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
Response to Arguments
Applicant's arguments filed 09/30/2025 have been fully considered but they are not persuasive.
Applicant argues that Bleasdale-Shepherd does not discloses (i) "generate, based on the input from the sensor, a predicted user command to the computer game, the generating comprising processing the input from the sensor using a first machine learning model" and (ii) "determine, based on the predicted user command, a time for controlling the computer game according to the predicted user command, the determining comprising processing the predicted user command using a second machine learning model different from the first machine learning model." Examiner respectfully disagrees. Referring to para [0062]-[0065] of applicant’s written publish specification, it appears the first machine learning model is based on sensor data and the second machine learning model is based on game state data. Bleasdale-Shepherd does utilize multiple machine learning models for the generating step and determining step as can be seen in at least para [0015]-[0016], [0034], [0040]-[0041], [0060]-[0061], [0077]. For example, the system utilizes a first machine learning to generate a predicted user command based on input from the sensor, wherein he first machine learning model is trained from sensor data and utilizes a second machine learning model, in addition to the first machine learning model, to determine the time for controlling the computer game, wherein the second machine learning model is trained from game states.
Para [0061] of Bleasdale-Shepherd, specifically states: “The trained machine learning model(s) 104 may represent a single model or an ensemble of base-level machine learning models, and may be implemented as any type of machine learning model 104… An "ensemble" can comprise a collection of machine learning models 104 whose outputs (predictions) are combined, such as by using weighted averaging or voting. The individual machine learning models of an ensemble can differ in their expertise, and the ensemble can operate as a committee of individual machine learning models that is collectively "smarter" than any individual machine learning model of the ensemble”.
Therefore, Bleasdale-Shepherd is still applicable to the current claim language and the rejection is maintained.
For purpose of compact prosecution, Dantas de Castro (US 2020/0078679 A1) is provided as a secondary reference for an alternative 103 obvious rejection. Please review rejection below.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-7, 9-18, 21 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bleasdale-Shepherd (US 2021/0146241 A1) or, in the alternative, under 35 U.S.C. 103 as obvious over Bleasdale-Shepherd (US 2021/0146241 A1) in view of Dantas de Castro (US 2020/0078679 A1).
1. Bleasdale-Shepherd discloses an apparatus, comprising:
at least one processor assembly (para [0058], the computing system 102 includes, among other components, one or more processors 300) programmed with instructions to:
execute a computer game (para [0011], The disclosed techniques may be implemented, at least in part, by a remote computing system that provides a video game service for a user community to play video games using client machines that are configured to access the video games from the computing system);
receive input from a sensor (para [0045], a computing system may receive sensor data from a client machine associated with a player of a video game while the video game is being played (e.g., in multiplayer mode). The sensor data may comprise raw-sensor data generated by one or more physical sensors of a game controller) ;
generate, based on the input, from the sensor, a predicted user command to the computer game, the generating comprising processing the input from the sensor using a first machine learning model (para [0015], [0038], [0052], [0077], generates a predicted user command based on the input using a first trained ML model, specifically relating to sensor data);
determine, based on the predicted user command, a time for controlling the computer game according to the predicted user command, the determining comprising processing the predicted user command using a second machine learning model different from the first machine learning model (para [0016], [0034], [0040], [0060]-[0061], a second trained ML model, relating to the game state, can be used in addition to the first trained ML model to determine the time for controlling the computer game), and
control the computer game according to the predicted user command at the determined time, the computer game controlled according to the predicted user command by one or more of: controlling the computer game according to the predicted user command in advance of a user completing the predicted user command (para [0017], [0042], [0069], [0084], controls the game in advance by proactively generating and inputting game control data before the user completes the command, e.g., simulating actions like jumps or movements ahead of actual input to reduce perceived latency), delaying control of the computer game according to the predicted user command subsequent to the user completing the predicted user command (para [0087], e.g., holding predictions until confirmed by actual data in high-latency scenarios).
