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 Status
Claims 1-20 are pending.
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 grouping of abstract ideas without significantly more. The claims, as exemplified by independent Claim 1, recites limitations directed to a grouping of abstract ideas such as:
1. A security support system comprising at least one processor in communication with at least one electronic gaming device, wherein the at least one processor is configured to:
analyze data transmitted between a game controller of the at least one electronic gaming device and a player tracking interface of the at least one electronic gaming device to identify operational data, the operational data associated with operation of the at least one electronic gaming device; - certain method of organizing human activity and/or mental processes
input the operational data into a machine-learning model, the machine-learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for identifying fraudulent player conduct from predefined normal player conduct; - certain method of organizing human activity;
identify suspected fraudulent player conduct based on an output generated by the machine-learning model based upon the input operational data; and in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed. – certain method of organizing human activity and/or mental process.
The limitations, as indicated above, are found to recite a grouping of abstract ideas because they recite steps and/or instructions for managing a social activity including mitigating and/or hedging associated with fraudulent player conduct and/or mental processes because they recite an observation, judgment, evaluation, and/or opinion (e.g., analyze data; identifying fraudulent player conduct from predefined normal player conduct, mitigating action when identified fraudulent player conduct). For at least these reasons, the claims, as exemplified by independent Claim 1, are found to recite a grouping of abstract ideas under Step 2A-prong 1.
This judicial exception is not integrated into a practical application because the additional limitations such as: “input the operational data into a machine-learning model, the machine-learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for” and “based on an output generated by the machine-learning model” recite mere data gathering and/or insignificant extra solution activity. Furthermore, the machine learning limitations recite resulted oriented functional language that utilizes well-known functions that amount to the way machine learning works such as “inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input” which would be required for any machine learning model (see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. April 18, 2025), pg. 8-9). Therefore the machine learning limitations are found to be a general use of ML in a data environment which is not indicative of an improvement that would integrate the abstract idea into a practical application. Stated differently, the additional limitations are not found to recite an integration into a practical application but directed to mere instructions to invoke a computer as a tool to implement the abstract idea and/or provide a technological environment in which to perform the abstract idea (see MPEP 2106.05(f) and (h)). For at least these reasons, the claims, as exemplified by independent Claim 1, are not found to integrate the claim into a practical application under Step 2A-prong 2.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements such as: “A security support system”, “at least one processor in communication with at least one electronic gaming device” when viewed individually and/or as a collection of elements recite highly-generalize computer components that are invoked as a tool to implement the abstract idea and/or provide a technological environment in which to perform the abstract idea (see MPEP 2106.05(f) and (h)). For instance, the Specification indicates that these are highly-generalized computer components and are similar to Alice v. CLS, in which the additional elements are directed to a common place business security method applied onto highly-generalized computer components known to one of ordinary skill in the gaming arts. It follows that the additional elements, as exemplified by independent Claim 1, are not found to amount to significantly more than the abstract idea under Step 2B.
Regarding independent Claims 10 and 19, the claims recite substantially the same subject matter as independent Claim 1 and the analysis is incorporated herein. The claims are different in that they are directed to a method and the non-transitory computer-readable media embodiments which do not change or alter the analysis above. For at least these reasons, independent Claims 10 and 19 are found to be directed to a grouping of abstract ideas without significantly more.
Regarding dependent Claims 2-9, 11-18, and 20, the additional limitations have been reviewed and were found to recite at least one of: a further limitation of a grouping of abstract ideas (see MPEP 2106.04(a)), invoking a computer as a tool to implement the abstract idea, insignificant extra solution activity, and/or provide a technological environment in which to perform the abstract idea.
For at least the reasons above, claims 1-20 are found to recite a grouping of abstract ideas without significantly more.
Double Patenting
The nonstatutory 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 nonstatutory 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 nonstatutory 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 § 2146 et seq. 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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 on the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, and 17 of U.S. Patent No. US 11,189,130 B2.
Claim 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of US Patent 11,741,782, claims 1-20 of 11,741,783, and claims 1-20 of US Patent 12,475,763.
The claims of the respective patents have been analyzed and are not patentably distinct as shown in the claim chart below.
