CTNF 18/919,981 CTNF 83393 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 2. 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. 3. Claims 1 and 21-39 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. 4. Step 1 Claims 1 and 21-39 are directed to an apparatus or method meeting the requirements for Step 1. 5. Step 2A Prong 1 In independent Claim 1 (and similarly for Claims 29 and 38), the following bolded steps recite an abstract measuring (observation), comparing, determining, and designating which are activities that can be performed in the mind and as such are mental processes. 1. A computer-implemented method comprising: measuring, for a user, a metric comprising: (i) a game-agnostic behavior metric that measures an expenditure of effort by the user in interactions with other individuals, or (ii) a game-dependent behavior metric that measures in-game patterns of interaction with other individuals by the user; comparing the metric with a corresponding reference metric for one or more other users; determining, based at least on comparing the metric with the corresponding reference metric, that a difference between the metric and the corresponding reference metric satisfies one or more criteria; and in response to determining that the difference satisfies the one or more criteria, designating the user as a bad actor. 6. Step 2A Prong II The abstract idea is not integrated into a practical application. According to MPEP 2106, a consideration indicative of integration into a practical application includes improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)) or adding a specific limitation other than what is well-understood, routine, conventional activity, or adding unconventional steps that confine the claim to a particular application (a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC , 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (MPEP § 2106.05(d)). Conversely, considerations not indicative of integration include adding words “apply it” (or equivalent) with the judicial exception or mere instructions to implement the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (MPEP 2106.05(f)); adding insignificant extra-solution activity (MPEP 2106.05(g)), or generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)). Here, processors, computer-readable media, and instructions (of Claims 29 and 39), are recited generically (no details whatsoever are provided other than in name only) that they represent no more than mere instructions to apply the judicial exception on a computer. Applicant’s Specification does not disclose that these elements are directed to a technological solution to a technological problem that “overcome some sort of technical difficulty.” (citing ChargePoint, Inc. v. SemaConnect, Inc., 920 F.3d 759, 768 (Fed. Cir. 2019). According to Applicant’s specification: Thus the required adaptation to existing parts of an equivalent device may be implemented in the form of a computer program product comprising processor implementable instructions stored on a non-transitory machine-readable medium such as a floppy disk, optical disk, hard disk, solid state disk, PROM, RAM, flash memory or any combination of these or other storage media, or 45 realized in hardware as an ASIC (application specific integrated circuit) or an FPGA (field programmable gate array) or other configurable circuit suitable to use in adapting the conventional equivalent device. (Page 10, ll. 41-47). of these or other networks. Consequently, these elements are viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer or as a means to automate the steps. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l , 573 U.S. 208, 224-26 (2014). There is no extra-solution activity (e.g., presenting, displaying, transmitting). Even when the limitations are viewed in combination, the elements in this claim do no more than automate the steps needed to be performed, using the one of more computer components as tools. While this type of automation is an improvement in a general sense as opposed to performance manually, there is no change to the computers and other technology that are recited in the claim as automating the abstract ideas, and thus this claim cannot improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc. , 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Latric Sys. , 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services , 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, each claim, as a whole, does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Thus, Claim 1, and similarly Claims 29 and 38, lack the eligibility requirements of Step 2 Prong II. 7. Step 2B According to MPEP 2106, in addition to the considerations discussed in Step 2A, an additional consideration indicative of an inventive concept (aka “significantly more”) is the addition of a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)). Conversely, an additional consideration not indicative of an inventive concept is simply appending well-understood, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea (MPEP 2106.05(d) and Berkheimer Memo, April 20, 2018). Thus, the additional elements evaluated under Step 2A are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. There are no extra-solution elements evaluated under Step 2A. Thus, Claim 1, and similarly Claims 29 and 38, do not recite additional elements, individually or in combination, that amount to significantly more than the abstract idea. Thus, Claims 1, 29 and 38 are ineligible. 8. Dependent Claims In Reference to Claims 21-28, 30-37, and 39 Claims 21-22, 26-27, 30-31, 35-36, and 39 recite extra-solution details of the metrics. Claims 23-24, 28, 32-33, and 37 recite additional abstract mental processes and extra-solution details of patterns. Claims 25 and 34 recite a trained machine learning model which is an abstract mathematical concept. Thus, none of the claims supply a practical application or inventive concept sufficient to transform the nature of the claim into a patent-eligible application. Additionally, the combination of additional elements adds nothing that is not already present when considered individually where the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. Thus, Claims 21-28, 30-37, and 39 are ineligible. Claim Objections 9. Claim 27 is objected to because of the following informality: a. Claim 22, Line 1: Change “the first game” to – the game --. b. Claim 27, Line 1: Change “claim 6” to – claim 26 --. c. Claim 28, Line 3: Change “the first user” to – the user --. d. Claim 31, Line 1: Change “the first game” to – the game --. e. Claim 37, Line 3: Change “the first user” to – the user --. Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 10. 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 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. 07-20-aia AIA 11. 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. 07-23-aia AIA 12. The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 13. Claim(s) 1 and 21-25, 28-34, and 37-39 is/are rejected under 35 U.S.C. 103 as obvious over U.S. Pat. Pub. No. 2023/0231861 to Chen in view of U.S. Pat. Pub. No. 2017/0294075 to Frenkel. In Reference to Claims 1, 29 and 38 A computer-implemented system and medium, comprising: a processor, memory and computer-readable medium with instructions (Fig. 8 CPU 801, computer-readable instructions tored in ROM 802 [0079]), the method, comprising: measuring, for a user, a game-agnostic behavior metric that measures an expenditure of effort by the user in interactions with other individuals, or a game-dependent behavior metric that measures in-game patterns of interaction with other individuals by the user (Examiner deems the types of behavior as non-functional descriptive matter as the type of behavior measured has no impact on the outcome on the subsequent a steps of the claim i.e., either behavior indicates a bad actor or not but not one as opposed to the other. Chen discloses “different types of behaviors” [0034] and measured as if Fig. 1 102, 102, and 112) see behavior features 340 [0043], Chen discloses that behaviors are given anomaly scores 120, 112); comparing the metric with a corresponding reference metric for a user (Fig. 1 comparison module 140 compares score 112 to threshold 130 [0029, 0053]); determining, based at least on comparing the metric with the corresponding reference metric, that a difference between the metric and the corresponding reference metric satisfies one or more criteria (Fig. 1 comparison module 140 determines differences between scores 112 and 130 when scores exceed the threshold [0029, 0053]); and in response to determining that the difference satisfies the one or more criteria, designating the user as a bad actor (Fig. 1 anomaly detection result 150 [0028-0029, 0053] when score exceeds threshold the behavior is anomalous.). Chen discloses the invention substantially as claimed to include “embodiments of the present disclosure may be used for any suitable type of user behavior” [0044]. However, Chen is silent as to a user in combination with others and of a game-agnostic behavior metric that measures an expenditure of effort by the user in interactions with other individuals, or a game-dependent behavior metric that measures in-game patterns of interaction with other individuals by the user. One of skill in the art would be aware of the teachings of Frenkel. Frenkel discloses monitoring of interactive games (Titl.) to include collusion detection (Fi3. 3118) as part of collecting player information, analysis via the collusion detection module, comparison to collusion policy, and enforcement (Fig. 5 steps 510-540). “The host computer system may include means for monitoring actions taken by one or more of the plurality of players to thereby detect collusion among the players.” [0008]). Frenkel teaches of both game-agnostic as well as game-dependent forms of collusion. For instance, Frenkel discloses game-agnostic collusion wherein “[d]spite all efforts to minimize cheating through visual means, players may nevertheless collude by communicating with one another via phone calls, texting, or the like.” [0053]. Likewise, game-dependent collusion wherein “[f]or example, if two players playing from the same location are within eyesight of each other's terminals, then they may be able to see each other's hole cards or signal each other their holding. This form of collusion provides these players with a significant advantage over other players in the game.” [0049]. Frenkel invents these collusions measures in order to “protect players from unsavory activities that have prevented the emergence of such gaming opportunities heretofore.” [0034]. The Supreme Court in KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007) identified a number of rationales to support a conclusion of obviousness (A) Combining prior art elements according to known methods to yield predictable results; (B) Simple substitution of one known element for another to obtain predictable results; (C) Use of known technique to improve similar devices (methods, or products) in the same way; and (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results. Here, it would require only routine skill in the art to modify Chen with the collusion behaviors of the user as well as the participation of others to achieve the predictable result of protecting the enjoyment of the game from all bad actors. The Courts have held that combining prior art elements according to known methods to yield predictable results to be indicia of obviousness. In Reference to Claims 21, 30, and 39 Frenkel discloses an absolute number metric that reflects a number of individuals with which the first user has interacted interacts within a predetermined period of time. (cheating or collusion monitoring that can occur between two people [0049] wherein “[t]he second system may analyze the recorded game play and signals from the gaming session in question, all gaming sessions of that player, and/or a gaming sessions within a specified time period (e.g., within one day, one week, one month, etc.). [0055] In Reference to Claims 22 and 31 Frenkel teaches a game-context based chat metric that reflects patterns of communication that have occurred between users during game play (“Despite all efforts to minimize cheating through visual means, players may nevertheless collude by communicating with one another via phone calls, texting, or the like.” [0053], see “patterns” [0055, 0057]) where “A collusion detection processor 318 monitors such things as unusual action taken by a specific player, frequent occurrences of the same players playing together in the same games.” [0070]). In Reference to Claims 23 and 32 Frenkel teaches evaluating a pattern of communication between the user and one or more of the other users during game play; (“For example, if two players playing from the same location are within eyesight of each other's terminals, then they may be able to see each other's hole cards or signal each other their holding. This form of collusion provides these players with a significant advantage over