on the initial position of the hands. The shooting width 207 is fixed and is not calibrated to the trainee's height. Either the shooting width 207, the hand calibration bubble 206, or both could be marked on the screen.
see para. [0038] The crosshair dashed lines in the upper left view 201 and the lower right view 204 are based on the camera position. They are not calibrated to a shooter position. The dashed lines are guides for how the gun is held. The vertical dashed line 209 indicates whether the gun is held too high (Break Too High) or too low (Break Too Low). The horizontal line 210 indicates whether the gun is held too loose (Too Light) or too tight (Too Tight). The guide lines do not have to be centered on the target).
Regarding claim 2, Ghani discloses wherein determining one or more body landmarks of the shooter comprises generating a wire frame model by connecting the body landmarks (see FIG. 2 and 4; see para. [0014] FIG. 4 illustrates how the hands and arms are converted to a skeletal representation; see para. [0027] Other orientations of the four view display could equally be used, such as the lower right screen being a side view of the skeletal hands, the upper right display being a side view of the trainee, etc.; see para. [0044] FIG. 4 illustrates how the skeletal hands and arms are recognized. The hands and arms 401 are converted to the skeletal representation 402 by identifying and matching the joints such as a wrist, finger digits).
Regarding claim 4, Ghani discloses further comprising determining, through image analysis of the video data of the shooter, a grip of the shooter.
(see para. [0018] The sensor 102 utilizes dual infrared cameras to track the position of the hands and arms. The cameras are separated to provide for depth perception utilizing the differences between the two camera images, similar to what human eyes do. The sensors look only at the infrared wavelength, and the arms-hands are illuminated by three infrared LEDs in line with the cameras. To keep power within the minimum amount that a USB power supply can provide, the LED's are pulsed and matched to the camera's frame rate. The sensor communicates with the training computer which identifies more than 21 points for each hand. Individual fingers are recognized including finger digits. One point represents the Palm of the hand, one point for the wrist, and 4 points for each arm. A rectangular projection of the arms approximates the Ulna and Radius bones with straight lines;
see para. [0022] FIG. 2 is an illustration of 4 views that are combined on a single monitor display; see para. [0023] 1. The upper left screen display 201 shows a hand calibration bubble 206 in crosshairs and a view update target as seen by the trainee with skeletal hands-arms 208; see para. [0024] 2. The lower left view screen display 203 shows a side view of the trainee's skeletal hands-arms 208 holding the gun; see para. [0026] 4. The lower right screen 204 is a side view video recorder of the trainee holding the gun. The video is recorded during the shooting (i.e. after foot pedal is pressed). It is a live view; see para. [0039] FIG. 3 illustrates how a score for the player is identified when a satisfactory shot is made (within a certain distance from target center 301) and within an acceptable hand position (i.e. hand position within hand calibration bubble 302 or shooting width). For example, the score would be 50% for 6 out of 12 shots when both criteria are met. Flexibility to establish an acceptable shot on target region and the size of the hand calibration bubble is part of the training session design. The target camera 107 is used as part of scoring an acceptable shot).
Regarding claim 5, Ghani discloses further comprising analyzing the grip of the shooter and providing, on a display screen, grip recommendations to alter the grip (see FIG. 2; see para. [0037] As seen in FIG. 2, a shooting width 207 is marked on the screen with two vertical lines when the foot calibrate pedal 109 is pressed. The shooting width provides a recommended window of acceptable hand positions based on the initial position of the hands. The shooting width 207 is fixed and is not calibrated to the trainee's height. Either the shooting width 207, the hand calibration bubble 206, or both could be marked on the screen; see para. [0038] The crosshair dashed lines in the upper left view 201 and the lower right view 204 are based on the camera position. They are not calibrated to a shooter position. The dashed lines are guides for how the gun is held. The vertical dashed line 209 indicates whether the gun is held too high (Break Too High) or too low (Break Too Low). The horizontal line 210 indicates whether the gun is held too loose (Too Light) or too tight (Too Tight). The guide lines do not have to be centered on the target)0.
