DETAILED ACTION
This final action is in response to the amendment and remarks filed 07/01/2025 for application 17/530,068.
Claims 1-2, 4-6, 8, and 16 have been amended. Claim 24 is a newly added claim. Claims 1-24 are pending in the application. Claims 1 is an independent claim.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed 04/21/2025 has been considered by the examiner.
Response to Amendment
The amendment filed 07/01/2025 has been entered.
Applicant’s amendment to the claims with respect to resolving claim objections and indefiniteness rejections under 35 U.S.C. 112(b) has been considered, and overcomes the objections and 112(b) rejections set forth in the office action mailed 03/07/2025. Consequently, the previous objections and rejections have been withdrawn.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, “the transition is supplementary to the selected content and that includes at least one of:” should read ““the transition is supplementary to the selected content and includes at least one of:” to make clearer that “includes at least one of” refers to “the transition”.
In claim 1, “providing the botcast to an audio player that renders the selected content of the botcast and the transition according to the sequence” should read “providing the botcast to an audio player that renders the selected content of the botcast and the transition according to the botcast audio playback sequence” to have proper antecedent basis.
Appropriate corrections are required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, 12, 14, 19, 21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy et al., (Pub. No. US 20210208842 A1, "Computerized Systems and Methods for Hosting and Dynamically Generating and Providing Customized Media and Media Experiences", filed 03/03/2021), hereinafter Cassidy, in view of Mishra et al., (Pub. No. US 20170280208 A1, “Dynamic Summaries for Media Content”, published 09/28/2017), hereinafter Mishra.
Regarding claim 1, Cassidy teaches A method implemented by a computing system for utilizing trained machine learning models for configuring and utilizing a botcast ("Some embodiments dynamically generate high-level instructions that describe how to produce an audio experience, which may be a single experience (that can be short or long) or can describe how to produce a succession of experiences that are chained together. Some embodiments make song mixes, podcasts, advertisements, and/or other content as desired. Some embodiments enable production of one or more lengthy (or practically perpetual) audio or video experiences through dynamic querying of databases that can include producer and/or user preferences regarding a large number of attributes and subjects, followed by dynamic scripting of content completely or substantially consistent with the preferences. In some embodiments, such preferences can be adjusted for different experiences, producers, users and branding goals" [Cassidy ¶ 0234]; “In Step 1816, the AI/machine learning models (e.g., CNNs and classifiers, for example) implemented by engine 400 (e.g., music processor 613, mixdown agent 614 and content generator 615, as discussed above) are then trained, or further trained on this information so that future search results, schedules and mixdowns can be performed more accurately and computationally efficiently” [Cassidy ¶ 0307]; Examiner has interpreted "botcast" as described in the instant specification ("…generating unique and/or customized botcasts for a plurality of different users, where each botcast comprises an audio file of assembled/compiled audio content that is personalized for each individual user" [specification ¶ 0014]). Cassidy discloses producing an audio experience (e.g., audio stream), including forms of assembled/compiled audio content (e.g., song mixes, podcasts), using machine learning models) the method comprising:
in response to a triggering event for generating or modifying the botcast for a particular user, the computing system automatically accessing, from memory or storage, one or more profile or preference settings of the particular user, (“The computing system 601 includes a database 603, a streaming service 611 and an audio (or music, used interchangeably) processor 613. In some embodiments, as discussed in detail below, system 601 can further or alternatively include, mixdown agent 614 and content generator 615 (the functionality of which are discussed in more detail below in relation to FIGS. 13-18)” [Cassidy ¶ 0130];” In some embodiments, the data stored in the database 603 includes an audio library 622, user profiles 625, and overlay content. The audio library 622 may comprise audio files” [Cassidy ¶ 0135]; “User profiles can include similar information as discussed above in relation to database 420 in FIG. 4. A user account may include a user name, password, credentials, and other subscription information. The user profiles 625 may include the audio preferences of a user” [Cassidy ¶ 0137]; “Also, various data is stored in the database 603 or other memory that is accessible to the computing system 601. The database 603 may represent one or more databases 603…The streaming service 611 and audio processor 613 mentioned above are components executed on the computing system 601. These components may generate data and store the data on the database 603 and/or access the contents of the database 603. The streaming service 611 may be an application implemented on one or more webservers that enable users to subscribe, create, edit, and manage stream audio (e.g., digital radio stations). The streaming service 611 receives user input and generates an encoded audio stream that is transmitted over the network 616 for playback.” [Cassidy ¶ 0132-0133]; Cassidy discloses creation of an audio stream in response to user input (i.e., triggering event), wherein the creation involves consideration of user preferences [¶ 0234] accessed from user profiles stored in database memory)
wherein the botcast comprises a data structure or file that includes or operably links to media content (“According to some embodiments, database 420 can store data and metadata associated with media content from an assortment of media and/or service providers and/or platforms” [Cassidy ¶ 0103]; [Cassidy ¶ 0130, 0132-0133, 0135] as detailed above; “Through a client device 633, a user may subscribe to a streaming service 611 and specify a preference to an audio stream. The audio service 611 selects various audio files from the audio library 622 and assembles them in serial order into an audio stream that is then transmitted over the network 616 to a client device 633. The streaming service 611 may dynamically create a playlist of audio files to be streamed in a particular order. The playlist may include the currently streamed audio file, the subsequently streamed audio file, and potentially additional audio files to be streamed in order” [Cassidy ¶ 0140]; "...when doing the mixdowns, only the heads and tails of content are to be considered (e.g., for mixing them together, adding content, slicing up, and then encoding the output (to AAC, MP3, and the like))" [Cassidy ¶ 0224]; Cassidy discloses encoding the audio stream, comprising selected audio files and additional audio files (i.e., transitions), into an audio format such as AAC or MP3 (i.e., file), wherein the audio stream is drawn from (i.e., operably links to) media content stored in memory) and corresponding transitions created for media content (“In some embodiments, asset features are generated using machine learning or other artificial intelligence algorithms. The asset features may indicate information about an audio item such as the key of the music…or any other attribute or quality about an audio or video item” [Cassidy ¶ 0244]; “In some embodiments, the audio assembler 1608 transitions from one audio item to another audio item... In some embodiments, the audio analyzer 1605 performs feature extraction and classifies asset features to describe aspects of audio items” [Cassidy ¶ 0256]; “In some embodiments, conditions may indicate when to generate content based on the secondary content library of library 625 and what kind of content to generate…In some embodiments, the formulae engine 1608 may select clips based on the asset features of audio items” [Cassidy ¶ 0260-0261]; “The system determines that the transition from the first song 1705 to the second song 1706 is an audio event is a condition that could use dynamically generated content. In some embodiments, the content generator 615 is instructed to generate content dynamically to be inserted at a time position around this transition event” [Cassidy ¶ 0267]; The additional audio files [¶ 0140] included in the audio stream may correspond to transition points between audio items, wherein the content of the additional audio is selected based on asset features of the surrounding audio items) in a sequenced ordering for sequenced playback ([Cassidy ¶ 0140] as detailed above), and wherein the botcast is personalized and customized for the particular user based on the one or more profile or preference settings associated with the particular user ([Cassidy ¶ 0234] as detailed above);
identifying media content for selection by a user to include in the botcast for a particular user based on one or more profile or preference settings associated with the particular user; ([Cassidy ¶ 0137, 0140, 0234] as detailed above; Cassidy discloses users indicating preferences that are used to select audio files and assemble a playlist of the selected audio files)
automatically generating a transition associated with selected content, the selected content being selected from the identified media content, the transition being personalized to the particular user, based on the one or more profile or preference settings of the particular user, wherein audio content of the transition is supplementary to the selected content ([Cassidy ¶ 0244, ¶ 0256, ¶ 0260-0261, ¶ 0267] as detailed above; Cassidy discloses automatically identifying transition events corresponding to selected audio files and inserting dynamically generated content accordingly via asset features of the surrounding audio items, wherein dynamically generated content is consistent with user preferences [¶ 0234]. Audio attached to other audio is implicitly supplementary) and that includes at least one of:
