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
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) 21, 22, 24, 25, 27, 29, 30, 33, 34, 35, 36, 38, and 39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sardari (U.S. PGPUB 2019/0001219).
Re claims 21, 27, 29, and 35: Sardari discloses a method for providing game guidance content, comprising:
receiving a recording recorded from a game being played by a user (see paragraph [0033]: The user computing system 110 may further include an audio capture system 102 that can capture one or more utterances made by a user interacting with the user computing system 110.”);
using machine learning to identify a game objective by using the recording as an input (see paragraph [0036, 0089]: “The utterance classifier 118 can determine whether the utterance is a command, a query, or a statement. In some embodiments, the utterance classifier may utilize a machine learning system that determines the classification of the utterance based at least in part on training data obtained or captured previously or at an earlier point in time;” paragraph [0085]: as identified by Table 1, the system uses machine learning to identify commands from user utterances, i.e. recordings, that include specific game objectives);
selecting a content item based on the game objective (see paragraph [0082-0088]: At block 220, the response generator 138 generates a query response based at least in part on the data obtained from the one or more knowledge sources. In some embodiments, the response generator 138 may generate the query response based at least in part on previous utterances made by the user during the current session and/or during previous sessions interacting with the companion application 106 or the video game 112.); and
providing the content item to the user (see paragraph [0082-0088]: the response is outputted to the player in the form of audio or adjusting the game).
Re claims 22, 30, and 36: Sardari discloses with respect to the method of claim 21, wherein the using machine learning to identify a game objective by using the recording as an input comprises:
identifying the game objective from a game objectives database with which the machine learning interacts (see paragraph [0036]: “The utterance classifier 118 can determine whether the utterance is a command, a query, or a statement. In some embodiments, the utterance classifier may utilize a machine learning system that determines the classification of the utterance based at least in part on training data obtained or captured previously or at an earlier point in time;” see paragraph [0077]: “At decision block 208, the utterance classifier 118 determines whether the utterance is classified at the block 204 as a command, a query, or a statement. If it is determined that the utterance is classified as a command, the command manager 120 identifies the command corresponding to the utterance at block 210. The command manager 120 may identify the particular command corresponding to the utterance or referenced by the utterance by accessing a repository of commands stored at or available to the command manager 120.”).
Re claims 24, 32 and 38: Sardari discloses with respect to the method of claim 21, wherein the selecting a content item based on the game objective comprises: using the machine learning to identify the content item (see paragraph [0006, 0036, 0086]: “Generating the query response may comprise accessing user interaction data for the user, determining a plurality of eligible query responses based at least in part on the data obtained from the one or more knowledge sources, and generating a score for each of the eligible query responses based on the user interaction data and a parameter function. The parameter function may be generated based at least in part on a machine learning algorithm. Moreover, the method may include selecting the query response from the plurality of eligible query responses based at least in part on the score for each of the eligible query responses”).
Re claim 25, 33, and 39: Sardari discloses with respect to the method of claim 24, wherein the selecting a content item based on the game objective further comprises:
selecting the content item from a game guide database with which the machine learning interacts (see paragraph [0006, 0036, 0086]: the content offered in response is from a knowledge database, i.e. a game guide database designed to answer queries presented in Table 1, wherein machine learning interacts with said database to select the response with the best engagement score).
Re claim 28: Sardari discloses with respect to the method of claim 21, further comprising:
receiving a game guide request from the user (see paragraph [0085]: user utterances can include game guide requests, requesting on how to accomplish specific game tasks as listed in Table 1).
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.
Claim(s) 23 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of (U.S. PGPUB 2015/0170053).
Re claims 23 and 31: Sardari fails to disclose with respect to the method of claim 22, wherein the game objectives database is one or more of arranged, added to, updated, or modified via the machine learning. However, Miao discloses a system like Sardari to listen and record user voice commands during play of a video game (see paragraph [0062]), however Miao further discloses organizing a database of utterances identifying game objectives within ROM and RAM based databases (see paragraph [0045]). Specifically, Miao uses machine learning to add and subtract user voice command utterances from a ROM and RAM database of user voice command utterances, depending on the usage rate of the command by the player (see paragraph [0045]). It would have been obvious to one of ordinary skill in the art at the time the invention was filed, to modify the databases of Sardari with the database addition and subtraction feature of Miao for the purposes of promoting efficiency by accessing utterances with higher usage rates more quickly from RAM, while also saving space in RAM by storing lesser used words in ROM.
Claim(s) 26, 34, 37, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Di Giacomo Toledo (U.S. PGPPUB 2019/0329139 herein’ known as ‘139).
Re claims 26, 34, 37, and 40: Sardari fails to disclose with respect to the method of claim 25, wherein the game guide database is one or more of arranged, added to, updated, or modified via the machine learning. However, ‘139 similarity discloses a recommendation platform wherein players input voice commands that include game objectives, and in response the system accesses a recommendation database for responding to said commands with content that includes recommendations on how to accomplish said adjectives (see Abstract and paragraphs [0059, 0078, 0101]). ‘139 further teaches that within the recommendation database “the recommendations may be configured through various methods including manually curated lists by humans and/or through machine learning,” wherein curation inherently includes modifying said list (see paragraph [0098]). It would have been obvious to one of ordinary skill in the art at the time the invention was filed, to modify the recommendation database with machine learning curation feature of ‘139 for the purpose of creating recommendation list that includes recommendation information that better encompasses the demands of players.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Syed (U.S. PGPUB 2020/0298094) discloses a system that encompasses many of the claim limitations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REGINALD A RENWICK whose telephone number is (571)270-1913. The examiner can normally be reached Monday-Friday 11am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at (571)270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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REGINALD A. RENWICK
Primary Examiner
Art Unit 3714
/REGINALD A RENWICK/Primary Examiner, Art Unit 3715