CTFR 16/889,362 CTFR 90209 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Amendment Received 03/16/2026 Claim(s) 1-30 is/are pending. Claim(s) 1, 2, 7, 8, 13, 14, 19, 20, 25, and 26 has/have been amended. The 35 U.S.C § 103 rejection to claim(s) 1-30 have been fully considered in view of the amendments received on 03/16/2026 and are fully addressed in the prior art rejection below. Response to Arguments Received 03/16/2026 Regarding independent claim(s) 1, 7, 13, 19, and 25: Applicant’s arguments (Remarks, Page 11: ¶ 4 to Page 12: ¶ 2), filed 03/16/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C § 103 have been fully considered and are persuasive. Wherein, Shekhar et al. (US Patent No. 10665030 B1) fails to disclose to determine whether a second, subsequent text segment of the description describes the one or more object depicted in the generated one or more images; and in response to determining that the second, subsequent text segment of the description describes the one or more objects depicted in the generated one or more images, use the generated one or more images and the second, subsequent text segment of the description as input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of Shekhar et al. , in view of Cohen et al. (US PGPUB No. 20030059758 A1) , and further in view of Amer et al. (US PGPUB No. 20190304157 A1) . Applicant’s arguments (Remarks, Page 12: ¶ 4 to Page 13: ¶ 1), filed 03/16/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C § 103 have been fully considered and are persuasive. Wherein, Amer et al. fails to disclose to determine whether a second, subsequent text segment of the description describes the one or more object depicted in the generated one or more images; and in response to determining that the second, subsequent text segment of the description describes the one or more objects depicted in the generated one or more images. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of prior art as mentioned above. Applicant’s arguments (Remarks, Page 13: ¶ 3 to page 14 : ¶ 1), filed 03/16/2026, with respect to the rejection(s) of claim(s) 7, 13, 19, and 25 under 35 U.S.C § 103 have been fully considered and are persuasive due claim 7's, claim 13’s, claim 19’s and claim 25's similarity to claim 1. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of the prior art as mentioned above. Regarding dependent claim(s) 3-6, 9-12, 15-18, 21-24, and 27-30: Applicant’s arguments (Remarks, Page 14: ¶ 2-5), filed 03/16/2026, with respect to the rejection(s) of claim(s) 3-6, 9-12, 15-18, 21-24, and 27-30 under 35 U.S.C § 103 have been fully considered and are persuasive due the dependency upon claims 1, 7, 13, 19, and 25 respectively. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of the prior art as mentioned above. Regarding dependent claim(s) 2, 8, 14, 20, and 26: Applicant’s arguments (Remarks, Page 14: ¶ 4-5), filed 03/16/2026, with respect to the rejection(s) of claim(s) 2, 8, 14, 20, and 26 under 35 U.S.C § 103 have been fully considered and are persuasive due the dependency upon claims 1, 7, 13, 19, and 25 respectively. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. 07-37 AIA Applicant's arguments filed 03/16/2026 have been fully considered but they are not persuasive ; as expressed below . Regarding independent claim(s) 1 : Applicant argues (Remarks, Page 12, ¶ 3), that “For example, the cited portions of Pahud at best describe a method of NLP parsing to XML, then selection and rendering by an ‘animation engine’ from a graphics library to generate a scene per sentence. Pahud, FIGS. 1-3; [0035]-[0041], [0051]. However, these portions do not disclose determining whether a second, subsequent text segment of a description describes objects depicted in previously generated images, nor do they disclose using the generated images together with the subsequent text segment as inputs to one or more neural networks to generate additional images of a video. Rather, Pahud at best appears to describe sentence-level parsing followed by template-based rendering of scenes, without any determination step comparing subsequent text segments to previously generated images to reuse such images as inputs for further neural-network-based image generation. The deficiency of Shekhar and Pahud is not cured by combination with Amer.” The Examiner disagrees. Wherein, Applicant fails to view the teachings of Pahud et al. (US PGPUB No. 20060217979 A1) in relation with a first text segment corresponding sentence 1 (as illustrated within Fig. 7) which creates a depicted object (e.g. dragon) ( Pahud; [¶ 0061] ). As illustrated within Fig. 8 : PNG media_image1.png 607 490 media_image1.png Greyscale PNG media_image2.png 565 509 media_image2.png Greyscale the object (i.e. dragon) within the sentence becomes a generated scene and generated image (i.e. dragon graphic). Additionally, the now depicted object (of the dragon) within Fig. 8 is further describe with a description within a 2 nd subsequent text segment corresponding to sentence 2 ( Pahud; [¶ 0062] moreover, detected and analyzed input [¶ 0070-0071] ). As illustrated within Fig. 9 : PNG media_image3.png 578 492 media_image3.png Greyscale the object (of the dragon) being depicted within the generated image based on the pervious sentence is used in an updated or newly generated image (i.e. dragon graphic) corresponding to additional images depicting the object ( Pahud; [¶ 0062]; additionally, pronouns [¶ 0045] ). As