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 .
Response to Arguments
Applicant’s arguments, see p. 9, filed December 29, 2025, with respect to the objections to Claims 1 and 10 have been fully considered and are persuasive. The objections to Claims 1 and 10 have been withdrawn.
Applicant’s arguments with respect to claim(s) 1-9 have been considered but are moot because new grounds of rejection are made in view of Colvin (US 20240378970A1).
Applicant's arguments filed December 29, 2025, with respect to Claims 10-16, 18, and 19 have been fully considered but they are not persuasive. Applicant argues that Claim 10 has been amended similarly to that of Claim 1. Therefore, Claim 10 is allowable for similar reasons to those noted above (p. 12).
In reply, the Examiner points out that the amendments to Claim 10 are different than the amendments to Claim 1. Benedetto (US 20240226734A1) and Smith (US012316720B1) still teach the limitations of amended Claim 10, as discussed in the rejection for Claim 10 below.
Applicant’s arguments with respect to claim(s) 17 have been considered but are moot because new grounds of rejection are made in view of Sato (US 20070232381A1).
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.
Claim(s) 1 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1).
As per Claim 1, Bean teaches an intelligent system for generating at least one image asset (text-to-image generator 142 may include artificial intelligence configured to generate images, [0033]), comprising: a data storage device (process 800 can be stored as computer-readable instructions on a non-transitory computer-readable medium, [0085]); digital information, said digital information comprising a plurality of text-image pairs and an asset generator model trained using the plurality of text-image pairs (text-to-image generator 142 may include Computer Vision (CV) processes configured to generate the images, text-to-image generator 142 may include neural networks, CV processes are trained on large datasets of image-text pairs, from which statistical relationships between the pairs may be learned, [0035]), the asset generator model is stored in said data storage device; and a processing device, coupled to the data storage device, to execute a model-driven image generation tool ([0085], process 800 includes operation 802, where the computer system can receive an image generated by an automated image generator, [0086]), wherein the image generation tool is configured to: receive input regarding one or more image characteristics, said input comprising text indicating one or more attributes for a desired asset image (text-to-image generator 142 configured to generate images from a text prompt, [0033]); encode said text into a numerical representation processable by said asset generator model; extract one or more visual features and patterns from said input by said asset generator model to determine one or more attributes of said desired asset image; use said asset generator model to transform said numerical representation into generated image output based on said one or more attributes (text-to-image generator 142 may encode the initial text prompt into predefined inputs usable by a trained model to generate visual features in an image, the predefined inputs may include numerical representations of the words in the initial text prompt, [0034]); process and refine the generated image output using an image information creator to generate asset information; translate said generated asset information to a plurality of particular images for the desired asset image; and configure said plurality of particular images for display (text-to-image generator 142 generates multiple images from an initial text prompt, as the text-to-image generator 142 begins generating images from the initial text prompt, the text-to-image generator 142 may apply a different variable to each generated image, the text-to-image generator 142 may generate a first image with a first type of the requested content, a second image with a second type of the requested content, and so on, [0037]).
However, Bean does not expressly teach that the image asset is a game image asset, the asset generator model is a game asset generator model, the model-driven image generation tool is a model-driven game image generation tool, the image characteristics are game image characteristics. However, Benedetto teaches an intelligent system for generating at least one game image asset, comprising: a game asset generator model; and executing a model-driven game image generation tool, wherein the game image generation tool is configured to: receive input regarding one or more game image characteristics, said input comprising text indicating one or more attributes for a desired game asset image; use said game asset generator model to transform said text into generated image output (IGAI is provided to enable text-to-image generation, when the IGAI is customized, the machine learning and deep learning algorithms are tuned to achieve specific custom outputs, such as unique image assets to be used in gaming technology, [0069], when the user enters user input 412 to generate an image, such input is processed by an interpreter based on a currently active profile, translate the user input 412 into translated input that is fed to the image generation AI 102, user input 412 might include the word dark, and based on the user’s active profile, the word “dark” is mapped to additional words such as “fantasy”, “H. R. Giger”, etc., [0054]). Thus, this teaching of the game from Benedetto can be implemented into the device of Bean so that the image asset is a game image asset, the asset generator model is a game asset generator model, the model-driven image generation tool is a model-driven game image generation tool, the image characteristics are game image characteristics.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean so that the image asset is a game image asset, the asset generator model is a game asset generator model, the model-driven image generation tool is a model-driven game image generation tool, the image characteristics are game image characteristics because Benedetto suggests that it is well-known in the art that video games continue to achieve greater immersion through sophisticated graphics, players are able to enjoy immersive gaming experiences in which that participate and engage in virtual environments, and new ways of interaction are sought [0002].