an identification of a relevance of the selected content in relation to the particular user based on the one or more profile or preference settings associated with the particular user, (“In some embodiments, scheduler 1614 selects a particular formula or formulae based on conditions. In some embodiments, conditions may indicate when to generate content based on the secondary content library of library 625 and what kind of content to generate” [Cassidy ¶ 0260]; “In some embodiments, an interstitial formulae may provide information about a listening context, or a name of the listening context. In some embodiments, an interstitial formula may include a combination of a music embedded effects and an audio clip stating the listening context's name. A listening context can be, but is not limited to, a radio station, playlist, a streaming service, content channel, area of a service, or other organizing factor that is used to differentiate an area where a set of behaviors apply” [Cassidy ¶ 0258]; A user profile can include audio preferences of a user (e.g., songs, radio stations) [¶ 0137], and a transition, via an interstitial formulae, can identify a current "listening context", such as a radio station, wherein the content corresponding to the current listening context was selected to be consistent with user audio preferences [¶ 0234]) or
a summary of the selected content that is formatted in a selected summary format that is selected from a plurality of available different summary formats, based on the one or more profile or preference settings associated with the particular user; (“A back sell formula may include a summary clip followed by a clip stating the artist's name. A summary clip may be an audio recording of a voice saying, ‘you just listened to’” [Cassidy ¶ 0259]; "The content of a clip can be recorded several times to correspond to different energy levels, intensities. Several recordings can be made using different words to convey a similar message....For example, one clip can say “up next is” while another clip can say “next is”. In addition, these clips may be recorded by different people and/or with different inflections and/or different energy levels" [Cassidy ¶ 0275]; Transitions, via a back sell formula, can include a summary clip of the selected audio file, wherein both the selected audio file and corresponding dynamically generated transitions are consistent with user preferences [¶ 0234]. Clips can have different formats (e.g., differences in words, voices, inflections, energy levels))
sequencing the selected content with the transition into a botcast audio playback sequence based on the profile or preference settings of the particular user; ([Cassidy ¶ 0133, 0140] as detailed above; Cassidy discloses assembling selected audio files, along with inserted transition content [¶ 0267] consistent with user preferences [¶ 0234] into a serial (i.e., sequential) order and into an audio stream that is transmitted for playback)
formatting the botcast, including the selected content and the transition, with an audio format selected from a plurality of different audio formats, the audio format being selected from the plurality of different audio formats based on the one or more profile or preference settings associated with the particular user; and ([Cassidy ¶ 0224] as detailed above; "...audio and listening experiences are generated and compiled from various types of audio formats and types in a unique, dynamically determined manner for a listening user" [Cassidy ¶ 0016]; Cassidy discloses encoding an output file, comprising selected audio files and transitions [¶ 0140], into an audio format such as AAC or MP3. User profiles 625 can include the audio preferences of a user [¶ 0137], and thereby implicitly result in a preferred audio format, as audio experiences are generated from “various types of audio formats” and “in a unique, dynamically determined manner” based on the preferences of “a listening user”)
providing the botcast to an audio player that renders the selected content of the botcast and the transition according to the sequence in the selected audio format. ("When the audio player 637 of the client device 633 plays back the output file 1502, it can recognize and process the metadata 1511. In response, the client device 633 can render for display the contents reflected in the metadata" [Cassidy ¶ 0228]; The encoded audio stream, sequenced in a particular order [¶ 0140] and encoded into a particular format [¶ 0224], can be transmitted to an audio player that further renders its content for display).
However, Cassidy does not expressly teach wherein audio content of the transition is different from audio content of one or more different transitions generated in association with the selected content, the audio content of the one or more different transitions being personalized to one or more different users based on one or more different profile or preference settings for the one or more different users.
In the same field of endeavor, Mishra teaches a method of managing streaming of content personalized for a user (“The content delivery service 215 receives content requests 224 from client devices 206 and sends content data 227 to the client devices 206. In this regard, the content delivery service 215 may facilitate streaming of large content files or real-time live content via the network 209 to the client devices 206” [Mishra ¶ 0022]) wherein audio content of the transition is different from audio content of one or more different transitions generated in association with the selected content, the audio content of the one or more different transitions being personalized to one or more different users based on one or more different profile or preference settings for the one or more different users (“The components executed on the computing environment 203, for example, include a content delivery service 215, a summary generation service 218…The content delivery service 215 is configured to deliver media content 221 over the network 209 for user consumption via the client devices 206” [Mishra ¶ 0022]; “In some cases, the media content 221 may already be split up into multiple files, where each file corresponds to an underlying logical division 248” [Mishra ¶ 0026]; “The summary generation service 218 is executed to dynamically generate summaries of the media content 221 that are personalized for a user. “The dynamic summaries 230 are generated by the summary generation service 218 to provide a personalized summary of a portion of media content 221 before or after a particular position in the media content 221. The dynamic summaries 230 are tailored to the particular position in the media content 221, which may be at a point within a logical division 248 that is not associated with a predefined summary 251, such as midway through an episode of a video content series. Moreover, the dynamic summaries 230 may be dynamically generated to be highly relevant to the portion of the media content 254 to be consumed (e.g., read, listened to, or viewed)…The length and content of the dynamic summaries 230 may depend upon user characteristics.” [Mishra ¶ 0029]; “The user data 236 may include a variety of data regarding users that may be leveraged by the summary generation service 218 in personalizing the dynamic summaries 230. To this end, the user data 236 may include characteristics 260, a media consumption history 263. The characteristics 260 may include any characteristic of the user that may have a bearing on what type of dynamic summary 230 would be viewed as most helpful or relevant to the particular user. For example, the characteristics 260 may include age, gender, location, summary length preferences, etc” [Mishra ¶ 0031]; “For example, the content delivery service 215 may stream or transfer the media content 221 after the point of resumption to the client device 206 via the network 209 in the content data 227. The content access application 272 on the client device 206 may then be configured to render the media content 221 on one or more output devices 269 after presenting the dynamic summary 230” [Mishra ¶ 0046]; The same portion of media content (i.e., selected content) being transmitted to different users, via their client devices, is delivered along with respective dynamically generated summaries (i.e., different transitions), wherein the generated summaries may have differences in length and content (i.e., audio content) dependent on the characteristics of the respective user (i.e., personalized to profile/preference settings)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein audio content of the transition is different from audio content of one or more different transitions generated in association with the selected content, the audio content of the one or more different transitions being personalized to one or more different users based on one or more different profile or preference settings for the one or more different users as taught by Mishra into Cassidy because they are both directed towards managing streaming of content personalized for a user. Given that Cassidy already teaches “summary clips” as a type of transition [¶ 0259], differences in clip formats [¶ 0275], and an intended objective of generating content consistent with user preferences [¶ 0234], a person of ordinary skill in the art would recognize the value of incorporating the teachings of Mishra to improve the customization (e.g., varying length and content) of dynamically generated summaries to further align with the preferences and characteristics of a user, thereby improving the user experience.
