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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an apparatus, comprising: at least one processor system programmed with instructions to: execute a model to identify a first video game to recommend to a video game player, the first video game being one not yet played by the video game player and based on the identification, generate a podcast in a voice of a video game character of a second video game already played by the video game player, the podcast discussing the first video game which is a mental process, that is all the limitations above could be done by a person analyzing a playing history of a player and generating a suggestion video using standard tools.
This judicial exception is not integrated into a practical application because the claims do not recite additional elements that would integrate the abstract idea into a practical application. The recited “model” would read on using a mental rubric to identify a suggestion candidate based upon. There is no improvement made to computer technology since the claims are generating a suggestion based upon play history by an observer. This is not related to a long standing problem in computer technology. Additionally, there is no practical application as there is no particular machine that is used to implement the claim language only generic computer components are used to perform the invention. Also, there is no transformation of the machine used in the application into a different state or thing. Lastly, the claims do not attempt to apply the abstract idea in a meaningful way beyond simply using the claimed machine.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims does not recite significantly more than a generic information processing machine consisting of one or more processors, a non-transitory memory and an evaluative model. However, each of these components are well-known and understood within the video game recommending art. Therefore, the claim is directed to an abstract idea that lacks significantly more and thus is not patent eligible.
All dependent claims are also rejected, because they merely further detail the abstract idea.
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 (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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5 and 10-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fear (pub. no. 20230177583) in view of foreign patent literature Kim (WO20250063363).
Regarding claim 1, Fear discloses an apparatus, comprising: at least one processor system programmed with instructions to (“Systems and methods are disclosed related to playstyle analysis for game recommendations. The recommendations described herein may be related to streaming content, local content, or a combination thereof. In addition, the video game recommendations may correspond to computer games, console-based games, virtual reality (VR) games, augmented reality (AR) games, mixed reality (MR) games, and/or other game types. Although the present disclosure is described primarily with respect to video games, this is not intended to be limiting. For example, the systems and methods described herein may be used for software recommendations, video recommendations, and/or other types of content recommendations without departing from the scope of the present disclosure.
Systems and methods are disclosed that provide approaches for generating game recommendations by capturing and analyzing a user's interaction with a game to determine a playstyle for the user for that game such that other games may be determined to have a similar playstyle (e.g., fast-paced, slow-paced, twitchy, calm, high-skill, etc.) and may be recommended to the user. In embodiments, the playstyle of a game may refer to the aspects of a game that relate to how a player interacts with a game independent of a genre or category of a game—although genre and category may still be considered, in embodiments. In further examples, the playstyle may represent the speed, response time, pace, control schemes, skill level, game rules, and/or any behavior associated with the gameplay of a game”, [0018] & [0019]):
execute a model to identify a first video game to recommend to a video game player, the first video game being one not yet played by the video game player (“Once a data representation of user interactions with a game has been generated, a game playstyle profile may be generated. The game playstyle profile may include data corresponding to the playstyle for any of a number of distinct users. For example, the game playstyle profile may be generated to include data representative of playstyles for any number (e.g., hundreds, thousands, etc.) of users for a particular game, and a game playstyle profile may be generated for any of a number of games. The game playstyle profile may include one or more playstyle attributes for the game. For example, a playstyle attribute may be a value, parameter, and/or distribution that indicates the playstyle of the users associated with the user interactions. As an example, a playstyle attribute may indicate that 85% of the players of a particular game move their input devices an average distance of 100 pixels per second, or that some percentage of the pixels are changed from frame to frame on average (e.g., where a larger percentage indicates a more twitchy or fast-paced playstyle and a smaller percentage indicates a more slow-paced playstyle). As a further example, a playstyle attribute may be represented using a histogram, probability distribution function (PDF), and/or cumulative distribution function (CDF) that represents how a set of users has interacted with a game. For example, a playstyle attribute may indicate that 50% of a game's players provide a mouse-click input between 10 and 35 times per minute. In some examples, the data representative of user interactions with a game can be provided to a machine learning model—e.g., neural network—to generate a game playstyle profile.