Claim 1 of the instant application
Claim 1, 8, and 14 of the ’763 Patent
Claims 1-2, and 4 of US Patent 11,741,782
Claims 1, 3, and 17 of US Patent 11,189,130
Claims 1, 8, and 11, of US Patent 11,741,783
Similarities and Differences
A security support system comprising at least one processor in communication with at least one electronic gaming device, wherein the at least one processor is configured to:
A security support device for an electronic gaming device, the security support device comprising:
A security support device installed within or affixed to a cabinet of an electronic gaming machine (EGM), the security support device comprising:
A security support device installed within or affixed to a cabinet of an electronic gaming machine, the security support device comprising:
A security support device installed within or affixed to a cabinet of an electronic gaming machine (EGM), the security support device comprising:
Claim of instant application and the claims of Patent ‘130; ‘782; ‘783; and ‘763 recite substantially the same subject matter.
Differences – Claim 1 of the instant application does not include (EGM) as an acronym for electronic gaming machine.
a communication port of a network communication path located within
a network interface configured to inspect network traffic, and
a first network interface configured to inspect network traffic being generated by one or more components of the electronic gaming machine;
a network interface configured to inspect network traffic; and
Claim 1 of the instant application recites a broader embodiment of ‘130; ‘782; ‘783; and ‘763.
a second network interface configured to communicatively couple with a local area network; and
a security support component comprising at least one processor communicatively coupled with a communication port of a network communication path located within the electronic gaming device and communicatively coupled between a game controller of the electronic gaming device and a player tracking interface of the electronic gaming device, the communication port configured to enable the at least one processor to inspect data sent between the game controller and the player tracking interface without interfering with data transmission between the game controller and the player tracking interface, wherein the at least one processor is configured to:
a security support component communicatively coupled to a network communications path via the network interface and between a game controller of the EGM and a player tracking interface of the EGM, the communicative coupled allowing the network interface to inspect data packets sent between the game controller and the player tracking interface without interfering with packet transmission between the game controller and the player tracking interface, wherein the security support component is configured to:
a security support components communicatively coupled, via the first network interface, to a network communications path between a game controller of the electronic gaming machine and a player tracking interface of the electronic gaming machine, the communicative coupling allows the first network interface to inspect packets sent between the game controller and the player tracking interface without interfering with packet transmission between the game controller and the player tracking interface, the security support component is configured to:
a security support component communicatively coupled to a network communications path via the network interface and between a game controller of the EGM and a player tracking interface of the EGM, wherein the security support component is configured to:
Claim 1 of the instant application recites a broader embodiment of the invention recites by Claims 1 of the ‘130; ‘782; ‘783 and ’763. Each of the claims are directed to a security support component comprising at least a processor that is in communicatively coupled with a game controller and a player tracking interface and configuring the security component to perform operations.
Differences – Claim 1 does not recite the narrower embodiment directed to the particular physical configuration of the security support device and recites the obvious variant of the processor of the security support component.
analyze data transmitted between a game controller of the at least one electronic gaming device and a player tracking interface of the at least one electronic gaming device to identify operational data, the operational data associated with operation of the at least one electronic gaming device;
detect data transmitted between the game controller and the player tracking interface, wherein the data is addressed to at least one of the game controller or the player tracking interface;
read, via the network interface, network packets from the network interface, wherein the network packets are transmitted between the game controller and the player tracking interface and are addressed to at least one of the game controller and the player tracking interface;
read, via the first network interface, network packets from the first network interface, the network packets are transmitted between the game controller of the electronic gaming machine and the player tracking interface and are addressed to one of the game controller and the player tracking interface;
read network packets from the network interface, wherein the network packets are transmitted between the game controller and the player tracking interface;
Claim recites substantially the same data transmitted operational data directed to the game controller and the player tracking interface.
Differences – Claim of the instant application use obvious substitutes to recite the steps of reading the data as opposed to detecting the data.
Input the operational data into a machine-learning model,
input the operational data into a machine-learning model;
extract operational data from the network packets, wherein the operational data is related to the operation of the EGM;
extract one or more components of operation data from the network packets, the operation data related to the operation of the electronic gaming machine;
Claim 1 of the instant application recites substantially the same subject matter.
