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 Amendment
This action is in response to applicant’s arguments and amendments filed 1/07/2026, which are in response to USPTO Office Action mailed 9/25/2025. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
Status of Claims
Claims 1-15 and 17-40 are currently pending in the present application. Claim 16 is currently cancelled.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 14-15, 22-26, 33 and 36-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter et al. (US PGPUB No. 2021/0074031; Pub. Date: Mar. 11, 2021) in view of CHANDRA (US PGPUB No. 2021/0084350; Pub. Date: Mar. 18, 2021) and Dey et al. (US PGUB No. 2008/0086570; Pub. Date: Apr. 10, 2008).
Regarding independent claim 1,
Richter discloses a method of generating and presenting customized content, the method comprising: receiving, by at least one electronic computer processor, an electronic request associated with content to be presented from an entertainment system; See FIG. 7 & Paragraph [0084], (Disclosing a system for causing display of synthesized reality (SR) content associated with a current plot setting of video content. FIG. 7 illustrates method 700 comprising step 7-1 of receiving a user input indicating a request to explore the current plot setting of currently displayed video content, i.e. a method of generating and presenting customized content, the method comprising: receiving, by at least one electronic computer processor, an electronic request associated with content to be presented from an entertainment system (e.g. Note [0085] wherein the user input corresponds to a voice command, selection of a TV remote button, movement of the TV remote, head movement, eye movement, etc.).)
identifying, by said at least one computer processor, at least one specific action to be performed in response to said electronic request; See FIG. 7 & Paragraph [0084], (FIG. 7 illustrates method 700 comprising step 7-1 of receiving a user input indicating a request to explore the current plot setting of currently displayed video content, i.e. identifying, by said at least one computer processor, at least one specific action to be performed in response to said electronic request (e.g. the SR content is presented to the user in response to an input).)
selecting at least one artificial intelligence software agent instruction from a plurality of artificial intelligence software agent instructions, wherein each selected artificial intelligence software agent instruction is: configured to perform the at least one specific action; See Paragraph [0046], (Disclosing a system for causing display of synthesized reality (SR) content associated with a current plot setting of video content. Companion content obtainer 246 is configured to obtain SR content associated with video content presented to a user via an electronic device 120 by leveraging instructions, logic, heuristics and metadata, i.e. selecting at least one artificial intelligence software agent instruction from a plurality of artificial intelligence software agent instructions (e.g. by utilizing the companion content obtainer 246), wherein each selected artificial intelligence software agent instruction is: configured to perform the at least one specific action (e.g. companion content obtainer 246 may retrieve or generate content on-the-fly to fulfill the user input);)
and selected based on a set of criteria, including a type of specific action and at least one of: timing of specific action; change in satisfaction level of the user; or availability of artificial intelligence software agent instruction; See Paragraph [0086], (User input corresponds to a voice command, selection of a TV remote button, movement of the TV remote, head movement, eye movement, or the like while the notification is displayed, i.e. selected based on a set of criteria, including a type of specific action and at least timing of specific action (e.g. user gestures are performed while the video content having a plot setting is being presented).)
communicating information between said at least one electronic computer processor and said at least one artificial intelligence software agent instruction;
electronically storing, by the at least one electronic computer processor, a plurality of content; See Paragraph [0091], (Companion content obtainer 246 may generate synthesized reality (SR) content on-the-fly based at least in part on video content and a current plot setting, i.e. communicating information between said at least one electronic computer processor and said at least one artificial intelligence software agent instruction (e.g. video content displayed on a display 130 is associated with plot setting information that is used by the companion content obtainer 246 to generate content on-the-fly).)
electronically storing, by the at least one electronic computer processor, a plurality of content; See Paragraph [0091], (SR content may be obtained from a library associated with video content being viewed by a user, i.e. electronically storing, by the at least one electronic computer processor, a plurality of content (e.g. the library associated with video content represents a storage system for storing content).)
first electronically determining, by the at least one electronic computer processor, a base content from the plurality of content, based at least in part on at least one user criteria of at least one or more of: at least one end user device, or at least one entertainment system, See Paragraph [0079], (The generated companion content may be displayed via display device 130. Note [0032] wherein display device 130 corresponds to a television or a computing device such as a desktop computer, kiosk, laptop computer, tablet, mobile phone, wearable computing device, etc., i.e. first electronically determining, by the at least one electronic computer processor, a base content from the plurality of content (e.g. the video content presented on display 130), based at least in part on at least one user criteria of at least one or more of: at least one end user device (e.g. electronic device 120 corresponds to a user device for presenting SR companion content).)
wherein the at least one user criteria is based on at least one user information, See Paragraph [0037], (SR content is presented to a user 150 while the user 150 is virtually and/or physically present within a physical setting 105 proximal to the user device 130 as displayed in at least FIG. 1A, i.e. wherein the at least one user criteria is based on at least one user information (e.g. information relating to a user's position in a physical setting 105).)
and electronically accessing, by the at least one electronic computer processor, at least one metadata associated with the base content; See FIG. 7 & Paragraph [0085], (Method 700 comprises receiving a user input indicating a request to explore a current plot setting of video content, i.e. electronically accessing, by the at least one electronic computer processor, at least one metadata (e.g. the plot setting information) associated with the base content;)
electronically analyzing by at least one artificial intelligence software agent instruction, at least one capability of at least two or more of: the at least one end user device, the at least one entertainment system, or at least one end user environment; See Paragraph [0087], (SR companion content may place a user into a current plot setting capable of expanding the setting beyond the fixed camera angle of the flat video content. Note [0082] wherein SR companion content may be displayed using a electronic device 120 located in a physical setting 660, i.e. electronically analyzing by at least one artificial intelligence software agent instruction, at least one capability of at least two or more of: the at least one end user device (e.g. electronic device 120) or at least one end user environment (e.g. the physical setting 660).)
second electronically determining, by the at least one electronic computer processor, at least one content subset based on both: the at least one user criteria, and the least one metadata associated with the base content; See Paragraph [0084], (The system may adjust the presentation of SR content in response to receiving user interaction data associated with the SR content.) See Paragraph [0088], (SR companion content is displayed based on user interactions such as body pose inputs/actions, eye movements, voice commands, etc., i.e. the at least one user criteria, wherein the user actions are associated with the plot setting of the SR content, i.e. the least one metadata associated with the base content;)
electronically selecting, by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction, at least one selected of said at least one content subsets based on at least two or more of the at least one or more entertainment system criteria, the at least one metadata associated with the base content, the at least one capability of the at least one end user device, the at least one capability of the at least one end user, the at least one capability of the at least one entertainment system, or the at least one capability of the at least one end user environment; See Paragraph [0046], (Companion content obtainer 246 is configured to obtain SR content associated with video content presented to a user via an electronic device 120 by leveraging instructions, logic, heuristics and metadata, i.e. the at least one metadata associated with the base content (e.g. the plot setting of a video content).) See Paragraph [0053], (One or more displays 312 may present the SR experience to a user which includes presentation of flat video content to a user such as a 2-dimensional or “flat” AVI, FLV, WMV, MOV, MP4, or the like file associated with a TV episode or a movie, or live video pass-through of the physical setting 105, i.e. at least one capability of the at least one entertainment system.)
electronically processing, by the at least one electronic computer processor, of the base content and the at least one selected of said at least one content subsets, See FIG. 6C & Paragraph [0082], (SR companion content associated with video content is presented via electronic device 120 such as by displaying AR content in a physical setting associated with video content 675, i.e. electronically processing, by the at least one electronic computer processor, of the base content (e.g. video content having a plot setting) and the at least one selected of said at least one content subsets (e.g. SR content generated and displayed based on the plot setting).)
wherein the electronic processing of the base content and the at least one selected of said at least one content subsets comprises: third electronically determining, by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction, at least one insertion location of the base content for the at least one selected of said at least one content subsets to be at least one or more of inserted, combined, merged or linked into the base content; See Paragraphs [0023]-[0024], (The mixed reality (MR) system may monitor orientation and/or location with respect to a physical setting to enable interaction between virtual objects and real objects which are physical elements from the physical setting. At least one virtual object may be superimposed over a physical setting. The system may combine images or video with virtual objects and displays the combination on an opaque display, i.e. determining, by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction, at least one insertion location of the base content for the at least one selected of said at least one content subsets to be at least one or more of inserted, combined (e.g. virtual objects may be combined with the flat video display), merged or linked into the base content;
electronically generating, by the at least one electronic computer processor, a customized content comprising the base content and the at least one selected of said at least one content subsets for the at least one end user device; See Paragraph [0024], (At least one virtual object may be superimposed over a physical setting. The system may combine images or video with virtual objects and displays the combination on an opaque display.) See Paragraph [0037], (Electronic device 120 may present SR content to a user 150 while the user is virtually and/or physically present within a physical setting 105, i.e. electronically generating, by the at least one electronic computer processor, a customized content comprising the base content and the at least one selected of said at least one content subsets for the at least one end user device;)
and electronically transmitting, by the at least one electronic computer processor, said customized content to the at least one end user device to be presented to the at least one end user. See Paragraph [0037], (Electronic device 120 may present SR content to a user 150 while the user is virtually and/or physically present within a physical setting 105, i.e. electronically transmitting, by the at least one electronic computer processor, said customized content to the at least one end user device to be presented to the at least one end user.)