Regarding claim 8, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the transition, including the identification of the relevance of the selected content to the particular user is based on the one or more profile or preference settings associated with the particular user, the relevance being determined according to a context of the particular user relative to the selected content ([Cassidy ¶ 0258, 0260] as detailed in claim 1 above; A user profile can include audio preferences of a user (e.g., songs, radio stations) [¶ 0137], and a transition, via an interstitial formulae, can identify a current "listening context", such as a radio station, wherein the content corresponding to the current listening context was selected to be consistent with user audio preferences (i.e., determined relevance according to user context) [¶ 0234])
Regarding claim 12, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the transition includes the summary of the selected content that is formatted in the selected summary format and wherein the method includes selecting the selected summary format from the plurality of available different summary formats, based on the one or more profile or preference settings associated with the particular user ([Cassidy ¶ 0259, 0275] as detailed in claim 1 above; Transitions, via a back sell formula, can include a summary clip of the selected audio file, wherein both the selected audio file and corresponding dynamically generated transitions are consistent with user preferences [¶ 0234]. Clips can have different formats (e.g., differences in words, voices, inflections, energy levels))
Regarding claim 14, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the identifying the selected content includes identifying the selected content from a plurality of different media content from a corresponding plurality of different media content sources ([Cassidy ¶ 0103] as detailed in claim 1 above; "In some embodiments, a set or plurality of audio files can be identified; however, for purposes of discussion in relation to Process 500, a single audio file will be discussed as being identified...the audio file has associated therewith information indicating, but not limited to, a type of audio file (e.g., music, voice over, and the like), and an audio identifier (ID) (which can be an internal ID or an ID associated with the provider of the file). In some embodiments, this information can further indicate a source of the audio file" [Cassidy ¶ 0111, 0113]; Cassidy discloses that sources of audio files can be identified, wherein the selected audio files can further be drawn from media content stored in memory, wherein the media content is drawn from a variety of sources)
Regarding claim 19, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the method further includes modifying the botcast by:
adding new content to the botcast with a new corresponding transition that is associated with the new content; (“The user may provide input to the streaming service 611, which can include such actions as, but not limited to, skipping to the next track, pausing, changing stations, providing feedback regarding an interest (e.g., “like” or “dislike”), and in some embodiments, as discussed below, can provide parameters to alter the output (e.g., change volume, energy level, speed of playback, aggressiveness of overlaying, factors relating to the personality or overall perception of the output, and the like). In response, according to the disclosed functionality, the streaming service 611 may access the audio library 622 to create an updated audio stream in response to the user input [Cassidy ¶ 0141]; “As mentioned above, the requesting user can provide input, settings or parameters for controlling how the playlist is managed… In some embodiments, Step 1818 can be performed, which monitors for these types of inputs by the requesting user. Should input be provided at this stage, Process 1800 would proceed back to at least Step 1810 or 1812 to search and/or recompile the schedule and mixdowns” [Cassidy ¶ 0308-0309]; “In Step 1812, the results are identified, analyzed and a schedule (e.g., a playback data structure) for each file in the search results and the audio file identified in the request (from Step 1802) is determined. According to some embodiments, Step 1812 involves receiving the compiled results, analyzing them (e.g., via scheduler 1614) and determining an order of each audio file identified in the search results, as well as any overlap, if any, between transitions of files” [Cassidy ¶ 0303]; In response to user feedback during playback, the audio stream may be updated to include new content (from results of updated search) and transitions corresponding to updated user preferences)
resequencing the botcast audio playback sequence ([Cassidy ¶ 0141, 0303, 0308-0309] as detailed above; The updated content is re-ordered when forming the updated audio stream)
Regarding claim 21, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the media content is identified by a machine learning model that is trained to search and crawl target resources for content that is determined to be relevant to different users based on different user profile or preference settings associated with the different users. ("…applications 342 may include computer executable instructions which, when executed by client device 300, transmit, receive, and/or otherwise process audio, video, images, and enable telecommunication with a server…In some embodiments, applications 342 may further include search client 345 that is configured to send, to receive, and/or to otherwise process a search query and/or search result" [Cassidy ¶ 0093]; “The database 420 can be any type of database or memory and can be associated with a content server on a network (e.g., cloud server, content server, a search server or application server) or a user's device (e.g., client devices discussed above in FIGS. 1-3)” [Cassidy ¶ 0097]; “In Step 1816, the AI/machine learning models (e.g., CNNs and classifiers, for example) implemented by engine 400 (e.g., music processor 613, mixdown agent 614 and content generator 615, as discussed above) are then trained, or further trained on this information so that future search results, schedules and mixdowns can be performed more accurately and computationally efficiently" [Cassidy ¶ 0307]; "…dynamic querying of databases that can include producer and/or user preferences…In some embodiments, such preferences can be adjusted for different experiences, producers, users" [Cassidy ¶ 0234]; Cassidy discloses AI/machine learning models trained to compile accurate search results, wherein the search results can be obtained from a client configured to search through audio/video/image resources, wherein the searching can be adjusted based on preferences of different users, and the media content stored in memory is drawn from the search results).
Regarding claim 24, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the audio content of the transition includes the identification of the relevance of the selected content in relation to the particular user based on the one or more profile or preference settings associated with the particular user ([Cassidy ¶ 0258, 0260] as detailed in claim 1 above; A user profile can include audio preferences of a user (e.g., songs, radio stations) [¶ 0137], and a transition, via an interstitial formulae, can identify a current "listening context" (e.g., via audio clip stating listening context’s name), such as a radio station, wherein the content corresponding to the current listening context was selected to be consistent with user audio preferences (i.e., determined relevance according to user context) [¶ 0234]).
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy and Mishra, as applied to claim 1 above, further in view of Ingrassia, Jr. et al., (Pub. No. US 20120079017 A1, “Method and Systems for Providing Podcast Content”, filed 09/28/2010), hereinafter Ingrassia.
Regarding claim 2, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the identifying the selected content to include in the botcast for the particular user further includes:
parsing the media content into a plurality of media subcomponents; ([Cassidy ¶ 0140] as detailed in claim 1 above; “Process 1100 begins with Step 1102 where the identified audio file(s) from Step 502 is parsed, from which portions (e.g., slices) of the audio file are identified. Such portions, for example, can include, but are not limited to, samples of the audio, normalized versions of the audio, segmentation of the audio, extracted audio and melodic portions, and the like” [Cassidy ¶ 0171]; “FIG. 12 provides Process 1200 for further processing of an audio file. Such processing can be a sub-process of Steps 504 and 506's operations, as mentioned above…Process 1200 begins with Step 1202 where the audio file is parsed (and analyzed) in a similar manner as discussed above in relation to Steps 504 and 1102. In Step 1204, a set of predetermined portions of the audio file are identified, and such portions correspond to a predetermined time period of the audio file” [Cassidy ¶ 0184])
identifying the one or more selected subcomponents as the selected content; ([Cassidy ¶ 0140], as detailed in claim 1 above) and for each selected subcomponent of the one or more selected subcomponents, automatically generating a separate transition corresponding to each selected subcomponent, each transition being automatically generated by a machine learning model that is trained to perform analysis of media content and to generate transitions for the media content that summarize a context and relevance of the media content and in a personalized manner based on different preference or profile settings of different users ([Cassidy ¶ 0244, 0256, 0260-0261, 0267]; The additional audio files [¶ 0140] (i.e., transitions) included in the audio stream may correspond to transition points between audio items (i.e., subcomponents), wherein the content of the additional audio is selected based on asset features of the surrounding audio items, wherein both the selected audio files and corresponding dynamically generated transitions are consistent with user preferences [¶ 0234]).