However, Bean and Benedetto do not expressly teach that the plurality of text-image pairs is stored in said data storage device. However, Gong teaches a data storage device; digital information stored in said data storage device, said digital information comprising a plurality of text-image pairs (stores the abstract image as text-image data pairs in the abstract image training dataset, [0096]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean and Benedetto so that the plurality of text-image pairs is stored in said data storage device because Gong suggests that the text-image pairs need to be stored in order to retrieve the text-image pairs in order to use the retrieved text-image pairs to train the asset generator model [0096].
However, Bean, Benedetto, and Gong do not expressly teach that the game image asset is a casino game image asset, the game asset generator model is a casino game asset generator model, the model-driven game image generation tool is a model-driven casino game image generation tool, the game image characteristics are casino game image characteristics for one or more casino game symbols, the desired game asset image is a desired casino game asset image; and configure said plurality of particular images for display as one or more symbols in a slot game on a gaming machine display. However, Colvin teaches generating at least one casino game image asset; a casino game asset generator model; execute a model-driven casino game image generation tool, wherein the casino game image generation tool is configured to: receive input regarding one or more casino game image characteristics for one or more casino game symbols; and configure said plurality of particular images for display as one or more symbols in a slot game on a gaming machine display (EGMs, generally utilized in casino environments, are commonly referred to as slot machines, [0065], specialized AI game design applications and associated machine learning may develop game aspects such as slot machine game art and graphics, [0441], specialized graphics-based AI game design system is able to modify the graphics files as instructed or desired, specialized graphics-based AI game design system produces new graphics which may be specifically tailored, [0532], the term graphic as used herein may include reel symbols, [0409], game symbols 915 on the primary game display after a spin of the reels, the primary slot game, [0163]). Thus, this teaching of the casino slot game and symbols from Colvin can be implemented into the combination of Bean, Benedetto, and Gong so that the game image asset is a casino game image asset, the game asset generator model is a casino game asset generator model, the model-driven game image generation tool is a model-driven casino game image generation tool, the game image characteristics are casino game image characteristics for one or more casino game symbols, the desired game asset image is a desired casino game asset image; and configure said plurality of particular images for display as one or more symbols in a slot game on a gaming machine display.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, and Gong so that the game image asset is a casino game image asset, the game asset generator model is a casino game asset generator model, the model-driven game image generation tool is a model-driven casino game image generation tool, the game image characteristics are casino game image characteristics for one or more casino game symbols, the desired game asset image is a desired casino game asset image; and configure said plurality of particular images for display as one or more symbols in a slot game on a gaming machine display as suggested by Colvin. Colvin suggests that casinos derive much of their gaming revenue from slot machines. Slot machines can become stale after even short game play sessions [0002]. Thus, new game development for these electronic gaming machines is needed, but it is a very labor intensive and time-consuming exercise [0005]. Thus, it is advantageous to use graphics-based AI game design system to produce new graphics that are specifically tailored as desired, to easily and quickly produce the new graphics [0532].
As per Claim 4, Bean does not teach wherein said processing device further implements an image decoder which receives said generated image output and converts said output to a particular image. However, Benedetto teaches wherein said processing device further implements an image decoder which receives said generated image output and converts said output to a particular image (the output is provided to a decoder 612 that transforms that output to the pixel space, the output is also upscaled to improve the resolution, [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean so that said processing device further implements an image decoder which receives said generated image output and converts said output to a particular image because Benedetto suggests that this is needed to convert said output to a particular image that is able to be displayed [0075].