other players in the game.” [0049]. Frenkel further teaches comparing the pattern of communication with a reference pattern of communication of different users within game play where ”Some embodiments may also use big data analytics systems or other analytic systems to analyze game play on the player that can predict whether the player is playing in an abnormal manner, showing different characteristics, or suspicious patterns to normal game play.” [0057]. One of skill in the art would modify the comparison module score and threshold of Chen with the pattern of communication and a reference pattern based on normal play of Frenkel to achieve the predictable result of determining players that are cheating from those that are not. In Reference to Claim 24 and 33 Frenkel teaches a timing of interactions between the user and one or more other users (“The second system may analyze the recorded game play and signals from the gaming session in question, all gaming sessions of that player, and/or a gaming sessions within a specified time period (e.g., within one day, one week, one month, etc. [0055]. In Reference to Claims 25 and 34 Chen discloses trained computer learning models ([0032], “trained models” [0034], “Anomaly detection model 120 may be a trained model. For example, anomaly detection model 120 may be trained based on user behavior data over a second time period.” [0048, 0049]). Frenkel teaches using machine learning models that correlate one or more game state indicators with patterns of normal communications between users within a game to model the pattern of communication, to evaluate a difference in in-game communication patterns of the user (See “artificial intelligence”, “patterns”, [0055, 0057], and “and wherein the artificial intelligence component accesses the recording database to access current and historical gameplay to identify abnormal game play or player interactions.” Claim 2 and “state” of the gaming system to capture “user interactions” [0088] via state recording module 440). 14. Claims 26-28 and 35-38 are rejected under 35 U.S.C. 103 as obvious over Chen, Frenkel further in view of CN 114630701 (‘701). In Reference to Claims 26-27 and 35-36 Chen discloses the invention substantially as claimed. However, the reference does not explicitly disclose a spatial metric that reflects how the user navigates a video game to interact with other users based on a spatial relationship of the user with one or more other users in-game when the user is interacting. One of skill in the art would be aware of ‘701. ‘701 discloses a machine learning trust score (Titl.) wherein “[a]lthough most video game players do not participate in the cheating behavior, there are usually a small number of players who cheat for obtaining more advantages than other players. In general, cheating players employ third-party software that provides them with information or more mechanical advantage than other players. For example, the third-party software can be configured to extract position data about the other player position, and can present the position to the cheating player. The information advantage allows the cheating players to attack other players, or otherwise utilize the position information by the third-party software.” (Background Page 1, Para. 2, See also Fig. 1 and associated text). ‘701 invents this system where “[u]sually, bad player behavior (such as, cheating) will destroy the game experience of the player who wants to play game legally.” (Background Page 2, Para. 1). Here, it would require only routine skill in the art to modify Chen’s monitoring to include the spatial metrics of ‘701 to achieve the predictable result of prevents bad players from ruining the gaming experience of others. The Courts have held that combining prior art elements according to known methods to yield predictable results to be indicia of obviousness. In Reference to Claims 28 and 37 ‘701 also teaches of a property of accounts associated with a client device and user account of a player wherein a trust score can be tied to specific user accounts (“For example, a part of data in the historical data associated with the sampling set of the user account can be represented by a group of features, and is marked to indicate the past when playing the video game player in a specific manner. The machine learning model of the data training can be output to the machine learning score of the user account registered to the video game service (e.g., trust score) to prediction the player behavior (See machine translation P.3 Para. 4) and the client machines associated with the user account (“the computing system may receive information a plurality of client machines, the information indicating the login account of the login application program executing the video game, and the computing system can define a plurality of players to be grouped into the matching for playing the video game in a plurality of people mode.” (See machine translation P.3 Para. 5) One of skill in the art would modify the comparison module and threshold of Chen with the trust scores associated with specific user accounts and client devices of players to achieve the predictable result of knowing the accounts of suspected cheaters. The Courts have held that combining prior art elements according to known methods to yield predictable results to be indicia of obviousness. Conclusion 07-96 AIA 15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is in the Notice of References Cited . 16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul A. D’Agostino whose telephone number is (571) 270-1992. 17. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 18. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-270-2992. /PAUL A D'AGOSTINO/Primary Examiner, Art Unit 3715 Application/Control Number: 18/919,981 Page 2 Art Unit: 3715 Application/Control Number: 18/919,981 Page 3 Art Unit: 3715 Application/Control Number: 18/919,981 Page 4 Art Unit: 3715 Application/Control Number: 18/919,981 Page 5 Art Unit: 3715 Application/Control Number: 18/919,981 Page 6 Art Unit: 3715 Application/Control Number: 18/919,981 Page 7 Art Unit: 3715 Application/Control Number: 18/919,981 Page 8 Art Unit: 3715 Application/Control Number: 18/919,981 Page 9 Art Unit: 3715 Application/Control Number: 18/919,981 Page 10 Art Unit: 3715 Application/Control Number: 18/919,981 Page 11 Art Unit: 3715 Application/Control Number: 18/919,981 Page 12 Art Unit: 3715 Application/Control Number: 18/919,981 Page 13 Art Unit: 3715
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