DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to amendment filed on 2/19/2026, in which claims 1, 8 – 9, 15, and 20 was amended, claims 6 and 13 was canceled, and claims 1 – 5, 7 – 12, and 14 – 20 was presented for further examination.
3. Claims 1 – 5, 7 – 12, and 14 – 20 are now pending in the application.
Response to Arguments
4. Applicant’s arguments with respect to claims 1 – 5, 7 – 12, and 14 – 20 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.
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.
5. Claims 1 and 5 - 7 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasaraghavan (US 2023/0195813 A1), in view of Senftner et al. (US 2008/0019576 A1).
As per claim 1, Srinivasaraghavan (US 2023/0195813 A1) discloses,
A method for personalized content generation (para.[0002]; “provide such visiting users with personalized content” and para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports, and the like”).
the method comprising: training, by a system operatively coupled to a processor (para.[0035]; “learned and/or trained personalization ( or taste or interest) vectors
that indicate the user's tastes, interests, behaviors and/or activities (e.g., real-world and digital actions), and the like”).
a personalization model that maps content comprising custom video content to genericized video custom content based on a parallel corpus linking the custom video content to the genericized custom video content (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or
free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
wherein the genericized custom video content preserves semantic meaning of the custom video content while removing a desired personality (para.[0040]; “engine 200 receives a request to have provided content be un-biased or depersonalized”, para.[0041]; “enables a particular type of un-biasing ( or depersonalization)”, and para.[0061]; “input can be provided by the user that corresponds to an "attribute"
( or attributes) type of depersonalization”).
wherein mapping the custom video content to genericized video custom content comprises: sensing, by the system and comprising an Internet of Things (IOT) sensor within the system, electronic video content (para.[0021]; “UE 102 may include mobile phones, tablets, laptops, sensors, Internet of Things (IoT) devices” and para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites”).
receiving, by the system, electronic video content and information indicative of the desired personality from which to personalize the electronic video content (para.[0036]; “involves executing the recommendation model which operates
by analyzing of the request from the user, which can include analysis of the data in the request, as well as the data and/or metadata about the user, and formulating a query to
search for requested content”, para.[0034]; “type of request that causes personalized content to be provided to a user”, para.[0037]; “to, image thumbnails that represent
movies on a streaming platform that are being recommended to the user”).
wherein the information indicative of the desired personality is based on a selection by an entity in response to a prompt output from the system and wherein the selection chooses between a plurality of types of personalities (para.[0020]; “provides users with an ability to customize their own personalizations …… the framework enables users to control personalization of the content” and para.[0044]; “UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed).
and applying, by the system, the information indicative of the desired personality to the electronic video content via application of a personalization model to the electronic video content (para.[0035]; “a set of personalized content is provided
to the user. ……..engine 200 receiving the request, and providing a recommended set of content items to a user by using a defined recommendation model” and para.[0044]; “a user being provided a recommended set of movies (items 402-410) ………UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed”).
Srinivasaraghavan does not disclose wherein the personalization model is versioned for different personalities, wherein the various different personalities are modelled and pre-loaded based on application such that a first one of the different personalities is associated with a first application and a second one of the different personalities is associated with a second application.
However, Senftner et al. (US 2008/0019576 A1) in an analogous art discloses,
wherein the personalization model is versioned for different personalities (para.[0060]; “an actor model has been created with process 100, it may be pared with any number of prepared videos to create different personalized videos featuring that actor”).
wherein the various different personalities are modelled (para.[0016]; “a computing device is provided to create a personalized version” and para.[0060]; “paired with any number of new actor models to create a personalized version of that video with that actor”).
and pre-loaded based on application such that a first one of the different personalities is associated with a first application and a second one of the different personalities is associated with a second application (para.[0014]; “providing an actor model library of one or more new actor models where each of the models resulting from an actor modeling process”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate actor model of the system of Senftner into depersonalization of the system of Srinivasaraghavan to replace an actor from original video based on user preferences to produce a personalized digital video.