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1) in view of Litvak (US 20220101053A1).
As per Claim 2, Bean, Benedetto, Gong, and Colvin are relied upon for the teachings as discussed above relative to Claim 1. Colvin teaches model-driven casino game image generation tool [0065, 0441, 0532], as discussed in the rejection for Claim 1.
However, Bean, Benedetto, Gong, and Colvin do not teach wherein model-driven casino game image generation tool further comprises a discriminator component, said discriminator component configured to determine acceptability of said generated image output by determining whether or not the generated image output falls outside a defined tolerance range. However, Litvak teaches wherein model-driven game image generation tool further comprises a discriminator component, said discriminator component configured to determine acceptability of said generated image output by determining whether or not the generated image output falls outside a defined tolerance range (generator is trained on epochs of the training dataset while the discriminator 308 is held constant, the discriminator 308 is then trained on epochs of the training dataset while the generator is held constant, this process is repeated until the GAN converges, until the generator produces images that are correctly determined to be real by the discriminator 308 based on ground truth at a rate that is determined to be acceptable by a user, 90%, [0043], game engine software, used to generate images for video game software, [0047], generates synthetic images based on scene descriptions using game engine software, scene descriptions include ground truth corresponding to the generated synthetic images, [0057]). Since Colvin teaches model-driven casino game image generation tool [0065, 0441, 0532], this teaching of the discriminator component from Litvak can be implemented into the model-driven casino game image generation tool of Colvin.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that model-driven casino game image generation tool further comprises a discriminator component, said discriminator component configured to determine acceptability of said generated image output by determining whether or not the generated image output falls outside a defined tolerance range because Litvak suggests that this way, more realistic looking images are generated [0043].
As per Claim 3, Bean, Benedetto, Gong, and Colvin do not teach wherein said discriminator component is further configured to receive user input of the acceptability of said generated image output. However, Litvak teaches wherein said discriminator component is further configured to receive user input of the acceptability of said generated image output (generator is trained on epochs of the training dataset while the discriminator 308 is held constant, the discriminator 308 is then trained on epochs of the training dataset while the generator is held constant, this process is repeated until the GAN converges, until the generator produces images that are correctly determined to be real by the discriminator 308 based on ground truth at a rate that is determined to be acceptable by a user, 90%, [0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that said discriminator component is further configured to receive user input of the acceptability of said generated image output because Litvak suggests that this way, the images are generated to be realistic looking enough to a user [0043].
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1) in view of Denison (US 20240193821A1).
Bean, Benedetto, Gong, and Colvin are relied upon for the teachings as discussed above relative to Claim 4.
However, Bean, Benedetto, Gong, and Colvin do not teach wherein said processing device further implements a background remover and said particular image is processed by said background remover to remove one or more background portions of the particular image. However, Denison teaches wherein said processing device further implements a background remover and said particular image is processed by said background remover to remove one or more background portions of the particular image (remove such clump of trees from the portion of the background of the generated image, [0058], output is provided to a decoder that transforms the output to the pixel space, the output is also upscaled to improve the resolution, [0074]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that said processing device further implements a background remover and said particular image is processed by said background remover to remove one or more background portions of the particular image because Denison suggests that this way, background portions of the particular image can be removed as desired by the user [0058].
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1) in view of Ranzinger (US 20250029206A1).
Bean, Benedetto, Gong, and Colvin are relied upon for the teachings as discussed above relative to Claim 4.
However, Bean, Benedetto, Gong, and Colvin do not teach wherein said processing device further implements an upscaler and said particular image is processed by said upscaler to resize said particular image. However, Ranzinger teaches wherein said processing device further implements an upscaler and said particular image is processed by said upscaler to resize said particular image (neural network circuit that is configured to perform an upscaler to upscale an image, modify an image to adjust its size, [0642], decoding of multimedia data, including image, [0452]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that said processing device further implements an upscaler and said particular image is processed by said upscaler to resize said particular image because Ranzinger suggests that this is needed in order to resize said particular image to prepare it for use by applications [0642, 0579].
