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
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character step “240” has been used to designate both discriminating step and validating step in Fig.3. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 38-57 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 38 and 57 claim training of a generative adversarial network (GAN) using a generator and discriminator and validating correctness as being supported in the specification “Fig. 2 presenting a schematic diagram of algorithm 150 of generative adversarial network (GAN). Algorithm 150 is combined with neural networks 60 and 90. Generative neural network 60 handles with prestored datasets 50 of locations of all players in the field in the past 10 seconds and generates the datasets 70 relating to predicted locations of the same players in the next 5 seconds. Numbers withing the diagram boxes refer to the dataset format handled in the present algorithm. Discriminative neural network 90 discriminates between predicted datasets 70 and really obtained datasets of players' locations generated candidate datasets from the true data distribution. The discrimination result is signed by numeral 95” (p. 7). Nevertheless, this is not enough disclosure with respect to the details on the structures of “Generative neural network 60” and “Discriminative neural network 90” (are they CNN, RNN, or any other type of NN for time-series data?), nor there is any structure on the “validating correctness” step to train the parameters for “Generative neural network 60” and “Discriminative neural network 90,” to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Thus claims 38-57 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 38-57 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 38-57 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, as being analyzed above. Since there are no clear disclosure on the structures of “Generative neural network 60” and “Discriminative neural network 90” and “validating correctness” step, these claim limitations rendering the claim limitation indefinite.
Claims 38 and 47 also recites the limitation "said real-time positions". There is insufficient antecedent basis for this limitation in the claim.
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 38-57 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses a system (i.e., machines) in claims 38-46 and a method (i.e., process) in claims 47-57, which are clearly directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “systems and methods for short-term prediction and, more particularly, to systems and methods for predicting and analysis through scenarios of sports games.” Claim 38 recites the following (with emphasis added):
Claim 38: A computer-implemented system for assisting a sports game analyst, comprising:
a. a user interface operable to interact with a user;
b. a memory storing records of positions of sports game players and game object within a playing ground; said memory comprises personal records of said sports game players;
c. a processor cooperatively operable with said user interface and said memory; said processor configured for performing an artificial intelligence (Al) algorithm, said Al algorithm comprising a generative adversarial network algorithm trained by steps of:
i. inquiring records of positions of said sports game players and game object within said playing ground for a first predetermined period of time;
ii. generating successive probable positions of said sports game players and game object within said playing ground for a second predetermined period of time within said first predetermined period of time;
iii. discriminating between corresponding generated probable positions and said records; and
iv. validating correctness of said generated probable positions relative to said real-time positions; and
d. a sensor configured for detecting and transmitting real-time positions of said sports game players and game object within said playing ground to said processor; said sensor is selected from the group consisting of a radar, an optical control sensor, a GPS wearable, an RFID beacon, and any combination thereof;
wherein said processor is configured for inquiring real-time positions of said sports game players and game object and predicting future positions of said sports game players and game object within said playing ground by performing said Al algorithm.
The bold and underlined portions of claim 38 encompass the abstract idea, which is also encompassed by the dependent claims 39-46, and substantially also encompassed by claims 47-57.
Claims 38 and 47 recite the steps for sports game players data collection and training AI model based on the collected data; and further using the trained AI model for prediction. Without any details on the technology implementation, under the broadest reasonable interpretation, it can be mere data collection and data analysis performed by human mind to analyze data and make prediction. These limitations, when given their broadest reasonable interpretation, are directed to certain methods of organizing human activity and mental processes.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on a computer, or merely using a computer as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the present claims include the additional elements other than the abstract idea which include a computer comprising a processor, a memory, and a sensor for collecting data. All these devices as presented, including the communication among them over a generic network connection, are directed to the components amount to merely field of use type limitations and/or extra solution activity to provide a computer implementation of data collection and data analysis for providing predictions. These additional elements to carry out these routine steps does not make the claim any less abstract. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The additional element(s) does/do not improve the functioning of these devices or provide any improvement to another technology or technical field. The claims do not recite any elements that appear to limit the invention to a particular machine. Thus the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a computer comprising a processor, a memory, and a sensor for collecting data. These additional elements are merely used for insignificant extra-solution activity in which these additional elements are related to acquiring the data and performing model predictions. Use of a machine or apparatus that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would weigh against eligibility. See Bilski, 138 S. Ct. at 3230 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, ___ (1978)), and Cybersource v. Retail Decisions, 654 F.3d 1366, 99 USPQ2d 1690 (Fed. Cir. 2011). Thus the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are generally linked to implement an abstract idea on a generic computer. When looked at individually and as a whole, the claim limitations are determined to be an abstract idea without "significantly more," and thus not patent eligible.
