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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/18/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 20, 22-32, and 35-37 are objected to because of the following informalities:
In each of claims 20, 22-32, and 36-37, “user operable input” should instead read “user-operable input”
In claim 24, “the operations comprises” should instead read “the operations comprise”
In claim 35, “computer implemented method” should instead read “computer-implemented method”
Appropriate correction is required.
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 (i.e., changing from AIA to pre-AIA ) 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 19-20, 28-32, 34-35, and 38 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 11,458,388 (hereinafter “Kestell”).
Regarding Claims 19, 35 and 38, Kestell discloses one or more processors (fig. 3: processor(s) 328); and
one or more storage devices storing instructions that, when executed by the one or more processors (fig. 3: storage device 326 storing instructions 306), cause the one or more processors to perform operations comprising:
receiving controller input information indicative of one or more user inputs for a controller device (col. 1, ll. 44-58: “a computer-implemented method for adjusting controller settings is provided. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user”), the controller device comprising one or more user-operable input elements (fig. 1: left and right joysticks 114 & 116, left and right triggers 110 & 112, directional pad 102, buttons 104);
predicting, for a given user-operable input element of the controller device having an initial sensitivity setting, sensitivity update information for the given user-operable input element using a trained machine learning model, wherein the trained machine learning model is configured to process the controller input information to generate the sensitivity update information (col. 1, ll. 25-58: “machine learning system may account for how users are interfacing with software… through a controller… The system may provide recommendations for configuration settings of the controller to aid each user's respective tendencies… providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising… controller sensitivity”), and
wherein the trained machine learning model has been trained using training data comprising previous controller input information for a plurality of users (col. 1, ll. 25-43: “The system may also account for configuration settings that are used by the most skilled users, and then makes a comparison to users who have similar tendencies to form recommendations”); and
updating the initial sensitivity setting for the given user-operable input element based on the sensitivity update information to obtain an updated sensitivity setting (fig. 2; col. 1, ll. 25-58: “The system may provide recommendations for configuration settings of the controller to aid each user's respective tendencies… The system may also be configured for automatic/dynamic adjustments, if desired by the user… the controller settings comprising at least one of controller sensitivity… also includes receiving approval of the user to implement the suggested adjustments to the controller settings… also includes adjusting the controller settings based on the approval of the user”).
Further regarding Claim 35, Kestell discloses a computer implemented method (col. 1, ll. 44-58: “a computer-implemented method for adjusting controller settings is provided”) comprising the above steps (see claim 19).
Further regarding Claim 38, Kestell discloses one or more non-transitory computer readable storage media storing instructions that, when executed by one or more processors (col. 2, ll. 10-20: “a non-transitory computer-readable storage medium is provided including instructions (e.g., stored sequences of instructions) that, when executed by a processor, cause the processor to perform a method for adjusting controller settings”), cause the one or more processors to perform operations comprising the above steps (see claim 19).
Regarding Claim 20, Kestell further discloses the given user operable input element is one of a control stick, a trigger button, or a touchpad of the controller device (fig. 1: left and right joysticks 114 & 116, left and right triggers 110 & 112).
Regarding Claim 28, Kestell further discloses the training data comprises instances of controller input information (claim 1: “a machine learning model trained at least in part by the controller input”),
each instance of controller input information having an associated sensitivity setting and associated metadata indicative of a degree of in-game success, wherein the trained machine learning model has been trained using the training data to learn a relationship between displacements for the given user operable input element, a sensitivity setting for the given user operable input element and a degree of in-game success (col. 5, ll. 30-49: “machine learning may be utilized to build a model that maps player skill level/play style built from all gathered player telemetry and settings to recommend changes to their control setup to improve certain aspects of how they play. For example, user performance data may be gathered through telemetry of the software. Additionally, configuration profiles (e.g., user profiles) can be shared on a social media platform and updated over time. The system may also account for configuration settings that are used by the most skilled users, and then makes a comparison to users who have similar tendencies to form recommendations;” col. 1, ll. 25-58: “the controller settings comprising… controller sensitivity”), and
wherein the operations comprise inputting at least the controller input information associated with the given user operable input element to the trained machine learning model and the trained machine learning model is configured to output the sensitivity update information indicative of a sensitivity setting for the given user operable input element correlated with an increase in in-game success (col. 5, ll. 59-65: “As another example, in a first-person shooter game, it may be determined that a player is often shot from behind. As a result, the system may recommend increasing turning sensitivity. Furthermore, if the player's aim with recoil removed is often sporadic and/or off target, the system may recommend reduced sensitivity while aiming down iron sites”).
