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 .
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-5, 7-14, and 16-18 of U.S. Patent No. 12,138,552. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations claimed within the current claims are already covered by the patented claims. Examiner has produced a comparison below between the patented claims and the current pending claims.
U.S. Patent No. 12,138,552
Current Claims
1. A method comprising:
generating, via a game analysis platform that includes at least one processor and at least one memory, a training data set based on game data collected from actual players;
training, via the game analysis platform, a primary artificial intelligence (AI) model using machine learning based on the training data set to detect player bots;
gathering, via the game analysis platform, actual game data from game play;
processing the actual game data via the primary AI model to generate primary detection results;
detecting, via the primary AI model, a potential use of a player bot when the primary detection results exceed a detection threshold;
training a secondary AI model to recognize actual players; and
in response to detecting the potential use of the player bot via the primary AI model:
processing the actual game data via the secondary AI model to generate secondary detection results; and
confirming use of the player bot when the secondary detection results indicate the use of the player bot.
2. The method of claim 1, further comprising: in response to detecting the potential use of the player bot:
prompting a user to evaluate the actual game data;
receiving user input regarding the game play; and
wherein confirming the use of the player bot is further based on when the user input indicates the use of the player bot.
3. The method of claim 2, further comprising: identifying a player associated with the game play for disqualification in response to confirming the use of the player bot.
4. The method of claim 2, wherein the method further comprises: updating the training data set when the user input indicates no player bot use.
5. The method of claim 2, wherein prompting the user includes utilizing a graphical user interface.
7. The method of claim 1, wherein the method further comprises: updating the training data set when the secondary AI model indicates an actual player.
8. The method of claim 1, wherein a probability of error of determining the use of the player bot via the secondary AI model is lower than a probability of error of determining the use of the potential player bot via the primary AI model.
9. The method of claim 1, where processing the actual game data via the primary AI model includes processing the actual game data corresponding to a time window.
10. A method comprising:
generating, via a game analysis platform that includes at least one processor and at least one memory, a training data set based on game data collected from actual players;
training, via the game analysis platform, a primary artificial intelligence (AI) model using machine learning based on the training data set to detect cheating software;
gathering, via the game analysis platform, actual game data from game play;
processing the actual game data via the primary AI model to generate primary detection results;
detecting, via the primary AI model, a potential use of cheating software when the primary detection results exceed a detection threshold;
training a secondary AI model to recognize actual players; and
in response to detecting the potential use of cheating software via the primary AI model:
processing the actual game data via the secondary AI model to generate secondary detection results; and
confirming use of the cheating software when the secondary detection results indicate the use of the cheating software.
11. The method of claim 10, further comprising: in response to detecting the potential use of cheating software:
prompting a user to evaluate the actual game data;
receiving user input regarding the game play; and
wherein confirming the use of the cheating software is further based on when the user input indicates the use of the cheating software.
12. The method of claim 11, further comprising: identifying a player associated with the game play for disqualification in response to confirming the use of cheating software.
13. The method of claim 11, wherein the method further comprises: updating the training data set when the user input indicates no use of cheating software.
14. The method of claim 11, wherein prompting the user includes utilizing a graphical user interface.
16. The method of claim 10, wherein the method further comprises: updating the training data set when the secondary AI model indicates an actual player.
17. The method of claim 10, wherein a probability of error of determining the use of cheating software via the secondary AI model is lower than a probability of error of determining the use of the potential use of cheating software via the primary AI model.
18. The method of claim 10, where processing the actual game data via the primary AI model includes processing the actual game data corresponding to a time window.
1. A method comprising:
generating, via a game analysis platform that includes at least one processor and at least one memory, a training data set based on game data collected from actual players;
training, via the game analysis platform, a primary artificial intelligence (AI) model using machine learning based on the training data set to detect cheating;
gathering, via the game analysis platform, actual game data from game play;
processing the actual game data via the primary AI model to generate primary detection results;
detecting, via the primary AI model, potential cheating when the primary detection results exceed a detection threshold;
training a secondary AI model to recognize actual players; and
in response to detecting the potential cheating via the primary AI model:
processing the actual game data via the secondary AI model to generate secondary detection results; and
confirming cheating when the secondary detection results further indicate cheating.
2. The method of claim 1, further comprising: in response to detecting the potential cheating:
prompting a user to evaluate the actual game data; and
receiving user input regarding the game play.
3. The method of claim 2,
wherein confirming the cheating is further based on when the user input indicates the cheating.
4. The method of claim 3, further comprising: identifying a player associated with the game play for disqualification in response to confirming the cheating.
