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 .
Response to Arguments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. US 2022/0339542 A 1 to Damian, which was provided by Applicant in an IDS filed 07/07/2026, teaches training a model using source videos, some of which comprise metadata describing game controller inputs and some of which lack this metadata. The trained model is then used to find similar game scenarios in current gameplay as in the source videos and suggest game actions to a player to aid in player advancement.
Claim Rejections - 35 USC § 102
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 1, 19-22, 24-25, 29-31 and 33-34 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2022/0339542 to Damian.
Re claim 1, Damian discloses: A computer-implemented method
The Abstract, [0003]-[0004] and [0021-[0026] disclose that Damian discloses a method of using a trained machine learning model for analyzing video game source videos, extracting player actions from them, analyzing current game scenarios and generating overlays such as ghost layers that can teach players suggested controller operations in similar game scenarios.
comprising: maintaining a computer recording that does not have associated controller data indicating controller operations executed during generation of the computer recording;
[0022], some source videos 305 exhibiting players playing a video game, tutorials, walkthroughs or the like “may not support embedded metadata that may identify each action of the player at each point in the game … an analyzer 350 may segment a source video 305 into individual player actions 360-362 using an analyzer 359.” Any source videos 305 accessible to the invention of Damian lacking embedded metadata meet the limitation of being maintained computer recordings that do not have controller data.
generating, using a machine learning model trained using a training set comprising sequences of video frames from plural recorded computer recordings and first controller data that indicates associated first controller operations executed during generation of a corresponding sequence of video frames from the sequences of video frames,
[0025], a machine learning model 5 is constructed to provide ghost layer 200 functionality for assisting a player who is currently playing a game. The model is usable to determine whether similar scenarios exist in source videos as in current game play and to overlay screen shots or segments of video to facilitate advancement in the current game.
[0029], the machine learning model 5 may be implemented using reinforcement learning algorithms trained from source gameplay videos 305 to attempt to mimic the gameplay shown in the source videos 305. Game controller inputs to perform actions suggested in videos used to train the model are output as a ghost layer that is synchronized to frames of current gameplay.
Regarding the source videos 305, [0022] describes that some source game videos 305 support embedded metadata 376 indicating each action of the player. These source videos 305 can have player actions directly extracted and identified for use in generating walkthrough content for overlaying with an instant video game being played.
And as noted in [0003], identified player actions from metadata “can have a one-to-one correspondence with a game controller input received from a user.”
second controller data
indicating predicted controller operations previously executed during generation of the computer recording and
[0029], a ghost layer 200 is displayed to the user in the form of a video using a reinforcement learning algorithm that attempts to mimic the gameplay shown in source videos 305.
ii) for the computer recording that does not have associated controller data indicating controller operations executed during generation of the computer recording
[0022], the source videos 305 used to train the model 5 can include source videos 305 lacking embedded metadata describing player actions (controller inputs, see [0003]). For source videos lacking metadata, player actions can be inferred using analytics. The ghost layer 200 comprising suggested player actions mimicking source videos 305 described in [0029] can be based at least in part on videos lacking embedded metadata.
and transmitting at least a portion of the computer recording with corresponding data from the second controller data that indicates the predicted controller operations executed during generation of the computer recording.
[0029], a ghost layer comprising suggested player actions is output to a player playing a current game.
Re claims 25, 34, refer to the rejection of claim 1.
Re claims 19-20, 29-30,[0003] discloses that there can be a one-to-one correlation between player actions and controller input operations. As described in [0022] and [0029] a machine learning model trained using controller input operations correlated to each frame of source video can be used to generate ghost layer video that mimics the source video in relevant scenarios.
Re claim 21, 31, [0021] and [0027] discloses that sounds may be inputs to and outputs from a machine learning model.
Re claim 22, refer to the list of game controller types that may be usable to generate input data for source videos 305 in the invention of Damian. These include a joystick, a gamepad, a keyboard and keypad (which by definition of these peripheral types have buttons).
Re claim 24, 33, [0029] describes that a ghost layer providing game controller inputs to perform is synchronized frame-by-frame with current gameplay.
Allowable Subject Matter
Claims 16-18, 23, 26-28, and 32 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/STEVEN J HYLINSKI/ Primary Examiner, Art Unit 3715
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