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 .
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)(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) 1-3, 10-14, and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Benedetto et al. (US 2019/0291010 A1).
Regarding claim 1, Benedetto discloses an apparatus, comprising:
at least one processor system configured to (see par. [0033], As shown, system 10 includes a game server 205 executing the game processor module 201 that provides access to a plurality of interactive gaming applications):
execute a first model to determine that a first video game player is a video game coach candidate (see par. [0045], For example, the qualification may be given to a player that is an expert of other games, or when a player has played the subject gaming application with high skill, or when a player achieves a certain task or quest identified as being a qualification standard (e.g., qualification boss, intermediate boss, end boss, etc.));
based on execution of the first model to determine that the first video game player is a video game coach candidate, present a prompt at a first device of the first video game player regarding whether the first video game player would like to be considered as a video game coach (see par. [0046], the request is sent to the available experts who are most likely to be able to help; also see par. [0102], In another embodiment, experts are polled one at a time to determine whether they want to provide assistance);
receive an affirmative response to the prompt (see par. [0046], In one implementation, the first expert from the filtered set to accept the help request; also see par. [0102], During the polling process, the first expert to respond affirmatively is assigned to provide assistance, as will be further described in FIG. 3B);
based on the affirmative response, execute a second model to match the first video game player with a second video game player that the first video game player is to coach in gameplay (see par. [0046], selected as the expert providing assistance; also see par. [0101], matching experts to players requesting help); and
based on the match, facilitate a communication channel between the first device of the first video game player and a second device of the second video game player for the first video game player to coach the second video game player regarding a particular aspect of a video game (see par. [0046], In that case, a help session is established between the player and the selected expert).
Regarding claims 2 and 13, Benedetto discloses wherein the second model uses one or more clustering algorithms to match the first video game player with the second video game player (see par. [0138], The matching engine 123 performs an expert selection process 570. For example, the matching vectors are given a matching value after performing criteria matching 540, weighting 550, and the consideration of additional factor 560. For example, for expert E1 a matching value 591 (3.0) is generated. Also, for expert E5 a matching value 595 (4.6) is generated. Between the two experts, expert E5 has a higher matching value, which may indicate a better quality match, such that expert E5 may be better suited in providing assistance for the query of player P1 than expert E1).
Regarding claims 3 and 14, Benedetto discloses wherein the second model is executed to use play proficiency at a particular task that the second video game player is facing within a particular video game as a factor in matching the first video game player with the second video game player (see par. [0136], In addition, matching engine 123 may apply a weighting application 550 is performed on the matching vectors. For example, col 555 shows weighting factors for each of the parameters 545 in the game contexts of the gaming application used by the matching engine 123… As shown, character race has a weight of 0.6, shield a weight of 0.8, sword a weight of 0.4, jump skill a weight of 1.2, stamina a weight of 1.4 . . . bombs a weight of 0.2. That is, stamina and jump skill of the character is highly valued in the comparison. These factors may be important in accomplishing a particular task or quest).
Regarding claims 10 and 16, Benedetto discloses wherein the first model is executed to use play proficiency at the particular aspect of the video game in determining that the first video game player is a video game coach candidate (see par. [0094], In another implementation, the experts may have some qualification, such as skill of player, accomplishing a task, finishing a quest, finishing a portion of the game within a time period, finishing the game within a time period, etc., as previously described).
Regarding claim 11, Benedetto discloses a method, comprising:
accessing game engine data from the game engines of respective video game players, the respective video game players comprising a first video game player (see par. [0045], For example, the qualification may be given to a player that is an expert of other games, or when a player has played the subject gaming application with high skill, or when a player achieves a certain task or quest identified as being a qualification standard (e.g., qualification boss, intermediate boss, end boss, etc.));
analyzing the game engine data using a first model to determine that the first video game player is a video game coach candidate (see par. [0045], For example, the qualification may be given to a player that is an expert of other games, or when a player has played the subject gaming application with high skill, or when a player achieves a certain task or quest identified as being a qualification standard (e.g., qualification boss, intermediate boss, end boss, etc.));
based on determining that the first video game player is a video game coach candidate, presenting a prompt at a first device of the first video game player regarding whether the first video game player would like to opt-in to a video game coach program (see par. [0046], the request is sent to the available experts who are most likely to be able to help; also see par. [0102], In another embodiment, experts are polled one at a time to determine whether they want to provide assistance);
receiving an affirmative response to the prompt (see par. [0046], In one implementation, the first expert from the filtered set to accept the help request; also see par. [0102], During the polling process, the first expert to respond affirmatively is assigned to provide assistance, as will be further described in FIG. 3B);
based on the affirmative response, executing a second model to match the first video game player with a second video game player that the first video game player is to coach in gameplay (see par. [0046], selected as the expert providing assistance; also see par. [0101], matching experts to players requesting help); and
based on the match, facilitating a communication channel between the first device of the first video game player and a second device of the second video game player for the first video game player to coach the second video game player regarding a particular aspect of a video game (see par. [0046], In that case, a help session is established between the player and the selected expert).
Regarding claim 12, Benedetto discloses analyzing the game engine data using the first model to determine that a third video game player is not a video game coach candidate regarding the particular aspect of a video game; and based on a determination that the third video game player is not a video game coach candidate regarding the particular aspect of a video game, noting in a log that the third video game player is not a video game coach candidate regarding the particular aspect of a video game (see par. [0044], Game criteria may include threshold information to filter the pool of experts to a manageable set. For example, the threshold may be a minimum quality standard (e.g., expert rating, valuation, etc.), or recency of playing the gaming application so that the expert can provide the freshest assistance that is not encumbered with lack of immediate recall; thus by filtering the pool of experts, the system is noting who is a qualified expert and who is not a qualified expert).