However, the combination does not expressly teach presenting the plurality of media subcomponents to the particular user for subsequent user selection and identifying user input selecting one or more selected subcomponents from the plurality of media subcomponents, at least not prior to assembly of the initial audio stream (The examiner notes that Cassidy does teach identifying user input providing user feedback in response to a presented audio stream [¶ 0141]).
In the same field of endeavor, Ingrassia teaches a method of compiling media into an audio stream (e.g. podcast) that is personalized for a user ("In some embodiments, the podcast parsing application may create a compilation of podcast content. Compiling will be understood to mean concatenating, or arranging in series, podcasts or portions of podcasts thereby forming a new podcast. Compiling podcast content, portions of podcast content, or combinations thereof, may provide a technique for delivering desirable content to a user" [Ingrassia ¶ 0018]) that present[s] the plurality of media subcomponents to the particular user for subsequent user selection (“In some approaches, compilations may be generated based on user input, may be manually assembled by a user, or both. For example, a user may specify content of interest by manually selecting portions of podcasts. The user may also input keywords, preference information, any other suitable indicators, or any combination thereof to the podcast parsing application for searching podcast content….The user input, output of automated processes, or both may be used by the podcast parsing application to, for example, identify podcasts and podcast portions to use in the compilation” [Ingrassia ¶ 0018]; “Extracting will be understood to mean distinguishing a portion of a podcast from a remainder of the podcast, and providing the distinguished portion for further action. Extraction includes, but is not limited to, removal, separation, and retrieval of portions of podcast content. Extraction of podcast content may allow for content of interest to be separated from content that is not of interest… In some approaches, the podcast parsing application may extract portions of podcast content based on user input, including providing options to a user for selecting or specifying a portion of a podcast.” [Ingrassia ¶ 0020-0021]) and identif[ies] user input selecting the one or more selected subcomponents from the plurality of media subcomponents ([Ingrassia ¶ 0018, 0020-0021], as detailed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated present[ing] the plurality of media subcomponents to the particular user for subsequent user selection and identify[ing] user input selecting the one or more selected subcomponents from the plurality of media subcomponents as taught by Ingrassia into the combination because both Cassidy and Ingrassia are directed towards compiling media into an audio stream (e.g. podcast) that is personalized for a user. Incorporating the teachings of Ingrassia would provide the user with more options to directly customize how their personalized media stream is assembled (e.g., via setting metadata tags) ([Ingrassia ¶ 0023-0024, 0058]).
Regarding claim 3, the combination of Cassidy, Mishra, and Ingrassia teaches the limitations of parent claim 2, and Cassidy further teaches wherein the method further includes a textual description of one or more topics associated with each corresponding media subcomponent via metadata tags (“In some embodiments, the asset features can also include metadata that is added at the time of import or any other point thereafter by humans or other sources or processes. In some embodiments, this also can include the text of the content (if speech) as extracted by ML/DSP processes or any other data extracted or produced by selected sources or processes, as discussed above” [Cassidy ¶ 0244]). Ingrassia further teaches presenting each of the plurality of media subcomponents with a textual description via its metadata tags (“The podcast parsing application may display information associated with a selected portion of a selected podcast, as shown by display region 980. The selected portion, tagged as the NPR segment "Who's Carl This Time" is displayed during playback time 00:31 to 7:00, or 31 seconds to 7 minutes. The tagging for this program is illustratively shown as "Segment Title", indicating that the portions of this podcast have been tagged in metadata by program segment title” [Ingrassia ¶ 0083]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated presenting each of the plurality of media subcomponents with a textual description as taught by Ingrassia into Cassidy because both of these methods are directed towards compiling media into an audio stream (e.g. podcast) that is personalized for a user. Incorporating the teachings of Ingrassia would further aid the objective of enabling the user to directly customize how their personalized media stream is assembled (e.g., via metadata tags [Ingrassia ¶ 0023-0024, 0058]), as it would present them with helpful description information to consider when making a decision on which audio items to include in a stream.
Regarding claim 4, the combination of Cassidy, Mishra, and Ingrassia teaches the limitations of parent claim 2, and Cassidy further teaches wherein the user input selecting the media content for subsequent parsing and presentation as potential content for the selected content comprises a selection of an audio file that includes the selected content and parsing the media content includes parsing the media content into a plurality of audio segments ([Cassidy ¶ 0140, 0171, 0184] as detailed in claim 2 above)
Regarding claim 5, the combination of Cassidy, Mishra, and Ingrassia teaches the limitations of parent claim 2, and Cassidy further teaches wherein the user input selecting the media content for subsequent parsing and presentation as potential content for the selected content comprises a selection of a control associated with generating the botcast while the media content is presented (“As illustrated in FIG. 6, streaming service 611 can function as a production service (referred to herein as “production service” 611 for purposes of the description of FIGS. 16-17)...The production service 611 may be an application implemented on one or more webservers that enable users to subscribe, create, edit, and manage standalone or sequences of content. In some embodiments, the production service 611 receives user input and generates an encoded audio stream that is transmitted over the network 616 for playback” [Cassidy ¶ 0238]; Cassidy discloses a webserver application that can serve as a user control associated with generating the audio stream and presenting content).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cassidy, Mishra, and Ingrassia, as applied to claim 5 above, further in view of Hou (Pub. No. US 20190349619 A1, “Methods and Systems for Generating and Providing Program Guides and Content”, filed 05/07/2019).
Regarding claim 6, the combination of Cassidy, Mishra, and Ingrassia teaches the limitations of parent claim 5, and Cassidy further teaches wherein the media content comprises a webpage ([Cassidy ¶ 0238]; Webservers inherently store and deliver webpages to users). Ingrassia further teaches a system of manually selecting content for inclusion such that, upon user selection of an icon, the system identifies content for inclusion in the selected content ([Ingrassia ¶ 0018, 0020-0021], as detailed in claim 2 above).
However, the combination does not expressly teach the user input selecting the media content for subsequent parsing and presentation as the potential content for the selected content comprises a selection of an icon on a browser that is displaying the webpage, such that, upon user selection of the icon, the system identifies content on the webpage for inclusion in the selected content.
In the same field of endeavor, Hou teaches a method of compiling media into a content stream that is personalized for a user ("For example, the curating tools may enable an independent user to create a linear, scheduled experience out of on-demand content. For example, a library of different content may be provided via which the user can assemble a program or channel." [Hou ¶ 0108]; "At block 910, a personalized, dynamically generated program guide is generated based at least in part on the explicitly provided user preferences and/or the inferred user preferences. For example, the explicitly provided user preferences and/or the inferred user preferences may be used in determining the ordering of channels in the electronic program guide" [Hou ¶ 0304]) wherein the user input selecting the media content for subsequent parsing and presentation as the potential content for the selected content comprises a selection of an icon on a browser that is displaying the webpage (“Optionally a library add control (e.g., a button) may be provided on third party pages (e.g., website pages) or via a browser plug in that enables a user to add a video on the third party page to the user's individual library (e.g., the user's DVR library for access via the DVR or otherwise)” [Hou ¶ 0084]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the user input selecting the media content for subsequent parsing and presentation as the potential content for the selected content comprises a selection of an icon on a browser that is displaying the webpage as taught by Hou into the combination of Cassidy, Mishra, and Ingrassia because all of these methods are directed towards compiling media content into a content stream that is personalized for a user. Given that Ingrassia already discusses how podcasts are typically hosted directly on provider websites [Ingrassia ¶ 0002], by adapting the disclosed content selection technique of Hou to enable user selection of webpage content, a person of ordinary skill in the art would recognize its value in further aiding the objective of Ingrassia, as it would enable the user to customize how their personalized media stream is assembled by interacting with and selecting content directly from provider webpages themselves.