Richter does not disclose the step of electronically tagging, by the at least one electronic computer processor, at least a portion of said plurality of content with predetermined information and real-time information;
and wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using: at least one artificial intelligence software agent instruction;
CHANDRA discloses the step of electronically tagging, by the at least one electronic computer processor, at least a portion of said plurality of content with predetermined information and real-time information; See Paragraph [0011], (Disclosing a system for providing a user with customized video content from a platform. The platform includes an inventive intelligence configured to create and provide customized video content to a user in the form of a logical playlist of video content generated based on user preferences and other factors. The system enables use of artificial intelligence tools to help create customized video content.) See Paragraph [0055], (Uploaded content is tagged according to the city or region it pertains to an includes a timestamp indicating when the content entered the platform, i.e. electronically tagging, by the at least one electronic computer processor, at least a portion of said plurality of content with predetermined information and real-time information;)
and wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using: at least one artificial intelligence software agent instruction; See Paragraph [0086], (The system may automatically obtain user data including a physical location of a user, i.e. wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using: at least one artificial intelligence software agent instruction (e.g. Note [0062] wherein machine learning tools are used to evaluate user behavior data such as by performing a clickstream analysis of user interactions).)
Richter and CHANDRA are analogous art because they are in the same field of endeavor, customized content generation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter to include the method of delivering content to a user according to network resource availability as disclosed by CHANDRA. Paragraph [0096] of CHANDRA discloses that the system allows for proper customized video content to be created from user preferences while also providing functionality for editing the customized content to better suit their needs at any given time.
Richter-CHANDRA does not disclose the step of electronically automatically adapting, by the at least one electronic computer processor, the at least one insertion location of said at least one selected of said at least one content subsets into the base content based on a real-time bandwidth connection characteristic with the at least one end user device;
and monitoring, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data;
determining a transmission route based on the monitored network congestion;
Dey discloses the step of electronically automatically adapting, by the at least one electronic computer processor, the at least one insertion location of said at least one selected of said at least one content subsets into the base content based on a real-time bandwidth connection characteristic with the at least one end user device; See Paragraph [0014], (Disclosing a system for transmission of live digital content from sources to receiving devices. User devices may receive a stream of live content, i.e. base content, which may be subsequently enhanced with live content insertion.) See Paragraph [0030], (Streaming server 106 selects from multiple sets of alternative representations of a live content feed so as to achieve the best trade-off between quality, bandwidth and error resiliency for delivery to receiving devices, i.e. adapting the insertion based on real-time bandwidth connection characteristics with the end user device.)
and monitoring, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data; See Paragraph [0038], (Content customizer 106 determines a set of customizing operations that specify multiple streams or paths of customized digital content data according to available network resources and selects customized data streams in accordance with network conditions as a function of estimated received quality, i.e. monitoring, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data (e.g. content customizer 106 makes automated decisions relating to the provisioning of content to a user based on network resource availability, i.e. network traffic data).)
determining a transmission route based on the monitored network congestion; See Paragraph [0038], (Content customizer 106 determines a set of customizing operations that specify multiple streams or paths of customized digital content data according to available network resources and selects customized data streams in accordance with network conditions as a function of estimated received quality. Content Customizer 106 may apply the selected customizing operations to provide the customized video stream to the respective devices 202, i.e. determining a transmission route based on the monitored network congestion.)
Richter, CHANDRA and Dey are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA to include the method of dynamic content insertion across a live video stream as disclosed by Dey. Paragraph [0035] of Dey discloses that the use of a stream buffer for encoding relatively short epochs of live content delivery allows the system to better respond to changing network conditions. This results in a reduction in source-to-screen delay, allowing for more efficient usage of network bandwidth and more efficient transfer rates.
Regarding dependent claim 14,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein the electronically storing of the plurality of content and the electronically tagging of the plurality of content with stored, predetermined, or real-time information, further comprises wherein the information further comprises: information about at least one or more of: a start scene, a stop scene, an actor, an author, an artist, a singer, a director, a content resolution, a length, a language, or a time stamp. See Paragraph [0029]-[0030], (Each bucket is created based on the duration of the content to be filled in each bucket which includes real-time criteria indicating a time duration of a bucket based on a Story Decision value (SDV) value of each video content associated with timestamp data as described in [0062], i.e. wherein the electronically storing of the plurality of content and the electronically tagging of the plurality of content with stored, predetermined, or real-time information, further comprises wherein the information further comprises: information about at least a time stamp.)
Regarding dependent claim 15,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
Richter further discloses the step wherein the electronically processing comprises wherein the third electronically determining, by the at least one electronic computer processor, the at least one insertion location of the base content, wherein the at least one insertion location comprises at least one compatible insertion location; See Paragraph [0024], (The MR setting comprises superimposing virtual objects over a physical setting.) See Paragraph [0028], (The system may generate SR content associated with flat video content for a user to consume, i.e. wherein the electronically processing comprises wherein the third electronically determining, by the at least one electronic computer processor, the at least one insertion location of the base content, wherein the at least one insertion location comprises at least one compatible insertion location (e.g. the system may display SR content relating to the plot setting of live video content by presenting additional virtual objects within a physical setting).)
wherein the at least one compatible insertion location comprises at least one or more of: at the beginning, within, or at an end of the base content, for each of the one or more selected content subsets to be inserted into the base content to generate the customized content. See Paragraph [0024], (The MR setting comprises superimposing virtual objects over a physical setting, i.e. wherein the at least one compatible insertion location comprises at least within the base content, for each of the one or more selected content subsets to be inserted into the base content to generate the customized content.)
Regarding dependent claim 21,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
Richter further discloses the step wherein the base content comprises a live video stream and the at least one selected of said at least one content subsets comprise at least one or more of: at least one text content subset, at least one image content subset, at least one audio content subset, at least one video content subset, at least one animation content subset, at least one game content, at least one augmented reality simulation content, at least one virtual reality simulation content, at least one hologram content, at least one three dimensional visual content, or at least one graphics content subset. See Paragraph [0053], (Displays 312 may be used to present the SR experience to the user including presenting flat video content to a user such as a TV episode, movie or live video pass-through of a physical setting 105. Note [0029] wherein SR companion content may include additional content such as an educational aide or auxiliary information associated the video content such as an AR version of the Ho Chi Minh Trail or an AR battlefield map showing the location of military battalions while watching a documentary on the Vietnam War, i.e. wherein the base content comprises a live video stream and the at least one selected of said at least one content subsets comprise at least: one text content subset.)
Regarding independent claim 22,
Richter discloses a computing system configured to generate and present customized content, the computing system comprising: at least one memory device configured to electronically store instructions, wherein the at least one memory device having electronically stored thereon at least one user criteria and a plurality of content; See Paragraph [0091], (SR content may be obtained from a library associated with video content being viewed by a user, i.e. a computing system configured to generate and present customized content, the computing system comprising: at least one memory device configured to electronically store instructions, wherein the at least one memory device having electronically stored thereon at least one user criteria and a plurality of content; (e.g. the library associated with video content represents a storage system for storing content).)
