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 .
DETAILED ACTION
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to the abstract idea of mental processes and/ or certain methods of organizing human activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter
More specifically, regarding Step 1, of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a machine, process, and/or an article of manufacturer, which are statutory categories of invention.
Step 2a – Prong 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
The claim recites:
a data collection engine configured to collect system inputs required to progress in the online game and user inputs generated by a plurality of users during gameplay, the system inputs and the user inputs used to generate and train a resource allocation model used to predict a particular resource configuration required for the online game based on the game state and success criteria defined for the online game, wherein the success criteria is defined by type of users accessing the online game for providing the user inputs; and
a resource allocation agent configured to optimize allocation of resources by controlling allocation of each type and a number of said each type of resource based on the particular resource configuration predicted for the online game;
wherein the resource allocation model is generated using machine learning algorithm...
These limitations, under their broadest reasonable interpretation, cover concepts that can be performed in the human mind (or by a human using pen and paper) and/or that manage personal or commercial interactions. Specifically:
Collecting system and user inputs, observing game state, and defining success criteria based on types of users (players, developers, spectators) are acts of observation and evaluation.
Generating and training a model to predict a resource configuration based on those inputs, game state, and success criteria is an act of judgment and prediction.
Optimizing and controlling the type and number of resources based on the predicted configuration is an act of evaluation and decision-making regarding resource management.
These steps are analogous to a human administrator or system operator who monitors player activity and game conditions, evaluates what resources will be needed to maintain a desired quality of experience for different categories of users, and then decides how many and what type of servers or processing units to allocate. Such activity can be performed mentally or with the aid of simple record-keeping.
The claims also recite certain methods of organizing human activity because they manage commercial or interpersonal interactions in the context of providing an online multiplayer game service (i.e., allocating computing resources to ensure satisfactory gameplay experiences for different types of users).
Dependent claims 2–14 merely refine the same abstract idea by adding further details of prediction timing, resource characteristics, geographic distribution based on user location/type, dynamic adjustment based on changes in user types or workload, use of developer inputs or simulated plays, specific node roles, and inclusion of particular user categories. These refinements remain within the same mental process / organizing human activity groupings.
Step 2a – Prong 2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea- see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)
The claim as a whole does not integrate the abstract idea into a practical application.
The additional elements recited in claim 1 include:
a distributed game engine that executes instances of the online game and gathers user inputs;
a data collection engine;
a resource allocation agent;
the use of a machine learning algorithm to generate the model;
the characterization of the online game as a massive multi-player game; and
(in the dependent claims) reusable components, a synchronization engine, geographic distribution of resources, management/processing/master nodes, and a communication interface.
These elements are recited at a high level of generality and amount to nothing more than instructions to implement the abstract idea of predicting and allocating resources on a generic distributed computing environment / generic game engine. The claims do not recite a particular technological improvement to the computer itself, to the functioning of a distributed system, or to another technology.
There is no improvement to the way a computer or distributed system operates (e.g., no specific improvement in processing speed, memory utilization, network latency reduction through a particular technical mechanism, or a new data structure).
The machine learning algorithm is recited generically (“generated using machine learning algorithm”) without any particular training technique, model architecture, loss function, or technical implementation detail that improves the functioning of the computer or the field of resource allocation technology.
The distributed game engine, nodes, and synchronization features are described functionally and at a high level of generality; they merely provide a generic technological environment in which to perform the abstract idea of resource prediction and allocation.
The claims do not transform a particular article into a different state or thing, nor do they apply the abstract idea with a particular machine that is integral to the claim in a non-conventional way.
Therefore, the additional elements do not integrate the abstract idea into a practical application under MPEP § 2106.04(d). They simply link the abstract idea to a particular technological environment (distributed online gaming systems) without improving that environment.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept).
Viewed individually, the additional elements are well-understood, routine, and conventional activities previously known in the industry:
Executing game instances and gathering user inputs on distributed servers is conventional in cloud and multiplayer gaming systems.
Collecting system and user data for monitoring and management is routine.
Dynamically allocating compute resources (servers, virtual machines, processing units) based on demand is a long-standing practice in distributed computing and cloud resource management.
Using machine learning models for prediction or forecasting is conventional and well-understood.
Providing management, processing, and coordination nodes with communication interfaces is a standard architectural pattern in distributed systems.
Viewed as an ordered combination, the additional elements do not transform the abstract idea into a patent-eligible application. The combination merely applies the abstract idea of predicting and allocating resources according to user types and game state using conventional distributed computing components and generic machine learning. There is no inventive concept that amounts to significantly more than the abstract idea itself.