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) 38, 40-42, 44-47, 49-53 and 55-57 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tormasov et al. [US20210170229], hereinafter Tormasov, in view of Koochali et al. [Eng. Proc. 2021, 5(1), 40; https://doi.org/10.3390/engproc2021005040 “If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN” Published: 8 July 2021], hereinafter Koochali.
Regarding claim 38, Tormasov discloses a computer-implemented system (Fig. 2) for assisting a sports game analyst, comprising:
a. a user interface operable to interact with a user ([0034], “user input module 206 may be a user interface through which a coach can draw out a game plan through arrows”);
b. a memory storing records of positions of sports game players and game object within a playing ground; said memory comprises personal records of said sports game players ([0047], “The system memory 22 may be any memory for storing data used herein and/or computer programs that are executable by the processor 21” and [0041], “module 208 may be configured to generate a strategic game recommendation for the player, wherein its neural network is trained using historic positional data and associated historic game events that led to successful results (e.g., successful scoring and passing) to identify the opposing players and optimize the chances of the player in achieving his/her intent.”);
c. a processor cooperatively operable with said user interface and said memory; said processor configured for performing an artificial intelligence (Al) algorithm ([0024], “The present disclosure uses artificial intelligence (A.I) and machine learning algorithms to control and analyze situations in a game and generate timely instructions to players on their location on the field in real time”), said Al algorithm comprising algorithm trained by steps of: i. inquiring records of positions of said sports game players and game object within said playing ground for a first predetermined period of time (claim 2, “determining that the game event is occurring further comprises inputting the positional data into a machine learning algorithm configured to classify game events based on player locations over a time period, wherein the machine learning algorithm is trained on a dataset comprising historic positional data and associated historic game events”); and
d. a sensor configured for detecting and transmitting real-time positions of said sports game players and game object within said playing ground to said processor; said sensor is selected from the group consisting of a radar, an optical control sensor, a GPS wearable, an RFID beacon, and any combination thereof ([0005], “images and depth information from a plurality of sensors distributed in an environment of a sports contest” and [0029], “Referring to FIG. 1, four depth sensors acquire image and depth information. The acquired information is then sent to reconstruction module 202. For example, sensor 101 transmits images 101 and data 101. Likewise, sensor 102 transmits images 102 and data 102. Sensor 103 transmits images 103 and data 103. Sensor 104 transmits images 104 and data 104. The information may be transmitted in real time such that images and data depicted in FIG. 3 is for a specific time t1”);
wherein said processor is configured for inquiring real-time positions of said sports game players and game object and predicting future positions of said sports game players and game object within said playing ground by performing said Al algorithm ([0024], “The present disclosure uses artificial intelligence (A.I) and machine learning algorithms to control and analyze situations in a game and generate timely instructions to players on their location on the field in real time” and [0044], “For example, in addition to capturing positional data of player 105, sensors 101-104 may capture additional positional data of at least one other player (e.g., player 106) in a second group of players (i.e., the opposing team). Game recommendation module may determine that the at least one other player is a game obstacle and recommend a game action that player 105 avoids contact with player 106. More specifically, movement prediction module 210 may predict, based on the positional data of player 105, that player 105 will be located at a first location (e.g., (x6, y6, z6) in environment 100 at a subsequent time”).