Regarding claim 6, Ghani discloses wherein determining the score of the shot comprises: receiving target video data; performing image analysis on the received target video data (see para. [0016] FIG. 1 shows the equipment set up for a training session. A side view camera 101 is connected to a computer 103 by wireless or by a suitable connection such as USB or ethernet. The side view camera records a side view of the trainee during a shooting session. An infrared sensor 102 detects the position of the trainee's arms and hands. It does this by an infrared sensor that utilizes two infrared cameras and a few illuminating infrared LEDs which illuminate the arms/hands of the trainees. The infrared cameras are spaced apart so as to provide depth (i.e. distance above the sensor) perspective. The infrared sensor 102 and the side view camera 101 are both connected to the computer; see para. [0018] The sensor 102 utilizes dual infrared cameras to track the position of the hands and arms. The cameras are separated to provide for depth perception utilizing the differences between the two camera images, similar to what human eyes do. The sensors look only at the infrared wavelength, and the arms-hands are illuminated by three infrared LEDs in line with the cameras. To keep power within the minimum amount that a USB power supply can provide, the LED's are pulsed and matched to the camera's frame rate. The sensor communicates with the training computer which identifies more than 21 points for each hand. Individual fingers are recognized including finger digits. One point represents the Palm of the hand, one point for the wrist, and 4 points for each arm. A rectangular projection of the arms approximates the Ulna and Radius bones with straight lines);
determining a hit on the target; and determining a score of the hit (see para. [0039] FIG. 3 illustrates how a score for the player is identified when a satisfactory shot is made (within a certain distance from target center 301) and within an acceptable hand position (i.e. hand position within hand calibration bubble 302 or shooting width). For example, the score would be 50% for 6 out of 12 shots when both criteria are met. Flexibility to establish an acceptable shot on target region and the size of the hand calibration bubble is part of the training session design. The target camera 107 is used as part of scoring an acceptable shot).
Regarding claim 8, Ghani discloses wherein determining one or more body landmarks includes determining 17 body landmarks (see para. [0018] The sensor communicates with the training computer which identifies more than 21 points for each hand. Individual fingers are recognized including finger digits. One point represents the Palm of the hand, one point for the wrist, and 4 points for each arm. A rectangular projection of the arms approximates the Ulna and Radius bones with straight lines).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7, 9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ghani (US 2020/0072578).
Regarding claim 7, and substantially similar limitations in claim 13, Ghani fails to explicitly disclose wherein receiving the video data includes capturing video data by a mobile phone. However, the Applicant’s use of a mobile phone is an obvious design choice. Applicant has not disclosed that the capturing video data by a mobile phone solves any stated problem or is for any particular purpose. Moreover, it appears that capturing video data using the device of Ghani or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani to obtain the device as specified in claims 7 and 13, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani.
Regarding claim 9, Ghani fails to explicitly disclose wherein tracking the one or more body landmarks includes generating a bounding box around each of the one or more body landmarks. However, the Applicant’s use of generating a bounding box around each of the one or more body landmarks is an obvious design choice. Applicant has not disclosed that generating a bounding box around each of the one or more body landmarks solves any stated problem or is for any particular purpose. Moreover, it appears that generating any style of graphical image for the purpose tracking one or more body landmarks using the device of Ghani or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani to obtain the device as specified in claim 9, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani.
Claims 3, 10, 11 and 14-17 rejected under 35 U.S.C. 103 as being unpatentable over Ghani in view of Zhang, et al. (hereinafter referred to as “Zhang” US 2021/0174700).
Regarding claim 3, Ghani does not explicitly disclose wherein associating the shot motion data with the score comprises executing a classification and regression tree machine learning model to identify causal relationship between the shot motion data and the score.
However, Zhang teaches executing a classification and regression tree machine learning model to identify causal relationship between data (see para. [0115] In particular, a supervised machine learning algorithm is shown, comprising an illustrative random forest algorithm. Random forest algorithms are a method for classification and regression. By using a multitude of decision tree predictors 504, each depending on the values of a random subset of a training data set 502, the chances of overfitting to the training data set may be minimized. The decision tree predictors are voted or averaged at a decision step 506 to obtain predictions 508 of the random forest algorithm. For the task of object recognition, input 502 to the machine learning algorithm may include feature values, while output 508 may include predicted gestures and/or poses associated with a user. Random forest is only one illustrative machine learning algorithm that is within the scope of the present invention, and the present invention is not limited to the use of random forest. Other machine learning algorithms, including but not limited to, nearest neighbor, decision trees, support vector machines (SVM), Adaboost, Bayesian networks, various neural networks including deep learning networks, evolutionary algorithms, and so forth, are within the scope of the present invention.