Claims 7, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy and Mishra, as applied to claims 1, 12, and 14 above, further in view of Ingel et al., (Pub. No. US 20210224319 A1, “Artificially Generating Audio Data from Textual Information and Rhythm Information”, filed 12/28/2020), hereinafter Ingel.
Regarding claim 7, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the media content is a file containing the selected content in a text format (According to some embodiments, database 420 can store data and metadata associated with media files, including, but not limited to, audio files, video files, text files, multi-media files, and the like, or some combination thereof...While the focus on this disclosure will refer to audio files, it should not be construed as limiting, as any other type of media file, whether known or to be known, can be implemented without departing from the scope of the instant application" [Cassidy ¶ 0104-0105]).
However, Cassidy does not expressly teach performing TTS (text-to-speech) processing on the selected content to format the selected content into an audio format.
In the same field of endeavor, Ingel teaches a method of compiling media into an audio stream that is personalized for a user ("Consistent with the present disclosure, system 100 may cause dubbing of a media stream (e.g., media stream 110) from an origin language to one or more target languages. The term “media stream” refers to digital data that includes video frames, audio frames, multimedia, or any combination thereof" [Ingel ¶ 0114]; "In some examples, step 444 may base the generation of speech data on desired voice characteristics and/or desired speech characteristics. For example, the desired voice characteristics and/or desired speech characteristics may be based on characteristics identified by step 438, on characteristics provided by a user" [Ingel ¶ 0172]) that perform[s] TTS (text-to-speech) processing on the selected content to format the selected content into an audio format (“In some embodiments, voice generation module 408 may include instructions to use the translated transcript and the determined voice profile to generate artificial dubbed version of the received media stream. Voice generation module 408 may use any suitable text-to-speech (TTS) algorithm to generate an audio stream from the translated transcript” [Ingel ¶ 0144]; Media content can be a transcript where text-to-speech is used to format the transcript into an audio stream format).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the method further includes: performing TTS (text-to-speech) processing on the selected content to format the selected content into an audio format as taught by Ingel into the combination because both Ingel and Cassidy are directed towards compiling media into an audio stream that is personalized for a user. Incorporating the teachings of Ingel would enable generation of an artificial dubbed voice from text content ([Ingel ¶ 0003-0004]) for further incorporation into an audio stream.
Regarding claim 13, the combination of Cassidy and Mishra teaches the limitations of parent claim 12 and Cassidy further teaches a selected summary format ([Cassidy ¶ 0259, 0275]). Ingel further teaches includ[ing] a prosody speaking style selected from a plurality of possible prosody speaking styles (“In some examples, the at least one characteristic of the avatar selected by step 3106 may comprise a characteristic of a voice of the avatar. Some non-limiting examples of such characteristic of the voice of the avatar may include pitch (such as pitch range), prosody, register, gender of the voice, a selection of the voice of a plurality of alternative voices, and so forth” [Ingel ¶ 0477]; Ingel discloses that a voice can be generated from a variety of different voice characteristics (e.g. speaking style), including prosody. The examiner notes that Cassidy also discloses that clips can have differences in speaking style (e.g., voices, inflections, energy levels) [Cassidy ¶ 0275], and therefore could be reasonably interpreted to teach a plurality of prosody speaking styles on its own merits).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated includ[ing] a prosody speaking style selected from a plurality of possible prosody speaking styles as taught by Ingel into the combination because both Ingel and Cassidy are directed towards compiling media into an audio stream that is personalized for a user. Incorporating the teachings of Ingel would provide more adjustment options ([Ingel ¶ 0199]) when generating an artificial voice ([Ingel ¶ 0003-0004]) for incorporation into an audio stream.
Regarding claim 15, the combination of Cassidy and Mishra teaches the limitations of parent claim 14, and Cassidy further teaches wherein the selected content includes at least some selected content in an audio format and at least some selected content in a text format ([Cassidy ¶ 0104-0105]; Media content is not limited in scope as being directed solely towards audio files, and may comprise any type of data including text files) and wherein formatting the selected content includes converting the selected content into the audio format ([Cassidy ¶ 0224] as detailed in claim 1 above). Ingel further suggests an embodiment wherein the selected content comprises content in an audio format and content in a text format, (“FIG. 39 is a flowchart of an example method 3900 for artificially generating audio data from textual information and rhythm information, in accordance with some embodiments of the disclosure. In this example, method 3900 may comprise: receiving textual information (step 3902); receiving rhythm information (step 3904); receiving voice characteristics (step 3906)” [Ingel ¶ 0684]; “In some examples, Step 3906 may comprise receiving a source audio data (for example using Step 702 and/or Step 802), and analyzing the source audio data to determine the voice characteristics based on voice characteristics of a speaker in the source audio data (for example using Step 708 and/or Step 810)” [Ingel ¶ 0687]) and all of the selected content is convert[ed] into an audio format (“receiving textual information (step 3902); receiving rhythm information (step 3904); receiving voice characteristics (step 3906)…and generating an audio stream based on the textual information, the rhythm information and the voice characteristics” [Ingel ¶ 0684]; Ingel teaches generating an output audio (i.e., converting into an audio format) from source information comprising textual information and voice characteristics, wherein voice characteristics can be further determined from source audio).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein formatting the selected content includes converting all of the selected content into the audio format as taught by Ingel into the combination because both Ingel and Cassidy are directed towards compiling media into an audio stream that is personalized for a user. Incorporating the teachings of Ingel would enable consideration of the additional vocal expression provided by text content (“…the first portion of the generated audio stream includes a vocal expression of the first portion of the textual information in accordance with the first portion of the rhythm information and in a voice corresponding to the voice characteristics” [Ingel ¶ 0684]) when generating an artificial voice [Ingel ¶ 0003-0004] for incorporation into an audio stream.
Claims 9, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy and Mishra, as applied to claims 1 and 8 above, further in view of Hou (Pub. No. US 20190349619 A1, “Methods and Systems for Generating and Providing Program Guides and Content”, filed 05/07/2019).
Regarding claim 9, the combination of Cassidy and Mishra teaches the limitations of parent claim 8.
However, the combination does not expressly teach wherein the transition includes temporal information associated with a scheduled calendar event associated with the particular user.
In the same field of endeavor, Hou teaches a method of compiling media into a content stream that is personalized for a user ([Hou ¶ 0108, 0304]) wherein the transition includes temporal information associated with a scheduled calendar event associated with the particular user ("For example, the programmer can drag a given program identifier to a calendar user interface (which lists days and time for specifying broadcast dates and times) and drop the program identifier at a desired month, week, day, and time for the selected channel" [Hou ¶ 0102]; "At block 2010, a determination is made as to when (in terms of time or available slot) a first of the selected set of two or more related interstitials are to be displayed...at block 2020, current time, current device location information, current channel metadata, program metadata, clip metadata, behavior of social network connections and/or content event timing information may be accessed. Some or all of the information accessed at block 2022 may be utilized to determine when the second interstitial is to be displayed" [Hou ¶ 0232, 0235]; Hou discloses that the user can schedule content events on a calendar interface, wherein a related interstitial (i.e., transition) may access content event timing information to determine at what time it should be displayed during the event; therefore, the interstitial can include a starting and stopping time (i.e., temporal information) associated with a scheduled event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the transition includes temporal information associated with a scheduled calendar event associated with the particular user as taught by Hou into the combination because both Hou and Cassidy are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Hou would allow the user to plan a desired viewing schedule that can be checked for time gaps and errors [Hou ¶ 0106].