Once an aggregate game playstyle profile has been generated for a particular game, the profile can be compared to a user's personal playstyle corresponding to a different game to determine a similarity between the two playstyles. The user playstyle corresponding to a different game may be determined using the methods described above with respect to capturing and analyzing data representative of user interactions with a game. For example, a user playstyle may be determined with respect to a first game and then compared to the aggregated game playstyle profiles associated with a set of other games, to identify which games of the set of other games may share a similar style of play to user's playstyle as exhibited during playthrough of the first game. As an example, a user may play a first game and have an average movement speed of 500 pixels per second. In such an example, using the game's aggregated playstyle profiles of other games, games in which 80% (e.g., 80th percentile) of other users have an average movement speed of 500 pixels per second may be determined to have a similar playstyle to the user. In some embodiments, a similarity score indicating the similarity between a game's aggregate playstyle profile and a user playstyle can be calculated. For example, the similarity score may be based on comparing a first data representative of a user interactions with a first game (e.g., a player of the first game) to a second data representative of user interactions with a second game (e.g., a plurality of players of the second game). In some examples, the similarity score may be calculated using a machine learning model trained to determine the similarity between a game playstyle profile and a user playstyle. In some embodiments, the similarity score may be based solely on data representative of user interactions with a game, while in some embodiments, the similarity score may be based on a combination of the data representative of user interactions with a game and associated game attributes such as genre, keywords, console compatibility, aesthetic, age rating, content rating, etc.
In some embodiments, the similarity score may be used to provide one or more game recommendations to a user. For example, based on determining similarity scores between a user playstyle and the aggregate game playstyle profiles of each game of a set of other games, a game of the set of other games which has an aggregate game playstyle profile most similar to the user playstyle may be provided to the user as a recommended game. In some embodiments, a game recommendation may be provided to a user based on determining a similarity between the aggregate playstyle profile for a game that the user has played and the aggregate playstyle profiles of other games. In some examples, multiple games may be recommended to the user based on the similarity scores of the games. An indication may be provided to the user in association with a game recommendation, which indicates that based on their detected playstyle, one or more games have been identified as candidates for having a similar playstyle. For example, a message or graphical element may indicate to the user that since their playstyle was identified as “fast-paced and twitchy,” they may enjoy these other games for which a significant number of other players were identified with a “fast-paced and twitchy” playstyle. In some embodiments, games corresponding to a similarity score that is greater that a threshold similarity value may be recommended to a user. As such, game recommendations may be provided to a user that most closely match the user's playstyle. In this way, user-specific recommendations can be provided to the user, resulting in higher relevance and more accurate game suggestions”, [0023] - [0025]).
Regarding claim 1, it is noted that Fear does not disclose generating a podcast in a voice of a character discussing the recommended content. Kim however, teaches generating a podcast in a voice of a character discussing the recommended content (“FIG. 1 is a block diagram showing an exemplary configuration of a system (10) for personalized content recommendation according to an embodiment of the present disclosure. In the following descriptions, the system (10) for personalized content recommendation will be referred to as a recommendation system (10). The recommendation system (10) may be a computing device/system that recommends content to a user. Specifically, the recommendation system (10) may determine recommended content based on a user's content viewing history, etc., and generate personalized recommendation messages and recommendation videos corresponding to the determined recommended content, thereby providing a personalized content recommendation experience to the user. Referring to FIG. 1, the recommendation system (10) may include a user terminal (11) and a service server (12)”, [31];
“In the recommended content list determined based on metadata, the recommendation order for each content can be determined in various ways. For example, if the user wants to receive recommendations based on genre, the recommendation order can be determined in the order of the user's preferred genre, and if the user wants to receive recommendations based on cast, the recommendation order of contents featuring the user's preferred cast can be determined as high as possible. In addition, the total number of contents to be included in the recommended content list can also vary depending on the user's input.