The examiner notes that Richter does not explicitly disclose the step of storing "at least one user criteria".
and at least one electronic computer processor communicatively coupled to the at least one memory device and configured to execute the instructions to cause the computing system to perform operations comprising to: receive, by said at least one computer processor, an electronic request associated with content to be presented from an entertainment system; See FIG. 7 & Paragraph [0084], (Disclosing a system for causing display of synthesized reality (SR) content associated with a current plot setting of video content. FIG. 7 illustrates method 700 comprising step 7-1 of receiving a user input indicating a request to explore the current plot setting of currently displayed video content, i.e. a method of generating and presenting customized content, the method comprising: receive, by said at least one computer processor, an electronic request associated with content to be presented from an entertainment system (e.g. Note [0085] wherein the user input corresponds to a voice command, selection of a TV remote button, movement of the TV remote, head movement, eye movement, etc.).)
identify, by said at least one computer processor, at least one specific action to be performed in response to said electronic request; See FIG. 7 & Paragraph [0084], (FIG. 7 illustrates method 700 comprising step 7-1 of receiving a user input indicating a request to explore the current plot setting of currently displayed video content, i.e. identifying, by said at least one computer processor, at least one specific action to be performed in response to said electronic request (e.g. the SR content is presented to the user in response to an input).)
select at least one artificial intelligence software agent instruction from a plurality of artificial intelligence software agent instructions, wherein each selected artificial intelligence software agent instruction is: configured to perform the at least one specific action; See Paragraph [0046], (Disclosing a system for causing display of synthesized reality (SR) content associated with a current plot setting of video content. Companion content obtainer 246 is configured to obtain SR content associated with video content presented to a user via an electronic device 120 by leveraging instructions, logic, heuristics and metadata, i.e. selecting at least one artificial intelligence software agent instruction from a plurality of artificial intelligence software agent instructions (e.g. by utilizing the companion content obtainer 246), wherein each selected artificial intelligence software agent instruction is: configured to perform the at least one specific action (e.g. companion content obtainer 246 may retrieve or generate content on-the-fly to fulfill the user input);)
and selected based on a set of criteria, including a type of specific action and at least one of: timing of specific action; change in satisfaction level of the user; or availability of artificial intelligence software agent instruction; See Paragraph [0086], (User input corresponds to a voice command, selection of a TV remote button, movement of the TV remote, head movement, eye movement, or the like while the notification is displayed, i.e. selected based on a set of criteria, including a type of specific action and at least timing of specific action (e.g. user gestures are performed while the video content having a plot setting is being presented).)
communicating information between said at least one electronic computer processor and said at least one artificial intelligence software agent instruction; See Paragraph [0091], (Companion content obtainer 246 may generate synthesized reality (SR) content on-the-fly based at least in part on video content and a current plot setting, i.e. communicating information between said at least one electronic computer processor and said at least one artificial intelligence software agent instruction (e.g. video content displayed on a display 130 is associated with plot setting information that is used by the companion content obtainer 246 to generate content on-the-fly).)
first electronically determine a base content from the plurality of content, based at least in part on the at least one user criteria of at least one or more of: at least one end user device, or at least one entertainment system; See Paragraph [0079], (The generated companion content may be displayed via display device 130. Note [0032] wherein display device 130 corresponds to a television or a computing device such as a desktop computer, kiosk, laptop computer, tablet, mobile phone, wearable computing device, etc., i.e. first electronically determining, by the at least one electronic computer processor, a base content from the plurality of content (e.g. the video content presented on display 130), based at least in part on at least one user criteria of at least one or more of: at least one end user device (e.g. electronic device 120 corresponds to a user device for presenting SR companion content).)
electronically analyze by at least one artificial intelligence software agent instruction, at least one capability of at least two or more of: at least one end user device, the at least one entertainment system, or at least one end user environment; See Paragraph [0037], (SR content is presented to a user 150 while the user 150 is virtually and/or physically present within a physical setting 105 proximal to the user device 130 as displayed in at least FIG. 1A, i.e. wherein the at least one user criteria is based on at least one user information (e.g. information relating to a user's position in a physical setting 105).)
second electronically determine at least one content subset based on both the at least one user criteria and the at least one metadata associated with the base content; See Paragraph [0084], (The system may adjust the presentation of SR content in response to receiving user interaction data associated with the SR content, i.e. second electronically determine at least one content subset based on both the at least one user criteria and the at least one metadata associated with the base content;) See Paragraph [0088], (SR companion content is displayed based on user interactions such as body pose inputs/actions, eye movements, voice commands, etc., i.e. the at least one user criteria, wherein the user actions are associated with the plot setting of the SR content, i.e. the least one metadata associated with the base content;)
electronically select by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction, at least one selected of the at least one content subsets based on at least two or more of: the at least one metadata associated with the base content, the at least one capability of the at least one end user device, the at least one capability of the at least one end user, or the at least one capability of the at least one end user environment; See Paragraph [0046], (Companion content obtainer 246 is configured to obtain SR content associated with video content presented to a user via an electronic device 120 by leveraging instructions, logic, heuristics and metadata.) See Paragraph [0087], (SR companion content may place a user into a current plot setting capable of expanding the setting beyond the fixed camera angle of the flat video content. Note [0082] wherein SR companion content may be displayed using a electronic device 120 located in a physical setting 660, i.e. electronically select by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction, at least one selected of the at least one content subsets based on at least two or more of: the at least one metadata associated with the base content (e.g. plot setting information associated with video content), the at least one capability of the at least one end user device (e.g. a user's electronic device 120 is used to display augmented video content).)
electronically process the base content and the at least one selected of the at least one content subsets comprising: third electronically determine, by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction at least one insertion location of the base content for the at least one selected of said at least one content subsets to be inserted into the base content; See Paragraphs [0023]-[0024], (The mixed reality (MR) system may monitor orientation and/or location with respect to a physical setting to enable interaction between virtual objects and real objects which are physical elements from the physical setting. At least one virtual object may be superimposed over a physical setting. The system may combine images or video with virtual objects and displays the combination on an opaque display, i.e. electronically determine, by the at least one electronic computer processor based on an output of at least one artificial intelligence software agent instruction at least one insertion location of the base content for the at least one selected of said at least one content subsets to be inserted into the base content (e.g. virtual objects may be combined with the flat video display).)
electronically generate a customized content comprising the base content and the at least one selected of the at least one content subsets for at least one end user device; See Paragraph [0024], (At least one virtual object may be superimposed over a physical setting. The system may combine images or video with virtual objects and displays the combination on an opaque display.) See Paragraph [0037], (Electronic device 120 may present SR content to a user 150 while the user is virtually and/or physically present within a physical setting 105, i.e. electronically generating, by the at least one electronic computer processor, a customized content comprising the base content and the at least one selected of said at least one content subsets for the at least one end user device;)
and electronically transmit said customized content to the at least one end user device to be presented to the at least one end user. See Paragraph [0037], (Electronic device 120 may present SR content to a user 150 while the user is virtually and/or physically present within a physical setting 105, i.e. electronically transmit said customized content to the at least one end user device to be presented to the at least one end user.)