Claim 1 of the instant application is different that it recites an obvious variant term to identify the data as oppose to extra the data to be used.
the machine learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for identifying fraudulent player conduct from predefined normal player conduct;
input operational data from the network packets to a machine-learning model, wherein the operational data is related to the operation of the EGM;
identify suspected fraudulent player conduct based on an output generated by the machine-learning model based upon the input operational data;,
identify suspected fraudulent player conduct based on an output from the machine-learning model, wherein the output is based on the operational data;
detect fraudulent player conduct based on the operational data;
detect fraudulent player conduct based on the one or more components of operational data; and
detect fraudulent player conduct based on an output from the machine-learning model;
Claim 1 of the instant application recites substantially the same subject matter for identify/detect suspected fraudulent player conduct and are not patentably distinct.
Claims differ in that the instant application recites the obvious variant of identify vs detect using a machine-learning model which is substantially similar to subject matter recited in dependent claims of ‘130, ‘782, and ‘782.
transmit a security alert on the local area network via the second network interface in response to the detected fraudulent player conduct.
in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed.
in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed, wherein the mitigating action comprises at least one of i) disabling the electronic gaming device, ii) generating a security alert, or iii) removing the electronic gaming device from participation in a multiplayer electronic game.
in response to detecting fraudulent player conduct, performing a mitigating action, wherein the mitigating action comprises at least one of i) automatically disabling the EGM or ii) automatically removing the EGM from participation in a multiplayer electronic game.
(Claim 17) The security support device of claim 1, wherein transmission of the security alert causes a mitigating action to be automatically in response to the detected fraudulent player conduct.
in response to detecting fraudulent player conduct, performing a mitigating action, wherein the mitigating action comprises at least one of i) automatically disabling the EGM; or ii) automatically removing the EGM from participation in a multiplayer electronic game.
The claims recite substantially the same subject matter to cause a mitigating action to cause an alert, automatically disabling and/or removing the player from the multiplayer game as recited in ‘782 and ‘783.
(Claim 2) The security support device of Claim 1, wherein the security support device further comprises a second network interface configured to communicatively couple with a local area network, and wherein the security support component is further configured to, in response to detecting fraudulent player conduct, transmit a security alert on the local area network via the second network interface.
(Claim 3) The security support device of claim 1, wherein detecting fraudulent player conduct includes applying the one or more components of operational data as inputs to a machine learned model, the output of the machine learned model identifies fraudulent player conduct.
(Claim 11) The EGM of Claim 8, wherein the security support device further comprises a second network interface configured to communicatively couple with a local area network, and wherein the security support device is further configured to, in response to detecting fraudulent player conduct, transmit a security alert on the local area network via the second network interface.
(Claim 4) The security support device of Claim 1, wherein detecting fraudulent player conduct includes applying the operational data as an input to a machine learned model, wherein an output of the of the machine learned model identifies fraudulent player conduct.
Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims under the obviousness analysis provide an “unjustified timewise extension” of the security support device and are obvious variations of the invention claimed in the US Patent 11,189,130, US Patent 11,741,782, US Patent 11,741,783, and 12,475,763 because they merely re-arranging the corresponding subject matter of the security support device. The claims each recite substantially the same subject matter directed to the security support system communicatively coupled with the game controller and the player tracking interface of the gaming machine to detect fraud and provide alerts and mitigating actions. For at least these reasons, the Applicant is not entitled to a patent for the generic or broader invention without maintaining common ownership and ensuring that the term of the later issued patent will expire at the end of the original term of the earlier issued patent.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Application 18/914,909 (US 2025/0037542 A1) and claims 1-20 of US 12,469,362 in view of Kaizerman (US 2020/0211325 A1).
Although the claims at issue are not identical, they are not patentably distinct from each other because the patentably claims under the obviousness analysis provide an “unjustified timewise extension” of the security support device and recites obvious variations of the invention claims in co-pending application (US Application 18/914,909) and US Patent 12,469,362. The claims recite substantially the same subject matter as being directed to a security support system of the electronic gaming device to identify suspected fraudulent behavior based on the operational data. The differences in the claim amount to inputting information into a machine-learning model that is used to identify the suspected fraudulent behavior. However, the prior art of Kaizerman teaches a machine-learning models that uses operational data of electronic gaming machines to identify suspected fraud to yield the predictable result to improve the user experience and increase user satisfaction (see Kaizerman, 0004, 00335-0037). It follows that the differences in the subject matter between the Claims 1-20 of the instant application incorporate obvious uses of the security support device that would have been known to one of ordinary skill in the gaming arts. For at least these reasons, claims 1-20 of the instant application are not patentably distinct from claims 1-20 of copending Application 18/351,962 and claims 1-20 of US 12,469,362 in view of Kaizerman.