As per claim 5, the rejection of claim 1 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization model, once trained for a content type, personalizes any content of the content type without additional training (para.[0035]; “recommendation model can comprise learned and/or trained personalization ( or taste or interest) vectors that indicate the user's tastes, interests, behaviors and/or activities (e.g., real-world and digital actions), and the like.”).
As per claim 6, the rejection of claim 1 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization model may be versioned for different personalities (para.[0036]; “involves executing the recommendation model which operates
by analyzing of the request from the user, which can include analysis of the data in the request”).
As per claim 7, the rejection of claim 1 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the electronic video content also comprises audio content (para.[0018]; “and the like. Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports, and the like”).
6. Claims 2 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasaraghavan (US 2023/0195813 A1), in view of Senftner et al. (US 2008/0019576 A1), and further in view of Yinhe et al (CN 110874402 A).
As per claim 2, the rejection of claim 1 is incorporated, Srinivasaraghavan (US 2023/0195813 A1) further discloses,
building the parallel corpus linking the custom video content to the genericized custom video content and training the personalization model that maps the custom video content to the genericized custom video content based on the parallel corpus (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
Neither Srinivasaraghavan nor Senftner specifically disclose wherein the training the personalization model comprises: training an encoder to embed generic video content into vectors that maintain a semantic meaning of the generic video content; training a decoder to decode the vectors into genericized version of the generic video content; genericizing custom video content via application of the encoder and the decoder to custom video content.
However, Yinhe et al (CN 110874402 A) in an analogous art discloses,
wherein the training the personalization model comprises: training an encoder to embed generic video content into vectors that maintain a semantic meaning of the generic video content (pg.4 lines 21 – 23; “a training method of a dialogue model, the dialogue model including an encoder, a decoder, and a user-customized hidden vector generation module, the training method including: on the encoder side, converting the training input into a word vector and generating an encoder hidden vector according to the word vector”).
training a decoder to decode the vectors into genericized version of the generic video content (pg.4 lines 24 – 25; “the decoder side, generating a decoder hidden vector according to the encoder hidden vector, and generating a reply according to the
decoder hidden vector”).
genericizing custom video content via application of the encoder and the decoder to custom video content (pg.4 line 1; “generating a user-personalized hidden vector and using the personalized hidden vector for the encoder and/or decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into actor model of the system of Senftner to generate a personalized answer to user question , thereby improving user experience .
As per claim 3, the rejection of claim 2 is incorporated and further Yinhe et al (CN 110874402 A) discloses,
wherein the training the decoder comprises minimizing an error between the input custom video content and the output genericized custom video content (pg.3 lines 12 – 13; “user personalized hidden vector is adopted to process an input hidden vector of a decoder, and the second user personalized hidden vector is adopted to process an output hidden vector of the decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into actor model of the system of Senftner to generate a personalized answer to user question , thereby improving user experience.
As per claim 4, the rejection of claim 2 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization of the electronic video content is based on the custom video content from which the parallel corpus and the personalization model are built (para.[0020]; “modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in”).
wherein the content also comprises text content and speech content in which the personalization model determines whether the content is the text content or the speech content and embeds vectors based on a Mel spectrogram based on a first decision that the content is the speech content and embeds vectors based on a second decision that the content is the text content (para.[0056]; “each item (remaining, if Step 608 is enabled) in the similarity and opposite groupings are analyzed ……. vector analysis model that analyzes the items in each grouping ( e.g., feature vectors of each item are analyzed) and determines their corresponding attributes ….. analysis can also be performed by any other known or to be known computational analysis technique and/or AI/ML classifier, algorithm, mechanism or technology”).
7. Claims 8 and 12 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasaraghavan (US 2023/0195813 A1), in view of Senftner et al. (US 2008/0019576 A1), in view of Rofe et al (US 2020/0186575 A1), and further in view of Degges Jr. et al (US 9,830,924 B1).