Alternatively, Dantas de Castro discloses a system for implementing animation actions in video games using machine learning models, including the use of different predictive models where the output of a first model serves as input to a second, different model to determine timing or outcomes for game control, para [0022], [0036]. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Bleasdale-Shepherd’s apparatus to incorporate Dantas de Castro’s teaching of using a second, different machine learning model to process the predicted user command (from the first model) in order to determine the precise time for game control, as this would enable more accurate, game-state-aware timing for applying predictions, improving latency compensation and responsiveness in video games. The combination yields the claimed two-model pipeline, where the first generates the command and the second determines the application time.
2. Bleasdale-Shepherd discloses the apparatus of Claim 1, wherein the sensor comprises a camera, and wherein the at least one processor assembly is programmed with instructions to: receive input from the camera (para [0045] - the game controller 106 includes one or more input/output (I/O) devices 200, such as...a camera(s));
generate, based on the input from the camera, the predicted user command and control the computer game according to the predicted user command in advance of the user completing the predicted user command (para [0055]-[0056], the camera can determine the physical state of the game controller and determine the predicted user command as discussed above.)
3. Bleasdale-Shepherd discloses the apparatus of Claim 2, wherein the at least one processor assembly is programmed with instructions to: identify a game action as occurring for which the predicted user command is to be executed and based on the identification, control the computer game according to the predicted user command in advance of the user completing the predicted user command (para [0017], [0042], [0069], [0078], [0084], the game predicts the user command by identifying a game action occurring in advance of the user completing the predicted user command, e.g. jumping).
4. Bleasdale-Shepherd discloses the apparatus of Claim 2, wherein the predicted user command comprises a gesture command (para [0035], [0045] - the game controller 106 includes one or more input/output (VO) devices 200, such as finger-operated controls (e.g., joysticks, trackpads, triggers, depressible buttons, etc.), potentially other types of input or output devices, such as a touchscreen(s), a microphone(s) to receive audio input, such as user voice input, a camera(s) or other types of sensor (e.g., sensor(s) that can function as an input device to receive gestural input, such as motion of the game controller and/or a hand of the user).
5. Bleasdale-Shepherd discloses the apparatus of Claim 2, wherein the predicted user command comprises a computer game controller command (para [0011] - predicting user input to a video game controller using a trained machine learning model(s), and proactively generating corresponding game control data in order to compensate for latency between player action and player perception).
6. Bleasdale-Shepherd discloses the apparatus of Claim 2, comprising the camera (para [0045] - the game controller 106 includes one or more input/output (I/O) devices 200, such as finger-operated controls (e.g., joysticks, trackpads, triggers, depressible buttons, etc.), potentially other types of input or output devices, such as a touchscreen(s}, a microphone(s) to receive audio input, such as user voice Input, a camera(s)).
7. Bleasdale-Shepherd discloses the apparatus of Claim 1, wherein the sensor comprises a controller button (para [0012] - raw sensor data may be sent along with the game control data (e.g., data generated by button presses, deflections of joysticks, etc., as well as altered sensor data) that is to be processed for controlling an aspect of the video game), and wherein the at least one processor assembly is programmed with instructions to: receive input generated based on actuation of the controller button (para [0034] - user inputs may include actuation of a control on the game controller 106, such as a press of a button, a deflection of a joystick, a swipe of a finger on a trackpad, etc., and/or a tilt or a movement of the game controller 106 in 3D space); generate, based on the input generated based on actuation of the controller button, the predicted user command (para [0012] - sensor data can be utilized for various purposes, such as to control an aspect(s) of a video game (e.g., to control a player-controlled character, to rotate a virtual camera that dictates what is visible on the display, etc.)); and control the computer game according to the predicted user command at the determined time by delaying control of the computer game according to the predicted user command until a time subsequent to the user completing the predicted user command, wherein the times subsequent to the user completing the predicted user command matches the determined time (para [0034] - Using the historical game control data 116 together with the historical sensor data 118 and/or the historical game state data 126 allows for correlations to be made between game control data 116 and sensor data 118 and/or game state data 126 generated at, or near, the same time as the game control data 116; para [0080] - the datastore 124 accessible to the computing system 102 can tag certain types of user input as ones for which the system is to proactively generate game control data 108, while other types of user input may be tagged as being excluded from consideration. In other words, there may be certain types of user input for which it is not deemed beneficial to predict on behalf of players 110, and itis better to wait for actual game control data 116 corresponding to those types of user input).