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1) in view of Azarian Yazdi (US 20250166236A1).
As per Claim 7, Bean, Benedetto, Gong, and Colvin are relied upon for the teachings as discussed above relative to Claim 1. The combination of Benedetto and Colvin teaches input regarding one or more casino game image characteristics, as discussed in the rejection for Claim 1.
However, Bean, Benedetto, Gong, and Colvin do not teach wherein said processing device is further configured to implement a stabilization component, wherein said input regarding said one or more casino game image characteristics is output by said stabilization component. However, Azarian Yazdi teaches wherein said processing device is further configured to implement a stabilization component, wherein said input regarding said one or more image characteristics is output by said stabilization component (normalize inputs to a model in order to stabilize a training process and improve the performance of the model, [0094], image generator creates an output image, the output image resemble text prompt entered into the text input field, user can then introduce a new text prompt into the text input field to generate additional images, [0034]). Since the combination of Benedetto and Colvin teaches input regarding one or more casino game image characteristics, as discussed in the rejection for Claim 1, this teaching of the stabilization component from Azarian Yazdi can be implemented into the combination of Benedetto and Colvin so that said processing device is further configured to implement a stabilization component, wherein said input regarding said one or more casino game image characteristics is output by said stabilization component.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that said processing device is further configured to implement a stabilization component, wherein said input regarding said one or more casino game image characteristics is output by said stabilization component because Azarian Yazdi suggests that this improves the performance of the asset generator model [0094, 0034].
As per Claim 8, Bean, Benedetto, Gong, and Colvin do not teach wherein said stabilization component receives one or more user prompts and processes said one or more user prompts by at least one of: optimizing said prompts and generating size information for one or more images to be created from said one or more user prompts. However, Azarian Yazdi teaches wherein said stabilization component receives one or more user prompts and processes said one or more user prompts by at least one of: optimizing said prompts and generating size information for one or more images to be created from said one or more user prompts (user input in the form of a text prompt 104, the text prompt 104, indicative of a desired image, is entered by a user via a text input field, transmission of the text prompt 104 to an image generation system for further processing, [0032], [0094]). This would be obvious for the reasons given in the rejection for Claim 7.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (US 20240320867A1), Benedetto (US 20240226734A1), Gong (US 20240037810A1), and Colvin (US 20240378970A1) in view of Zhang (US 20240386621A1).
Bean, Benedetto, Gong, and Colvin are relied upon for the teachings as discussed above relative to Claim 1.
However, Bean does not teach wherein said learned casino game asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with one or more text-image pairs used in training said casino game asset generator model. However, Benedetto teaches wherein said learned game asset generator model [0069] is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with data used in training said game asset generator model ([0069], system configured to learn the user’s understanding with words used as input for the image generation AI 102, this understanding can define a profile for the user that is used to interpret the user’s input words, user may wish to create different profiles to facilitate generation of images with different styles, based on different learned understandings of user preferences associated with the different profiles, a given profile maps words to other words and data defining a semantic understanding of words as determined for the specific profile, [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean so that said learned game asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with data used in training said game asset generator model because Benedetto suggests that this way, the learned game asset generator model is trained to recognize and process semantic relationships according to the user’s understanding and intent with words [0052].
However, Bean, Benedetto, and Gong do not teach said learned game asset generator model is a learned casino game asset generator model. However, Colvin teaches said learned casino game asset generator model, as discussed in the rejection for Claim 1.
However, Bean, Benedetto, Gong, and Colvin do not teach wherein said learned casino game asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with one or more text-image pairs used in training said casino game asset generator model. However, Zhang teaches wherein said learned asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with one or more text-image pairs used in training said asset generator model (during training, text-image pairs can be placed in semantic space, [0049]). Thus, this teaching of text-image pairs from Zhang can be implemented into said learned casino game asset generator model of the combination of Benedetto and Colvin so that said learned casino game asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with one or more text-image pairs used in training said casino game asset generator model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bean, Benedetto, Gong, and Colvin so that said learned casino game asset generator model is trained to recognize and process one or more of syntactical patterns, semantic relationships, and visual associations in accordance with one or more text-image pairs used in training said casino game asset generator model because Zhang suggests that it is well-known in the art to train said learned asset generator using text-image pairs [0001].