7. The method of claim 1, further comprising: identifying a player associated with the game play for disqualification in response to confirming the cheating.
5. The method of claim 3, wherein the method further comprises: updating the training data set when the user input indicates no cheating.
6. The method of claim 3, wherein prompting the user includes utilizing a graphical user interface.
8. The method of claim 1, wherein the method further comprises: updating the training data set when the secondary AI model indicates no cheating.
9. The method of claim 1, wherein a probability of error of determining the cheating via the secondary AI model is lower than a probability of error of determining the cheating via the primary AI model.
10. The method of claim 1, where processing the actual game data via the primary AI model includes processing the actual game data corresponding to a time window.
11. A game platform comprising: at least one processor; and a memory that stores operational instructions that, when executed by the at least one processor, cause the at least one processor to perform operations that include:
generating a training data set based on game data collected from actual players;
training a primary artificial intelligence (AI) model using machine learning based on the training data set to detect cheating;
gathering actual game data from game play;
processing the actual game data via the primary AI model to generate primary detection results;
detecting, via the primary AI model, potential cheating when the primary detection results exceed a detection threshold;
training a secondary AI model to recognize actual players; and
in response to detecting the potential cheating via the primary AI model:
processing the actual game data via the secondary AI model to generate secondary detection results; and
confirming cheating when the secondary detection results further indicate cheating.
12. The game platform of claim 11, wherein in response to detecting the potential cheating, the operations further include:
prompting a user to evaluate the actual game data; and
receiving user input regarding the game play.
13. The game platform of claim 12,
wherein confirming the cheating is further based on when the user input indicates the cheating.
14. The game platform of claim 13, wherein the operations further include: identifying a player associated with the game play for disqualification in response to confirming the cheating.
17. The game platform of claim 11, wherein the operations further include: identifying a player associated with the game play for disqualification in response to confirming the cheating.
15. The game platform of claim 13, wherein the operations further include: updating the training data set when the user input indicates no cheating.
16. The game platform of claim 13, wherein prompting the user includes utilizing a graphical user interface.
18. The game platform of claim 11, wherein the operations further include: updating the training data set when the secondary AI model indicates no cheating.
19. The game platform of claim 11, wherein a probability of error of determining the cheating via the secondary AI model is lower than a probability of error of determining the cheating via the primary AI model.
20. The game platform of claim 11, where processing the actual game data via the primary AI model includes processing the actual game data corresponding to a time window.
Claim Rejections - 35 USC § 103
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 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cox et al (U.S. 10,603,593) in view of Niknafs et al (U.S. 11,253,785).
Regarding claims 1 and 11, Cox discloses:
a method (1:64-2:31, system for automatically reducing cheating in an online game environment) comprising:
generating, via a game analysis platform (11:5-28, Fig. 3, server systems 300 which execute GCSD systems 340 that automatically reduce cheating in an online game), that includes at least one processor (11:5-28, Fig. 3, server computing system 300 includes one or more hardware processors 305) and at least one memory (11:5-28, Fig. 3, server computing system 300 includes storage 320 and memory 330), a training data set based on game data collected from actual players (3:8-56, 4:39-43, training data is gathered that includes examples of gameplay actions and is used to train a model to recognize gameplay actions as being authorized or not);
training, via the game analysis platform, a primary artificial intelligence (AI) model using machine learning based on the training data set to detect cheating (5:56-6:10, GCSD Model Training component 105 obtains training data 160, including gameplay actions with labeled cheat software detection decisions, and uses the training data to train one or more models 165 and 170a for subsequent use in determining authorization of gameplay actions based on cheat software detection);
gathering, via the game analysis platform, actual game data from game play (6:11-49, the trained models 170a are made available for use in subsequent activities, for example users 115 interact with online gaming environments 120 performing various activities and producing candidate gameplay actions 185 to be assessed);
processing the actual game data via the primary AI model to generate primary detection results (6:50-7:27, candidate gameplay actions 185 are supplied as input to one or more of the trained models 170a and a determination is made whether the gameplay actions are authorized or not);
detecting, via the primary AI model, potential cheating when the primary detection results exceed a detection threshold (6:50-7:27, if the likelihood of actions being unauthorized exceeds an upper threshold the system may impose a penalty);
training a secondary AI model (3:8-56, 6:50-7:27, one or more additional models may be trained and used, including automated systems); and
in response to detecting the potential cheating via the primary AI model (6:50-7:27, the first trained model 170a determines likelihood of actions being unauthorized exceeds an upper threshold):
processing the actual game data via the secondary AI model to generate secondary detection results (6:50-7:27, actions identified by trained model 170a as being potentially unauthorized are supplied as input to one or more of the additional trained models 165 and a determination is made whether the gameplay actions are authorized or not); and
confirming cheating when the secondary detection results further indicate cheating (6:50-7:27, the additional trained model processes the input and generates a decision of whether the actions are unauthorized).