see para. [0064] It would be understood by persons of ordinary skill in the art that training or performance training activities discussed in this disclosure broadly refer to any physical exercise, workout, drill, or practice that improve a user's fitness and skill levels to better his or her ability to perform a given physical activity or sport. Training activities thus disclosed can maintain, condition, correct, restore, strengthen, or improve the physical ability, power, agility, flexibility, speed, quickness, reaction, endurance, and other physical and technical skills necessary for a physical activity or sport. Such a physical activity or sport may be competitive or non-competitive in nature, with or without specific goals or challenges, and may or may not be scored according to specific rules. A user of the system as disclosed herein is referred to as a player, including in non-competitive activities such as rehabilitative physical therapies and occupational therapies. A training session may involve one or more individual players. During a training session, individual skills such as power, speed, agility, flexibility, posture, balance, core strength, upper and lower-body strength, rhythm, swing, stroke, flick, running, stopping, dribbling, juggling, passing, catching, throwing, smashing, tackling, shooting, jumping, sprinting, serving, and goalkeeping may be isolated, broken down into specific movements, and worked upon. Such skills may be inter-dependent. For example, better core strength may lead to better stance and balance, and better body-eye and hand-eye coordination may lead to faster speed, shorter stopping time, and better control of a ball. Some training activities are tailored for specific demands of a particular sport. Embodiments of the present invention may be used for interactive virtual coaching in ball sports as well as other types of sports or physical activities, including but not limited to, basketball, soccer, baseball, football, hockey, tennis, badminton, juggling, archery, softball, volleyball, boxing, canoeing, kayaking, climbing, cycling, diving, equestrian, fencing, golf, gymnastics, handball, judo, karate, modern pentathlon, roller sport, rowing, rugby, sailing, shooting, swimming, surfing, table tennis, taekwondo, track and field, triathlon, water polo, weightlifting, wrestling, squash, wakeboard, wushu, dancing, bowling, netball, cricket, lacrosse, running, jogging, yo-yo, foot bagging, hand sacking, slinky, tops, stone skipping, and many other types of sports, games, and other activities in a similar fashion.
Ghani and Zhang fail to explicitly disclose executing a classification and regression tree machine learning model associating the shot motion data with the score to identify causal relationship between the shot motion data and the score. However, the Applicant’s use of a machine learning model to identify causal relationship between the shot motion data and the score is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that identifying a causal relationship between the shot motion data and the score using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claim 3, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Regarding claim 10, and substantially similar limitations in claim 14, Ghani does not explicitly disclose further comprising executing a machine learning model to correlate the shot motion data with the score. However, Zhang teaches executing a machine learning model (see para. [0115] In particular, a supervised machine learning algorithm is shown, comprising an illustrative random forest algorithm. Random forest algorithms are a method for classification and regression. By using a multitude of decision tree predictors 504, each depending on the values of a random subset of a training data set 502, the chances of overfitting to the training data set may be minimized. The decision tree predictors are voted or averaged at a decision step 506 to obtain predictions 508 of the random forest algorithm. For the task of object recognition, input 502 to the machine learning algorithm may include feature values, while output 508 may include predicted gestures and/or poses associated with a user. Random forest is only one illustrative machine learning algorithm that is within the scope of the present invention, and the present invention is not limited to the use of random forest. Other machine learning algorithms, including but not limited to, nearest neighbor, decision trees, support vector machines (SVM), Adaboost, Bayesian networks, various neural networks including deep learning networks, evolutionary algorithms, and so forth, are within the scope of the present invention.
Ghani and Zhang fail to explicitly disclose executing a machine learning model to correlate the shot motion data with the score. However, the Applicant’s use of a machine learning model to correlate the shot motion data with the score is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that correlating the shot motion data with the score using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claims 10 and 14, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Regarding claim 11, Ghani and Zhang do not explicitly disclose wherein the machine learning model is configured to determine the motion data that results in an off-center target hit. However, the Applicant’s use of a machine learning model configured to determine the motion data that results in an off-center target hit is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that determining the motion data that results in an off-center target hit using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claim 11, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Regarding claim 15, Ghani and Zhang do not explicitly disclose wherein the machine learning model is configured to determine the motion data that results in a reduced score. However, the Applicant’s use of a machine learning model configured to determine the motion data that results in a reduced score is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that determining the motion data that results in a reduced score using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claim 15, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Regarding claim 16, Ghani and Zhang do not explicitly disclose predicting, by the machine learning model, a predicted score based on the motion data. However, the Applicant’s use of a machine learning model configured to predict a score based on the motion data is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that predicting a score based on the motion data using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claim 16, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Regarding claim 17, Ghani and Zhang do not explicitly disclose comparing the predicted score with the score. However, the Applicant’s use of a machine learning model configured to compare the predicted score with the score is an obvious design choice. Applicant has not disclosed that use of a machine learning model solves any stated problem or is for any particular purpose. Moreover, it appears that comparing the predicted score with the score using the device of Ghani and Zhang or the Applicant would perform equally well. Therefore, it would have been prima facie obvious to modify Ghani and Zhang to obtain the device as specified in claim 17, because such a modification would have been considered a mere design consideration which fails to patentably distinguish over the prior art of Ghani and Zhang.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT P. BULLINGTON whose telephone number is (313) 446-4841. The examiner can normally be reached on Monday through Friday from 8 A.M. to 4 P.M. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Peter Vasat, can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free).
/Robert P Bullington, Esq./ Primary Examiner, Art Unit 3715
Prosecution Insights