However, Tormasov does not disclose said Al algorithm comprising a generative adversarial network algorithm trained by steps of: ii. generating successive probable positions of said sports game players and game object within said playing ground for a second predetermined period of time within said first predetermined period of time; iii. discriminating between corresponding generated probable positions and said records; and iv. validating correctness of said generated probable positions relative to said real-time positions.
Nevertheless, Koochali teaches using a generative adversarial network algorithm (title, “Probabilistic Multivariate Times Series Forecast with GAN”) trained by steps of: ii. generating successive probable positions of time series data within a second predetermined period of time within said first predetermined period of time (p. 3 – p. 4, “In this article, we consider Conditional GAN as a method for training a probabilistic forecast model using adversarial training. In this perspective, the generator is our probabilistic model (i.e., ProbCast) and the discriminator provides the required gradient for optimizing ProbCast during training. To learn P(Xt+1|Xt, .., X0), the historical information {Xt, .., X0} is used as the condition of our Conditional GAN and the generator is trained to generate Xt+1. Hence, the probability distribution, which is learned by the generator, corresponds to P(Xt+1|Xt, .., X0), that is, our target distribution”); iii. discriminating between corresponding generated probable positions and said records; and iv. validating correctness of said generated probable positions relative to said records (p. 6, “Finally, we searched for the optimal architecture of the discriminator (Figure 2) and trained the ProbCast. The discriminator concatenated Xt+1 to the end of the input window and constructed {Xt+1, Xt, .., X0}. Then it utilized a GRU block followed by two layers of MLP to inspect the consistency of this window. We used the genetic algorithm to search for the optimal architecture” and p. 7, “Table 2 presents the optimal hyperparameters we found for each dataset using our framework during the experiment”).
Thus, it would have been obvious to one having ordinary skill in the art before the time the invention was effectively filed to have modified the system disclosed by Tormasov, to have the GAN comprising a generator and a discriminator trained with records data, as taught by Koochali, in order to be able to use the probabilistic forecast from GAN to better predict the time series data in a time period time in the near future.
Regarding claim 40, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said generative adversarial network algorithm comprises parameterizing said successive probable positions by applying at least one predetermined sports game technique; said sports game technique is selected from the group consisting of a single lunge, a rabona, a stepover, a Cruyff turn, an inside rollover, a Matthews cut, an ellastico, an around-the-world, a Ronaldo chop and any combination thereof (Tormasov, [0034], “For example, in basketball, an example game plan may be a pick and roll, in which one player serves as a temporary obstacle (called “setting a pick”) for an opposing player while another teammate with the basketball runs past the opposing player. This enables the teammate to either score (because the opposing player was temporarily slowed down from defending), or allows the one player to run towards the basketball hoop (called “rolling”) in an attempt to score while the teammate serves as a distraction. In the latter, the teammate passes the ball to the one player when the one player arrives underneath the basketball hoop and the one player shoots”).
Regarding claim 41, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said personal records are selected from the group consisting of ball control skills, dribbling skills, tackling skills, heading skills, dead ball skills, passing accuracy, body control skills, spatial awareness, tactical knowledge, risk assessment, physical endurance, balance and coordination, speed, and any combination thereof (Tormasov, [0033], “Suppose, that three objects are identified in a given scenario: Lebron James, a basketball hoop, and a basketball. The data structure may be [{Lebron James, (4 meters, 5 meters, 1.5 meters)}, {basketball hoop, (10 meters, 0 meters, 2.7 meters), {basketball, (4 meters, 5.05 meters, 0.5 meters)}]. In some aspects, the output position of a given object describes the center point of the object (e.g., determined by averaging out all depth points on the surface of the object). In other aspects, each object is represented by a basic shape, such as a rectangular prism, to account for the volume of the object. For example, if Lebron James is 2.06 meters tall, 0.91 meters wide, and 0.305 meters in depth, when identifying Lebron James in the output data structure, these dimensions along with the location coordinates of Lebron James may be provided. This is allows for collision prevention when the data structure is provided to game recommendation module 208 as module 208 will not recommend going to a location where an obstacle exists”).