Richter does not disclose the step wherein the at least one memory device having electronically stored thereon at least one user criteria;
electronically tag at least a portion of the plurality of content with predetermined information and real-time information;
wherein the at least one user criteria is based on at least one user information, and wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using at least one artificial intelligence software agent instruction, and electronically access at least one metadata associated with the base content;
CHANDRA disclose the step wherein the at least one memory device having electronically stored thereon at least one user criteria; See Paragraph [0011], (Disclosing a system for providing a user with customized video content from a platform. The platform includes an inventive intelligence configured to create and provide customized video content to a user in the form of a logical playlist of video content generated based on user preferences and other factors. The system enables use of artificial intelligence tools to help create customized video content.) See Paragraph [0029], (The system may create user preference buckets designating a storage space for a user providing user preferences, i.e. at least one memory device configured to electronically store instructions, wherein the at least one memory device having electronically stored thereon at least one user criteria.)
electronically tag at least a portion of the plurality of content with predetermined information and real-time information; See Paragraph [0011], (Disclosing a system for providing a user with customized video content from a platform. The platform includes an inventive intelligence configured to create and provide customized video content to a user in the form of a logical playlist of video content generated based on user preferences and other factors. The system enables use of artificial intelligence tools to help create customized video content.) See Paragraph [0055], (Uploaded content is tagged according to the city or region it pertains to an includes a timestamp indicating when the content entered the platform, i.e. electronically tagging, by the at least one electronic computer processor, at least a portion of said plurality of content with predetermined information and real-time information;)
wherein the at least one user criteria is based on at least one user information, and wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using at least one artificial intelligence software agent instruction, and electronically access at least one metadata associated with the base content; See Paragraph [0086], (The system may automatically obtain user data including a physical location of a user, i.e. wherein the at least one user information comprises being: electronically, automatically, obtained, and analyzed using: at least one artificial intelligence software agent instruction (e.g. Note [0062] wherein machine learning tools are used to evaluate user behavior data such as by performing a clickstream analysis of user interactions).)
Richter and CHANDRA are analogous art because they are in the same field of endeavor, customized content generation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter to include the method of delivering content to a user according to network resource availability as disclosed by CHANDRA. Paragraph [0096] of CHANDRA discloses that the system allows for proper customized video content to be created from user preferences while also providing functionality for editing the customized content to better suit their needs at any given time.
Richter-CHANDRA does not disclose the step of electronically automatically adapting, by the at least one electronic computer processor, the at least one insertion location of said at least one selected of said at least one content subsets into the base content based on a real-time bandwidth connection characteristic with the at least one end user device;
monitor, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data;
determine an optimal transmission route based on the monitored network congestion;
Dey discloses the step of electronically automatically adapting, by the at least one electronic computer processor, the at least one insertion location of said at least one selected of said at least one content subsets into the base content based on a real-time bandwidth connection characteristic with the at least one end user device; See Paragraph [0014], (Disclosing a system for transmission of live digital content from sources to receiving devices. User devices may receive a stream of live content, i.e. base content, which may be subsequently enhanced with live content insertion.) See Paragraph [0030], (Streaming server 106 selects from multiple sets of alternative representations of a live content feed so as to achieve the best trade-off between quality, bandwidth and error resiliency for delivery to receiving devices, i.e. electronically automatically adapting, by the at least one electronic computer processor, the at least one insertion location of said at least one selected of said at least one content subsets into the base content based on a real-time bandwidth connection characteristic with the at least one end user device;)
monitor, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data; See Paragraph [0038], (Content customizer 106 determines a set of customizing operations that specify multiple streams or paths of customized digital content data according to available network resources and selects customized data streams in accordance with network conditions as a function of estimated received quality, i.e. monitor, by the at least one electronic computer processor based on an output from the at least one artificial intelligence software agent instruction, network congestion using network traffic data (e.g. content customizer 106 makes automated decisions relating to the provisioning of content to a user based on network resource availability, i.e. network traffic data).)
determine an optimal transmission route based on the monitored network congestion; See Paragraph [0038], (Content customizer 106 determines a set of customizing operations that specify multiple streams or paths of customized digital content data according to available network resources and selects customized data streams in accordance with network conditions as a function of estimated received quality. Content Customizer 106 may apply the selected customizing operations to provide the customized video stream to the respective devices 202, i.e. determine an optimal transmission route based on the monitored network congestion (e.g. content customizer 106 defines a path based on estimated received quality).)
Richter, CHANDRA and Dey are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA to include the method of dynamic content insertion across a live video stream as disclosed by Dey. Paragraph [0035] of Dey discloses that the use of a stream buffer for encoding relatively short epochs of live content delivery allows the system to better respond to changing network conditions. This results in a reduction in source-to-screen delay, allowing for more efficient usage of network bandwidth and more efficient transfer rates.
Regarding dependent claim 23,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
Dey further discloses the step wherein the at least one memory device comprises at least one or more of a local database, a distributed database, or a network connected database. See FIG. 2, (FIG. 2 illustrates a system 200 comprising a plurality of storage systems including a data source storing digital content 206 and streaming buffer 103 comprising prepped content 216, i.e. wherein the at least one memory device comprises at least a network connected database.)
Regarding dependent claim 24,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
Richter further discloses the step wherein at least a portion of the system resides on the at least one end user device. See Paragraph [0031], (Electronic device 120 is configured to present the SR experience to the user via a display 122, i.e. wherein at least a portion of the system resides on the at least one end user device.)
Regarding dependent claim 25,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
Richter further discloses the step wherein the system resides on a cloud system and is communicatively coupled to the at least one end user device through at least one content delivery network. See FIG. 1B & Paragraph [0034], (FIG. 1B illustrates a system 100B comprising a controller 110 configured to manage and coordinate an SR experience for a user wherein controller 110 is a remote server located outside of physical setting 105 and may be embodied as a cloud server 106. Controller 110 is communicatively coupled with electronic device 120 via one or more communication channels, i.e. wherein the system resides on a cloud system and is communicatively coupled to the at least one end user device through at least one content delivery network.)
Regarding dependent claim 26,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein the electronically generate a customized content occurs without receipt of any explicit input from the at least one end user during presentation.
See Paragraph [0058], (The system may generate the user newscast created by the platform by composing a plurality of buckets of video content that are played out automatically one after another, i.e. step wherein the electronically generate a customized content occurs without receipt of any explicit input from the at least one end user during presentation.)
Regarding independent claim 33,
The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 36,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein said electronically generating comprises at least one or more of: electronically generating said customized content without requesting input from a user; electronically generating said customized content absent requesting interactive input during the presenting of the content; electronically generating said customized content dynamically without requesting input from a user; electronically generating said customized content automatically without requesting input from a user; electronically generating said customized content dynamically without requesting input from a user during the presenting of the content; electronically generating said customized content automatically without requesting input from a user during the presenting of the content; electronically generating said customized content dynamically without requesting input from a user avoiding interruption; electronically generating said customized content automatically without requesting input from a user avoiding interruption; electronically generating the customized content avoiding interruption during the presenting of the content; electronically generating said customized content eliminating interruption during the presenting of the content; or electronically generating said customized content to obtain an absence of interruption during the presenting of the content. See Paragraph [0039], (A user may request the system play customized video content such as via a voice command). See Paragraph [0058], (The system may generate a newscast in the form of a playlist created by the platform via the creation of buckets and is played out automatically on after another, i.e. electronically generating said customized content to obtain an absence of interruption during the presenting of the content, (e.g. the system may automatically commence playback of the newscast playlist uninterrupted without requiring further user input.)
Regarding dependent claim 37,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step of electronically presenting, at the at least one end user device, by at least one electronic computer processor, comprising at least one or more of: electronically presenting said customized content at the at least one end user device; presenting said customized content without requesting input from a user; presenting said customized content absent requesting interactive input during the presenting of the content; presenting said customized content dynamically without requesting input from a user; presenting said customized content automatically without requesting input from a user; presenting said customized content dynamically without requesting input from a user during the presenting of the content; presenting said customized content automatically without requesting input from a user during the presenting of the content; presenting said customized content dynamically without requesting input from a user avoiding interruption; presenting said customized content automatically without requesting input from a user avoiding interruption; presenting said customized content avoiding interruption during the presenting of the content; presenting said customized content eliminating interruption during the presenting of the content; or presenting said customized content to obtain an absence of interruption during the presenting of the content. See Paragraph [0082], (User terminals 101 may be any suitable device with at least ad display, storage unit and network connectivity.) See Paragraph [0099], (The user terminal may be used to initiate playback of customized video content such as a customized newscast, i.e. electronically presenting, at the at least one end user device, by at least one electronic computer processor, comprising at least one or more of: electronically presenting said customized content at the at least one end user device;)
Regarding dependent claim 38,
As discussed above with claim 37, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein said electronically presenting, at the at least one end user device, by the at least one electronic computer processor, comprises at least one or more of: electronically presenting said customized content in video display form; electronically presenting said customized content in audio form; electronically presenting said customized content in virtual reality form; electronically presenting said customized content in augmented reality form; electronically presenting said customized content in mixed reality form; electronically presenting said customized content in a smart television form; electronically presenting said customized content in a theatrical performance form; or electronically presenting said customized content on a mobile device. See Paragraph [0101], (The system may create buckets including video content to be consumed by the user.) See Paragraph [0058], (The system may generate a newscast in the form of a playlist created by the platform via the creation of buckets and is played out automatically on after another, i.e. wherein said electronically presenting, at the at least one end user device, by the at least one electronic computer processor, comprises at least one or more of: electronically presenting said customized content in video display form;)
Regarding dependent claim 39,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein the electronically generate a customized content occurs in response to receipt of an explicit input from the at least one end user during presentation. See Paragraph [0039], (A user may request the system play customized video content such as via a voice command). See Paragraph [0058], (The system may generate a newscast in the form of a playlist created by the platform via the creation of buckets and is played out automatically on after another, i.e. wherein the electronically generate a customized content occurs in response to receipt of an explicit input from the at least one end user during presentation.)
