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 .
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 an abstract idea without significantly more. The claim(s) recite(s) a method comprising: receiving, by one or more processors, one or more sets of play data for a football specialist, each set of play data comprising data descriptive of one or more of a process and an outcome of a football action performed by the football specialist; applying, by the one or more processors, an evaluative model to the one or more sets of play data for the football specialist; and outputting, by the one or more processors and based on the application of the evaluative model, one or more evaluations for the football specialist, each evaluation indicating a skill level of a respective attribute for the football specialist which is a mental process, that is all the limitation above could be done by a scout or coach viewing live play or a video of play and making an evaluation in their mind and or using pen and paper.
This judicial exception is not integrated into a practical application because the claims do not recite additional elements that would integrate the abstract idea into a practical application. The recited “sensors” would read on viewing a video of play from a camera and are consequently are held to be conventional data gathering and insignificant extra-solution activity. There is no improvement made to computer technology since the claims are generating an evaluation of an athlete by an observer. This is not related to a long standing problem in computer technology. Additionally, there is no practical application as there is no particular machine that is used to implement the claim language only generic computer components are used to perform the invention. Also, there is no transformation of the machine used in the application into a different state or thing. Lastly, the claims do not attempt to apply the abstract idea in a meaningful way beyond simply using the claimed machine.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims does not recite significantly more than a generic information processing machine consisting of one or more processors, a non-transitory memory and an evaluative model. However, each of these components are well-known and understood within the sports monitoring art. Therefore, the claim is directed to an abstract idea that lacks significantly more and thus is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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) 1-7, 12-14 and 16-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Thurman et al. (pub. no. 20140135956).
Regarding claim 1, Thurman discloses a method comprising: receiving, by one or more processors, one or more sets of play data for a football specialist each set of play data comprising data descriptive of one or more of a process and an outcome of a football action performed by the football specialist (“In one implementation, football travel parameter module 460 directs processor 26 to determine or identify at least one football event by comparing at least one attribute of the football, based upon signals received from sensor 252 or derived from such signals, to one or more predetermined signature characteristics of different football events. For purpose of this disclosure, a "football event" is one or more particular action of the football with respect to one or more of a playing field, a player or goalpost. Examples of different individual "football events" include but are not limited to, an under center snap of the football; a shotgun/quick snap of the football; a multi-step drop back with the football; a handoff of the football; a pass release of the football; pass flight of the football; a catch of the football; a drop of the football; a fumble of the football; an initiation of a pass of the football; a run with the football; a punt of the football; initial ground impact of the football; a kickoff of the football; and an onside kick of the football”, [0086]);
applying, by the one or more processors, an evaluative model to the one or more sets of play data for the football specialist (“In one implementation, the one or more predetermined signature characteristics of different football events are stored in event signal storage 462. Such football event signatures comprise distinct sets of ball travel parameters or characteristics associated with each different football event. For example, an under center snap of a football may be associated with one or more distinct acceleration characteristics over time as compared to acceleration characteristics over time of the steps taken by a quarterback during a multi-step drop following the snap, as compared to acceleration characteristics over time of the initiation of a pass (when the quarterback or thrower begins to cock his or her arm prior to a throw), and the like. In some implementations, signature characteristics for an event may comprise unique sets or groups of multiple football travel parameters. For example, different football events may be distinguished from one another based upon a combination of two or more of a sensed acceleration of the football, a sensed internal pressure of the football, a sensed height of the football, a sensed speed/velocity of the football, a sensed spin of the football, a sensed rotation of the football using gyro sensed information, a sensed movement of the football using magnetometer sensed information, and combinations thereof.
Pattern recognition through the use of a neural network or a machine learning techniques can be employed to determine complicated motion or timing events involving the football and an act or event with the football, such as football event signatures. In one implementation, such football event signatures are obtained by sports performance system 420 through use of a "neural network" in which the football event signatures are identified or learned through the analysis of multiple calibration football events. For example, multiple football events with football 450 may be sensed and stored, wherein processor 26, following instructions contained in football travel parameters module 460 or another set of computer code, compares one or more of the sensed ball travel parameters (acceleration values, spin, orientation, height, velocity composition over a period of time) with the known identity of each football event to associate each known football event with a specific football event signature comprising a group of one or more of the sensed ball travel parameters. Such football event signatures are stored for subsequent use in identifying subsequent football events. Neural network can also be referred to as machine learning. A neural network is a form of pattern recognition, and can involve analysis of multiple events or variables occurring over time.