9. Bleasdale-Shepherd discloses the apparatus of Claim 1, wherein the at least one processor assembly is programmed with instructions to: use the first machine learning model to infer the predicted user command in advance of the user completing the predicted user command (para [0011], [0060] - predicting user input to a video game controller using a trained machine learning model(s), and proactively generating corresponding game control data in order to compensate for latency between player action and player perception.).
10. Bleasdale-Shepherd discloses the apparatus of Claim 1, comprising the at least one processor assembly (para [0058] - the computing system 102 includes, among other components, one or more processors 300 (e.g., a CPU(s))).
11. Bleasdale-Shepherd discloses a method, comprising: receiving input from a sensor; generating, based on the input from the sensor, a predicted user command to a first computer game, the generating comprising processing the input from the sensor using a first machine learning model; determining, based on the predicted user command, a time for controlling the first computer game according to the predicted user command, the determining comprising processing the predicted user command using a second machine learning model different from the first machine learning model; and controlling the first computer game according to the predicted user command at the determined time, the first computer game controlled according to the predicted user command by one or more of: controlling the first computer game according to the predicted user command in advance of a user completing the predicted user command, delaying control of the first computer game according to the predicted user command subsequent to the user completing the predicted user command as similarly discussed above.
12. Bleasdale-Shepherd discloses the method of Claim 11, comprising: providing, as an input to the first machine learning model, camera input, the camera input indicating the predicted user command (para [0045] - the game controller 106 includes one or more input/output (l/O) devices 200, such as finger-operated controls (e.g., joysticks, trackpads, triggers, depressible buttons, etc.), potentially other types of input or output devices, such as a touchscreen(s), a microphone(s) to receive audio input, such as user voice input, a camera(s) or other types of sensor (e.g., sensor(s) 202) that can function as an input device to receive gestural input, such as motion of the game controller 106 and/or a hand of the user 110; para [0015] - the sensor data as input to a trained machine learning model(s) to predict user input to the game controller that will cause corresponding game control data to be received from the player's client machine within a time period since receiving the sensor data); receiving, as an output from an activation layer of the first a machine learning model, an inference of the predicted user command (para [0015] - the trained machine learning model(s) is configured to process the sensor data and generate a score as output, the score indicative of a probability that a type of user input will be provided to the game controller); and using the inference for generating the predicted user command (para [0015] - based at least in part on the machine-learned score, the computing system may generate, on behalf of the player, game control data that corresponds to the type of user input, and may provide the proactively-generated game control data to the video game as input so that video game data that is output from the video game can be sent to a client machine (e.g., a second client machine of a second player of the video game) to compensate for latency of the video game platform).
13. Bleasdale-Shepherd discloses the method of Claim 12, wherein the first machine learning model is trained on at least one dataset of camera inputs and respective ground truth user commands (para [0033] - the computing system 102 may train a machine learning model(s) 104 using historical data sampled from the datastore 124. For example, the computing system 102 may access a portion of the historical data as sampled data, and use the sampled data to train the machine learning model(s) 104. In some embodiments, the portion of the data used as training data is represented by a set of features, and is labeled with a label that indicates one of multiple types of user input corresponding to historical game control data 116; para [0034] - the machine learning model(s) 104 may be trained using historical sensor data 118 collected in the datastore 124. Additionally, or alternatively, the machine learning model(s) 104 may be trained using historical game state data 126 that includes game sates of a video game 120 that occurred during past sessions of the video game 120; para [0071] - this may be implemented as an error-driven learning technique (a type of reinforcement learning) where the machine learning model(s) being trained are tasked with predicting the outputs based on the sample training data provided as input, and the training component 308 determines (e.g., measures) the prediction error of the model's prediction, which may be based on the labels from the training data (e.g., the known correct output values, often referred to as ground truth data)).
14. Bleasdale-Shepherd discloses the method of Claim 13, wherein at least some of the camera inputs of the at least one dataset indicate user gestures to actuate computer game controllers (para [0034] - the machine learning model(s) 104 may be trained using historical sensor data 118 collected in the datastore 124; para [0045] - the game controller 106 includes one or more input/output (I/O) devices 200, such as finger-operated controls (e.g., joysticks, trackpads, triggers, depressible buttons, etc.), potentially other types of input or output devices, such as a touchscreen(s), a microphone(s) to receive audio input, such as user voice input, a camera(s) or other types of sensor (e.g., sensor(s) 202) that can function as an input device to receive gestural input, such as motion of the game controller 106 and/or a hand of the user 110).