Regarding dependent claim 40,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein: said electronically selecting said at least one selected of said at least one content subset, comprises electronically selecting, by the at least one electronic computer processor, without requesting an acceptance confirmation from the end user, See Paragraph [0101], (See Paragraph [0101], (The system may create buckets including video content to be consumed by the user. Buckets are filled with determined content based on user preferences to form customized content. Note [0089] wherein the video clips/content to be filled in the buckets is determined by determination unit 312, i.e. wherein: said electronically selecting said at least one selected of said at least one content subset, comprises electronically selecting, by the at least one electronic computer processor, without requesting an acceptance confirmation from the end user)
said electronically transmitting, by the at least one electronic computer processor, said customized content to the at least one end user device comprises evaluating communications network conditions. See Paragraph [0080], (Users may request a newscast be created and delivered to their handheld device. The newscast may be switched to different content formats based on the availability of network bandwidth to the user. A user may be asked to switch to audio or textual modes if the system determines that there is not sufficient network bandwidth to view the video format of the newscast, i.e. evaluating communications network condition (e.g. by monitoring network bandwidth).)
Claim(s) 2-3, 5-6, 8-11, 13, 17-18, 27-32 and 35-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA and Dey as applied to claim 1 above, and further in view of Punja et al. (US PGPUB No. 2021/0266637; Pub. Date; Aug. 26, 2021).
Regarding dependent claim 2,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein the first electronically determining the base content comprises: electronically storing, by the at least one electronic computer processor, the at least one user criteria in an electronic database, wherein the at least one user criteria comprises at least one or more of: at least one user physical factor, at least one cognitive factor, at least one social factor, at least one political factor, at least one economic factor, or at least one consumption factor; User preferences 402 may be related to characteristics of a news item including categories or classes of content preferred by a user, i.e. wherein the first electronically determining the base content comprises: electronically storing, by the at least one electronic computer processor, the at least one user criteria in an electronic database, wherein the at least one user criteria comprises at least at least one cognitive factor (e.g. a user preference is a cognitive factor as it relates to a user’s enjoyment of a particular piece of content).)
electronically storing, by the at least one electronic computer processor, a plurality of content in an electronic database, wherein the plurality of content comprises at least one or more of: at least one text, at least one image, at least one audio, at least one animation, at least one graphics, at least one three dimensional visuals, or at least one video; See Paragraph [0055], (Platform 103 enables uploading of news/video clips to a cloud server by authentic news providers, i.e. electronically storing, by the at least one electronic computer processor, a plurality of content in an electronic database, wherein the plurality of content comprises at least one video.)
electronically accessing, by the at least one electronic computer processor, at least one metadata associated with the user selected content; See Paragraph [0055], (Content uploaded to the platform cloud is classified and meta-tagged on the basis of a plurality of parameters including a) geography, b) character, c) popularity, etc. Meta-tags are used by the platform to create the personalized newscasts, i.e. electronically accessing, by the at least one electronic computer processor, at least one metadata associated with the user selected content;)
and electronically analyzing, by the at least one electronic computer processor, the at least one user criteria with the user selected content to fourth electronically determine the base content from the plurality of content. See Paragraph [0092], (The system may create a plurality of buckets representing storage space for generating elements of a customized newscast. The content of each bucket are determined based on user preferences 402 and other factors, i.e. electronically analyzing, by the at least one electronic computer processor, the at least one user criteria with the user selected content to fourth electronically determine the base content from the plurality of content.)
Richter-CHANDRA-Dey does not discloses the step of electronically accessing, by the at least one electronic computer processor, user selected content from said plurality of content based at least in part on receiving an input from the at least one end user, wherein said input comprises at least one or more of: a title of a book, a name of a book, a name of a movie, a name of a game, or a name of a music band;
Punja discloses the step of electronically accessing, by the at least one electronic computer processor, user selected content from said plurality of content based at least in part on receiving an input from the at least one end user, wherein said input comprises at least one or more of: a title of a book, a name of a book, a name of a movie, a name of a game, or a name of a music band; See Paragraph [0024], (Disclosing a method for generating an image depiction of content using a machine learning system. User profile elements may correspond to media consumption information including movies, television programs, etc., i.e. accessing user-selected content from a plurality of content based in part on receiving an input from the user (e.g. receiving user profile data), wherein said input includes at least a movie (e.g. user profile data corresponding with consumption of movie content).)
Richter, CHANDRA, Dey and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing user selections as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Regarding dependent claim 3,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
Richter-CHANDRA-Dey does not discloses the step wherein the second electronically determining the at least one selected of said at least one content subsets comprise: analyzing, by the at least one electronic computer processor, the at least one metadata associated with the base content;
analyzing, by the at least one electronic computer processor, at least one metadata associated with the at least one of said at least one content subsets;
analyzing, by the at least one electronic computer processor, the compatibility of the base content with the at least one of said at least one content subsets;
identifying, by the at least one electronic computer processor, at least one of said at least one content subsets based on a correlation between the user criteria and the at least one of said least one content subsets;
and analyzing, by the at least one electronic computer processor, the at least one user criteria with the at least one of said at least one content subsets to determine the at least one selected of said at least one content subsets.
Punja discloses the step wherein the second electronically determining the at least one selected of said at least one content subsets comprise: analyzing, by the at least one electronic computer processor, the at least one metadata associated with the base content; See Paragraph [0035], (Disclosing a method for generating an image depiction of content using a machine learning system. Generator 230 may pre-process metadata 210 to determine particular preferences associated with a profile. Note [0030] wherein metadata 210 pertains to particular content and other content, i.e. analyzing metadata associated with base content.)
analyzing, by the at least one electronic computer processor, at least one metadata associated with the at least one of said at least one content subsets; See Paragraph [0035], (Generator 230 may pre-process metadata 210 to determine particular preferences associated with a profile. Note [0030] wherein metadata 210 pertains to particular content and other content, i.e. analyzing metadata associated with the at least one content subsets (e.g. metadata 210 represents information associated with every existing piece of content on the system.)
analyzing, by the at least one electronic computer processor, the compatibility of the base content with the at least one of said at least one content subsets; See Paragraph [0034], (Discriminator 240 may determine if a generated content portion represents a passable depiction of content, i.e. analyzing compatibility of base content with the at least one content subset.) Note [0022] wherein machine learning system 120 receives depictions 110 from which to base and compare a generated depiction.)
identifying, by the at least one electronic computer processor, at least one of said at least one content subsets based on a correlation between the user criteria and the at least one of said least one content subsets; See Paragraph [0024], User profile preferences may be correlated with particular attributes of images such that the machine learning system may determine that the profile consumes content having particular genres, actors, or other features, i.e. identifying at least one of said at least one content subsets based on a correlation between the user criteria (e.g. profile elements that identify for example movies or television programs consumed by said profile) and the at least one of said least one content subsets (e.g. content having features in common with the profile input).)
and analyzing, by the at least one electronic computer processor, the at least one user criteria with the at least one of said at least one content subsets to determine the at least one selected of said at least one content subsets. See Paragraph [0025], (Reference depictions are considered according to attributes corresponding to preferences of profile 125, i.e. analyzing the user criteria to determine at least one selected of said at least one content subsets.)