Regarding Claim 29, Kestell further discloses the operations comprise updating the sensitivity setting for the given user operable input element by incrementing or decrementing a parameter associated with the sensitivity setting by a fixed amount (fig. 2; col. 6, ll. 45-57: “the recommendations 206 may be incremental, so that the user is able to adjust to the new controller settings. For example, if the user is accustomed to a joystick sensitivity setting of 5, and the sensitivity is suddenly increased to 10, then the user will likely not be able to interact as efficiently with the software because the change is too large. Instead, the system may increase the sensitivity incrementally to allow the user time to become accustomed to the new settings. Eventually, the user may be able to have a sensitivity of 10”).
Regarding Claim 30, Kestell further discloses the sensitivity update information is indicative of a value to be used for a parameter associated with the sensitivity setting, and wherein the operations comprise updating the sensitivity setting for the given user operable input element with the value for the parameter (fig. 2; col. 6, ll. 45-57: “the adjustments may be implemented automatically through continuous monitoring so that the user slowly becomes more adept with each new adjustment over time;” Examiner notes example settings of “5” or “10” may be the chosen value of the update information which will then be set as new sensitivity setting).
Regarding Claim 31, Kestell further discloses the controller device is a two-handed controller device (fig. 1; col. 4, ll. 37-48: “directional pad 102, left joystick 114, left bumper 106, and left trigger 110 may be controlled by a user's left hand… the right joystick 116, the buttons 104, the right bumper 108, and the right trigger 112 may be controlled by a user's right hand),
wherein the operations comprise predicting second sensitivity update information for another user operable input element having another sensitivity setting (fig. 2; col. 4, ll. 50-61: “the sensitivities of the joysticks 114, 116 may not be optimized for the user, which may cause unintended errors by the user;” col. 5, ll. 59-65: “As another example, in a first-person shooter game, it may be determined that a player is often shot from behind. As a result, the system may recommend increasing turning sensitivity. Furthermore, if the player's aim with recoil removed is often sporadic and/or off target, the system may recommend reduced sensitivity while aiming down iron sites;” Examiner notes the sensitivities of left joystick 114 and right joystick 116 are set separately and would compromise the “given” and “another” user operable inputs), and
wherein the given user operable input element is associated with a portion of the controller device configured to be operated by a first hand of a user and the another user operable input element is associated with another portion of the controller device configured to be operated by a second hand of the user (fig. 1: left joystick 114 and right joystick 116).
Regarding Claim 32, Kestell further discloses the given user operable input element and the other user operable input element are control sticks (fig. 1: left joystick 114 and right joystick 116).
Regarding Claim 34, Kestell further discloses the operations comprise processing a session of a video game based on the controller input information and the updated sensitivity setting (figs. 2-3; col. 3, ll. 56-67: “a machine learning system may account for how users are interfacing with software (e.g., a simulation, a video game…) through a controller;” col. 5, ll. 21-29: “an exemplary graphical user interface (GUI) 200 for automatically adjusting controller settings (e.g., settings of a controller), according to certain aspects of the present disclosure. The GUI 200 may include a listing of controller settings 202 and a log 204 of implemented adjustments 206 (e.g., recommendations)”).
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 21-22, 33, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Kestell as applied to claims 19 and 35 above, and further in view of US 2021/0146241 (hereinafter “Bleasdale”).
Regarding Claim 21, Kestell discloses predicting the sensitivity update information based on received controller input information but does not explicitly disclose measuring positional displacements for the control sticks or trigger buttons. However, Kestell modified by Bleasdale discloses the controller input information is indicative of at least one of: positional displacements for a control stick, positional displacements for a trigger button, or positions of touch inputs with respect to a touch pad (Bleasdale, par. 0012: “This raw sensor data may be sent along with the game control data (e.g., data generated by button presses, deflections of joysticks;” par. 0075: “The sensor data 118 received at block 502 may include, without limitation… displacement values”).
Kestell and Bleasdale are analogous arts because they both teach systems which use machine learning techniques to analyze previous controller use to make predictions with the goal of improving future gameplay experiences. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the prediction of Kestell with the particular measurement of Bleasdale because it is an objective means to measure the controller input information (Bleasdale, pars. 0012, 0075).
Regarding Claim 22, Kestell discloses predicting the sensitivity update information based on received controller input information but does not explicitly disclose measuring positional displacements during a given period of time. However, Kestell modified by Bleasdale discloses the operations for predicting the sensitivity update information comprises predicting the sensitivity update information based on at least some of the received controller input information indicative of positional displacements for the given user operable input element during a period of time (Bleasdale, par. 0052: “The sensor data 118 may include, for example, measurements in terms of displacement (e.g., displacement since the preceding time log)… sensor data 118 may further include times at which the sensor data 118 is generated and/or transmitted, e.g., at any suitable time interval, so that a history of sensor data 118 can be collected and temporarily, or permanently, stored on the game controller 106;” par. 0013: “sensor data… can be represented by a set of features and labeled to indicate one of multiple types of user input that caused corresponding game control data to be received within a time period since the sensor data was received”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the prediction of Kestell with the particular measurement of Bleasdale because it is an objective means to measure the controller input information (Bleasdale, pars. 0012, 0075).