illustrated within Fig. 10 : PNG media_image4.png 608 486 media_image4.png Greyscale wherein the animation engine uses the image of the object (i.e. dragon graphic) from a previous image generation in response to determining the object (i.e. dragon) is further described within the subsequent sentence. Additionally, the reuse of the image of the object (i.e. dragon graphic) is further taught within further modifications of the scene ( Pahud; [¶ 0065] ). As illustrated within Figs. 13-15 : PNG media_image5.png 531 445 media_image5.png Greyscale PNG media_image6.png 550 464 media_image6.png Greyscale PNG media_image7.png 576 475 media_image7.png Greyscale PNG media_image8.png 604 508 media_image8.png Greyscale wherein the image of the object (i.e. dragon graphic) is used/reused in response to several subsequent sentences are a description that describes the object (i.e. dragon). Moreover, graphics library ( Pahud; [¶ 0052-0053] ) in relation with retrieving and maintaining relationships/links ( Pahud; [¶ 0056-0058]; moreover, reuse of graphic based on keywords within a sentence [ id. ]; and moreover, selecting graphics in response to inputs in a repeatable manner [¶ 0070-0072 and ¶ 0074] ). Lastly, Applicant argues “previously generated images” and “determination step comparing subsequent text segments to previously generated images”, however the claim language of the independent claims is broader than as inferred by Applicant. Therefore, Applicant’s arguments above are not persuasive. Additionally, Pahud et al. has been removed from the current rejection (below) because of the nature of the claim language. Wherein, the amended subject matter is more directed toward determining the descriptions within subsequent text segments, than the reuse of image data based on a comparison/determination of an object within a being within a generated image as inferred by Applicant. Applicant should note that the independent claims recite no 1 st subsequent and therefore a 2 nd subsequent is redundant or indicative of a further text segment than that of a 2 nd text segment or a subsequent text segment. 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) . Claim Rejections - 35 USC § 112 07-30-01 AIA The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. 07-31-01 Claims 2, 8, 14, 20, and 26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Wherein, the incorporation of the limitation of “use only” within the subject matter of “… use only the one or more subsequent text segments as input to the one or more neural networks” fails to be supported within the Specification. For example, ¶ 0062 discloses a subsequent text segment and ¶ 0063 discloses that captured segments can be encoded 404 using layers of a convolution neural network (CNN), however the CNN is salient regarding only using subsequent text segments. Additionally, ¶ 0056 discloses a segment of text can be encoded using layers of a convolutional neural network (CNN), however this too is silent regarding the CNN only using segments. Still further, ¶ 0062-0064 which mention one or more text segments, CNN, and variational autoencoder (VAE) are silent regarding an input of only subsequent text segments as inputs into a neural network. Although, the Specification discloses “… when a text segment detected that then corresponds to these grape vines, these vines have appeared naturally and consistently, and do not just appear out of nowhere” within ¶ 0062, this is not equivalent and/or properly claimed within the amended subject matter of claim 2 (and similar claims 8, 14, 20, and 26). Even further, the Specification discloses a less limiting input for a neural network within ¶ 0054 than as amended. Additionally, the determination step/method of “… determining that one or more subsequent text segments of the description do not describe the one or more objects depicted in the generated one or more images” is silent within the disclosure. Although, ¶ 0062-0064 support a consistent of objects throughout an animation, the Specification is silent regarding determining a description do/does not describe one or more objects depicted. The Specification supports “… when a text segment detected that then corresponds to these grape vines, these vines have appeared naturally and consistently, and do not just appear out of nowhere” within ¶ 0062, “… a transformer can also have an ability to change previously generated animation if a determination is made that such change is appropriate based at least in part upon further context determined for this story” within ¶ 0063, and “… this process can check 410 cache to determine whether one or more objects to be included in animation have been previously generated, and thus have representations stored in cache … if it is determined 412 that an object was previously generated then its representation can be pulled 414 from cache and used to guide new animation of that object … if it is determined that an object was not previously generated, or does not have a representation in cache, then a new object animation can be generated 406 by this VAE” [¶ 0064]. However, ¶ 0062-0064 as mentioned above is silent regarding the amended subject matter of “ determining that one or more subsequent text segments of the description do not describe the one or more objects depicted in the generated one or more images ” (emphasis added). Wherein, ¶ 0062-0064 discloses determining if an object is generated in relation with memory as oppose to the depicted images. And wherein, ¶ 0062-0064 discloses when a text segment corresponds to an object that has appeared (i.e. detected that then corresponds to these grape vines, these vines have appeared naturally and consistently, and do not just appear out of nowhere) as oppose to detecting a description that does not describe an object depicted in the generated image. Therefore, there exists gaps in support within the Specification regarding the manner in which the language of claim 2’s (and similar claims 8’s, 14’s, 20’s, and 26’s) subject matter is claimed. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1, 4, 5, 7, 10, 11, 13, 16, 17, 19, 22, 23, 25, 26, 28, and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar et al., US Patent No. 10665030 B1, hereinafter Shekhar, in view of Cohen et al., US PGPUB No. 20030059758 A1, hereinafter Cohen, and further in view of Amer et al., US PGPUB No. 20190304157 A1, hereinafter Amer . Regarding claim 7 , Shekhar discloses a system ( Shekhar; a system [Col. 3, lines 28-50 and Col. 12, lines 36-54] ) comprising: one or more processors to use one or more neural networks to generate one or more images depicting one or more objects based, at least in part, on a first textual segment of a description of the one or more objects and one or more different images depicting the one or more objects generated ( Shekhar; the system [as addressed above] comprises one or more processors [Col. 3, lines 28-50 and Col. 13, lines 17-29], as illustrated within Fig. 1, to use one or more NNs [Col. 2, line 49 to Col. 3, line 26] to generate one or more images depicting one or more objects, as illustrated within Fig. 4, based (at least in part) on a 1 st textual segment of a description of the one or more objects, as illustrated within Fig. 3, and one or more different images depicting the one or more objects generated by the system [Col. 4, line 18 to Col. 5, line 7 and Col. 4, lines 36-52], as illustrated within Fig. 4; additionally, natural language processing [Col. 5, line 53 to Col. 6, line 33], and natural language scene description into a 3D scene in augmented reality [Col. 11, lines 22-65] ). Shekhar fails to explicitly disclose generating one or more images of a video based one or more textual descriptions and one or more different images of the video depicting the one or more objects generated by the one or more neural networks; determine whether a second, subsequent text segment of the description describes the one or more object depicted in the generated one or more images; and in response to determining that the second, subsequent text segment of the description describes the one or more objects depicted in the generated one or more images, use the generated one or more images and the second, subsequent text segment of the description as input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects. However, Cohen teaches using a computer in relation with generating one or more images ( Cohen; using a computer [¶ 0016], as illustrated within Fig. 1A, in relation with generating one or more images [¶ 0021 and ¶ 0024], as illustrated within Fig. 1C ); determine whether a second, subsequent text segment of the description describes the one or more object depicted in the generated one or more images ( Cohen; determine whether a 2 nd subsequent text segment of the description describes the one or more object depicted in the generated one or more images [¶ 0028-0030]; moreover, a computer (via a reader) is able to process a printed book [¶ 0023] and interact in a manner that causes a visual (and/or visual modification) [¶ 0024-0026], and further involves resume reading (corresponds to a 2 nd numerous subsequent text segments as well as 3 rd , 4 th , and/or numerous subsequent text segments implicitly) [¶ 0027 and ¶ 0029-0030], as illustrated within Fig. 2 ); and in response to determining that the second, subsequent text segment of the description describes the one or more objects depicted in the generated one or more images ( Cohen; in response to determining that the 2 nd subsequent text segment of the description describes the one or more objects depicted in the generated one or more images [¶ 0027 and ¶ 0029-0030] ), use the generated one or more images and the second, subsequent text segment of the description as input to the computer to generate one or more additional images of the video depicting the one or more objects ( Cohen; use the generated one or more images and the 2 nd subsequent text segment of the description as input to the computer to generate one or more additional images of the video depicting the one or more objects [¶ 0023-0025 and ¶ 0027]; moreover, visual cue(s) [ id. ] ). Shekhar and Cohen are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce a visualization effect. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar , to incorporate using a computer in relation with generating one or more images; determine whether a second, subsequent text segment of the description describes the one or more object depicted in the generated one or more images; and in response to determining that the second, subsequent text segment of the description describes the one or more objects depicted in the generated one or more images, use the generated one or more images and the second, subsequent text segment of the description as input to the computer to generate one or more additional images of the video depicting the one or more objects ( as taught by Cohen ), in order provide improved interactive storytelling that is engaging to a user while retaining the experience of printed books ( Cohen; [¶ 0003-0004 and ¶ 0007-0008] ). Shekhar as modified by Cohen fails to disclose use one or more neural networks to generate one or more images of a video depicting one or more objects; the video depicting the one or more objects generated by the one or more neural networks; and input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects. However, Amer teaches one or more processors ( Amer; the system [¶ 0037 and ¶ 0071], as illustrated within Figs. 1A and 1C, one or more processors [¶ 0108 and ¶ 0116] ) to: use one or more neural networks to generate one or more images of a video depicting one or more objects based, at least in part, on one or more text segments of a description of the one or more objects and one or more different images of the video depicting the one or more objects generated by the one or more neural networks ( Amer; the processors [as addressed above] to use one or more NNs to generate one or more images of a video [¶ 0008-0009] depicting one or more objects (i.e. visual representation(s) or animations of a scene) based (at least in part) on one or more text descriptions of the one or more objects (i.e. nodes and/or actors) [¶ 0034-0037 and ¶ 0047-0048] and implicitly one or more different images/visualizations of the video depicting the one or more objects (i.e. nodes and/or actors) (given information parsing of video, object detection, and object tracking/mapping) [¶ 0035, ¶ 0102-0103, and ¶ 0107-0108], as illustrated within Fig. 2C, generated by the one or more NNs [¶ 0048-0051]; wherein, a command/request creates an object or an actor to be included within the story, such that a story description describes attributes of a scene or event or describes an event or sequence of events within the story [¶ 0038]; wherein, an AI agent or system generating an animation or video in response to human input and/or human interactions relating to interactive storytelling [¶ 0032-0033]; and wherein, parsing [¶ 0090, ¶ 0092, and ¶ 0094] as well as detection and tracking/mapping within video [¶ 0094 and ¶ 0097-0099]; moreover, training one or more learning models [¶ 0110-0111, ¶ 0113, and ¶ 0134-0135] assists in animation using machine learning (e.g. CNN-RNN) [¶ 0160, ¶ 0186, ¶ 0188-0189], as illustrated within Fig. 6B and Fig. 9; such that, animation/video is/are generated by using searched/stored video data [¶ 0129-0130 and ¶ 0132]; wherein, text/queries and video data [¶ 0150 and ¶ 0152] produce a score [¶ 0155-0160] in relation with providing a video for playback [¶ 0163, ¶ 0166, and ¶ 0168-0169] ); and use the generated one or more images and the second, subsequent text segments of the description as input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects ( Amer; use the generated one or more images and one or more text segments (implicitly corresponding to a 2 nd subsequent text given a UI allows for multiple inputs of inputs) of the description as input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects [¶ 0034 and ¶ 0036-0039]; wherein, techniques for training a machine learning system to understand narrative and context, and employ both explicit and implicit knowledge about a scene or sequence of scenes or events to intelligently and/or accurately represent (e.g., as a data structure, an animation, or a video) scenes, sequences of events, or narrative [¶ 0033 and ¶ 0043]; in other words, a ML is configured to process a narrative and context that is indicative of a sequence corresponding to subsequent text segments; moreover, ML module receives textual information in relation with generating an animation [¶ 0043-0045]; additionally, generating a next frame through adding a difference to a previous frame [¶ 0051] ). Shekhar in view of Cohen and Amer are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce a visualization effect. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen , to incorporate one or more processors to: use one or more neural networks to generate one or more images of a video depicting one or more objects based, at least in part, on one or more text segments of a description of the one or more objects and one or more different images of the video depicting the one or more objects generated by the one or more neural networks; and use the generated one or more images and one or more text segments of the description as input to the one or more neural networks to generate one or more additional images of the video depicting the one or more objects ( as taught by Amer ), in order provide improved interactive storytelling that is automated and easy to use while being intelligent, accurate, and/or relevant to a user’s design ( Amer; [¶ 0005-0006, ¶ 0014, and ¶ 0031-0033] ). Regarding claim 10 , Shekhar in view of Cohen and Amer further discloses the system of claim 7, wherein the one or more processors are further to enable interaction through an AR application to enable one or more textual characters and the one or more images to be selectively or concurrently presented ( Shekhar; the one or more processors [as addressed within the parent claim(s)] are further to enable interaction through an AR application [Col. 3, lines 28-50] to enable one or more textual characters and the one or more images to be implicitly selectively or concurrently presented (given objects are