Regarding claim 42, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said generative adversarial network algorithm comprises parameterizing said successive probable positions by applying said personal records of sports game players and selecting a candidate to be a substitute in said sports game (Tormasov, [0033], “Suppose, that three objects are identified in a given scenario: Lebron James, a basketball hoop, and a basketball. The data structure may be [{Lebron James, (4 meters, 5 meters, 1.5 meters)}, {basketball hoop, (10 meters, 0 meters, 2.7 meters), {basketball, (4 meters, 5.05 meters, 0.5 meters)}]. In some aspects, the output position of a given object describes the center point of the object (e.g., determined by averaging out all depth points on the surface of the object). In other aspects, each object is represented by a basic shape, such as a rectangular prism, to account for the volume of the object. For example, if Lebron James is 2.06 meters tall, 0.91 meters wide, and 0.305 meters in depth, when identifying Lebron James in the output data structure, these dimensions along with the location coordinates of Lebron James may be provided. This is allows for collision prevention when the data structure is provided to game recommendation module 208 as module 208 will not recommend going to a location where an obstacle exists”).
Regarding claim 44, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said system is configured for outputting a recommendation to a coach indicating which players to substitute during a match and an optimal team line-up for the coming match (Tormasov, [0040], “Game recommendation module 208 may accordingly determine a fatigue level indicative of how exhausted a player is, as a function of the biometric data. In response to determining that the fatigue level (e.g., 90%) surpasses a threshold fatigue level (e.g., 50%), game recommendation module 208 may recommend calling a timeout and switching the player” and [0041], “recommendation to change the intent/strategy (e.g., to pass the ball instead of shoot), or even a recommendation to exit the sports contest (e.g., go to the bench and switch out with a different player)”).
Regarding claim 45, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said system is configured for modelling a dribbling-and-losing-the-ball game episode performed by one player and predicting an alternative outcome of said episode performed by another player (Tormasov, [0034], “User input module 206 enables an administrative device or a player device to provide a game plan. For example, in basketball, an example game plan may be a pick and roll, in which one player serves as a temporary obstacle (called “setting a pick”) for an opposing player while another teammate with the basketball runs past the opposing player. This enables the teammate to either score (because the opposing player was temporarily slowed down from defending), or allows the one player to run towards the basketball hoop (called “rolling”) in an attempt to score while the teammate serves as a distraction”).
Regarding claim 46, the combination of Tormasov and Koochali discloses the system according to claim 38, wherein said system is configured for modelling a scenario of a team attack if player A plays instead of player B (Tormasov, [0033], “Suppose, that three objects are identified in a given scenario: Lebron James, a basketball hoop, and a basketball. The data structure may be [{Lebron James, (4 meters, 5 meters, 1.5 meters)}, {basketball hoop, (10 meters, 0 meters, 2.7 meters), {basketball, (4 meters, 5.05 meters, 0.5 meters)}]. In some aspects, the output position of a given object describes the center point of the object (e.g., determined by averaging out all depth points on the surface of the object). In other aspects, each object is represented by a basic shape, such as a rectangular prism, to account for the volume of the object. For example, if Lebron James is 2.06 meters tall, 0.91 meters wide, and 0.305 meters in depth, when identifying Lebron James in the output data structure, these dimensions along with the location coordinates of Lebron James may be provided. This is allows for collision prevention when the data structure is provided to game recommendation module 208 as module 208 will not recommend going to a location where an obstacle exists”).
Regarding claim 47, please refer to the claim rejection of claim 38 for the training steps of the AI model. With respect to “inquiring, predicting and outputting for a third predetermined period of time,” Tormasov discloses Fig. 3 which deploys the trained model for inquiring, predicting and outputting for a third predetermined period of time ([0019]).
Regarding claim 49, please refer to the claim rejection of claim 38 and Tormasov, Fig. 3.