Regarding Claims 33 and 37, Kestell further discloses the operations for predicting the sensitivity update information comprise predicting the sensitivity update information for updating the sensitivity setting for the given user operable input element to obtain the updated sensitivity setting in advance of a subsequent session of the video game or another video game (fig. 4; col. 1, ll. 25-58: “The system may provide recommendations for configuration settings of the controller to aid each user's respective tendencies… The system may also be configured for automatic/dynamic adjustments, if desired by the user… the controller settings comprising at least one of controller sensitivity… also includes receiving approval of the user to implement the suggested adjustments to the controller settings… also includes adjusting the controller settings based on the approval of the user;” Examiner notes these updated settings would inherently be received and set before the subsequent video game session).
Kestell does not explicitly disclose a recording of user inputs. However, Bleasdale discloses the received controller input information is a recording of user inputs for the controller device recorded during a previous session of a video game (par. 0052: “a history of sensor data 118 can be collected and temporarily, or permanently, stored on the game controller 106”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the prediction of Kestell with the recorded input information of Bleasdale in order to maximize training data for the machine learning model (Bleasdale, par. 0077).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Kestell in view of Bleasdale as applied to claim 22 above, and further in view of US 2023/0081279 (hereinafter “Fuller”).
Regarding Claim 23, modified Kestell does not explicitly disclose checking whether an amount of motion exceeds a threshold amount of motion. However, Fuller discloses predicting the sensitivity update information for increasing sensitivity associated with the given user operable input element based on whether the at least some of the received controller input information is indicative of an amount of motion exceeding a threshold amount of motion (par. 0085: “a computer program that sets the sensitivity on an input controller… that corrects for biases in a player's movements. A player makes a series movements from each of a plurality of starting positions to each of a plurality of target positions. The position error (e.g., the amplitude and direction of the difference between the landing position of the movement and the position of the target) is measured for each movement, and the average movement error is computed for each combination of a starting position and a target position. The computer program then manipulates the sensitivity of the input controller to correct for systematic biases in the player's movements;” Examiner notes a particular position error is the threshold amount; abstract: “comparing the input from the player during the period of time to an optimal input during the period of time in the game”),
the threshold amount of motion being identified based on the trained machine learning model (par. 0087: “receiving information about at least one persons' movements when interacting with a virtual environment… monitoring at least one of the persons' movements to assess the persons' performance interacting with the virtual environment, wherein performance comprises at least one of speed and accuracy of movement… applying… machine learning operations to analyze performance assessment”).
Kestell and Fuller are analogous arts because they both teach machine learning systems which evaluate and set the sensitivity of a controller. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the motion threshold of Fuller with the prediction model of Kestell in order to more effectively make a determination/prediction about the sensitivity by utilizing the gathered motion data and a comparison between optimal and actual motion (Fuller, abstract; pars. 0085-0087).
Claims 24 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Kestell as applied to claims 19 and 35 above, and further in view of Fuller.
Regarding Claims 24 and 36, Kestell further discloses the operations comprise evaluating, using the trained machine learning model, the sensitivity setting for the given user operable input element relative to the previous controller input information for the plurality of users, and to predict the sensitivity update information based on the evaluation relative to the previous controller input information (col. 11, ll. 38-42: “the process 400 may further include comparing the user profile of the user with other user profiles of other users. According to an aspect the process 400 may further include providing the suggested adjustments based on the comparing;” col. 6, ll. 35-44: “The log 204 may be updated to reflect each change that was made. The log 204 may also include a history of which recommendations were accepted or denied as well. With each choice by the user, the machine learning system may understand better how to adjust the controller settings to fit the tendencies of that specific user”).