Regarding claim 20, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the method further includes generating an additional botcast [Cassidy ¶ 0234]. Hou further teaches an additional botcast that includes:
the selected content; and
a different transition associated with the selected content, the different transition being personalized to a different user than the particular user, the additional botcast also omitting the transition associated with the selected content that is personalized to the particular user ("Optionally, consumers may also be provided with access to some or all features of the channel scheduling tool to enable users to program their own channels and to share their channels with other users…For example, the curating tools may enable an independent user to create a linear, scheduled experience out of on-demand content. For example, a library of different content may be provided via which the user can assemble a program or channel" [Hou ¶ 0107-0108]; "By way of illustration and with reference to the example illustrated in FIG. 22, 10 interstitials may be displayed during a program to two different users. For the first user 2202, whose history indicated a high tolerance or preference for interstitials (e.g., advertisements), 2 interstitials may be scheduled to be immediately played before the beginning of the program and the other 8 interstitials may be distributed throughout the program (including during the first quarter of the program) and immediately after the program. For the second user 2204, whose history indicated a low tolerance for interstitials (e.g., advertisements), no interstitials may be displayed immediately before the program begins or during the first third of the program, and the 10 interstitials may be distributed through the last two thirds of the program" [Hou ¶ 0329]; Users can generate independent content channels (i.e., botcasts) comprised of the same programs (i.e., selected content) as other users. Based on their preferences, users may have interstitials (i.e., transitions) shown at different transition events while viewing the same program (e.g., first user has interstitials in first quarter, whereas first quarter interstitials are omitted for second user)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated generating an additional botcast that includes: the selected content; and a different transition associated with the selected content, the different transition being personalized to a different user than the particular user, the additional botcast also omitting the transition associated with the selected content that is personalized to the particular user as taught by Hou into the combination because both Hou and Cassidy are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Hou would improve user engagement in cases where advertisements are used as transition content ("Thus, for the second user 2204, the display of interstitials may be delayed to enable the second user to have sufficient time to be fully engaged in the program, and hence less likely to navigate away from the interstitials (e.g., to another program on a different channel)" [Hou ¶ 0329]).
Regarding claim 22, the combination of Cassidy and Mishra teaches the limitations of parent claim 1 and wherein the summary is generated by a machine learning module that is trained to generate summaries [Cassidy ¶ 0244, 0259, 0260-0261] and select[ing] formats for formatting the summaries based on the different profile or preference settings associated with the different users [Cassidy ¶ 0234, 0275]. Hou further teaches generat[ing] summaries based on contextual relevance to different users (“Thus, the broader population may be provided with curating tools to let independent users generate channels” [Hou ¶ 0107]; "Program/program segment metadata may be used to populate an interstitial. By way of illustration, a given item of content (e.g., a program or program segment) may be associated with metadata. The metadata may include information regarding or related to the content (e.g.,…plot summaries…)...with respect to the trivia quiz interactive interstitial, the quiz may be composed using an artificial intelligence engine that accesses a content information database (e.g.,...plot summaries…)...Optionally, the artificial intelligence engine may take into account the current elapsed play time of the content being watched to avoid providing a question that cannot be answered until the user has viewed a later portion of the content" [Hou ¶ 0145-0146]; An artificial intelligence (e.g., machine learning) engine can generate plot summaries of currently viewed based on contextual relevance to the current user (e.g. elapsed play time of the content being watched)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated generat[ing] summaries based on contextual relevance to different users as taught by Hou into Cassidy because both of these methods are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Hou would prevent generation of summaries that may "spoil" content that has not yet been watched by the user ("Optionally, the artificial intelligence engine may take into account the current elapsed play time of the content being watched to avoid providing a trivia question that will disclose or “spoil” an upcoming event in the content being watched." [Hou ¶ 0146]).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy and Mishra, as applied to claim 8 above, in view of Diamant et al., (Pub. No. US 20190341050 A1, “Computerized Intelligent Assistant for Conferences”, published 11/07/2019), hereinafter Diamant.
Regarding claim 10, the combination of Cassidy and Mishra teaches the limitations of parent claim 8.
However, the combination does not expressly teach wherein the transition includes information referencing a statement made about the particular user in the selected content.
In the same field of endeavor, Diamant teaches a method of compiling media into a content stream that is personalized for a user ("At 205, determining participant identities further includes recognizing pre-registered content of interest for a participant. “Content of interest” may be used herein to refer to any topic or subject which may be of interest to a conference participant. Non-limiting examples of content of interest include any of: 1) a word and/or phrase...4) a digital file (e.g., a particular document), 5) analog multimedia and/or audiovisual content (e.g., a particular photo or diagram, such as a diagram shared on a whiteboard)...In some examples, content of interest for a conference participant may be inferred based on a personal preference of the conference participant" [Diamant ¶ 0077]; "The reviewable transcript may be filtered to focus on content of interest (e.g., name mentions and action items) for any individual receiving the reviewable transcript" [Diamant ¶ 0110]) wherein the transition includes information referencing a statement made about the particular user in the selected content ("Similarly, the reviewable transcript may focus on specific times in the transcript when the conference participant's name, or content of interest to the participant, was mentioned. For example, if a conference participant leaves early, the reviewable transcript may focus on a time at which the conference participant's name was mentioned, along with a previous and following sentence, phrase, or summary to provide context" [Diamant ¶ 0108]; "Returning briefly to FIG. 14, creating the transcript at 211 further includes recognizing content of interest in the transcript at 216. Such content of interest may include any suitable content (e.g., pre-registered content of interest for a conference participant, as described above). In some examples, recognizing content of interest may include recognizing a name mention of a participant at 217. For example, as shown in FIG. 16, when local participant 164 (Carol) mentions local participant 165 (Dan)'s name, such mention may be recognized as an event “E2” of potential interest to local participant 165 (Dan) and/or to other conference participants" [Diamant ¶ 0118]; A user receiving the transcript may filter the transcript by content of interest [¶ 0110], wherein content of interest comprises name mentions of the user; furthermore, a transcript may provide transitional content around name mention content that provides context, therefore further referencing the selected content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the transition includes information referencing a statement made about the particular user in the selected content as taught by Diamant into the combination because both Diamant and Cassidy are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Diamant would enable the content stream to further highlight moments of particular relevance to the user within a piece of selected content [Diamant ¶ 0108].
Regarding claim 11, the combination of Cassidy and Mishra teaches the limitations of parent claim 8, and Diamant further teaches wherein the transition includes information referencing a statement made by the particular user in the selected content ([Diamant ¶ 0108, 0118]; A user receiving the transcript may filter the transcript by content of interest [¶ 0110], wherein content of interest comprises action items by the user; furthermore, a transcript may provide transitional content around action item content that provides context, therefore further referencing the selected content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the transition includes information referencing a statement made by the particular user in the selected content as taught by Diamant into Cassidy because both of these methods are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Diamant into Cassidy would enable the content stream to further highlight moments of particular relevance to the user within a piece of selected content [Diamant ¶ 0108].