Claim(s) 10, 12, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) in view of Smith (US012316720B1).
As per Claim 10, Benedetto teaches a method of presenting a game at a gaming device (plays a video game 204 executed by a game machine 202, [0038]) comprising: receiving, at a game asset generation device comprising a processor, memory and machine-readable code stored in said memory and executable by said processor, player input regarding one or more game image characteristics (embodiments can be fabricated as computer readable code on a computer readable medium, the computer readable medium is any data storage device that can store data, which can be thereafter be read by a computer system, [0095], [0069, 0054]); implementing, by said processing device a model-driven game image generation tool, wherein the game image generation tool is configured to: receive said input regarding one or more game image characteristics; extract one or more visual features and patterns from said input by a learned game asset generator model to determine one or more attributes of said one or more game image characteristics; transform said input into a plurality of generated images using said learned game asset generator model [0069, 0054]; process and refine the plurality of generated images using an image information creator to generate asset information; and translate said generated asset information to a plurality of particular images for the one or more game image characteristics (conditioning process assists in shaping the output toward the desired output, using structured metadata, the structured metadata may include information gained from the user input to guide a machine learning model to denoise progressively in stages until the processed denoising is decoded back to a pixel space, in the decoding stage, upscaling is applied to achieve an image that is of higher quality, when the IGAI is customized, the machine learning algorithms are tuned to achieve specific custom outputs, such as unique image assets to be used in gaming technology, [0069]); storing said plurality of particular images in a database ([0069], graphics memory storing pixel data for each pixel of an output image, data defining the desired output images can be stored in graphics memory, [0078]); and transmitting said plurality of particular images [0069] to said gaming device having a video display (client device required to receive video output from the cloud-based video game and render it on a local display, [0086], client device 100 can be a game console, [0029]), a processor, a memory, and machine-readable code stored in said memory and executable by said processor (game machine 202 is a local device (e.g. computer, game console), [0038], [0095]) to present a game by displaying game information via said video display, said game information comprising one or more of said plurality of particular images [0086, 0069].
However, Benedetto does not teach that the game is a wagering game. However, Smith teaches generative artificial intelligence services to generate images based on a prompt (generative artificial intelligence services (to generate images based on a prompt), col. 7, lines 52-54). Smith teaches presenting a wagering game by displaying game information via said video display, said game information comprising one or more of said plurality of particular images (this transaction path could be employed to facilitate various times of online games, such as reel-based games, and other types of wagering games, col. 25, lines 48-50; col. 7, lines 52-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto so that the game is a wagering game as suggested by Smith. Smith suggests that it is well-known in the art that online entertainment services have grown in popularity in recent years, as most of the world’s population now has reliable, high-speed Internet access. Online gaming is also a popular and growing set of entertainment services. Wagering games are well-known (col. 23, line 66-col. 24, line 17).
As per Claim 12, Benedetto teaches wherein said input comprises text indicating one or more attributes for at least one desired game asset image [0069, 0054].
As per Claim 14, Benedetto does not teach wherein said plurality of particular images comprise one or more slot game images. However, Smith teaches wherein said plurality of particular images comprise one or more slot game images (reel-based games are primarily slot games that consist of spinning reels with symbols, users win when the reels stop spinning to display specific combinations of symbols, symbols can be fruits, or other types such as those related to a specific theme, col. 24, lines 18-23; col. 7, lines 52-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto so that said plurality of particular images comprise one or more slot game images as suggested by Smith. Smith suggests that it is well-known in the art that online entertainment services have grown in popularity in recent years, as most of the world’s population now has reliable, high-speed Internet access. Online gaming is also a popular and growing set of entertainment services. Slot games are well-known (col. 23, line 66-col. 24, line 23).