Richter, CHANDRA, Dey and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing metadata as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Regarding dependent claim 6,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
Richter-CHANDRA-Dey does not disclose the step wherein the at least one stored user information analyzed and obtained is analyzed and obtained in real-time using the at least one of the at least one artificial intelligence technique, or the at least one machine learning techniques, wherein the first electronically determining the base content comprises fourth electronically determining, by the at least one electronic computer processor, the user criteria based on the at least one stored user information, and wherein the at least one stored user information is determined from merging and weighting of user criteria associated with two or more end users.
Punja discloses the step wherein the at least one stored user information analyzed and obtained is analyzed and obtained in real-time using the at least one of the at least one artificial intelligence technique, or the at least one machine learning techniques, See Paragraph [0054], (Disclosing a method for generating an image depiction of content using a machine learning system. Machine learning/artificial intelligence systems are used to generate a content depiction 730 to reflect preferences of a user profile. Note [0057] wherein the system may provide content depictions based on user profile data in real-time, i.e. wherein the at least one stored user information analyzed and obtained is analyzed and obtained in real-time using the at least one machine learning technique.)
wherein the first electronically determining the base content comprises fourth electronically determining, by the at least one electronic computer processor, the user criteria based on the at least one stored user information, See Paragraphs [0004] & [0009], (The ML system may generate a tailored content depiction based on elements of a user profile or collection of user profiles by processing and interpreting elements of the user profiles into classifications of features and levels of preference for different kinds of features of content, i.e. wherein the first electronically determining the base content comprises fourth electronically determining, by the at least one electronic computer processor, the user criteria based on the at least one stored user information (e.g. user preferences are determined based on user profile information).)
and wherein the at least one stored user information is determined from merging and weighting of user criteria associated with two or more end users. See Paragraph [0009], (The ML system may use deconstructed segments or features of content in order to tailor content depictions to a particular user profile or collection of user profiles.) See Paragraph [0023], (The system may employ a plurality of machine learning models 123 including locally weighted learning models to generate a new content depiction based on profile data 125 and content data 130, i.e. wherein the at least one stored user information is determined from merging and weighting of user criteria associated with two or more end users.)
Richter, CHANDRA, Dey and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing metadata as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Regarding dependent claim 8,
As discussed above with claim 2, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
Punja further discloses the step wherein the at least one consumption factor associated with the at least one end user comprises at least one or more of a public or private information associated with activity of the at least one end user including content, social media, viewing activities, reviews, posts, share, or recommendations, See Paragraph [0032], (Generator 230 may determine content preferences associated with a profile based on profile data 200 which may include internet browsing history, social media posts, content “likes” or “dislikes,” etc., i.e. wherein the at least one consumption factor associated with the at least one end user comprises at least one or more of a public or private information associated with activity of the at least one end user including social media.)
wherein the consumption factors are determined automatically by a system using at least one of: artificial intelligence (AI), or machine leaning (ML) methods. See Paragraph [0045], (The machine learning system may consume profile data including content consumption history in order to generate an output depiction relevant to the user profile information, i.e. consumption factors are determined automatically by a system using ML methods.)
Regarding dependent claim 9,
As discussed above with claim 2, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step further comprising at least one ambience criteria comprising at least one or more of: a type of the at least one end user device, a processing capability, an audio capability, a video capability, a resolution capability, communications connectivity, or network connectivity of the at least one end user device, outside light, outside light at a location, a light characteristic, or an acoustic characteristics at a location of the at least one end user device, or temperature, or a humidity at a location of the at least one end user device. See Paragraph [0080], (The customized video content may be switched to different content formats based on the availability of network bandwidth to the user device, i.e. ambience criteria comprising at least network connectivity.)
Regarding dependent claim 10,
As discussed above with claim 2, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein storing the at least one user criteria in said electronic database comprises storing, by the at least one electronic computer processor, at least one or more of: user entered information, publicly available information associated with the at least one end user, or information entered using a human machine interface method. See Paragraph [0037], (User preferences may be input using a graphic slider, bar, numeric digits and/or tapping gestures on a user terminal, i.e. wherein storing the at least one user criteria in said electronic database comprises storing, by the at least one electronic computer processor, at least one or more of: user entered information.)
Regarding dependent claim 11,
As discussed above with claim 10, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the human machine interface method comprises providing, by the at least one electronic computer processor, an input to the at least one end user device based on at least one or more of: a gesture from the at least one end user, facial expression of the at least one end user, brain activity of the at least one end user, eye movement of the at least one end user, or an audio or sound generated by the at least one end user. See Paragraph [0037], (User preferences may be input using a graphic slider, bar, numeric digits and/or tapping gestures on a user terminal, i.e. wherein the human machine interface method comprises providing, by the at least one electronic computer processor, an input to the at least one end user device based on at least one or more of: a gesture from the at least one end user (e.g. via tapping gestures on a user terminal).)
Regarding dependent claim 13,
As discussed above with claim 2, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the at least one end user device comprises at least one or more of: a tablet, phablet, a smart speaker, a smart phone, a virtual reality display, an augmented reality display, a mixed reality display, a display monitor, a touchscreen, a projector, a pop-up display, a heads up display (HUD), or at least one network connected lens. See Paragraph [0082], (The one or more user terminals may be embodied as a smartphone, i.e. wherein the at least one end user device comprises at least, a smart phone.)
Regarding dependent claim 17,
As discussed above with claim 15, Richter-CHANDRA-Dey discloses all of the limitations.
Richter-CHANDRA-Dey does not disclose the step wherein the compatible insertion location comprises fourth electronically automatically determining, by the at least one electronic computer processor, using an artificial intelligence or a machine learning method.
Punja discloses the step wherein the compatible insertion location comprises fourth electronically automatically determining, by the at least one electronic computer processor, using an artificial intelligence or a machine learning method. See Paragraph [0054], (Disclosing a method for generating an image depiction of content using a machine learning system. Machine learning/artificial intelligence systems are used to generate a content depiction 730 to reflect preferences of a user profile. Note [0023] wherein a content depiction is generated by combining and modifying elements of image data from content data 130 and/or content depictions 110 based on provide data 125 and content data 130, i.e. wherein the compatible insertion location comprises fourth electronically automatically determining, by the at least one electronic computer processor, using an artificial intelligence or a machine learning method (e.g. generating content depictions includes determining a placement of the elements of the depiction such as characters, geographic elements, etc.).)
Richter, CHANDRA, Dey and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing metadata as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Regarding dependent claim 18,
As discussed above with claim 17, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
Punja further discloses the step wherein the machine learning method comprises at least one or more of a supervised learning method, an unsupervised learning method, or a reinforcement learning method. See Paragraph [0053], (Connections in the neural network used to generate content depictions may be modified or reinforces based on a degree of consumption of content associated with the content depiction, i.e. wherein the machine learning method comprises at least a reinforcement learning method.)
Regarding dependent claim 27,
As discussed above with claim 22, Richter-CHANDRA-Dey discloses all of the limitations.
Richter-CHANDRA-Dey does not disclose the step wherein the system comprises at least one artificial intelligence software agent instructions, wherein the at least one artificial intelligence software agent instructions is configured to determine the customized content based on the base content, the one or more content subsets, and at least one content subset criteria, wherein the at least one content subset criteria is determined based on at least one or more of: the at least one user criteria, the at least one capability of the at least one end user device, the at least one capability of the at least one end user, or the at least one capability of the at least one end user environment.
Punja discloses the step wherein the system comprises at least one artificial intelligence software agent instructions, wherein the at least one artificial intelligence software agent instructions is configured to determine the customized content based on the base content, the one or more content subsets, and at least one content subset criteria, See Paragraph [0030], (Disclosing a method for generating an image depiction of content using a machine learning system. Generator 230 receives data for a particular content, profile data 200 and metadata 210 pertaining to the particular content and other content, the artificial intelligence software agent instructions is configured to determine the customized content based on the base content (e.g. content data 215) , one or more content subsets , and a content subset criteria (e.g. metadata 210 as it relates to more than one piece of content).)
wherein the at least one content subset criteria is determined based on at least one or more of: the at least one user criteria, the at least one capability of the at least one end user device, the at least one capability of the at least one end user, or the at least one capability of the at least one end user environment. See Paragraph [0030], (Metadata 210 may include the following: content consumption statistics for the content to be depicted and/or other related content, data pertaining to the actors of the content, their relative popularity, the success of particular content they have been involved in, the success of particular content depictions related to the content, and other data that may be used to tailor a content depiction, i.e. content criteria.