The same analysis applies to dependent claims 2–14. The additional limitations in those claims (pre-provisioning based on predicted demand, reusable components, geographic distribution based on user location and type, dynamic adjustment based on changes in user types or workload, use of developer inputs or simulated plays, specific node roles, and inclusion of developers/players/spectators) further describe the abstract idea or recite conventional computer functions performed in a conventional way. They do not add an inventive concept.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
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 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) 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bruno (US 2014/0344457 A1) in view of Malan (US 2019/0244137 A1).
1. Bruno discloses a system for provisioning resources for an online game, comprising: a distributed game engine to execute instances of the online game and to gather user inputs provided by a plurality of users, the user inputs used to affect a game state of the online game and to generate game data (i.e. remote gaming service that executes game sessions (each session runs an instance of a game title) on distributed computing resources across multiple server farms. Player clients provide inputs that affect game state; the service generates the corresponding game data), [0018]–[0020], [0028], (Figs. 1–4),
the distributed game engine having, a data collection engine configured to collect system inputs required to progress in the online game and user inputs generated by a plurality of users during gameplay, the system inputs and the user inputs used to generate and train a resource allocation model used to predict a particular resource configuration required for the online game based on the game state and success criteria defined for the online game (i.e. resource manager continuously collects system inputs (number of active game sessions, CPU/usage characteristics, session churn, historical usage patterns, demand characteristics) and user inputs (player joins/leaves that change session count and game-state load). These data are used to generate predictive forecasts of the required resource configuration based on current game state and historical demand patterns that reflect successful service operation, [0021], [0058]-[0060], [0077], [0083], [0094], [0099]-[0101], (Figs. 7–10),
wherein the success criteria is defined by type of users accessing the online game for providing the user inputs (i.e. player profiles, usage history and different player populations (types) drive different demand profiles and therefore different resource needs for successful operation, [0054]-[0057], [0063],
and a resource allocation agent configured to optimize allocation of resources by controlling allocation of each type and a number of said each type of resource based on the particular resource configuration predicted for the online game (i.e. resource manager dynamically adds or subtracts resource units of different types and quantities according to the predicted configuration, optimizing efficiency while meeting demand), [0097]-[0102];
wherein the online game is a massive multi-player game (i.e. multiplayer game sessions shared by many players), [0020], [0071], [0090].
Bruno does not expressly teach or suggest that the resource allocation model is generated using a machine learning algorithm. Malan teaches using machine learning to estimate or forecast resource use with time-varying demand in gaming platforms. Malan trains a machine learning model on historical activity data to predict the number of resources needed to meet a target success metric under fluctuating demand, [0015], [0057]-[0070], [0082]-[0095], (Fig. 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive resource allocation model of Bruno so that it is generated using a machine learning algorithm as taught by Malan and would have been motivated to do so because both references address the identical problem of provisioning compute resources for online multiplayer gaming platforms under variable, time-varying user demand and enhancing Bruno’s forecasting with Malan’s machine-learning model would have produced more accurate predictions under complex demand patterns, with a reasonable expectation of success.
2. Bruno and Malan discloses the system of claim 1, wherein the distributed game engine is configured to provision said each type and the amount of said each type of resource specified in the particular resource configuration prior to receiving game play requests from users of the online game for a subsequent game play session, the provisioning of the type and the amount of each type of resource based on predicted demand driven by the type of users predicted to access the online game during the subsequent game play session (maintains standby resources and uses forecasts based on historical usage data or demand patterns to provision resources in advance of actual session demand), Bruno [0059], [0092], [0094], [0098]-[0102].
3. Bruno and Malan discloses the system of claim 1, wherein the resources are reusable components used to process functional portions of the distributed game engine related to features of game data generated for the online game, Bruno (i.e. recycle computing resource), Bruno [0060], [0098]-[0102].
4. Bruno and Malan discloses the system of claim 3, wherein the distributed game engine further includes a synchronization engine to manage allocation and synchronization of the functional portions allocated to the resources (i.e. the resource manager and matchmaker coordinate allocation of resources across distributed sessions and ensure proper formation and operation of multiplayer sessions), Bruno [0065]-[0067], [0069].
5. Bruno and Malan discloses the system of claim 3, wherein each resource is configured to perform at least one functional portion of the distributed game engine (each allocated resource executes game code/functional portions for its sessions), Bruno [0050], [0061]-[0062].
6. Bruno and Malan discloses the system of claim 3, wherein the resources allocated for processing the functional portions are distributed across a geographical area and are identified based on physical location of the plurality of users providing the user inputs for the online game and type of users providing the user inputs (allocating resources across geographically distributed server farms, preferring proximate resources based on player location and player profiles/types), Bruno [0056], [0069].
