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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/5/2026 has been entered.
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.
Claims 22-27, 29-34, and 36-40 are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0115743 to McLoughlin et al. (hereinafter McLoughlin) in view of US 2012/0142429 to Muller (hereinafter Muller).
Regarding claims 22, 29, and 36, McLoughlin teaches a system and computer storage, and method therefor, comprising:
an electronic data store configured to store user interaction data associated with a video game (e.g., database 140); and
a hardware processor (e.g., host system 110A and/or computing device 120) in communication with the electronic data store, the hardware processor configured to execute specific computer-executable instructions to at least:
cause a set of computing resources to be provided, the set of computing resources being usable to execute one or more tasks within a video game (e.g., a frame generator 112 that uses network resources to generate the predicted partial frames corresponding to the most likely user actions in ¶ 75 and/or network and graphics resources as discussed in ¶ 169);
receive, from the electronic data store, user interaction data indicative of a set of user interactions associated with one or more of a plurality of users interacting with the video game (e.g., a “frame generator 112 generates a set of predicted partial frames corresponding a set of possible user actions relative to a current position associated with the static frame 102” in ¶ 73);
determine, using a prediction model, a set of predicted user interactions based at least one the user interaction data (e.g., “the host system 110A may use a variety of information sources to improve accuracy of predicted scene changes that are represented in a partial frame” which “can include statistical models and machine learning, e.g., models that are learned empirically on per-application and per-user basis” in ¶ 128);
determine, based at least in part on the set of predicted user interactions, a set of computing resources (e.g., “the frame generator 112 may prioritize certain actions that are more likely to occur relative to other less probable user actions” in ¶ 74 and/or “an event can refer to receiving a specific type of input indicating that a user has entered a gaming session, which is then used to adjust the operations performed so that reduction of input latency is prioritized over graphics fidelity of the frames that are displayed on the computing device 120” in ¶ 169); and
cause the set of computing resources associated with the video game to be adjusted based at least in part on the set of predicted computing resources (e.g., “the frame generator 112 may determine a subset of user actions that are most likely to be used by the user and intelligently use network resources to generate the predicted partial frames corresponding to the most likely user actions” in ¶ 75 and/or “adjust the operations performed so that reduction of input latency is prioritized over graphics fidelity of the frames that are displayed on the computing device 120” in ¶ 169).
Further regarding claims 22, 29, and 36, McLoughlin teaches the invention substantially as described above, but lacks in explicitly teaching wherein the adjustment comprises one or more of (i) changing a resource type associated with the set of computing resources to a different resource type based on the determined set of predicted computing resources, (ii) increasing or decreasing an amount of computing resources in the set of computing resources based on the determined set of predicted computing resources, and (iii) changing an available time period during which the set of computing resources is to be made available based on the determined set of predicted computing resources. In a related disclosure, Muller teaches a system that tracks and analyzes user behavior in a game environment (abstract). More particularly, Muller teaches that the invention permits conservation of resources, such as reducing, adjusting or optimizing the number of active servers in a network system that provide access to the game or other online endeavor (¶ 57). Furthermore, Muller teaches that multiple servers may be employed to handle peak loads on the system, but, to conserve energy and resources, players may be aggregated on certain servers while other servers are powered down or put in standby mode during non-peak times (¶ 93). Muller states that the system can predict that more servers must be powered up at certain times of day when players are expected to join, and may be shut down at certain times of day (¶ 248 and Fig. 14B). As such, Muller teaches the claimed features lacking from McLoughlin. It would have been obvious to one of ordinary skill in the art before the effective date to modify the system of McLoughlin to include the prediction-based system of Muller that increases or decreases resources according to expected user demand in order to achieve “reduced energy consumption, […] reduced wear and tear on servers, data storage devices, and other network resources, thereby reducing a number of servers or other network elements that could crash and need to be scrapped/recycled” (Muller ¶ 248).
Regarding claims 23, 30, and 37, McLoughlin teaches wherein causing the set of computing resources to be adjusted comprises causing a resource type of the set of computing resources to be adjusted (e.g., adjusting graphics fidelity in ¶ 169).
Regarding claims 24, 31, and 37, McLoughlin teaches wherein causing the set of computing resources to be adjusted comprises causing a resource amount of the set of computing resources to be adjusted (e.g., adjusting graphics fidelity in ¶ 169).
Regarding claims 25, 32, and 38, McLoughlin teaches wherein causing the set of computing resources to be adjusted comprises causing a time period during which the set of computing resources is to be made available to be adjusted (e.g., the time period during which the user takes certain actions, such as movements or inputs, as described in at least ¶ 97).
Regarding claims 26, 33, and 39, McLoughlin teaches wherein the hardware processor is further configured to execute specific computer-executable instructions to cause one or more tasks triggered by subsequent user interactions within the video game to be executed using the adjusted set of computing resources (e.g., subsequent tasks within the timer period described in at least ¶ 97).
Regarding claims 27, 34, and 40, McLoughlin teaches wherein determining the set of predicted computing resources comprises:
determining a first set of predicted user interactions for a first user playing the video game;
determining a second set of predicted user interactions for a second user playing the video game;
determining an aggregated set of predicted user interactions based at least in part on the first set of predicted user interactions and the second set of predicted user interactions; and
determining, based at least in part on the aggregated set of predicted user interactions, the set of predicted computing resources (e.g., “the frame generator 112 may use various machine learning and/or other statistical classifier techniques to identify the most likely user inputs in certain scenarios and/or locations of a game based on prior inputs submitted by the user or other users when in the same scenario and/or locations of the game” in ¶ 75).
Claims 28, 35, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over McLoughlin and Muller in view of US 10,243,870 to Alton et al. (hereinafter Alton).
Regarding claims 28, 35, and 41, the combination of McLoughlin and Muller teaches the invention substantially as described above, including wherein the set of computing resources comprises those of a virtual machine, which are configured to perform one or more tasks in response to at least one of the user interactions (e.g., virtual machines in ¶¶ 47-49 of McLoughlin). McLoughlin and Muller lack in explicitly teaching a microservice executing on the virtual machine. In a related disclosure, Alton teaches techniques for distributed computing system node management (abstract). More particularly, Alton teaches that the distributed computing system 100 may include a compute node implemented as one or more virtual machines (3:11-42). The compute nodes may also execute one or more services, which may be micro-services (3:42-60). The micro-services may be used for rendering, distribution, video game execution, and streaming services (3:42-60). It would have been obvious to one of ordinary skill in the art before the effective date to modify the combination of McLoughlin and Muller to include the microservices of Alton in order to improve the control of rendering, distribution, and video game execution, as beneficially taught by Alton.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in view of the new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM H MCCULLOCH whose telephone number is (571)272-2818. The examiner can normally be reached M-F 9:30-5:30.
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/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715