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 1-7, 10-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaizerman (US 2020/00211325 A1).
Regarding claim 1, Kaizerman discloses a security support system comprising at least one processor in communication with at least one electronic gaming device (see Kaizerman, Fig 1-2, 5, 9, 12, 0039, 0075, 0077), wherein the at least one processor is configured to:
analyze data transmitted between a game controller of the at least one electronic gaming device and a player tracking interface of the at least one electronic gaming device to identify operational data (see Kaizerman,210, 230, 250 of Fig. 2, 0039-0043, wherein the analysis is a method for detecting, flagging, or categorizing anomalies or other error conditions in activities or data which include gaming activities of players and/or received outcomes, such as bets and wins, log in times, levels, scores, bonus amounts, and other gaming related player actions and received outcomes (e.g., data transmitted between a game controller of at least one electronic device and the server 110 of system 100); 0044-0047, Data may be collected or obtained from various data sources such as game databases (e.g., as database 130 as presented in Fig. 1) storing the different game related actions and from a client module running on user devices (e.g., user devices 120) which may record actions taken by the player client module and related outcomes; data describing various events which may arrive asynchronously from the various databases and use devices, may be arranged according to player identity number (ID) and sorted by time of occurrence; the time series data may include, for each player ID, a plurality of channels including event values of a particular event type over time; 0066-0068), the operational data associated with operation of the at least one electronic gaming device (see Kaizerman, Fig. 2, 0039-0047, 0050, wherein the operational data is data associated with the outlier event which is associated with operation of at least one player of the at least one electronic gaming device of system 100);
input the operational data into a machine-learning model (see Kaizerman, 0039-0042, 0046-0048), the machine-learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for identifying fraudulent player conduct from predefined normal player conduct (see Kaizerman, Fig. 2, 0047-0048, 0072 – wherein the historical operation data is training data that includes events labeled as normal/abnormal by a human operator and/or by the method of detecting anomalies disclosed herein such as by machine learning models);
identify suspected fraudulent player conduct based on an output generated by the machine-learning model based upon the input operational data (see Kaizerman, Fig. 5, 0051, 0058-0060, 0066, 0071, wherein the detecting, flagging, or categorizing anomalies of fraud or operational problems are detected by gaming activity and patterns detected by a machine learning model; Fig. 9, 0066-0070); and
in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed (see Kaizerman, 0069-0072).
Regarding claim 2, Kaizerman discloses the security support system of Claim 1, wherein the identified operational data is addressed to at least one of the game controller or the player tracking interface (see Kaizerman, Fig. 1-2, 5, 0039, 0042-0045, wherein the system 100 identified operational data includes data of gaming activities of players using from a database storing time series data of gaming patterns that are addressed to at least one of a game controller of the gaming device performing the gaming activity and/or associated with the player id on different channels such as in a multi-channel time series data format; 0068, a relation between players may be determined based on player features or attributes, IP addresses, same or similar timing of outlier events, geographic location and other statistics; 0070 – wherein the report of the outlier events includes the gamer details (e.g., user ID and IP address) which indicates that the operational data is addressed to at least one of a game controller of a user device or a player tracking interface)).
Regarding claim 3, Kaizerman discloses the security support system of Claim 1, wherein the mitigating action comprises at least one of i) disabling the at least one electronic gaming device, ii) generating a security alert, or iii) removing the at least one electronic gaming device from participation in a multiplayer electronic game (see Kaizerman, Fig. 12, 0039-0044; 0070-0072, wherein in block 950, a notification may be provided to a user or operator, as indicated in block 952. The notification may be provided in the form of a report describing the timing of the outlier events, the gamer details (e.g., user ID and IP address) of the suspected players, and any other relative data.. The notification may also include generating an alarm to attract the attention of the operator to the possible fraud); 0075-0078, 0080-0083 – input devices and output devices may be connected to computing devices such as a wired or wireless network interface card; These elements in combination are found to indicate a second network output interface that generates a notification/alert/alarm).
Regarding claim 4, Kaizerman discloses the security support system of Claim 1, wherein the at least one processor is further configured to input the operational data to the machine-learning model by transmitting the operational data to a security support server (see Kaizerman, Figs. 1-2, 0039-0048), wherein the security support server is configured to: apply the operational data into the machine-learning model (see Kaizerman, 0039-0043, 0069-0072); and transmit the output from the machine-learning model to the at least one electronic gaming device (see Kaizerman, 0069-0072).