As per claim 8, Srinivasaraghavan (US 2023/0195813 A1) discloses,
A computer program product for personalized content generation (para.[0002]; “provide such visiting users with personalized content” and para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports, and the like”).
the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method (para.[0136]; “a non-transitory computer readable medium ( or computer-readable storage medium/media) stores computer data, which data can include computer program code ( or computer-executable instructions) that is executable by a computer”).
training a personalization model that maps custom video content to the genericized custom video content based on a parallel corpus linking the custom video content to the genericized custom video content (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
wherein the genericized custom video content preserves semantic meaning of the custom video content while removing a desired personality (para.[0040]; “engine 200 receives a request to have provided content be un-biased or depersonalized”, para.[0041]; “enables a particular type of un-biasing ( or depersonalization)”, and para.[0061]; “input can be provided by the user that corresponds to an "attribute" ( or attributes) type of depersonalization”).
sampling, by the system and comprising an Internet of Things (IOT) sensor, electronic video content, wherein the sampling comprises sampling, within the electronic video content, style and personality characteristics as well as speech patterns, cadence, speed or verbiage (para.[0021]; “UE 102 may include mobile phones, tablets, laptops, sensors, Internet of Things (IoT) devices”, para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites”, para.[0061]; “identifying an attribute(s) (e.g., a characteristic or feature) of a content item(s) that can be excluded or "escaped" from during content selection”).
receiving electronic video content and information indicative of the desired personality from which to personalize the electronic video content (para.[0036]; “involves executing the recommendation model which operates by analyzing of the request from the user, which can include analysis of the data in the request, as well as the data and/or metadata about the user, and formulating a query to search for requested content”, para.[0034]; “type of request that causes personalized content to be provided to a user”, para.[0037]; “to, image thumbnails that represent movies on a streaming platform that are being recommended to the user”).
wherein the information indicative of the desired personality is based on a selection by a model that associates particular categories of content with corresponding respective personality preferences (para.[0020]; “provides users with an ability to customize their own personalizations …… the framework enables users to control personalization of the content” and para.[0044]; “UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed).
and wherein a first category of content corresponds to a first one of the respective personality preferences and wherein a second category of content corresponds to a second one of the respective personality preferences (para.[0067]; “When it is determined that the attribute is categorical,
Process 700 proceeds from Step 710 to Step 714 where engine 200 automatically determines and presents categories that can be related to the attribute so that the user can select the categories to escape from”).
and wherein the selection by the model is based on feedback from an entity external to the system specifying the first category of content and the corresponding first one of the respective personality preferences for the category of content (para.[0096]; “the sentiment data from Step 912 can be stored and used to train a new model and/or re-train the recommendation model”).
applying the information indicative of the desired personality to the electronic video content via application of a personalization model to the electronic video content (para.[0035]; “a set of personalized content is provided
to the user. ……..engine 200 receiving the request, and providing a recommended set of content items to a user by using a defined recommendation model” and para.[0044]; “a user being provided a recommended set of movies (items 402-410) ………UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed”).
and training a personalization model that maps the custom video content to the genericized custom video content based on the parallel corpus (para.[0046]; “recommendation model can be further trained ( or retrained, used interchangeably ) based on this information so as to provide automatic un-biasing for the user for future
searches”).
Srinivasaraghavan does not specifically disclose wherein the personalization model may be versioned for different personalities.
However, Senftner et al. (US 2008/0019576 A1) in an analogous art discloses,
wherein the personalization model may be versioned for different personalities (para.[0060]; “an actor model has been created with process 100, it may be pared with any number of prepared videos to create different personalized videos featuring that actor”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate actor model of the system of Senftner into depersonalization of the system of Srinivasaraghavan to replace an actor from original video based on user preferences to produce a personalized digital video.
Neither Srinivasaraghavan nor Senftner specifically disclose performing a calibration of genericized video content prior to processing in an encoder.
However, Rofe et al (US 2020/0186575 A1) in an analogous art discloses
the method comprising: performing a calibration of genericized video content prior to processing in an encoder (para.[0088]; “an audio calibration signal is generated by the distribution server 610. This may comprise an audio impulse. In another case, it may comprise a predetermined waveform such as a sine or square wave. The audio calibration signal may be encoded as per audio data generated by a client device”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate audio calibration process of the system of Rofe into actor model of the system of Senftner to provide bi-direction interaction among users that reduce connection latency, thereby improving users experience.