15. Bleasdale-Shepherd discloses the method of Claim 11, comprising: providing, as an input to the second machine learning model, game state data and the predicted user command; receiving, as an output from an activation layer of the second machine learning model, an inference based on the game state data, the inference indicating the determined time for controlling the first computer game according to the predicted user command; and controlling, at the determined time indicated by the inference, the first computer game according to the predicted user command (para [0040], [0060]-[0061]).
16. Bleasdale-Shepherd discloses the method of Claim 15, wherein the second machine learning model is trained on at least one dataset of game state data, user commands, and respective ground truth times for controlling one or more computer games (para [0040], [0060]-[0061]).
17. Bleasdale-Shepherd discloses the method of Claim 11, wherein determining the time for controlling the first computer game according to the predicted user command comprises using a rules-based software algorithm to determine, in part and based on game state data and the predicted user command, the time for controlling the first computer game according to the predicted user command [0040], [0060], [0077].
18. Bleasdale-Shepherd discloses a system comprising: at least one computer medium that is not a transitory signal and that comprises instructions executable by at least one processor assembly to: generate a predicted user command to a computer game, the generating comprising processing input from a sensor using a first machine learning model; determine, based on the predicted user command, a time for controlling the computer game according to the predicted user command, the determining comprising processing the predicted user command using a second machine learning model different from the first machine learning model; and release the predicted user command to the computer game at the determined time, the determined time being a time during which a game action occurs for which the predicted user command is to be executed, the determined time being different from a second time at which the predicted user command is completed as similarly discussed above.
21. Bleasdale-Shepherd discloses the apparatus of claim 1, wherein the first machine learning model and the second machine learning model are elements of a multi-function machine learning model [0061].
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) 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bleasdale-Shepherd (US 2021/0146241 A1) as applied above and further in view of Osman (US 2021/0154583 A1).
8. Bleasdale-Shepherd discloses the apparatus of Claim 7, but does not expressly disclose wherein the at least one processor assembly is programmed with instructions to: delay control of the computer game according to the predicted user command until a game action occurs at the determined time for which the predicted user command is to be executed; and based on the game action occurring at the determined time, control the computer game according to the predicted used command. However, Osman discloses a system to predict game states to reduce latency wherein the at least one processor assembly is programmed with instructions to (para [0005], [0016]): delay control of the computer game according to the predicted user command until a game action occurs at the determined time for which the predicted user command is to be executed and based on the game action occurring at the determined time, control the computer game according to the predicted user command (para [0254]). It would have been obvious to one of ordinary skill in the art to modify Bleasdale-Shepherd with Osman and would have been motivated to do so to allow for accurate display of corresponding scenes based on known events in the game (para [0254] - “An example of the known event is an end of a game scene, which is a challenging scene to predict with the pre-determined level of prediction.").
19. Bleasdale-Shepherd discloses the system of Claim 18, wherein the instructions are executable to: release the predicted user command in advance of receiving input generated based on actuation of a computer game controller button that is predicted to be used to provide the predicted user command (para [0016]).
Bleasdale-Shepherd does not disclose the instructions being executable to: delay release of the predicted user command subsequent to a user completing the predicted user command based on a determination that the game action has not yet occurred for which to apply the predicted user command. However, Osman discloses a system to predict game states to reduce latency (para [0005], [0016]) wherein the instructions are executable to: delay release of the user command subsequent to the user completing the user command based on a determination that a game action has not yet occurred for which to apply the user command (para [0254]). It would have been obvious to one of ordinary skill in the art to modify Bleasdale-Shepherd with Osman and would have been motivated to do so to allow for accurate display of corresponding scenes based on known events in the game (para [0254] - “An example of the known event is an end of a game scene, which is a challenging scene to predict with the pre-determined level of prediction.").
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)
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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, David L. Lewis can be reached at (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seng H Lim/Primary Examiner, Art Unit 3715