The service server (12) can determine the content with the highest recommendation ranking among the recommended content list determined based on the user's metadata as the recommended content with priority. Thereafter, the service server (12) according to the embodiment of the present disclosure can generate a recommendation message for the determined recommended content based on the user's metadata. For example, the recommendation message can be generated based on the like history, such as "You like content A. Then, I think you'll like this content as well.", or can be a message describing the reason for recommending the corresponding content based on the user's content viewing history, such as "You like genre B. Then, I think you'll like this content as well."
Next, the service server (12) can generate a recommendation video composed of a generated recommendation message and the context of the recommended content using a generative AI algorithm. For example, the context of the recommended content may include the cast appearing in the recommended content, the content of the recommended content, etc. For example, if the recommended content is a movie, the recommended video may be a video in which the lead actor of the movie appears and a recommendation message is played in the voice of the lead actor for about 10 seconds.
In some cases, the recommended video may feature one cast member or multiple cast members. In cases where multiple cast members are featured, the recommended message may be played as a conversation between the cast members. In this way, personalized recommended videos are generated through generative AI algorithms, which can improve the accuracy of recommendations by reflecting the user's personal history related to viewing content rather than simply displaying recommended content.
The service server (12) can display the recommended video generated in this way in the recommended content area on the content service page and automatically play it. For example, the service page can be an execution screen of an application for receiving an OTT service or content streaming service from the service server (12) on the user terminal (11). The configuration of the content service page is described in more detail with reference to the drawings in FIG. 3 and below“, [36] – [40] ).
Exemplary rationales that may support a conclusion of obviousness include use of a known technique to improve similar devices (methods, or products) in the same way. Here both Fear and Kim are directed to systems for recommending content. To modify Fear so that the recommendation was delivered as a podcast narrated by a content related character as taught by Kim would be to use a known technique to improve a similar device in the same way. Therefore, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the claimed invention to add the Kim podcast to the Fear system. To do so would be to market new content in an exciting fashion thereby increasing the popularity of the new content.
Regarding claim 2, Fear discloses the model identifies the first video game based on the first video game being similar to the second video game ([0024]).
Regarding claim 3, Fear discloses the model identifies the first video game based on the video game player's playstyle ([0023] & [0024]).
Regarding claim 4, Fear discloses the model identifies the first video game based on the first video game being in a same game genre as one or more other video games played by the video game player ([0019]).
Regarding claim 5, Fear discloses the model identifies the first video game based on the first video game being in a group of video games that a game platform has indicated for recommendation to video game players (“In some embodiments, the similarity score may be provided to a recommendation generator 126, that may be configured to use the similarity scores corresponding to a list of games to determine which games may be used to generate a recommendation to a user”, [0045]).
Regarding claim 10, the combination of Fear and Kim discloses the podcast is generated to verbally recommend that the video game player play the first video game (Kim: [38] – [40]).
Regarding claim 11, the combination of Fear and Kim discloses the podcast comprises audio (Kim: [38] – [40]).
Regarding claim 12, the combination of Fear and Kim discloses the podcast comprises video, the video showing the video game character discussing the first video game (Kim: [38] – [40]).
Regarding claim 13, the combination of Fear and Kim discloses present the podcast at a device associated with the video game player (Kim: [38] – [40]).
Regarding claim 14, the combination of Fear and Kim discloses execute a generative artificial intelligence (AI) model to generate the podcast (Kim: [38]).
Claims 15-18 are directed to the methods implemented by the apparatuses of claims 1, 2, 5 and 13 respectively and are rejected for the same reasons as claims 1, 2, 5 and 13 respectively.
Claim 19 is directed to an article of manufacture that contains code that implements the apparatus of claim 1 and is rejected for the same reasons as claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAWRENCE STEFAN GALKA whose telephone number is (571)270-1386. The examiner can normally be reached M-F 6-9 & 12-5.
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 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LAWRENCE S GALKA/Primary Examiner, Art Unit 3715