Neither Srinivasaraghavan nor Senftner nor Rofe specifically disclose wherein the calibration comprises standardizing an average pitch of speed of the genericized video content into the encoder to achieve better alignment between speakers with similar speaking dynamics at differing frequencies.
However, Degges Jr. et al (US 9,830,924 B1) in an analogous art disclose,
wherein the calibration comprises standardizing an average pitch of speed of the genericized video content into the encoder to achieve better alignment between speakers with similar speaking dynamics at differing frequencies (col.7 lines 49 – 64; “calculated a pitch (e.g., center or average frequency) of the speech command, or a range of the pitch (i.e., the range of frequency components included in the speech command). Based on the calibration data (i.e., from step 502), the pitch data can be used to normalize the relative intensity of the speech command, …….. .., normalizing the response of the system so that the output volume is normalized to be approximately the same for a same speech intensity without regard to the pitch of the speaker's voice”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate normalization of speech intensity of the system of Degges into audio calibration process of the system of Rofe and actor model of the system of Senftner to perform task based on the user’s spoken command in the system of Srinivasaraghavan.
As per claim 12, the rejection of claim 8 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization model, once trained for a content type, personalizes any content of the content type without additional training (para.[0035]; “recommendation model can comprise learned and/or trained personalization ( or taste or interest) vectors that indicate the user's tastes, interests, behaviors and/or activities (e.g., real-world and digital actions), and the like.”).
As per claim 14, the rejection of claim 8 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the electronic video content also comprises audio content (para.[0018]; “and the like. Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports, and the like”).
8. Claims 9 - 11 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasaraghavan (US 2023/0195813 A1), in view of Senftner et al. (US 2008/0019576 A1), in view of Rofe et al (US 2020/0186575 A1), in view of Degges Jr. et al (US 9,830,924 B1), and further in view of Yinhe et al (CN 110874402 A).
As per claim 9, the rejection of claim 1 is incorporated, Srinivasaraghavan (US 2023/0195813 A1) further discloses,
building a parallel corpus linking the custom video content to the genericized custom video content (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
Neither Srinivasaraghavan nor Senftner nor Rofe nor Degges specifically disclose wherein the personalization model is generated by: training the encoder to embed generic video content into vectors that maintain a semantic meaning of the generic video content; training a decoder to decode the vectors into genericized version of the generic video content; genericizing custom video content via application of the encoder and the decoder to custom video content.
However, Yinhe et al (CN 110874402 A) in an analogous art discloses,
wherein the personalization model is generated by: training the encoder to embed generic video content into vectors that maintain a semantic meaning of the generic video content (pg.4 lines 21 – 23; “a training method of a dialogue model, the dialogue model including an encoder, a decoder, and a user-customized hidden vector generation module, the training method including: on the encoder side, converting the training input into a word vector and generating an encoder hidden vector according to the word vector”).
training a decoder to decode the vectors into genericized version of the generic video content (pg.4 lines 24 – 25; “the decoder side, generating a decoder hidden vector according to the encoder hidden vector, and generating a reply according to the
decoder hidden vector”).
genericizing custom video content via application of the encoder and the decoder to custom video content (pg.4 line 1; “generating a user-personalized hidden vector and using the personalized hidden vector for the encoder and/or decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into the combine teaching of Rofe and Degges to generate a personalized answer to user question in the system of Srinivasaraghavan, thereby improving user experience.
As per claim 10, the rejection of claim 9 is incorporated and further Yinhe et al (CN 110874402 A) discloses,
wherein the decoder is trained by minimizing an error between the input custom video content and the output genericized custom video content (pg.3 lines 12 – 13; “user personalized hidden vector is adopted to process an input hidden vector of a decoder, and the second user personalized hidden vector is adopted to process an output hidden vector of the decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into personalization and depersonalization of the system of Srinivasaraghavan to generate a personalized answer to user question, thereby improving user experience.