Regarding claims 50 and 51, please refer to the claim rejections of claims 40 and 41.
Regarding claim 52, the combination of Tormasov and Koochali discloses the method according to claim 47, further comprising a step of modelling a fake game between rival teams and generating a game outcome on a basis of said personal records of said rival teams (Tormasov, [0041], “wherein its neural network is trained using historic positional data and associated historic game events that led to successful results (e.g., successful scoring and passing) to identify the opposing players and optimize the chances of the player in achieving his/her intent.”).
Regarding claim 53, please refer to the claim rejection of claim 42.
Regarding claims 55-57, please refer to the claim rejections of claim 44-46.
Claim(s) 39, 43, 48 and 54 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tormasov, in view of Koochali, further in view of Hobbs et al. [US11935298], hereinafter Hobbs.
Regarding claim 39, the combination of Tormasov and Koochali discloses the system according to claim 38. However, the combination of Tormasov and Koochali does not disclose wherein said sports game is soccer.
Nevertheless, Hobbs teaches in a like invention, using AI to predict formation in soccer (abstract, “The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data” and col. 2, lines 65-66, “A team's formation is of key interest in continuous sports, but particularly soccer”).
Thus, it would have been obvious to one having ordinary skill in the art before the time the invention was effectively filed to have modified the system disclosed by the combination of Tormasov and Koochali, to have the prediction of team formation for soccer, as taught by Hobbs, in order to be able to let more sports enjoy the benefit of the advanced prediction.
Regarding claim 43, the combination of Tormasov and Koochali discloses the system according to claim 42. However, the combination of Tormasov and Koochali does not explicitly disclose wherein said applying said personal records of sports game players comprises outputting recommended game formation and scenario of a sports game performed by alternative game players characterized by said personal records; said game formation is selected from the group consisting of 4-5-1, 4-3-3, 4-2-3-1, 3-5-2, 4-4-2, 3-4-2, and any combination thereof; said game scenario is selected from the group consisting of a tiki-taka scenario, a park-the-bus scenario, a counter-attack scenario, a high- press scenario, a long-ball scenario, and any combination thereof.
Nevertheless, Hobbs teaches in a like invention, applying said personal records of sports game players comprises outputting recommended game formation and scenario of a sports game performed by alternative game players characterized by said personal records; said game formation is selected from the group consisting of 4-5-1, 4-3-3, 4-2-3-1, 3-5-2, 4-4-2, 3-4-2, and any combination thereof; said game scenario is selected from the group consisting of a tiki-taka scenario, a park-the-bus scenario, a counter-attack scenario, a high- press scenario, a long-ball scenario, and any combination thereof (col. 5, lines 19-33, “For example, given a set of inputs (e.g., team, opponent, ball location, possession, etc.), prediction engine 120 may be configured to predict expected positions of the players. In some embodiments, the expected positions of the players may be parameterized by a set (e.g., a mixture) of n p-dimensional means and (p×p)-dimensional covariances, where n may be representative of the number of mixtures and p may be representative of the number of players. As output, prediction engine 120 may generate an optimal permutation or optimal formation. In some embodiments, prediction engine 120 may also output a semantic label associated with the optimal formation. For example, prediction engine 120 may output “4-4-2 formation,” “4-3-3 formation,” “3-5-2 formation,” “1-3-1” formation,” and the like”).
Thus, it would have been obvious to one having ordinary skill in the art before the time the invention was effectively filed to have modified the system disclosed by the combination of Tormasov and Koochali, to have outputting recommended game formation and scenario characterized by said personal records, as taught by Hobbs, in order to be able to generate customized game formation and scenario specific to certain condition to optimize the result.
Regarding claim 48, please refer to the claim rejection of claim 39.
Regarding claim 54, please refer to the claim rejection of claim 43.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YINGCHUAN ZHANG whose telephone number is (571)272-1375. The examiner can normally be reached 8:00 - 4:30 M-F.
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/YINGCHUAN ZHANG/Primary Examiner, Art Unit 3715