Regarding claim 17, Benedetto discloses an apparatus, comprising:
at least one computer medium that is not a transitory signal and that comprises instructions executable by at least one processor system to (see par. [0007], a non-transitory computer-readable medium storing a computer program for providing gaming assistance is disclosed):
execute a clustering model to match a first video game player with a second video game player that the first video game player is to coach in gameplay (see par. [0138], The matching engine 123 performs an expert selection process 570. For example, the matching vectors are given a matching value after performing criteria matching 540, weighting 550, and the consideration of additional factor 560. For example, for expert E1 a matching value 591 (3.0) is generated. Also, for expert E5 a matching value 595 (4.6) is generated. Between the two experts, expert E5 has a higher matching value, which may indicate a better quality match, such that expert E5 may be better suited in providing assistance for the query of player P1 than expert E1); and
based on the match, facilitate a communication channel between a first device of the first video game player and a second device of the second video game player for the first video game player to coach the second video game player regarding a particular aspect of a video game (see par. [0046], In that case, a help session is established between the player and the selected expert).
Regarding claim 18, Benedetto discloses wherein the instructions are executable to execute a second model to determine that the first video game player is a video game coach candidate (see par. [0045], For example, the qualification may be given to a player that is an expert of other games, or when a player has played the subject gaming application with high skill, or when a player achieves a certain task or quest identified as being a qualification standard (e.g., qualification boss, intermediate boss, end boss, etc.)); and
based on execution of the second model to determine that the first video game player is a video game coach candidate, present a prompt at the first device of the first video game player regarding whether the first video game player would like to be considered as a video game coach (see par. [0046], the request is sent to the available experts who are most likely to be able to help; also see par. [0102], In another embodiment, experts are polled one at a time to determine whether they want to provide assistance).
Regarding claim 19, Benedetto discloses wherein the instructions are executable to responsive to receiving a response to the prompt that the first video game player would like to be considered as a video game coach, begin parsing data associated with the first video game player to match the first video game player with the second video game player (see par. [0138], The matching engine 123 performs an expert selection process 570. For example, the matching vectors are given a matching value after performing criteria matching 540, weighting 550, and the consideration of additional factor 560. For example, for expert E1 a matching value 591 (3.0) is generated. Also, for expert E5 a matching value 595 (4.6) is generated. Between the two experts, expert E5 has a higher matching value, which may indicate a better quality match, such that expert E5 may be better suited in providing assistance for the query of player P1 than expert E1).
Regarding claim 20, Benedetto discloses wherein the instructions are executable to provide the data as input to the clustering model as part of execution of the clustering model (see par. [0138], The matching engine 123 performs an expert selection process 570. For example, the matching vectors are given a matching value after performing criteria matching 540, weighting 550, and the consideration of additional factor 560. For example, for expert E1 a matching value 591 (3.0) is generated. Also, for expert E5 a matching value 595 (4.6) is generated. Between the two experts, expert E5 has a higher matching value, which may indicate a better quality match, such that expert E5 may be better suited in providing assistance for the query of player P1 than expert E1).
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.
Claim(s) 4 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto et al. (US 2019/0291010 A1) in view of Zavesky et al. (US 2020/0193264 A1).
Regarding claims 4 and 7-9, Benedetto discloses the apparatus as discussed above. However, Benedetto does not explicitly disclose wherein the second model is executed to use language type/manner of speech/speaking speed/speaking cadence as a factor in matching the first video game player with the second video game player.
Zavesky teaches a system that connects users with virtual agents where the system perform a voice analysis on the user to determine language type/manner of speech/speaking speed/speaking cadence in order to modify a virtual agent’s engagement attributes (i.e. matching the user with a similar virtual agent according to speaking characteristics) (see par. [0089], For example, the VAMC 114 can perform an audio analysis on the voice of the user 104 (or another voice(s) of another person(s) in the background) to facilitate determining what the user 104 (or other person(s)) said, characteristics (e.g., tone, cadence, timbre, volume, language, dialect, vocabulary level, . . . ) of the voice of the user 104 (or voice(s) of the other person(s)); also see par. [0155], voice or speech recognition and analysis techniques to recognize or determine characteristics such as word speed); also see par. [0091], For example, based at least in part on the results of the analysis 322 and/or the determination of the sentiment and dialog state 334, the VAMC 114 can determine modifications to the speech modulation or other characteristics of the verbal words presented by the VA 102 to the user 104). It would have been obvious to one of ordinary skill in the art to combine the apparatus of Benedetto with the matching based on voice attributes as taught by Zavesky in order to enhance the progress of the interaction to achieve a desired goal of the interaction (see Zavesky, par. [0091]).
Claim(s) 5, 6, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto et al. (US 2019/0291010 A1) in view of Cordell et al. (US 8,559,618 B1).
Regarding claims 5, 6, and 15, Benedetto discloses the apparatus as discussed above. However, Benedetto does not explicitly disclose wherein the second model is executed to use accent/dialect as a factor in matching the first video game player with the second video game player.
Cordell teaches a system for matching a caller and an agent based on various voice attributes including accent and dialect (see col. 2, lines 14-18, A customer's accent or regional dialect may be detected by an automated speech recognition function and routed to a virtual contact center agent of like accent/dialect to provide a sense of a neighborhood or local support interface for the client). It would have been obvious to one of ordinary skill in the art to combine the apparatus of Benedetto with the matching based on voice attributes as taught by Cordell in order to match users based on similar attributes (see Cordell, col. 2, lines 1-3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fear (US 2015/0067745 A1), Van Luchene (US 2014/0357352 A1), Summa et al. (US 2023/0211241 A1)
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/ALLEN CHAN/Primary Examiner, Art Unit 3715 12/17/2025