As per claim 11, the rejection of claim 9 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization of the video content is based on the custom video content from which the parallel corpus and the personalization model is built (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
9. Claims 15 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasaraghavan (US 2023/0195813 A1), in view of Senftner et al. (US 2008/0019576 A1), and further in view of Yinhe et al (CN 110874402 A).
As per claim 15, Srinivasaraghavan (US 2023/0195813 A1) discloses,
A computer system for personalized content generation (para.[0002]; “provide such visiting users with personalized content” and para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports, and the like”).
the system comprising: an Internet of Things (IOT) sensor that senses electronic video content, wherein the IOT sensor senses, within the electronic video content, style and personality characteristics as well as speech patterns, cadence, speed or verbiage (para.[0021]; “UE 102 may include mobile phones, tablets, laptops, sensors, Internet of Things (IoT) devices”, para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites”, para.[0061]; “identifying an attribute(s) (e.g., a characteristic or feature) of a content item(s) that can be excluded or "escaped" from during content selection”).
and one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method (para.[0125]; “computer storage media for the storage of information such as computer-readable instructions, data structures, program modules, or other data” and para.[0136]; “a non-transitory computer readable medium ( or computer-readable storage medium/media) stores computer data, which data can include computer program code ( or computer-executable instructions) that is executable by a computer”).
the method comprising: training a personalization model that maps custom video content to genericized custom video content based on a parallel corpus linking the custom video content to the genericized custom video content (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
wherein the genericized custom video content preserves semantic meaning of the custom video content while removing a desired personality (para.[0040]; “engine 200 receives a request to have provided content be un-biased or depersonalized”, para.[0041]; “enables a particular type of un-biasing ( or depersonalization)”, and para.[0061]; “input can be provided by the user that corresponds to an "attribute" ( or attributes) type of depersonalization”).
receiving sensed electronic video content and information indicative of the desired personality from which to personalize the content (para.[0018]; “Personalization of content for a user spans, for example, movies, shows, news, books, music, web sites, retail products, sports” and para.[0036]; “involves executing the recommendation model which operates by analyzing of the request from the user, which can include analysis of the data in the request, as well as the data and/or metadata about the user, and formulating a query to search for requested content”, para.[0034]; “type of request that causes personalized content to be provided to a user”, para.[0037]; “to, image thumbnails that represent movies on a streaming platform that are being recommended to the user”).
wherein the information indicative of the desired personality is based on a selection by an entity in response to a prompt output from the system,
wherein the prompt allows the entity to select from one or more pre-set personalities (para.[0020]; “provides users with an ability to customize their own personalizations …… the framework enables users to control personalization of the content” and para.[0044]; “UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed).
and applying the information indicative of the desired personality to the electronic video content via application of a personalization model to the electronic video content (para.[0035]; “a set of personalized content is provided
to the user. ……..engine 200 receiving the request, and providing a recommended set of content items to a user by using a defined recommendation model” and para.[0044]; “a user being provided a recommended set of movies (items 402-410) ………UI 500 is provided to the user, which enables the user to specifically select items they desire to break or escape from”, where selecting item to break or escape from is interpreted as entity selecting “desire personality” a claimed”).
Srinivasaraghavan does not specifically disclose wherein the personalization model is versioned for different personalities, wherein the various different personalities are modelled and pre-loaded based on application such that a first one of the different personalities is associated with a first application and a second one of the different personalities is associated with a second application.
However, Senftner et al. (US 2008/0019576 A1) in an analogous art discloses,
wherein the personalization model is versioned for different personalities (para.[0060]; “an actor model has been created with process 100, it may be pared with any number of prepared videos to create different personalized videos featuring that actor”).
wherein the various different personalities are modelled (para.[0016]; “a computing device is provided to create a personalized version” and para.[0060]; “paired with any number of new actor models to create a personalized version of that video with that actor”).
and pre-loaded based on application such that a first one of the different personalities is associated with a first application and a second one of the different personalities is associated with a second application (para.[0014]; “providing an actor model library of one or more new actor models where each of the models resulting from an actor modeling process”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate actor model of the system of Senftner into depersonalization of the system of Srinivasaraghavan to replace an actor from original video based on user preferences to produce a personalized digital video.