Kestell does not explicitly disclose that the motion of the input element during a given period of time is utilized in this evaluation. However, Kestell modified by Fuller discloses the operations comprise evaluating, using the trained machine learning model, motion of the given user operable input element during a given period of time and the sensitivity setting for the given user operable input element relative to the previous controller input information for the plurality of users, and to predict the sensitivity update information based on the evaluation relative to the previous controller input information (par. 0086: “the performance of the relating to the player of can comprise comparing the input of the relating to the player of during the period of time in the game to an optimal input from the player during the period of time in the game;” par. 0008: “comparing the input from the player during the period of time in the game to an optimal input from the player during the period of time in the game;” par. 0085: “computer program then manipulates the sensitivity of the input controller to correct for systematic biases in the player's movements;” par. 0059: “input controllers that sense or measure physical movements (e.g., mouse, keyboard, eye tracker, motion capture, etc.)… each such input controller senses, measures, or estimates human movement and provides input signals to a computer to interact with the virtual environment”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the prediction model of Kestell with the input element motion evaluation of Fuller in order to utilize another metric (i.e., how the given input element motion compares to that of the training data set) to increase the accuracy of the prediction/recommendation (Fuller, abstract; pars. 0085-0086).
Claims 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Kestell in view of Fuller as applied to claim 24 above, and further in view of Bleasdale.
Regarding Claim 25, Kestell modified by Fuller further discloses the operations further comprise:
calculating an amount of in-game motion based on the motion for the given user operable input element during the given period of time and the sensitivity setting for the given user operable input element (Fuller, par. 0059: “input controllers that sense or measure physical movements (e.g., mouse, keyboard, eye tracker, motion capture, etc.), there are various methods for quantifying the accuracy of a player's movements. As noted above, each such input controller senses, measures, or estimates human movement and provides input signals to a computer to interact with the virtual environment. For example, the input signals could change 2D or 3D position and orientation of the virtual character being controlled by the player, or the 2D or 3D position and orientation of a virtual object in the virtual environment of the game. In one embodiment, the input signals control a cursor that is rendered on a computer monitor. The accuracy of a player's movements can be quantified either in terms of the physical movement in the real environment (e.g., the position or relative position of the mouse), or in terms of the virtual movement in the virtual environment (e.g., the position of a cursor that is controlled by the mouse);” par. 0085: “player makes a series movements from each of a plurality of starting positions to each of a plurality of target positions. The position error (e.g., the amplitude and direction of the difference between the landing position of the movement and the position of the target) is measured for each movement, and the average movement error is computed for each combination of a starting position and a target position. The computer program then manipulates the sensitivity of the input controller to correct for systematic biases in the player's movements”). The combination of the prediction model of Kestell with the input element motion evaluation of Fuller described above for Claim 24 would have included the calculation.
Modified Kestell discloses classifying the raw data (Fuller, pars 0080-0083) but does not explicitly disclose that the classifications are related to levels of in-game motion. However, Bleasdale discloses classifying, using the trained machine learning model, the received controller input information according to a classification from a plurality of candidate classifications corresponding to different levels of in-game motion (Bleasdale, fig. 4: “label data – e.g., sensor data and… game state data – with a label that indicates one of multiple types/classes of user input corresponding to the game control data received within the time period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the prediction model of modified Kestell with the classifications of Bleasdale in order to more effectively process the new input information and to improve the functioning of the learning model (Bleasdale, pars. 0060-0061).
Regarding Claim 26, Kestell modified by Fuller further discloses the operations comprise: in response to a classification corresponding to a level of in-game motion being greater than a first threshold level of in-game motion, predicting the sensitivity update information for decreasing the sensitivity setting for the given user operable input element (Fuller, par. 0085: “The position error (e.g., the amplitude and direction of the difference between the landing position of the movement and the position of the target) is measured for each movement, and the average movement error is computed for each combination of a starting position and a target position. The computer program then manipulates the sensitivity of the input controller to correct for systematic biases in the player's movements;” par. 0086: “the performance of the relating to the player of can comprise comparing the input of the relating to the player of during the period of time in the game to an optimal input from the player during the period of time in the game”).
Regarding Claim 27, Kestell modified by Fuller further discloses the operations comprise: in response to a classification corresponding to a level of in-game motion lower than a second threshold level of in-game motion, predicting the sensitivity update information for increasing the sensitivity setting for the given user operable input element (Fuller, par. 0085: “The position error (e.g., the amplitude and direction of the difference between the landing position of the movement and the position of the target) is measured for each movement, and the average movement error is computed for each combination of a starting position and a target position. The computer program then manipulates the sensitivity of the input controller to correct for systematic biases in the player's movements;” par. 0086: “the performance of the relating to the player of can comprise comparing the input of the relating to the player of during the period of time in the game to an optimal input from the player during the period of time in the game”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2022/0134239 (Constantin) teaches a skill assessment system in which machine learning techniques are used, in part, to make predictions and recommendations about adjustments to a user’s sensitivity settings
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIE DOSHER whose telephone number is (571) 272-4842. The examiner can normally be reached Monday - Friday, 10 a.m. - 6 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached at (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.G.D./Examiner, Art Unit 3715
/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
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