In one implementation, module 460 may utilize the identification of the initiation of a football pass (the cocking of the arm) and the identification of a pass release to track a quarterback pass release time (a quick release) for display, comparison or coaching. For display or communication purposes, the term "pass release" includes the upward and/or rearward movement of the player's arm in "cocking" or drawing back his or her arm to initiate a pass and the forward and/or upward movement and/or extension of the player's arm to launch or impart acceleration and/or spin onto the ball as it releases from the player's throwing hand. In yet another implementation, module 460 may utilize the identification of a punt of the football and an identification of either a catch of the football or a ground impact of the football to determine, display and/or record hang time of the football for the punt. In one implementation, module 460 may utilize the identification of football drops and football catches to track, display and store pass completion percentages for analysis, comparison between players, training and game use (as described above)“, [0087] – [0089]);
and outputting, by the one or more processors and based on the application of the evaluative model, one or more evaluations for the football specialist, each evaluation indicating a skill level of a respective attribute for the football specialist (“Once football travel parameter module 460 has identified or determined one or more football events, module 460 directs processor 26 to output graphics, information, lights, sound or other indicators based upon and/or utilizing the determined or identified football events. In one implementation, module 460 cooperates with display module 239 to display graphics representing the one or more football events by displaying a simulation of football 450 experiencing or undergoing the one or more football events. In one implementation, the timing, distances and/or positioning of the football in the graphical simulation are based upon football travel parameters received from sensor 252 of football 450.
In one implementation, module 460 stores and displays different data based upon identified football events in the timing of such identified football events for evaluation, comparison and/or training. For example, by identifying a snap of a football, module 460 may also identify the time elapsed from the identified snap to a second football event such as a punt, kick or pass of the football. By identifying a cocking of a football (a first football event) and the past release or launch of the football (a second football event), module 460 may identify the time elapsed to determine a quarterback release time or quick release for storage, display and/or comparison/training purposes. By identifying a snap of the football and receipt of the snap football by holder, punter or quarterback (during a quick snap or shotgun snap), the quality of the long snap may be stored, displayed and evaluated by module 460. By identifying when the football initially impact the ground following a kickoff for punt and by identifying each bounce of the football as well as a velocity and spin of football, model 460 made determine and display a travel distance of the football following the determined initial ground impact. Such a determination may facilitate training for kickoffs and onside kicks. As will be described below, the spiral efficiency of such long snaps may further be evaluated, displayed and compared by module 460. The present system provides the ability for a player, coach, team or organization to analyze one or more football events in a variety of different ways, simply, accurately, and comprehensively to evaluate a practice, an exercise, an in game play, or other football event(s). Additionally, the present system can be used to identify what event or events occurred to the football. In other words, a player could pick up the football and perform a series of football events, and the system can determine what the football event or events were based upon the signature trace. For example, the system can be configured to communicate that the football was just snapped, thrown and caught by a receiver. The system can also communicate more details such as the duration of each event or combination of events”, [0100] & [0101];
“As shown by FIG. 19, processor 26, following instructions contained in memory 428 provides a user (David P. in the example) with the options to learn 1500, perform 1502, stats 1504 or compare 1506. As further shown by FIG. 19, under the learn tab or option 1500, the user is further provided with the option to select categories of punt, pass or kick. Each of such selections can be made using a touchscreen or may be made using a keyboard, touchpad or other input device. As shown by FIG. 19, under the perform option, the user is further provided with the option to select categories of punt, pass and kick. As shown by FIG. 19, similar categories are provided under the option of stats”, [0122];
“Upon completion of the kick sample, processor 26 displays the ball travel parameters. In the example illustrated, the data collected comprises launch angle, speed, spin and direction of the football. As shown by FIG. 23, processor 26 prompts the user to indicate whether or not the particular field-goal kick attempt was successful by selecting either the make 1526 or miss 1528 inputs. In other implementations, the screenshot of FIG. 23 may be omitted where processor 26 determines whether or not the field-goal attempt was successful based upon the received values for the ball travel parameters, the environmental conditions, the field position and the field-goal length. In some implementations, processor 26 may indicate on display 22 at what distance the field-goal attempt would've been successful, or at what distances the field-goal attempt would not have been successful. The processor may indicate with what types of field-goal post the kick would've been successful or unsuccessful. This may be beneficial in those circumstances where the kick attempt is being made without actual field-goal posts. As shown by FIGS. 22 and 23, processor 26 displays the outcome. As shown by FIG. 22, processor 26 further presents a graphic 1530 depicting the trajectory of the football during the field-goal kick attempt. As shown by FIG. 22, processor 26 may present on display 22 a graphic 1532 indicating a rotation of the ball during the kick. As shown by FIG. 24, processor 26 may further display on display 22 a side view of the ball trajectory. Similar presentations may be made with the field-goal attempt is indicated to be wide left, wide right or short”, [0127];
“FIG. 38 illustrates a screenshot presented on display 22 by processor 26 in response to the user selecting the compare option. In the example shown in FIG. 38, the user is presented with ranking information and all-time high scores for a particular kick accuracy (or for other ball travel parameters) with respect to other users. Such users may be a select group of friends or those in a league. In one implementation, such accuracy or ball travel parameter values may also be compared to accuracies or ball travel parameters of celebrities”, [0134];
“Celebrity storage 34 comprises that portion of processor 26 in which data pertaining to travel of the ball imparted by a celebrity in the sport is stored. For purposes of this disclosure, a "celebrity" shall mean a person who has attained notoriety for his or her performance in the sport. Examples of such celebrities include professional athletes, college athletes, Olympians and athletes who have acquired notoriety due to their skill level. Although celebrity storage 34 is illustrated as being part of processor 26 which also includes user storage 32 for storing user data pertaining to travel of the ball, in other implementations, celebrity storage 34 may be located remote of processor 26. For example, celebrity storage 34 may be alternatively provided at a remote server which may be accessed across a local or wide area network”, [0060]).