provided to a scene from a dataset based on textual inputs, i.e. image/graphic objects are selected for a scene or mapped to a scene based on the terms/words recognized within the textual input they relate to) [Col. 4 , line 60 to Col. 5, line 52 and Col. 8, lines 40-59]; wherein, the natural language input, through NLP, converts to a scene graph [Col. 5, line 54 to Col. 6, line 5] which is further used to generate (visualizations/images corresponding to) a scene [Col. 10, lines 28-41 and Col. 12, lines 25-34]; moreover, text-to-AR scene conversion application [Col. 3, lines 51-56] ). Amer further teaches to enable one or more textual characters and the one or more images of the video to be selectively or concurrently presented at different points in time ( Amer; to enable one or more textual characters and the one or more images of the video to be selectively or concurrently presented implicitly at different points in time (given that the selected or concurrent images are presented as an animation) [¶ 0034 and ¶ 0040-0041]; moreover, NN driven animation through a sequence of frames associated with storytelling [¶ 0046 and ¶ 0051-0053]; additionally, [¶ 0047, ¶ 0156-0158, and ¶ 0160] ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate enabling one or more textual characters and the one or more images of the video to be selectively or concurrently presented at different points in time ( as taught by Amer ), in order provide improved interactive storytelling that is automated and easy to use while being intelligent, accurate, and/or relevant to a user’s design ( Amer; [¶ 0005-0006, ¶ 0014, and ¶ 0031-0033] ). Regarding claim 11 , Shekhar in view of Cohen and Amer further discloses the system of claim 7, wherein the one or more processors are further to provide information for the one or more objects to a transformer that is able to correlate those objects and determine a relevant importance of those objects ( Shekhar; the one or more processors [as addressed within the parent claim(s)] are further to provide information for the one or more objects to a transformer that is able to correlate those objects and determine a relevant importance (i.e. scene graph) of those objects [Col. 6, line 52 to Col. 7, line 21], as illustrated within Fig. 6; moreover, scene augmentation [Col. 7, line 22 to Col. 8, line 10] ), over a sequence of text including one or more textual characters based, at least in part, on the provided information ( Shekhar; determine a relevant importance (i.e. scene graph) [as addressed above] over a sequence of text including one or more textual characters based (at least in part) on the provided information [Col. 4, line 60 to Col. 5, line 52 and Col. 6, line 54 to Col. 7, line 2], as illustrated within Fig. 6 ). Amer further teaches to provide information for the one or more objects to a transformer that is able to correlate those objects and determine a relevant importance of those objects ( Amer; to provide information [¶ 0065-0067] for the one or more objects to a transformer that is able to correlate those objects and determine a relevant importance (i.e. composition graph) of those objects [¶ 0101-0104], as illustrated within Figs. 2B-2C; moreover, composition graph module [¶ 0070-0072]; still further, relevancy of importance also corresponds to ranking in relation with a score related to input/query data and video data [¶ 0150-0152 and ¶ 0155]; even further, relevancy of importance also corresponds to ranking in relation with a scores to identify relevant videos [¶ 0157-0161] ), over a sequence of text including one or more textual characters based, at least in part, on the provided information ( Amer; over a sequence of text including one or more textual characters based (at least in part) on the provided information [¶ 0101-0104], as illustrated within Figs. 2B-2C ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate to provide information for the one or more objects to a transformer that is able to correlate those objects and determine a relevant importance of those objects, over a sequence of text including one or more textual characters based, at least in part, on the provided information ( as taught by Amer ), in order provide improved interactive storytelling that is automated and easy to use while being intelligent, accurate, and/or relevant to a user’s design ( Amer; [¶ 0005-0006, ¶ 0014, and ¶ 0031-0033] ). Regarding claim 1 , the rejection of claim 1 is addressed within the rejection of claim 7, due to the similarities claim 1 and claim 7 share , therefore refer to the rejection of claim 7 regarding the rejection of claim 1. Regarding claim 4 , the rejection of claim 4 is addressed within the rejection of claim 10, due to the similarities claim 4 and claim 10 share , therefore refer to the rejection of claim 10 regarding the rejection of claim 4. Regarding claim 5 , the rejection of claim 5 is addressed within the rejection of claim 11, due to the similarities claim 5 and claim 11 share , therefore refer to the rejection of claim 11 regarding the rejection of claim 5. Regarding claim 13 , the rejection of claim 13 is addressed within the rejection of claim 7, due to the similarities claim 13 and claim 7 share , therefore refer to the rejection of claim 7 regarding the rejection of claim 13. Regarding claim 16 , the rejection of claim 16 is addressed within the rejection of claim 10, due to the similarities claim 16 and claim 10 share , therefore