Neither Srinivasaraghavan nor Senftner does not specifically disclose wherein the training the personalization model comprises training an encoder to embed generic content into vectors, that maintain a semantic meaning of the generic video content.
However, Yinhe et al (CN 110874402 A) in an analogous art discloses,
wherein the training the personalization model comprises training an encoder to embed generic content into vectors, that maintain a semantic meaning of the generic video content (pg.4 lines 21 – 23; “a training method of a dialogue model, the dialogue model including an encoder, a decoder, and a user-customized hidden vector generation module, the training method including: on the encoder side, converting the training input into a word vector and generating an encoder hidden vector according to the word vector”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into actor model of the system of Senftner to generate a personalized answer to user question in the system of Srinivasaraghavan, thereby improving user experience.
As per claim 16, the rejection of claim 15 is incorporated, Srinivasaraghavan (US 2023/0195813 A1) discloses,
building a parallel corpus linking the custom video content to the genericized custom video content; and training the personalization model that maps the custom video content to the genericized custom video content based on the parallel corpus (para.[0020]; “analyze the type of request as well as the modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in by providing the user with a broader range of content from a larger pool of content then previously made available to the user”).
Srinivasaraghavan does not specifically disclose wherein the generic video content is embedded into 512-dimension vectors, and wherein the training the personalization model further comprises: training a decoder to decode the vectors into genericized version of the generic video content; genericizing custom video content via application of the encoder and the decoder to custom video content.
However, Yinhe et al (CN 110874402 A) in an analogous art discloses,
wherein the generic video content is embedded into 512-dimension vectors, and wherein the training the personalization model further comprises: training a decoder to decode the vectors into genericized version of the generic video content (pg.4 lines 24 – 25; “the decoder side, generating a decoder hidden vector according to the encoder hidden vector, and generating a reply according to the
decoder hidden vector”).
genericizing custom video content via application of the encoder and the decoder to custom video content (pg.4 line 1; “generating a user-personalized hidden vector and using the personalized hidden vector for the encoder and/or decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into actor model of the system of Senftner to generate a personalized answer to user question in the system of Srinivasaraghavan, thereby improving user experience.
As per claim 17, the rejection of claim 16 is incorporate and further Yinhe et al (CN 110874402 A) discloses,
wherein the decoder is trained by minimizing an error between the input custom video content and the output genericized custom video content (pg.3 lines 12 – 13; “user personalized hidden vector is adopted to process an input hidden vector of a decoder, and the second user personalized hidden vector is adopted to process an output hidden vector of the decoder”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate conversion of attribute information into vector of the system of Yinhe into actor model of the system of Senftner to generate a personalized answer to user question in the system of Srinivasaraghavan, thereby improving user experience.
As per claim 18, the rejection of claim 16 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization of the content is based on the custom video content from which the parallel corpus and the personalization model is built (para.[0020]; “modeled behavior and preferences of a user, and un-bias or break ( or free) the user from the preconceived notions of what the user is expected to be interested in”).
As per claim 19, the rejection of claim 15 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization model, once trained for a content type, personalizes any content of the content type without additional training (para.[0035]; “recommendation model can comprise learned and/or trained personalization ( or taste or interest) vectors that indicate the user's tastes, interests, behaviors and/or activities (e.g., real-world and digital actions), and the like.”).
As per claim 20, the rejection of claim 15 is incorporated and further Srinivasaraghavan (US 2023/0195813 A1) discloses,
wherein the personalization model may be versioned for different personalities ((para.[0036]; “involves executing the recommendation model which operates
by analyzing of the request from the user, which can include analysis of the data in the request”).
and wherein the system prompts an entity external to the computer system for user data or feedback to determine which of a plurality of personalities to associate with the entity (para.[0084]; “where an input can be provided by the user that corresponds to a "sentiments" type of depersonalization” and para.[0085]; “the sentiment can correspond to comments about items, feedback, rendering history”).
receives the user data or the feedback (para.[0084]; “where an input can be provided by the user that corresponds to a "sentiments" type of depersonalization”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
4/22/2026
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