Richter, CHANDRA, Dey and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing metadata as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Regarding dependent claim 28,
As discussed above with claim 27, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the at least one user criteria comprise at least one or more of: at least one user physical factor, at least one cognitive factor, at least one social factor, at least one political factor, at least one economic factor, at least one consumption factor, at least one cultural factor, at least one geographic factor, at least one educational factor, at least one activity factor, or at least one other factor. See Paragraph [0089], (User preferences include genres, duration of the video content from the one or more user terminals. Note [0091] wherein user preferences are selected on the basis of geography, character, popularity etc. of a news item, i.e. wherein the at least one user criteria comprise at least one geographic factor.)
Regarding dependent claim 29,
As discussed above with claim 27, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the at least one capability of the at least one user device comprises at least one or more of: a type of the at least one end user device, at least one software capabilities, at least one hardware capability, at least one audio capability, at least one video capability, at least one processing capability, or at least one communication capability. See Paragraph [0084], (User terminals 101 may access data over a communication network 104 utilizing one or more applications having a graphical user interface designed to display and fetch data from platform 103, i.e. wherein the at least one capability of the at least one user device comprises at least one video capability (e.g. newscasts may be displayed as video), at least one processing capability (e.g. retrieving/fetching data for display), or at least one communication capability (e.g. communicating with platform 103 via a network 104).)
Regarding dependent claim 30,
As discussed above with claim 27, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the at least one end user device is at least one or more of: a tablet, a phablet, a smart speaker, a smart phone, a virtual reality headset, an augmented reality headset, a mixed reality headset, a monitor, a display, a smart television, a projector, or a network connected lens. See Paragraph [0068], (A user may utilize a smartphone application or webpage connected to server 200 in order to interact with the system, i.e. wherein the at least one end user device is a smart phone.)
Regarding dependent claim 31,
As discussed above with claim 27, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
Dey further discloses the step wherein the system further comprises at least one or more of a content encoder, a content decoder, a multiplexer, a de-multiplexer, or a transcoder. See Paragraph [0031], (Streaming Buffer 103 may be populated with prepped content encoded by streaming encoder 102 using predicting coding compression, i.e. wherein the system further comprises at least one or more of a content encoder.)
Regarding dependent claim 32,
As discussed above with claim 27, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
CHANDRA further discloses the step wherein the plurality of content is stored on a cloud system and the customized content is generated on the cloud system. See Paragraphs [0054]-[0055], (Platform 103 analyzes and transforms content uploaded to a cloud by authentic news providers in order to generate personalized content playlists to be delivered to user terminal devices, i.e. wherein the plurality of content is stored on a cloud system and the customized content is generated on the cloud system.)
and transmitted to the at least one end user device in at least one or more of: real time; on the fly; immediately; dynamically; automatically; without noticeable delay by the user; with negligible delay; without user noticeable delay; without interruption during presentation; in the absence of user interruption; or dynamically with immediate sub-second response time. See Paragraph [0058], (The newscast created by the platform is composed of buckets of video content that may be delivered toa user terminal and played out automatically open after another, i.e. transmitted to the at least one end user device in at least automatically.)
Regarding dependent claim 34,
The claim is analogous to the subject matter of dependent claim 2 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 35,
The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA, Dey and Punja as applied to claim 2 above, and further in view of CHOE (US PGPUB No. 2021/0264221; Pub. Date: Aug. 26, 2021).
Regarding dependent claim 7,
As discussed above with claim 2, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
Richter-CHANDRA-Dey-Punja does not disclose the step wherein the physical factors associated with the at least one end user comprise at least one or more of: age, gender, height, eye sight abilities, hearing abilities, or physical health of the at least one end user.
CHOE discloses the step wherein the physical factors associated with the at least one end user comprise at least one or more of: age, gender, height, eye sight abilities, hearing abilities, or physical health of the at least one end user. See Paragraph [0019], (Model personal information includes at least one of a name, a nationality, gender classification, age, occupation, race, address, favorite food, and favorite color of the model, i.e. wherein the physical factors associated with the at least one end user comprise at least one or more of: age, gender.)
Richter-CHANDRA-Dey-Punja and CHOE are analogous art because they are in the same field of endeavor, content generation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey-Punja to include the method of maintaining user personal data used to train a machine learning model as disclosed by CHOE. Paragraphs [0004]-[0005] of CHOE discloses that the process of creating virtual content by leveraging user data may be used to determine a user’s emotional state and deliver content that may help a user in case when a user is sad, troubled, etc.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA, Dey and Punja as applied to claim 3 above, and further in view of Lee (US PGPUB No. 2009/0006643; Pub. Date: Jan. 1, 2009).
Regarding dependent claim 4,
As discussed above with claim 3, Richter-CHANDRA-Dey-Punja discloses all of the limitations.
Punja further discloses the step wherein the electronic processing of the base content and the at least one selected of said at least one content subsets comprises: electronically correlating, by the at least one electronic computer processor, each of the at least one selected of said at least one content subsets with the base content; See FIG. 5 and Paragraph [0048], (FIG. 5 illustrates machine learning techniques including step 560 wherein the machine learning model may generate a content structure/image depiction of identified content based on profile preferences and correlated content structures, images and/or image/content structure features, i.e. correlating each of the at least one selected of said at least one content subsets with the base content (e.g. the initial content structures are correlated with user profile preferences and additional image/content structures).)
fourth electronically determining, by the at least one electronic computer processor, a characteristic of each of the at least one selected of said at least one content subsets with the base content and a characteristic of each of the at least one content subsets with others of the at least one selected of said at least one content subsets based on at least one or more of: a data signature, fingerprint code, a timestamp, or the metadata of the base content, or the at least one selected of said at least one content subsets; See FIG. 5 and Paragraph [0048], (FIG. 5 illustrates machine learning techniques including step 560 wherein the machine learning model may generate a content structure/image depiction of identified content based on profile preferences and correlated content structures, images and/or image/content structure features, i.e. determining a characteristic of each of the at least one selected of said at least one content subsets with the base content and a characteristic of each of the at least one content subsets with others of the at least one selected of said at least one content subsets based on at least the metadata of the base content, or the at least one selected of said at least one content subsets, i.e. (e.g. the correlated content structures, images or image features represent metadata shared/correlated across the initial image and the additional image data).)
Richter-CHANDRA-Dey-Punja does not disclose the step of electronically transcoding, by the at least one electronic computer processor, the at least one selected of said at least one content subsets for compatibility with the base content.
Lee discloses the step of electronically transcoding, by the at least one electronic computer processor, the at least one selected of said at least one content subsets for compatibility with the base content. See Paragraph [0069], (Disclosing a method for transcoding an internet media stream requiring insertion of new media segments into a transcoded media stream to support advanced features.) See Paragraph [0078], (Prefix and Postfix insertion of content to a transcoded media stream requires rendering compatibility between time bases and formats, i.e. transcoding the at least one selected at least one content subset (e.g. the appended stream elements) for compatibility with the base content (e.g. the pre-appended transcoded media stream.)
Richter, CHANDRA, Dey, Punja and Lee are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey-Punja to include the method of transcoding an appended content stream as disclosed by Lee. Doing so would ensure that the additional stream content is compatible with the stream it is being appended to, thereby ensuring that it may be delivered to user devices properly.
Regarding dependent claim 5,
As discussed above with claim 4, Richter-CHANDRA-Dey-Punja-Lee discloses all of the limitations.
Lee further discloses the step wherein the electronically generating the customized content comprises: electronically receiving, by the at least one electronic computer processor, the at least one capability of the at least one end user device comprising device criteria, wherein the device criteria include at least one of: type of device, software and hardware capabilities, audio and video capabilities, processing capabilities, or communication capabilities; See Paragraph [0024]-[0025], (Transcoding server system requests corresponding media data from a source media content server and then performs on-the-fly transcoding to convert the media data into a format with encoding parameters that are compatible with the user's device. The transcoded media data is then passed to the streaming server for delivery to the user's device using compatible communication protocols, i.e. receiving device criteria of the end user device including at least communication capabilities.)