As per Claim 18, Benedetto teaches wherein said input regarding one or more game image characteristics comprises one or more descriptive terms or phrases [0069, 0054].
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) and Smith (US012316720B1) in view of Morishita (US010937066B2).
Benedetto and Smith are relied upon for the teachings as discussed above relative to Claim 10. Benedetto teaches storing said plurality of particular images [0069, 0078].
However, Benedetto and Smith do not teach wherein said plurality of particular images are stored in association with an account of a player and said plurality of particular images are transmitted to said gaming device based upon identification of said account of said player at said gaming machine. However, Morishita teaches wherein said plurality of images are stored in association with an account of a player and said plurality of images are transmitted to said gaming device based upon identification of said account of said player at said gaming machine (game application corresponds to one of the plurality of images that are stored in at least one of the memory and a storage device connected to the terminal device via the network, the plurality of images being in association with predetermined user identification information, col. 14, lines 36-41). Since Benedetto teaches storing said plurality of particular images [0069, 0078], this teaching from Morishita can be implemented into the device of Benedetto so that said plurality of particular images are stored in association with an account of a player and said plurality of particular images are transmitted to said gaming device based upon identification of said account of said player at said gaming machine.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto and Smith so that said plurality of particular images are stored in association with an account of a player and said plurality of particular images are transmitted to said gaming device based upon identification of said account of said player at said gaming machine because Morishita suggests that this way, a player can easily retrieve images for their particular gameplay (col. 14, lines 36-41).
Claim(s) 13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) and Smith (US012316720B1) in view of Bean (US 20240320867A1).
As per Claim 13, Benedetto and Smith are relied upon for the teachings as discussed above relative to Claim 12. Benedetto teaches wherein said processing device is further configured to implement said game asset generator model [0069].
However, Benedetto and Smith do not teach wherein said processing device is further configured to implement said game asset generator model and encode said text into a numerical representation processable by said game asset generator model. However, Bean teaches wherein said processing device is further configured to implement said asset generator model and encode said text into a numerical representation processable by said asset generator model [0034]. Since Benedetto teaches wherein said processing device is further configured to implement said game asset generator model [0069], this teaching of the numerical representation from Bean can be implemented into the device of Benedetto so that said processing device is further configured to implement said game asset generator model and encode said text into a numerical representation processable by said game asset generator model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto and Smith so that said processing device is further configured to implement said game asset generator model and encode said text into a numerical representation processable by said game asset generator model because Bean suggests that this is needed so that the text can be processed by the asset generator model [0034].
As per Claim 16, Benedetto teaches wherein said game asset generator model utilized by said model-driven generation tool is trained using a machine learning algorithm to transform said input into a plurality of particular images (generation of an output image by an image generation AI (AGAI), can include artificial intelligence processing engines, AI model is generated using training data from a data set, the data set selected for training, [0068], [0069]).
However, Benedetto and Smith do not teach wherein said game asset generator model utilized by said model-driven generation tool is trained using a machine learning algorithm and a plurality of text-image pairs to transform said input into said plurality of particular images. However, Bean teaches wherein said asset generator model utilized by said model-driven generation tool is trained using a machine learning algorithm and a plurality of text-image pairs to transform said input into said plurality of particular images [0035]. Thus, this teaching of the text-image pairs from Bean can be implemented into the game asset generator of Benedetto so that said game asset generator model utilized by said model-driven generation tool is trained using a machine learning algorithm and a plurality of text-image pairs to transform said input into said plurality of particular images.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto and Smith so that said game asset generator model utilized by said model-driven generation tool is trained using a machine learning algorithm and a plurality of text-image pairs to transform said input into said plurality of particular images because Bean suggests that this way, statistical relationships between the pairs are learned, and subsequently utilized to produce high-quality images from subsequent text prompts [0035].
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) and Smith (US012316720B1) in view of Litvak (US 20220101053A1).