7. Bruno and Malan discloses the system of claim 1, wherein the resource allocation agent is configured to dynamically adjust the type and the number of each type of resource provisioned for the online game based on changes to the success criteria influenced by changes to type of users providing the user inputs, Bruno [0098]-[0102].
8. Bruno and Malan discloses the system of claim 1, wherein the resource allocation model is generated using game inputs specified by developers of the online game and trained using game data generated from user inputs of the plurality of users collected for the online game (allowing developers to specify expected demand events; the model is also trained on collected historical and real-time user-driven session data), Bruno [0098]-[0102].
9. Bruno and Malan discloses the system of claim 8, wherein the system inputs are obtained by querying game logic of the online game or from simulated game plays of the online game, wherein user inputs for the simulated game plays provided by a controlled group of users selected based on skill level (Bruno monitors actual game-session metrics (inherently querying running game logic) and uses player skill/rank profiles. Controlled skill-selected groups for simulation are an obvious extension of Bruno’s existing skill-based matching for improving model training), [0055]-[0057], [0098]-[0102].
10. Bruno and Malan discloses the system of claim 1, wherein the distributed game engine includes a plurality of management nodes, a plurality of processing nodes, and a master node, wherein each management node of the plurality of management nodes and each processing node of the plurality of processing nodes executes an instance of a game engine and game logic of the online game, and wherein each management node of the plurality of management nodes is configured to manage assignment of resources for processing one or more of the functional portions of the distributed game engine, and each processing node of the plurality of processing nodes is configured to provide the resources for processing the one or more functional portions of the distributed game engine, and the master node including a communication interface to coordinate synchronization of the functional portions of the distributed game engine (Bruno’s architecture includes resource managers that manage assignment of resources across distributed servers, and the game-session servers themselves that execute the game engine instances and provide the processing resources. Higher-level service components coordinate across the distributed farms), [0058]-[0060], [0081], [0083], [0085], [0100].
11. Bruno and Malan discloses the system of claim 1, wherein the plurality of users includes any one or combination of developers, players and spectator, Bruno [0071].
12. Bruno and Malan discloses the system of claim 1, wherein the type and the number of each type of resource defined in the particular resource configuration is based on specific combination of users providing the user inputs, wherein users include any one or combination of developers, players and spectators (the allocation is driven by the specific mix of active players and their profiles, and can incorporate developer-specified events), Bruno [0071], [0081], [0086].
13. Bruno and Malan discloses the system of claim 1, wherein the particular resource configuration is dynamically adjusted to adapt to workload changes detected for the online game, wherein the workload changes are influenced by changes to game state and success criteria of the online game, Bruno [0098]-[0102].
14. Bruno and Malan discloses the system of claim 13, wherein the workload changes are influenced by changes to type of users and number of users of each type accessing the online game, the changes to the type and number of users of each type influenced by new users accessing the online game or existing users exiting the online game, Bruno [0098]-[0102].
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 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,077,362 and over claims 1–20 of U.S. Patent No. 12,102,912.
Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are directed to the same inventive concept as the patented claims.
The patented claims are directed to a distributed game engine comprising management nodes and processing nodes, a resource allocation model constructed from game play training data that includes user inputs, game states, and success criteria, and a resource allocation agent that uses the model to identify the type and number of processing nodes (resources) required for processing specific functional portions of the distributed game engine for an online multiplayer game.
The pending claims recite the same core combination of elements: a distributed game engine, a data collection / training process that generates a resource allocation model using machine learning based on user inputs, game state, and success criteria defined by type of users, and a resource allocation agent that controls allocation of each type and number of resources based on the model.
Any differences in claim language (including the specific recitation of success criteria defined by type of users, reusable components, geographic distribution, or particular node terminology) are minor variations that would have been obvious to one of ordinary skill in the art. The pending claims do not define a patentably distinct invention over the claims of either patents.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached USPTO form PTO-892.
Colenbrander (US 2018/0288133 A1) discloses systems and methods for a distributed game engine that enables an elastic compute architecture for cloud and multiplayer gaming. Multiple compute nodes are clustered to execute the game engine across nodes. A node assembly / resource management function dynamically allocates the number and type of nodes based on demand, user requirements, and game needs. Nodes communicate via low-latency internal protocols to share game state and synchronize functional portions. The system supports both single-player and multiplayer scenarios and can broadcast frames to multiple clients.
Filing of New or Amended Claims
The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b)
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENG H LIM whose telephone number is (571)270-3301. The examiner can normally be reached Monday-Friday (9-5).
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Seng H Lim/Primary Examiner, Art Unit 3715
Prosecution Insights