Claims 16-17 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Cassidy and Mishra, as applied to claim 1 above, in view of Davidsson et al., (PGPUB, Pub. No. US 20200309547 A1, “Aligning Content Playback With Vehicle Travel”, filed 03/29/2019), hereinafter Davidsson.
Regarding claim 16, the combination of Cassidy and Mishra teaches the limitations of parent claim 1.
However, the combination does not expressly teach omitting a portion of the selected content that has been determined to have a diminished relevance to the particular user subsequent to a formatting the selected content and transition as the botcast; modifying or deleting the transition associated with the selected content having the portion omitted; and reformatting the botcast to reflect the selected content with the portion omitted and the modified or deleted transition associated with the selected content having the portion omitted.
In the same field of endeavor, Davidsson teaches a method of compiling media into a content stream that is personalized for a user ("Further, the system can comprise a content playback component that generates and respectively plays personalized streams of ranked content to a first entity and a second entity on respective playback devices. Additionally, the system can comprise a presentation component that controls presentation of the pruned content within the vehicle, and the first set of content can include at least one of: video content or audio content" [Davidsson ¶ 0006]) wherein the method further includes modifying the botcast by:
omitting a portion of the selected content that has been determined to have a diminished relevance to the particular user subsequent to a formatting the selected content and transition as the botcast; (“In various embodiments, a runtime of the first set of contents can be edited and/or a playback speed of the first set of contents can be edited. Further, the editing component can edit the first set of content using an artificial intelligence technique that learns an entity preference from previous experiences” [Davidsson ¶ 0006]; “In addition, the editing component 302 can alter the runtime of the content based on one or more preferences of an entity of the system 100…In another example, the editing component 302 can add or remove portions of the content based on the one or more preferences of the entity…the editing component 302 can remove dialog scenes from video content (e.g., a movie) based on an entity preference indicating a favorability of action scenes” [Davidsson ¶ 0064]; Subsequent to previous selected content streamed, the system can determine portions of the selected content (e.g., dialog scenes in a movie) to have diminished relevance based on learned preferences of the user/entity, and remove said portions from following content streamed)
modifying or deleting the transition associated with the selected content having the portion omitted; (“In another example, the editing component 302 can add or remove portions of the content based on the one or more preferences of the entity. For instance, the editing component 302 can remove credit scenes from a movie based on an entity preference indicating a disinterest in the credits" [Davidsson ¶ 0064]; Davidsson discloses further deleting a concluding transition portion of a piece of content (e.g., movie credits) that is associated with the edited content (e.g., movie with dialog scenes omitted))
and reformatting the botcast to reflect the selected content with the portion omitted and the modified or deleted transition associated with the selected content having the portion omitted (“In various embodiments, the editing component 302 can alter the runtime of subject content by adding or removing portions of the content” [Davidsson ¶ 0063]; Davidsson discloses the editing component altering the runtime by removing portions of content, therefore reformatting the content stream).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated omitting a portion of the selected content that has been determined to have a diminished relevance to the particular user subsequent to a formatting the selected content and transition as the botcast; modifying or deleting the transition associated with the selected content having the portion omitted; and reformatting the botcast to reflect the selected content with the portion omitted and the modified or deleted transition associated with the selected content having the portion omitted as taught by Davidsson into the combination because both Davidsson and Cassidy are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Davidsson would allow the content stream to prioritize the most relevant content in situations where the user has limited time available (e.g. vehicle travel time) ("In one or more embodiments, the alignment between content runtime and vehicle travel time can be facilitated by ranking the content by relevancy and selectively presenting the content based on the ranking...Further, the one or more devices can compose a set of the content based on the ranking, wherein the total runtime of the set of content can be less than or equal to the length of the vehicle's estimated travel time" [Davidsson ¶ 0026]).
Regarding claim 17, the combination of Cassidy, Mishra, and Davidsson teaches the limitations of parent claim 16, and Cassidy further teaches wherein the method includes performing the modifying dynamically in response to detecting the diminished relevance of the selected content to the particular user, the method further including detecting the diminished relevance which comprises determining the particular user has listened to the portion of the selected content that has been determined to have the diminished relevance ("In some embodiments, scheduler 1614 can leverage play history (for a single user, group of users, or content channel/station) via rules that distribute individual content items across time. This way, the same audio is not being consistently rendered, thereby creating a redundant listening experience" [Cassidy ¶ 0276]; Cassidy discloses implementing rules to avoid consistently repeating the same audio. An audio just listened to by the user will not be repeated for a period of time, and therefore temporarily has a diminished relevance to the user).
Regarding claim 23, the combination of Cassidy and Mishra teaches the limitations of parent claim 1, and Cassidy further teaches wherein the sequencing is performed by a machine learning model that is trained to sequence the selected content into a sequenced playback ordering [Cassidy ¶ 0140, 0307]. Davidsson further teaches wherein the sequenced playback ordering is based on relative relevance based on different profile or preference settings associated with different users ("The prioritization component 504 can prioritize one or more subsets of the set of contents selected by the content selection component 206 as a function of relevancy to, or preference of, an entity of the system 100. For example, wherein the content selection component 206 selects multiple pieces of content, the prioritization component 504 can generate a play order of the selected content that is in accordance with the ranking performed by the ranking component 204" [Davidsson ¶ 0070]; “In another example, the ranking component ranks relevancy of the classified content based upon preferences and context of two or more individuals in the vehicle. Further, the system can comprise a content playback component that generates and respectively plays personalized streams of ranked content to a first entity and a second entity on respective playback devices” [Davidson ¶ 0006]; The examiner has interpreted "relative relevance" as described in instant specification (“The relevance can also be a relative relevance, rather than a relevance of magnitude or category per se. For instance, the different items of content identified for a user for a particular botcast can be sorted in an ordering of relative relevance (e.g., reference C is more relevant than reference B, but less relevant than A reference, etc.” [specification ¶ 0069]); Davidsson discloses content pieces ordered based on a ranking of relevance relative to one another, wherein relevance can be determined based on different preferences of different users).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the sequenced playback ordering is based on relative relevance based on different profile or preference settings associated with different users as taught by Davidsson into the combination because both Davidsson and Cassidy are directed towards compiling media into a content stream that is personalized for a user. Incorporating the teachings of Davidsson would allow the content stream to prioritize the most relevant content in situations where the user has limited time available (e.g. vehicle travel time) ([Davidsson ¶ 0026]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Cassidy, Mishra, and Davidsson, as applied to claim 16 above, further in view of Hamano et al., (PGPUB, Pub. No. US 20100333137 A1, “Methods and Systems for Content Scheduling Across Multiple Devices”, filed 06/30/2009), hereinafter Hamano.
Regarding claim 18, the combination of Cassidy, Mishra, and Davidsson teaches the limitations of parent claim 16, and Cassidy further teaches wherein the method includes performing the modifying dynamically in response to detecting the diminished relevance of the selected content to the particular user [Cassidy ¶ 0276].
However, the combination does not expressly teach detecting the diminished relevance which comprises a detected change in a scheduled event associated with a calendar of the particular user.