Regarding claim 2, Thurman discloses the football specialist comprises a kicker (“In other implementations, the accuracy for other targets may be determined by target accuracy module 134 and displayed by display model 136. For example, other targets in football include, not limited to, a receiver to catch the football at a particular location on the football field and at a particular distance from the person throwing the ball or a region on the field at which the ball lands following a kick or punt. In some implementations, such predictions may be determined without a receiver actually catching the football or prior to the ball actually landing at the region on the field. For example, a person may throw, kick or punt the football into a wall, screen, net or other obstruction, wherein target accuracy module 134, using signals from sensor 252 carried by the football, to predict the ultimate travel path such as distance, height, spin and/or trajectory of the football in the hypothetical absence of the obstruction to predict whether or not the passing, kicking or punting objectives or target would be met. As a result, target accuracy module 134 allows a person to practice passing, kicking and/or punting in a relatively confined area, yet see predicted results as if the person had been practicing on a complete football field, with goalposts and with receivers”, [0066]).
Regarding claim 3, Thurman discloses each set of play data comprises one or more of: kickoff distance; distance deviation from a middle of a football field; a touchback indication; a kick hangtime; a ball flight speed; a ball contact angle; a field location of the kick; a clean maximum weight; a broad jump length; a kicker height; a kicker weight; a kicker grade point average; and a landed ball field location ([0066]).
Regarding claim 4, Thurman discloses each set of play data comprises one or more of: distance of kick; one of a make indication or a miss indication; a distance deviation from a center of a goal post opening; an indication of whether the ball was held on a block or directly on a field; a field location of the kick; a kick apex; a clean maximum weight; a broad jump length; a kicker height; a kicker weight; a kicker grade point average; and a ball height at a line of scrimmage ([0066]).
Regarding claim 5, Thurman discloses the football specialist comprises a punter, and wherein each set of play data comprises one or more of: a punt distance; a deviation from an intended location; a hangtime; a punt apex; a touchback indication; a time from a snap to a kick; a landed ball field location; a field location of the punt; a ball flight speed; a ball contact angle; a spin direction; a clean maximum weight; a broad jump length; a punter height; a punter weight; a punter grade point average; and a rotation speed (“In yet another implementation, module 460 may utilize the identification of a punt of the football and an identification of either a catch of the football or a ground impact of the football to determine, display and/or record hang time of the football for the punt”, [0089]).
Regarding claim 6, Thurman discloses the football specialist comprises a long snapper, and wherein each set of play data comprises one or more of: a snap accuracy; a snap precision; a ball speed; a time from a snap to a ball making contact with an intended target; a time from the snap to taking a blocking stance; a spiral tightness; a location of laces of the ball after being caught by the intended target; a 40-yard dash time; an agility measurement; a body height; a body weight; a bench press maximum; a clean maximum weight; a broad jump length; a long snapper grade point average; and a squat maximum (“In one implementation, module 460 stores and displays different data based upon identified football events in the timing of such identified football events for evaluation, comparison and/or training. For example, by identifying a snap of a football, module 460 may also identify the time elapsed from the identified snap to a second football event such as a punt, kick or pass of the football. By identifying a cocking of a football (a first football event) and the past release or launch of the football (a second football event), module 460 may identify the time elapsed to determine a quarterback release time or quick release for storage, display and/or comparison/training purposes. By identifying a snap of the football and receipt of the snap football by holder, punter or quarterback (during a quick snap or shotgun snap), the quality of the long snap may be stored, displayed and evaluated by module 460. By identifying when the football initially impact the ground following a kickoff for punt and by identifying each bounce of the football as well as a velocity and spin of football, model 460 made determine and display a travel distance of the football following the determined initial ground impact. Such a determination may facilitate training for kickoffs and onside kicks. As will be described below, the spiral efficiency of such long snaps may further be evaluated, displayed and compared by module 460”, [0101]).