refer to the rejection of claim 10 regarding the rejection of claim 16. Regarding claim 17 , the rejection of claim 17 is addressed within the rejection of claim 11, due to the similarities claim 17 and claim 11 share , therefore refer to the rejection of claim 11 regarding the rejection of claim 17. Regarding claim 19 , the rejection of claim 19 is addressed within the rejection of claim 7, due to the similarities claim 19 and claim 7 share , therefore refer to the rejection of claim 7 regarding the rejection of claim 19; however, the subject matter/limitations not addressed by claim 7 is/are addressed below. Shekhar discloses a non-transitory machine-readable medium having stored thereon a set of instructions ( Shekhar; a non-transitory machine readable medium (i.e. computer software product) having stored thereon a program/instructions [Col. 12, line 55 to Col. 13, line 29] ), which if performed by one or more processors, cause the one or more processors to at least perform ( Shekhar; the program, as addressed above, causes the one or more processors to at least perform is performed by one or more processors [Col. 13, lines 16-29] ). (further refer to the rejection of claim 7) Regarding claim 22 , the rejection of claim 22 is addressed within the rejection of claim 10, due to the similarities claim 22 and claim 10 share , therefore refer to the rejection of claim 10 regarding the rejection of claim 22. Regarding claim 23 , the rejection of claim 23 is addressed within the rejection of claim 11, due to the similarities claim 23 and claim 11 share , therefore refer to the rejection of claim 11 regarding the rejection of claim 23. Regarding claim 25 , the rejection of claim 25 is addressed within the rejection of claim 7, due to the similarities claim 25 and claim 7 share , therefore refer to the rejection of claim 7 regarding the rejection of claim 25; however, the subject matter/limitations not addressed by claim 7 is/are addressed below. Shekhar discloses an augmented reality content generation system ( Shekhar; an AR content generation system [Col. 3, lines 28-56 and Col. 12, lines 36-55] ), comprising: memory for storing network parameters ( Shekhar; memory for storing network parameters [Col. 12, line 55 to Col. 13, line 29] ) for the one or more neural networks ( Shekhar; memory for storing network parameters [as addressed above] implicitly for the one or more NNs [Col. 2, line 49 to Col. 3. line 26, Col. 8, line 40-53, and Col. 9, lines 10-46] ). (further refer to the rejection of claim 7) Regarding claim 28 , the rejection of claim 28 is addressed within the rejection of claim 10, due to the similarities claim 28 and claim 10 share , therefore refer to the rejection of claim 10 regarding the rejection of claim 28. Regarding claim 29 , the rejection of claim 29 is addressed within the rejection of claim 11, due to the similarities claim 29 and claim 11 share , therefore refer to the rejection of claim 11 regarding the rejection of claim 29 . 07-22-aia AIA Claim (s) 3, 9, 15, 21, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar in view of Cohen and Amer as applied to claim (s) 1, 7, 13, 19, and 25 above, and further in view of Chang et al., US PGPUB No. 20190080207 A1, hereinafter Chang . Regarding claim 9 , Shekhar in view of Cohen and Amer further discloses the system of claim 7, wherein the one or more inputs associated with an augmented reality (AR) application ( Shekhar; the one or more inputs associated with an augmented reality (AR) application [Col. 4, lines 29-58] ), wherein the one or more processors are further to cause the one or more images to be displayed along with one or more textual characters through the AR application ( Shekhar; the one or more processors are further to cause the one or more images to be displayed along with one or more textual characters through the AR application [Col. 4, line 60 to Col. 5, line 52], as illustrated within Fig. 4 ). Cohen further teaches the text segment of the description is obtained in connection with inputs ( Cohen; the text segment of the descriptions [as addressed within the parent claim(s)] is obtained in connection with inputs [¶ 0024 and ¶ 0027] ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate the text segment of the description is obtained in connection with inputs ( as taught by Cohen ), in order provide improved interactive storytelling that is engaging to a user while retaining the experience of printed books ( Cohen; [¶ 0003-0004 and ¶ 0007-0008] ). Shekhar as modified by Cohen and Amer fails to disclose the one or more text segment of the descriptions are obtained in connection with one or more cameras associated with an application. However, Chang teaches the one or more text segments of the description is obtained in connection with one or more cameras associated with an application ( Chang; the one or more text segments of the description/information [¶ 0005 and ¶ 0009] is obtained in connection with one or more cameras [¶ 0038 and ¶ 0059-0060] associated with an application [¶ 0045 and ¶ 0047], as illustrated within Fig. 1, Fig. 2, and Fig. 3A; additionally, a system captures or receives visual and/or text data from a 3 rd party [¶ 0119]; moreover, contextual analysis [¶ 0065-0067] and proximity analysis [¶ 0061-0062] ). Shekhar in view of Cohen and Amer and Chang are considered to be analogous art because they pertain to generating and/or managing data in relation with processing and/or producing media data, wherein one or more computerized units are utilized in order to configure media data using logic. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate the one or more text segments of the description is obtained in connection with one or more cameras associated with an application ( as taught by Chang ), in order provide improved efficiency and/or accuracy associated with computer recognition techniques through information analysis/extraction ( Chang; [¶ 0005-0009] ). Regarding claim 3 , the rejection of claim 3 is addressed within the rejection of claim 9, due to the similarities claim 3 and claim 9 share , therefore refer to the rejection of claim 9 regarding the rejection of claim 3. Regarding claim 15 , the rejection of claim 15 is addressed within the rejection of claim 9, due to the similarities claim 15 and claim 9 share , therefore refer to the rejection of claim 9 regarding the rejection of claim 15. Regarding claim 21 , the rejection of claim 21 is addressed within the rejection of claim 9, due to the similarities claim 21 and claim 9 share , therefore refer to the rejection of claim 9 regarding the rejection of claim 21. Regarding claim 27 , the rejection of claim 27 is addressed within the rejection of claim 9, due to the similarities claim 27 and claim 9 share , therefore refer to the rejection of claim 9 regarding the rejection of claim 27 . 07-22-aia AIA Claim (s) 6, 12, 18, 24, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shekhar in view of Cohen and Amer as applied to claim (s) 1, 7, 13, 19, and 25 above, and further in view of Smith et al., US Patent No. 10896534 B1, hereinafter Smith . Regarding claim 12 , Shekhar in view of Cohen and Amer further discloses the system of claim 7, the one or more neural networks ( Shekhar; the one or more NNs [as addressed within the parent claim(s)] ); and to generate the one or more images using the one or more objects, based at least in part, on output of a transformer that is able to correlate those objects ( Shekhar; to generate the one or more images using the one or more objects based (at least in part) on output of a transformer that is able to correlate those objects [Col. 4, line 60 to Col. 5, line 52 and Col. 6, line 52 to Col. 7, line 21] ). Amer further teaches wherein the one or more neural networks include one or more variational autoencoders (VAEs) to generate the one or more images of the video depicting the one or more objects, based at least in part, on output of a transformer that is able to correlate those objects ( Amer; the one or more NNs include implicit one or more VAEs (given the GAN) [¶ 0035 and ¶ 0048-0050] to generate the one or more images of the video depicting the one or more objects based (at least in part) on output of a transformer that is able to correlate those objects [¶ 0101-0104], as illustrated within Figs. 2B-2C; moreover, composition graph module uses ML techniques [¶ 0070-0072]; and moreover, GANs [¶ 0008-0009 and ¶ 0011-0012] and dense validation [¶ 0055-0056] ), wherein the one or more different images of the video are to be pulled from a cache for use by the one or more VAEs to ensure consistency across the one or more images and the one or more different images ( Amer; the one or more different images of the video are to be pulled from a cache (i.e. store) for use by the implicit one or more VAEs (given the GAN) to ensure consistency across the one or more images (of the video) and the one or more different (or 1 st ) images (of the video) [¶ 0108 and ¶ 0110-0111]; moreover, generating image data from data store(s) in relation with establishing consistency across inputs and outputs [¶ 0156-0158 and ¶ 0160] ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate the one or more neural networks include one or more variational autoencoders (VAEs) to generate the one or more images of the video depicting the one or more objects, based at least in part, on output of a transformer that is able to correlate those objects, wherein the one or more different images of the video are to be pulled from a cache for use by the one or more VAEs to ensure consistency across the one or more images and the one or more different images ( as taught by Amer ), in order provide improved interactive storytelling that is automated and easy to use while being intelligent, accurate, and/or relevant to a user’s design ( Amer; [¶ 0005-0006, ¶ 0014, and ¶ 0031-0033] ). Shekhar in view of Cohen and Amer fails to explicitly disclose one or more variational autoencoders (VAEs). However, Smith teaches wherein the one or more neural networks include one or more variational autoencoders (VAEs) to generate the one or more images and a transformer that is able to make correlations ( Smith; the one or more NNs include one or more VAEs to generate the one or more images and a transformer (i.e. encoder and/or decoder) that is able to correlations [Col. 5, lines 9-27 and Col. 12, line 33 to Col. 13, line 38, and Col. 14, lines 23-58] ), wherein pulled from a cache for use by the one or more VAEs to ensure consistency across the one or more images and the one or more different images ( Smith; pulled from a memory/cache for use by the one or more VAEs to ensure consistency across the one or more images [Col. 7, lines 26-58, Col. 7, line 63 to Col. 8, line 8, Col. 16, lines 36-65 and Col. 17, lines 11-31] ). Shekhar in view of Cohen and Amer and Smith are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce a visualization effect. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Shekhar as modified by Cohen and Amer , to incorporate wherein the one or more neural networks include one or more variational autoencoders (VAEs) to generate the one or more images and a transformer that is able to make correlations, wherein pulled from a cache for use by the one or more VAEs to ensure consistency across the one or more images and the one or more different images ( as taught by Smith ), in order provide improved computing resource efficiency when translating motion in real-time ( Smith; [Col. 2, line 56 to Col. 3, line 21] ). Regarding claim 6 , the rejection of claim 6 is addressed within the rejection of claim 12, due to the similarities claim 6 and claim 12 share , therefore refer to the rejection of claim 12 regarding the rejection of claim 6. Regarding claim 18 , the rejection of claim 18 is addressed within the rejection of claim 12, due to the similarities claim 18 and claim 12 share , therefore refer to the rejection of claim 18 regarding the rejection of claim 12. Regarding claim 24 , the rejection of claim 24 is addressed within the rejection of claim 12, due to the similarities claim 24 and claim 12 share , therefore refer to the rejection of claim 12 regarding the rejection of claim 24. Regarding claim 30 , the rejection of claim 30 is addressed within the rejection of claim 12, due to the similarities claim 30 and claim 12 share , therefore refer to the rejection of claim 12 regarding the rejection of claim 30 . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 2, 8, 14, 20, and 26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Regarding the subject matter of the independent claims: In order to given the neural network cited within the independent claims more consideration over the applied prior art, it is suggested that the neural network be claimed with further limitations that detail the manner in which the neural network is able to generating images based on text segments in the form of determinations and/or actions. The limitations should distinguish the neural network from general computer programing (e.g. animation engine) and/or from other neural networks that take inputs and output generated images based on said inputs. 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pahud et al. (US PGPUB No. 20060217979 A1) . Wherein, Pahud et al. teaches the dynamic creation of scenes associated with a natural language inputs in relation with creating, manipulating, and animating graphics within the scenes ( Pahud; [¶ 0036-0039, ¶ 0051-0057, ¶ 0061-0063, ¶ 0065, and ¶ 0068-0069] ). Although, Pahud et al. fails to teaches use of a neural network to create animations, the one or more neural networks taught by the applied prior art (within the rejection of claims 1, 7, 13, 19, and 25) can be modified to perform the actions of the animation engine as taught by Pahud et al. As expressed in more detail above, regarding ¶ 0060-0065 and Figs. 5-18, Pahud et al teaches the animation engine implicitly determines whether one or more text segments in a sequence of inputs describes an object in order to use the image of the object again in additional imaging (e.g. continuity) . 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art . 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles Lloyd Beard whose telephone number is (571)272-5735. The examiner can normally be reached Monday - Friday, 8:00 AM - 5: 00 PM, alternate Fridays EST. 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, Tammy Goddard can be reached at (571) 272-7773. 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. CHARLES LLOYD. BEARD Primary Examiner Art Unit 2611 /CHARLES L BEARD/ Primary Examiner, Art Unit 2611 Application/Control Number: 16/889,362 Page 2 Art Unit: 2611 Application/Control Number: 16/889,362 Page 3 Art Unit: 2611 Application/Control Number: 16/889,362 Page 4 Art Unit: 2611 Application/Control Number: 16/889,362 Page 5 Art Unit: 2611 Application/Control Number: 16/889,362 Page 6 Art Unit: 2611 Application/Control Number: 16/889,362 Page 7 Art Unit: 2611 Application/Control Number: 16/889,362 Page 8 Art Unit: 2611 Application/Control Number: 16/889,362 Page 9 Art Unit: 2611 Application/Control Number: 16/889,362 Page 10 Art Unit: 2611 Application/Control Number: 16/889,362 Page 11 Art Unit: 2611 Application/Control Number: 16/889,362 Page 12 Art Unit: 2611 Application/Control Number: 16/889,362 Page 13 Art Unit: 2611 Application/Control Number: 16/889,362 Page 14 Art Unit: 2611 Application/Control Number: 16/889,362 Page 15 Art Unit: 2611 Application/Control Number: 16/889,362 Page 16 Art Unit: 2611 Application/Control Number: 16/889,362 Page 17 Art Unit: 2611 Application/Control Number: 16/889,362 Page 18 Art Unit: 2611 Application/Control Number: 16/889,362 Page 19 Art Unit: 2611 Application/Control Number: 16/889,362 Page 20 Art Unit: 2611 Application/Control Number: 16/889,362 Page 21 Art Unit: 2611 Application/Control Number: 16/889,362 Page 22 Art Unit: 2611 Application/Control Number: 16/889,362 Page 23 Art Unit: 2611 Application/Control Number: 16/889,362 Page 24 Art Unit: 2611 Application/Control Number: 16/889,362 Page 25 Art Unit: 2611 Application/Control Number: 16/889,362 Page 26 Art Unit: 2611 Application/Control Number: 16/889,362 Page 27 Art Unit: 2611 Application/Control Number: 16/889,362 Page 28 Art Unit: 2611
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