However, Cox does not specifically disclose that:
the secondary AI model is trained to recognize actual players.
Niknafs teaches:
a system for detecting whether a player is engaging in cheating by using a bot during play of an online game (abstract), wherein an AI model is trained to recognize actual players (7:50-8:31, 10:48-11:8, Fig. 5, the bot detection systems 140 are configured to train the bot detection models 152, wherein the detection models are used to determine if a human player is playing a game or if a bot is being utilized).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to integrate the secondary machine learning bot detection model trained to distinguish human players from bots, as taught by Niknafs, as the secondary AI model of Cox in order to verify primary alerts with a secondary human recognition model, thereby reducing false-positive cheating detections and improving the overall anti-cheating system accuracy.
Regarding claims 2 and 12, Cox discloses that which is discussed above, and further discloses that:
in response to detecting the potential cheating:
prompting a user to evaluate the actual game data (3:8-56, 9:6-43, if the trained model determines a particular gameplay action is not authorized the system initiates a referral for a review by one or more humans (e.g., other users of the online game environment)); and
receiving user input regarding the game play (3:8-56, 9:6-43, receiving user input from the human reviewer indicating whether the gameplay action is authorized or unauthorized).
Regarding claims 3 and 13, Cox discloses that which is discussed above, and further discloses that:
confirming the cheating is further based on when the user input indicates the cheating (3:8-56, 9:6-43, the cheating is confirmed when input from the human reviewer is received and indicates detection of cheating).
Regarding claims 4 and 14, Cox discloses that which is discussed above, and further discloses:
identifying a player associated with the game play for disqualification in response to confirming the cheating (18:24-67, if the human reviewer confirms the gameplay action is cheating the player is penalized).
Regarding claims 5 and 15, Cox discloses that which is discussed above, and further discloses:
updating the training data set when the user input indicates no cheating (3:8-56, 14:42-15:2, decisions by the secondary AI model are used as training data to retrain the primary AI model).
Regarding claims 6 and 16, Cox discloses that which is discussed above, and further discloses that:
prompting the user includes utilizing a graphical user interface (7:28-63, reviews are conducted by other users utilizing the same online game environment which the gameplay actions took place).
Regarding claims 7 and 17, Cox discloses that which is discussed above, and further discloses:
identifying a player associated with the game play for disqualification in response to confirming the cheating (18:24-67, if the review confirms cheating the system directly imposes penalties to the offending player).
Regarding claims 8 and 18, Cox discloses that which is discussed above, and further discloses that:
the method further comprises: updating the training data set when the secondary AI model indicates no cheating (3:8-56, 14:42-15:2, decisions by the secondary AI model are used as training data to retrain the primary AI model).
Regarding claims 9 and 19, Cox discloses that which is discussed above, however, does not specifically disclose that:
a probability of error of determining the cheating via the secondary AI model is lower than a probability of error of determining the cheating via the primary AI model.
Niknafs teaches:
a system for detecting whether a player is engaging in cheating by using a bot during play of an online game (abstract), wherein the system tests individual AI detection models to measure their error rates (i.e., false positives and false negatives) and weight and select higher-performing models with lower error probabilities to prevent false positives (10:60-11:30, the bot detection models are weighted proportional to their relative performance in bot detection in different online games).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to configure the secondary AI verification model of the multi-tiered verification scheme, as taught by Cox, to have a lower probability of error than the primary AI model, as taught by Niknafs, in order that preliminary cheat flags are confirmed with high precision before applying penalties.
Regarding claims 10 and 20, Cox discloses that which is discussed above, and further discloses that:
processing the actual game data via the primary AI model includes processing the actual game data corresponding to a time window (16:35-17:3, game play data is processed as the actions occur or immediately afterwards (i.e., in real-time)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The Applicant is directed to the attached "Notice of References Cited" for additional relevant prior art. The Examiner respectfully requests the Applicant to fully review each reference as potentially teaching all or part of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON PINHEIRO whose telephone number is (571)270-1350. The examiner can normally be reached M-F 8:00A-4:30P ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached on (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jason Pinheiro/Examiner, Art Unit 3715
/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
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