Regarding claim 5, Kaizerman discloses the security support system of Claim 4, wherein the security support server is further configured to train the machine-learning model based on the operational data (see Kaizerman, 0039-0048).
Regarding claim 6, Kaizerman discloses the security support system of Claim 1, wherein the machine-learning model comprises a classification model trained with labeled data associated with a plurality of electronic gaming devices (see Kaizerman, 0014, 0048, 0063, 0068-0072).
Regarding claim 7, Kaizerman discloses the security support system of Claim 1, wherein the machine-learning model comprises an unsupervised anomaly detection model configured to identify instances of abnormal activity in the operational data by comparing the operational data to historical training data associated with prior game play (see Kaizerman, Figs. 1-9, 0052-0072).
Regarding claim 10, Kaizerman discloses a method for detecting fraudulent player conduct on at least one electronic gaming device (see Kaizerman, Fig. 1-2, 5-12, 0038-0047, 0075, 0077), the method comprising:
analyzing data transmitted between a game controller of at least one electronic gaming device and a player tracking interface of the at least one electronic gaming device to identify operational data (see Kaizerman,210, 230, 250 of Fig. 2, 0039-0047, 0060-0068), the operational data associated with operation of the at least one electronic gaming device (see Kaizerman, Fig. 2, 0039-0047, 0050);
inputting the operational data into a machine-learning model (see Kaizerman, 0039-0042, 0046-0048), the machine-learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for identifying fraudulent player conduct from predefined normal player conduct (see Kaizerman, Fig. 2, 0047-0048, 0072);
identifying suspected fraudulent player conduct based on an output generated by the machine-learning model based upon the input operational data (see Kaizerman, Fig. 5, 0051, 0058-0060, 0066, 0071); and
in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed (see Kaizerman, Fig. 1-2, 0068-0072).
Regarding claim 11, Kaizerman discloses the method of Claim 10, wherein the identified operational data is addressed to at least one of the game controller or the player tracking interface (see Kaizerman, Fig. 1-2, 5, 0039, 0042-0045, 0068, 0070).
Regarding claim 12, Kaizerman discloses the method of Claim 10, wherein the mitigating action comprises at least one of i) disabling the at least one electronic gaming device, ii) generating a security alert, or iii) removing the at least one electronic gaming device from participation in a multiplayer electronic game (see Kaizerman, Fig. 12, 0039-0044; 0070-0072, 0075-0078, 0080).
Regarding claim 13, Kaizerman discloses the method of Claim 10, further comprising inputting the operational data to the machine-learning model by transmitting the operational data to a security support server (see Kaizerman, Fig. 1-2, 5-9, 0039-0048), wherein the security support server is configured to: apply the operational data into the machine-learning model (see Kaizerman, Fig. 1-2, 5-9, 0039-0048); and transmit the output from the machine-learning model to the at least one electronic gaming device (see Kaizerman, 0069-0072).
Regarding claim 14, Kaizerman discloses the method of Claim 13, wherein the security support server is further configured to train the machine-learning model based on the operational data (see Kaizerman, 0014, 0048, 0063, 0068-0072).
Regarding claim 15, Kaizerman discloses the method of Claim 10, wherein the machine-learning model comprises a classification model trained with labeled data associated with a plurality of electronic gaming devices (see Kaizerman, 0014, 0048, 0063, 0068-0072).
Regarding claim 16, Kaizerman discloses the method of Claim 10, wherein the machine-learning model comprises an unsupervised anomaly detection model configured to identify instances of abnormal activity in the operational data by comparing the operational data to historical training data associated with prior game play (see Kaizerman, Figs. 1-9, 0052-0072).