Benedetto and Smith are relied upon for the teachings as discussed above relative to Claim 10. Benedetto teaches that the user is a player (player playing a game segment, [0076]).
However, Benedetto and Smith do not teach wherein said processing device is configured to receive player input regarding acceptability of said plurality of particular images and to modify said model-drive generation tool based thereon. However, Litvak teaches generator is trained on epochs of the training dataset while the discriminator 308 is held constant. The discriminator 308 is then trained on epochs of the training dataset while the generator is held constant. This process is repeated until the GAN converges, until the generator produces images that are correctly determined to be real by the discriminator 308 based on ground truth at a rate that is determined to be acceptable by a user, 90% [0043]. Thus, if it is not determined to be acceptable by the user, then the process repeats to train and modify the GAN. Thus, Litvak teaches wherein said processing device is configured to receive user input regarding acceptability of said plurality of particular images and to modify said model-driven generation tool based thereon [0043]. Since Benedetto teaches that the user is a player [0076], this teaching from Litvak can be implemented into the device of Benedetto so that said processing device is configured to receive player input regarding acceptability of said plurality of particular images and to modify said model-drive generation tool based thereon. This would be obvious for the reasons given in the rejection for Claim 3.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) and Smith (US012316720B1) in view of Pacey (US 20090075721A1) and Sato (US 20070232381A1).
Benedetto and Smith are relied upon for the teachings as discussed above relative to Claim 10.
However, Benedetto and Smith do not teach wherein said processing device is further configured to link said plurality of particular images to a math model for said game. However, Pacey teaches wherein said processing device is further configured to link said plurality of particular images to a math model for said game (generating the output images, displaying images of symbols, the symbols can be of the type generally found on a slots game (lemon, cherry), display images that simulate the mechanical reels of a slots machine, [0168], different game assets, such as math (probability distribution tables, pay tables), [0191]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto and Smith so that said processing device is further configured to link said plurality of particular images to a math model for said game because Pacey suggests that math, such as probability distribution tables and pay tables, are needed for a slot game [0168, 0191].
However, Benedetto, Smith, and Pacey do not expressly teach wherein said math model includes a table of game outcomes which are randomly selected for display to a player, wherein said table is tied to particular odds of winning and losing outcomes and a particular household, wherein said plurality of particular images are matched to said table. However, Sato teaches wherein said math model includes a table of game outcomes which are randomly selected for display to a player, wherein said table is tied to particular odds of winning and losing outcomes and a particular household (payout table indicating the line odds winning combinations and the payouts thereof, [0065]), wherein said plurality of particular images are matched to said table (the winning combination and the payout corresponding to the composite image data are determined based on the symbol pattern corresponding to the composite image data and the payout tables, [0097]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto, Smith, and Pacey so that said math model includes a table of game outcomes which are randomly selected for display to a player, wherein said table is tied to particular odds of winning and losing outcomes and a particular household, wherein said plurality of particular images are matched to said table because Sato suggests that this way, the awards are not always awarded based on the predetermined pattern, so it does not cause the player to feel that the video slot machine is monotonous [0006, 0065, 0097].
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto (US 20240226734A1) and Smith (US012316720B1) in view of Abdelkarim (US 20200384356A1).
Benedetto and Smith are relied upon for the teachings as discussed above relative to Claim 18.
However, Benedetto and Smith do not teach wherein said processing device is further configured to utilize information regarding a configuration of said video display of said gaming device to generate said plurality of particular images. However, Abdelkarim teaches wherein said processing device is further configured to utilize information regarding a configuration of said video display of said gaming device to generate said plurality of particular images (software installed on the gaming attachment is configured to generate an image on the combined adjoining displays (the computing device display, and the gaming attachment display) based on detected configuration information, and relative orientation or angle between the displays, [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Benedetto and Smith so that said processing device is further configured to utilize information regarding a configuration of said video display of said gaming device to generate said plurality of particular images because Abdelkarim suggests that this image is resized and converted according to the configuration and orientation of the display so that the image is displayed correctly [0048].
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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JH
/JONI HSU/Primary Examiner, Art Unit 2611