In the same field of endeavor, Hamano teaches a method of compiling media into a content stream that is personalized for a user ("Methods and systems for scheduling media content presentation across multiple devices are disclosed. A media planner application generates media event presentation schedules for one or more users based on user-scheduled media events and automatic media event recommendations...Media event recommendations can be generated based at least in part on user preferences and habits" [Hamano Abstract]; "The accessible media content may include hundreds of digital broadcast television channels, interactive applications (e.g., interactive games), digital music, on-demand programming (e.g., video-on-demand (VOD) programming), Internet resources, and recorded content (e.g., content recorded to a local video recorder)" [Hamano ¶ 0002]) that detect[s] the diminished relevance which comprises a detected change in a scheduled event associated with a calendar of the particular user ("In some embodiments, the media planner may be configured to connect to other electronic calendars or portable devices associated with the user and to monitor viewer habits and/or generate viewer preferences through these means. For example, if a user has a meeting scheduled with a home contractor, the media planner may use this information to decide that the user is interested in home renovation, and may schedule or recommend content related to this topic, such as a home improvement program or a workshop at a nearby home improvement store" [Hamano ¶ 0087]; "Indirect user interaction with the media planner may comprise consuming scheduled and/or recommended content, and may also comprise the failure of a user to consume scheduled and/or recommended content....User habits may also be monitored via electronic calendars" [Hamano ¶ 0128]; Hamano discloses the media planner system can be synced with a user's personal calendar, and determine user preferences and associated relevant content based on user habits and scheduled events, wherein diminished relevance can also be determined based on changes to scheduled events (e.g., user failure to consume previously scheduled content))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated detecting the diminished relevance which comprises a detected change in a scheduled event associated with a calendar of the particular user as taught by Hamano into the combination because both Hamano and Cassidy are directed towards compiling media content into a content stream that is personalized for a user. Incorporating the teachings of Hamano into the combination would allow the system to indirectly determine user preferences based on their behavior without requiring direct input from the user [Hamano ¶ 0128].
Response to Arguments
The remarks filed 07/01/2025 have been fully considered.
Applicant’s remarks [Remarks pages 13-14] traversing the anticipation rejections under 35 U.S.C. 102 set forth in the office action mailed 03/07/2025 have been considered, but are moot because the new grounds of rejection set forth above does not rely on the reference(s) applied in the prior rejection of record for the subject matter being challenged in applicant's argument.
Applicant's remarks [Remarks pages 14-16] traversing the obviousness rejections under 35 U.S.C. 103 set forth in the office action mailed 03/07/2025, with respect to claims 2-7, 9-11, 13, 15-18, 20 and 22-23 as amended, have been considered.
Although a new grounds of rejection has been applied, the examiner has determined a response necessary for the portion of the remarks [Remarks pages 15-16 – Additional Distinctions Provided by Dependent Claims] wherein the reference(s) applied in the prior rejection of record are still being relied upon in the new grounds of rejection to teach or suggest the subject matter being challenged in applicant’s argument.
The remaining remarks, while having been considered, are moot because the new grounds of rejection set forth above does not rely on the reference(s) applied in the prior rejection of record for the subject matter being specifically challenged in applicant’s argument.
Applicant alleges that the dependent claims provide additional distinctions over the cited prior art.
The examiner respectfully disagrees. Applicant is directed towards the grounds of rejection under 35 U.S.C. 103 with respect to claims 1-24 set forth above. Applicant’s arguments are further summarized and addressed below.
Applicant argues [Remarks page 15, para. 1] that Ingrassia fails to teach or suggest presenting "the plurality of media subcomponents to the particular user for subsequent user selection" and identifying “user input selecting the one or more selected subcomponents from the plurality of media subcomponents" as recited in claim 2, because the disclosed method of having a user manually select portions of a podcast to teach the system what kind of podcast portions the user likes does not correspond to using a machine learning model to parse media content into subcomponents, presenting those subcomponents from the selected content to the user, and then having the user interact with the user interface to select subcomponents for inclusion in the botcast.
In response, the examiner has clarified the rejection of the claim (see Claim Rejections – 35 USC § 103 [pages 18-21]) to emphasize that the rejection is based on a combination of references, wherein Cassidy was explained to already teach “parsing the media content into a plurality of media subcomponents” within an overall framework of processing media components for inclusion in an audio stream via utilization of machine learning models. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (see In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986)), and there is nothing to suggest that references Cassidy and Ingrassia would be incompatible to combine in a manner understood by one of ordinary skill in the art to arrive at the claimed invention. Further, nowhere, in the portion of the claim that Ingrassia is relied upon to teach, does it recite using a machine learning model to present media subcomponents or identify user input selecting one or more selected subcomponents. In fact, a machine learning model is only invoked in a later step with respect to “each transition being automatically generated by a machine learning model”, which is taught by Cassidy as already discussed. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Finally, the examiner notes that the manual selection procedure in Ingrassia is not limited to simply “teaching the system what kind of podcast portions the user likes”, but rather with the direct purpose of selecting content of interest for inclusion in the generated podcast [Ingrassia ¶ 0020-0021].
Applicant argues [Remarks pages 15, para. 2] that Cassidy fails to teach or suggest “presenting each of the plurality of subcomponents with textual description of one or more topics associated with each corresponding media subcomponent” as recited in claim 3 because the asset features disclosed in Cassidy are mainly metadata tags used to categorize content and are not disclosed as being presented with each piece of content.
In response, the examiner has clarified the rejection of the claim (see Claim Rejections – 35 USC § 103 [pages 21-22]) to emphasize that the rejection is based on a combination of references, wherein Cassidy teaches metadata tags including textual description of content, and Ingrassia teaches presenting subcomponents for selection along with corresponding textual description drawn from metadata tags. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (see In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986))
Applicant argues [Remarks page 16, para. 1] that Cassidy and Ingel fail to teach or suggest “wherein the selected content includes at least some selected content in an audio format and at least some selected content in a text format and wherein formatting the selected content includes converting all of the selected content into the audio format" as recited in claim 15 because Cassidy generally discloses storing metadata associated with various file types and Ingel generally discloses text-to-speech systems and using voice characteristics from source audio data.
In response, the examiner has clarified the rejection of the claim (see Claim Rejections – USC § 103 [pages 28-29]) to emphasize that the rejection is based on a combination of references, wherein Cassidy already teaches media content including both audio and text files (it is noted that text is not only limited to stored metadata, but that media content itself may also comprise text files), and converting selected content into a common audio format (e.g., MP3). Thus, wherein Cassidy already provides an implicit teaching, Ingel further suggests, via the disclosed embodiment, an example use case for converting text content and audio content into a combined audio format. The combined teachings of these references would thereby clearly lead one of ordinary skill in the art to arrive at the claimed invention.
Applicant argues [Remarks page 16, para. 2] that Cassidy and Hou fail to teach or suggest “generating an additional botcast that includes the selected content and a different transition associated with the selected content, the different transition being personalized to a different user than the particular user, the additional botcast also omitting the transition associated with the selected content that is personalized to the particular user" as recited in claim 20.
In response, the examiner notes that applicant has not provided any explanation or basis to support their finding that Cassidy and Hou fails to teach the limitations at issue, besides a general reference to remarks “expressed above”. However, the standing basis of rejection for claim 20 is different than the basis of rejection for the previously discussed claims in the remarks, as it is specific to the limitations of the claim. Consequently, it is unclear what portion of the remarks is being referenced, and applicant’s argument cannot be responded to or evaluated further.
Applicant has not presented further arguments with respect to the remaining dependent claims. As such, claims 1-24 stand rejected under 35 U.S.C. 103.
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 VIJAY M BALAKRISHNAN whose telephone number is (571) 272-0455. The examiner can normally be reached 10am-5pm EST Mon-Thurs.
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/V.M.B./
Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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