Punja further discloses the step of electronically processing, by the at least one electronic computer processor, the base content and the at least one selected of said at least one content subsets based on the device criteria; See Paragraph [0027], (A depiction may be generated and transmitted to a destination associated with a user profile, wherein a destination may include devices for personal displays of content linked to a profile. The broadcasting of the content using the associated device may include display of the generated depiction and may include providing information or an interface for accessing content associated with the generated depiction. Note [0023] wherein a depiction 145 is generated by combining and modifying elements of image data and/or content depictions based on profile data and content data., i.e. electronically processing, by the at least one electronic computer processor, the base content and the at least one selected of said at least one content subsets based on the device criteria (e.g. the device association with a user profile).)
fifth electronically determining, by the at least one electronic computer processor, interleaving characteristics of the base content and the at least one selected of said at least one content subsets based on at least one or more of: the user criteria, the device criteria, the ambient criteria, or the content criteria; See FIG. 5 and Paragraph [0048], (FIG. 5 illustrates machine learning techniques including step 560 wherein the machine learning model may generate a content structure/image depiction of identified content based on profile preferences and correlated content structures, images and/or image/content structure features, i.e. determining interleaving characteristics of the base content and the at least one selected of said at least one content subsets based on at least, the user criteria, the content criteria (e.g. the identified content used to generate a new piece of content are determined to correlate with profile preferences as well as correlated content structures, images and/or image/content structure features, wherein features represent content criteria and profile preferences represent user criteria).)
electronically wrapping, by the at least one electronic computer processor, the base content and the one or more selected content subsets for transmission to the at least one end user device; See Paragraph [0023], (Machine learning system 120 generates a new content depiction by combining and modifying elements of image data from content data 130 and/or content depictions 110 based on profile data and content data, i.e. wrapping the base content and selected content subsets for transmission to the user device (e.g. see FIG. 1 wherein following output of content 145, said content is transmitted to target user account at 150).)
Richter further discloses the step of electronically merging, by the at least one electronic computer processor, the base content and the one or more selected content subsets into a group of temporally overlapping content; See Paragraph [0078], (Both video content and SR companion content may be presented via electronic device 120, i.e. electronically merging, by the at least one electronic computer processor, the base content and the one or more selected content subsets into a group of temporally overlapping content;)
and electronically synthesizing, by the at least one electronic computer processor, the group of temporally overlapping content into the customized content. See Paragraph [0078], (Both video content and SR companion content may be presented via electronic device 120. Note [0024] wherein the system combines images or video with virtual objects and displays the combination on an opaque display, i.e. electronically synthesizing, by the at least one electronic computer processor, the group of temporally overlapping content into the customized content.)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA and Dey as applied to claim 1 above, and further in view of Doucette et al. (US PGPUB No. 2018/0373994; Pub. Date: Dec. 27, 2018).
Regarding dependent claim 12,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
Richter-CHANDRA-Dey does not disclose the step wherein said transmitting to present said customized content for display, further comprises presenting of the customized content on the at least one end user device following the transmitting, wherein said transmitting is from at least one distributed electronic content storage systems.
Doucette discloses the step wherein said transmitting to present said customized content for display, further comprises presenting of the customized content on the at least one end user device following the transmitting, wherein said transmitting is from at least one distributed electronic content storage systems. See Paragraph [0099], (Content management server 102 gathers information from one or more internal components 402-408. Internal components 402-408 gather and/or process information relating to such things as: content provided to users; content consumed by users; responses provided by users; user skill levels; content difficulty levels; next content for providing to users; etc. and may subsequently report the gathered and/or generated information in real-time, near real-time, i.e. providing content for display wherein transmission is from distributed electronic content storage systems.)
Richter, HANDRA, Dey and Doucette are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of maintaining a profile repository and content repository in a distributed network as disclosed by Doucette. Paragraph [0201] of Doucette discloses that the content provisioning system can be based on real-time and dynamic prioritization models that can accurately select content most suited for delivery, which increases the efficiency with which content is provided to the user.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA, Dey and Warren, as applied to claim 18 above, and further in view of Punja et al. (US PGPUB No. 2021/0266637; Pub. Date; Aug. 26, 2021).
Regarding dependent claim 19,
As discussed above with claim 18, Richter-CHANDRA-Dey-Warren discloses all of the limitations.
Richter-CHANDRA-Dey-Warren does not disclose the step wherein the machine learning method comprises at least one or more of: linear regression, logistic regression, decision tree, support vector machine (SVM), naïve bayes, k-nearest neighbors (kNN), k- means clustering, random forest, dimensionality reduction, or gradient boosting algorithm.
Punja discloses the step wherein the machine learning method comprises at least one or more of: linear regression, logistic regression, decision tree, support vector machine (SVM), naïve bayes, k-nearest neighbors (kNN), k- means clustering, random forest, dimensionality reduction, or gradient boosting algorithm. See Paragraph [0023], (Machine learning system 120 may employ any of the following techniques: linear regression, logistic regression, multivariate adaptive regression, locally weighted learning, Bayesian, Gaussian, Bayes, neural network, generative adversarial network (GAN), and/or others known to those of ordinary skill in the art.)
Richter, CHANDRA, Dey ¸Warren and Punja are analogous art because they are in the same field of endeavor, creation of customized content. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of processing metadata as disclosed by Punja. Paragraph [0007] of Punja discloses that the use of a machine learning system may improve or modify a generated depiction to more closely represent and approved or authentic depiction, thereby improving the system output.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richter in view of CHANDRA and Dey as applied to claim 1 above, and further in view of Rothman (US PGPUB No. 2021/0133830; Pub. Date: May 6, 2021).
Regarding dependent claim 20,
As discussed above with claim 1, Richter-CHANDRA-Dey discloses all of the limitations.
CHANDRA further discloses the step wherein the electronically processing comprises electronically inserting, by the at least one electronic computer processor, at least one selected of said at least one content subsets into the base content based on a score for each of said at least one content subset, See Paragraph [0061], (The platform may rank all of the available content on the cloud according to a Story Decision Value (SDV) that indicates how important it is to display a particular piece of content to a particular individual such that the SDV for a particular content item will be different for two users, i.e. electronically inserting, by the at least one electronic computer processor, at least one selected of said at least one content subsets into the base content based on a score for each of said at least one content subset (e.g. the customized content is comprised of elements that are most relevant to a user which takes the SDV value into account).)
Richter-CHANDRA-Dey does not disclose the step wherein the score is fourth electronically determined, by the at least one electronic computer processor, based on at least one weighted summation of a plurality of the at least one user criteria of at least one, two or more of the at least one end users.
Rothman discloses the step wherein the score is fourth electronically determined, by the at least one electronic computer processor, based on at least one weighted summation of a plurality of the at least one user criteria of at least one, two or more of the at least one end users. See Paragraph [0091], (Disclosing a system for publishing content including identifying one or more reviews (i.e. content subsets) associated with content included in the content item (i.e. base content). The system comprises functionality for overlaying third-party review information with content items wherein individual reviews are associated with aggregate rating information.) See Paragraph [0078], (Aggregate rating information comprises a normalized rating that is an average of ratings from individual reviews, i.e. a score determined based on weighted summation of user criteria score of two or more users.)
Richter, CHANDRA, Dey and Rothman are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Richter-CHANDRA-Dey to include the method of combining content with additional elements according to scores as disclosed by Rothman. Doing so would allow the system to enhance a user’s enjoyment of a particular piece of content by providing additional, relevant information that enhances a user’s enjoyment of said content.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 22 and 33 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments necessitated the new grounds of rejection presented in this Office Action.
Applicant’s amendments to independent claims 1, 22 and 33 have incorporated subject matter from now-cancelled claim 16 which was not previously rejected under 35 USC 101 as referring to a process of dynamically delivering content to a user device according to real-time network conditions. These steps were considered to not be able to be performed in the human mind. Therefore, the corresponding rejection has been withdrawn.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159