Regarding claim 7, Thurman discloses calculating, by the one or more processors, statistical data based on the one or more sets of play data, wherein the statistical data comprises one or more of an average, a variance, or a percentage of any type of data point in the one or more sets of play data (“FIG. 26 illustrate an example screenshot presented by processor 26 on display 22 in response to the user selecting detail icon 1612 (shown in FIG. 25) for launch angle data 1600. As shown by FIG. 26, in response to selection of interface icon 612 associated with launch angle data 1600 (shown in FIG. 25), processor 26 presents on display 22 data regarding launch angle of the kick attempts and compares such data with objective or goal launch angles. In the example illustrated, in response to receiving signals indicating that the screen of FIG. 26 has been clicked upon, processor 26 advances through a series or progression of different presentations regarding information about launch angle data. FIGS. 26 and 27 illustrate an example presentation of data by processor 26 which allows a person to choose amongst several different yardages for field-goal kicks so as to visibly ascertain the average launch angle and trajectory for kicks at the chosen distance and compare such launch angles/trajectories with respect to goal launch angles/trajectories for the particular distance”, [0129]).
Regarding claim 12, Thurman discloses controlling, by the one or more processors, one or more sensors to collect each set of play data while the play is occurring, the one or more sensors being installed in one or more of a football used in the play, a field on which the play occurs, pylons used to mark the field, and goalposts installed on the field (“Ball sensing system 240 provides signals or data through input 24 regarding one or more parameters pertaining to travel imparted to a ball by the user. Ball sensing system 240 comprises a ball 250, a sensor 252 and a transmitter 254. Ball 250 comprises a physical ball to which travel or motion is imparted directly or indirectly by the user. Examples of ball 250 include, but are not limited to, footballs, basketballs, golf balls, volleyballs, arrows, hockey pucks, baseballs, soccer balls, bowling balls, kick balls, tennis balls and the like.
Sensor 252 comprises one or more sensors carried by ball 250 to sense one or more travel parameters of ball 250. Examples of sensor 252 include, not limited to, micro-electromechanical sensors (MEMS), an accelerometer, a magnetometer, a gyro, a 9 degrees of freedom or motion sensor, a 6 degrees of freedom or motion sensor, pressure sensor, active RFID, passive RFID, temperature sensor, near field sensor, strain gauge, load sensor, and the like, and combinations thereof. In some implementations, sensors 252 and include a global positioning system (GPS) sensor or other presently known or future developed sensors. Examples of travel parameters that may be sensed by the one or more sensors 252 include, but are not limited to, the speed (velocity and acceleration/deceleration) of the ball as it travels, the launch angle of the ball, the trajectory of the ball, the distance traveled by the ball, the spin or rotation of the ball, and the like”, [0068] & [0069]).
Regarding claim 13, Thurman discloses receiving, by the one or more processors, indications of user input entering the data contained in each set of play data (”In one implementation, module 460 is configured to allow or prompt a user to input various settings, varying what information, such as what data is presented, the number of plays presented, how such plays and events are graphically distinguished from one another upon the selection of a particular event on the graphical user interface formed by football field 1100 and the presented plays. In this manner, module 460 facilitates evaluation of an entire possession of the football by a team or a longer period of time such as a quarter, half or entire game”, [0111]).
Regarding claim 14, Thurman discloses the football specialist comprises a first football specialist in a plurality of football specialists, and wherein the method further comprises: ranking, by the one or more processors, the plurality of football specialists based on the one or more evaluations for each respective football specialist ([0134]).
Claim 16 is directed to an article of manufacture containing code that implements the method of claim 1 and is rejected for the same reasons as claim 1.
Claim 17 is directed to a system that implements the method of claim 1 and is rejected for the same reasons as claim 1.
Regarding claim 18, Thurman discloses the one or more sensors comprise one or more of: a camera, a motion sensor, an accelerometer, a gyroscope, a magnetic sensor, a photoelectric sensor, a radar sensor, a lidar sensor, and a proximity sensor ([0069]).
Regarding claim 19, Thurman discloses the one or more sensors are installed in one or more of: a football used in the play, a field on which the play occurs, one or more pylons used to mark the field, and one or more goalposts installed on the field ([0068]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAWRENCE STEFAN GALKA whose telephone number is (571)270-1386. The examiner can normally be reached M-F 6-9 & 12-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Lewis can be reached at 571-272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAWRENCE S GALKA/Primary Examiner, Art Unit 3715