Regarding claim 19, Kaizerman discloses at least one non-transitory computer-readable media having instructions embodied thereon (see Kaizerman, Figs. 1-2, 5-9, 12, 0038-0047, 0075, 0077)), wherein when executed by at least one processor in communication with at least one electronic gaming device, the instructions cause the at least one processor to (see Kaizerman, Figs. 1-2, 12, 0075-0077):
analyze data transmitted between a game controller of the at least one electronic gaming device and a player tracking interface of the at least one electronic gaming device to identify operational data (see Kaizerman,210, 230, 250 of Fig. 2, 0039-0047, 0060-0068), the operational data associated with operation of the at least one electronic gaming device (see Kaizerman, Fig. 2, 0039-0047, 0050);
input the operational data into a machine-learning model (see Kaizerman, 0039-0042, 0046-0048), the machine-learning model trained with historical operational data of a plurality of other electronic gaming devices including labeled data for identifying fraudulent player conduct from predefined normal player conduct (see Kaizerman, Fig. 2, 0040-0048, 0072);
identify suspected fraudulent player conduct based on an output generated by the machine-learning model based upon the input operational data (see Kaizerman, Fig. 5, 0051, 0058-0060, 0066, 0071); and
in response to identifying the suspected fraudulent player conduct, cause a mitigating action to be performed (see Kaizerman, Fig. 1-2, 0068-0072).
Regarding claim 20, Kaizerman discloses the non-transitory computer-readable media of Claim 19, wherein the identified operational data is addressed to at least one of the game controller or the player tracking interface (see Kaizerman, Fig. 1-2, 5, 0039, 0042-0045, 0068, 0070).
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 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 8-9 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over obvious over Kaizerman as applied to Claims 1 above, in further view of Gururajan et al. (US 2006/0252554 A1).
Regarding claims 8 and 17, Kaizerman discloses the security support system of Claim 1 and the method of Claim 10, wherein the operational data includes data associated with the at least one electronic gaming device, and wherein the at least one processor is further configured to input the data associated with the at least one electronic gaming device to the machine-learning model (see Kaizerman, Fig. 1-2, 0038-0047, Data may be collected or obtained from various data sources such as game databases storing the different game related actions and from a client module running on user devices (e.g., user devices 120) which may record actions taken by the player client module and related outcomes), and wherein the output is further generated based on the data (see Kaizerman, 0070-0075, wherein the outlier detection is based on the time related series data). However, Kaizerman is silent with respect to the data including video data.
Gururajan et al. teach a security system for identifying and tracking game objects and game states for tracking game events, game states and general game progressions for reporting and analysis (see Garurajan, abstract, 0068, 0165). Specifically, Gururajan teaches wherein the operational data used to identify the suspected fraudulent player conduct is based on operational data which includes video data (see Gururajan, Fig. 3, 0061- wherein the system utilizes periodic imaging to capturing a video stream at specific frames over a specific period of time, a specific event, and optical chip detection utilizing the overhead imaging system; 0068, wherein the module records video data from imaging system and links game event data to recorded video for analysis and reporting for violation alerts or fraud alerts; 0165). One would have been motivated to incorporate the teachings of Gururajan to use known fraud detection techniques to yield the expected result to improve robustness of game tracking (see Garurajan, Fig. 6, abstract, 0004, 0068). Therefore it would have been obvious to one of ordinary skill at the time of filing the application to wherein the operational data includes video data.
Regarding claims 9 and 18, Kaizerman discloses the security support system of Claim 1 and the method of Claim 10, wherein the operational data includes data associated with the at least one electronic gaming device (see Kaizerman, 0037-0047), and wherein the at least one processor is further configured to input the data associated with the at least one electronic gaming device to the machine-learning model (see Kaizerman, 0068-0073), and wherein the output is further generated based on the data (see Kaizerman, Fig. 1-9, 0038-0047, 0068-0073). However, Kaizerman is silent with respect to the operational data including audio data.
Gururajan et al. teach a security system for identifying and tracking game objects and game states for tracking game events, game states and general game progressions for improving game tracking and monitoring (see Garurajan, abstract, 0003-0004, 0068, 0165). Specifically, Gururajan teaches wherein the operational data used to identify the suspected fraudulent player conduct is based on operational data which includes video data (see Gururajan, Fig. 3, 0061- wherein the system utilizes periodic imaging to capturing a video stream at specific frames over a specific period of time, a specific event, and optical chip detection utilizing the overhead imaging system; 0068, wherein the module records video data from imaging system and links game event data to recorded video for analysis and reporting for violation alerts or fraud alerts; 0165). One would have been motivated to incorporate the teachings of Gururajan to use known fraud detection techniques to yield the expected result for improving robustness of game tracking (see Garurajan, Fig. 6, abstract, 0004, 0068). Therefore it would have been obvious to one of ordinary skill at the time of filing the application to wherein the operational data includes video data.
Conclusion
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/RYAN HSU/EXAMINER, Art Unit 3715
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