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 .
DETAILED ACTION
This office action is responsive to application No. 17/948,655 filed on 09/09/2025. Claim(s) 2-6, 8, 10-69, 71-77, 79, 83, and 86-90 are canceled. Claim(s) 1, 7, 9, 70, 78, 80-82, 84-85, and 91-100 is/are pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1, 70, 94, and 95 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant claims:
Claims 1, 70, and 94:
“wherein the trained model is trained on one or more video frames of the plurality of content items to extract one or more attributes of the plurality of content items”
Claim 95:
“the trained model is trained on the one or more video frames of the plurality of content items”
Applicant’s specification (also PGPUB 2024/0098338):
[0080] Any of the features of the methods and systems above are obtained with a trained model. The model is trained with one or more knowledge graphs. The one or more knowledge graphs are determined by a trained model.
[0197] The predictive model 1850 is trained with data. The training data is developed in some embodiments using one or more data techniques including but not limited to data selection, data sourcing, and data synthesis. The predictive model 1850 is trained in some embodiments with one or more analytical techniques including but not limited to classification and regression trees (CART), discrete choice models, linear regression models, logistic regression, logit versus probit, multinomial logistic regression, multivariate adaptive regression splines, probit regression, regression techniques, survival or duration analysis, and time series models. The predictive model 1850 is trained in some embodiments with one or more machine learning approaches including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and dimensionality reduction. The predictive model 1850 in some embodiments includes regression analysis including analysis of variance (ANOVA), linear regression, logistic regression, ridge regression, and/or time series. The predictive model 1850 in some embodiments includes classification analysis including decision trees and/or neural networks. In FIG. 18, a depiction of a multi-layer neural network is provided as a non-limiting, exemplary predictive model 1850, the exemplary neural network including an input layer (left side), three hidden layers (middle), and an output layer (right side) with 32 neurons and 192 edges, which is intended to be illustrative, not limiting. The predictive model 1850 is based on data engineering and/or modeling techniques. The data engineering techniques include exploration, cleaning, normalizing, feature engineering, and scaling. The modeling techniques include model selection, training, evaluation, and tuning. The predictive model 1850 is operationalized using registration, deployment, monitoring, and/or retraining techniques.
[0056] The knowledge graph is based on at least one of an analysis of closed caption data, a video analysis using machine vision, or a deep neural network model.
[0058] Analysis of at least one of the closed caption data, the machine vision, or the deep neural network model extracts one or more features from one or more video frames.
[0059] Analysis of at least one of the closed caption data, the machine vision, or the deep neural network model creates tags and/or vectors for one or more frames for the one or more content items.
[0061] The method further comprises updating an existing knowledge graph to include output from the analysis of at least one of the closed caption data, the machine vision, or the deep neural network model to creates tags and/or vectors for one or more frames for the one or more content items.
[0160] The knowledge graph is created for many digital content items (e.g., movies) based on closed caption data including analysis of the sentences as well as other data described below, along with video analysis (using machine vision and deep neural network models) to extract features from video frames and create tags or vectors for individual frames, and for a content item.
[0167] The methods and systems include analysis based on at least one of collaborative filtering; content-based recommendations; context-aware recommendation systems; prediction of quality and/or popularity of a movie from metadata (e.g., a plot summary and a character description using contextualized embeddings); or a feature extractor. Recommendations are based on at least one of determination of what other viewers with similar tastes liked, personal viewing history, user preference, genre, sub-genre, actor, cast, time of day, device type, location, language, knowledge graph, NLP, plot summaries, tokenization, stemming, TF-IDF, K-means, similarity distance, deep learning-based classification models, ML analysis, or knowledge acquisition systems. In some embodiments, customized metadata is generated for each viewer for each content item, as opposed to generic metadata provided for a given content item.
[0185] In the generating step 1440, the knowledge graph (e.g., FIG. 7) is based on at least one of an analysis of closed caption data, a video analysis using machine vision, or a deep neural network model. In the generating step 1445, the closed caption data includes an analysis of sentences. In the generating step 1450, analysis of at least one of the closed caption data, the machine vision, or the deep neural network model extracts one or more features from one or more video frames. In the generating step 1455, analysis of at least one of the closed caption data, the machine vision, or the deep neural network model creates tags and/or vectors for one or more frames for the one or more content items 1700. In the generating step 1460, the closed caption data includes a textual representation of at least one of audio, a non-speech element, a character identification, a sound effect, a language identification, an expressed emotion, a music lyric, or timing metadata.
[0186] FIG. 15 depicts additional processes 1500 relating to the knowledge graph (e.g., FIG. 7), weightings, and determinations of relationship strength according to an exemplary embodiment. The process 1500 includes updating 1505 an existing knowledge graph (e.g., FIG. 7) to include output from the analysis of at least one of the closed caption data, the machine vision, or the deep neural network model to create tags and/or vectors for one or more frames for the one or more content items 1700. The process 1500 includes weighting 1510 one or more events in the one or more content items 1700 with one or more attributes 110-150, 905-955 determined by the analysis of at least one of the closed caption data, the machine vision, or the deep neural network model. The process 1500 includes determining 1515 weighting based on a video phase and/or an analysis phase of a computer vision system. The process 1500 includes determining 1520 a relationship strength between two or more content items 1700. Determining 1525 the relationship strength is based on one or more labels determined from the analysis of at least one of the closed caption data, the machine vision, or the deep neural network model. Determining 1530 the relationship strength is based on an extent of an overlap between the one or more labels determined from the analysis of at least one of the closed caption data, the machine vision, or the deep neural network model. The relationship strength is based 1535 on an analysis of a timing of events within the one or more content items 1700.
[0196] The predictive model 1850 receives as input metadata 1840. The predictive model 1850 is based on at least one of metadata of the streaming service, metadata of the requesting media device, metadata of the media content item, metadata of the communication system or network, metadata of the profile, or metadata of the currently streaming media device. The metadata includes information of the type represented in the media device manifest.
[0197] The predictive model 1850 is trained with data. The training data is developed in some embodiments using one or more data techniques including but not limited to data selection, data sourcing, and data synthesis. The predictive model 1850 is trained in some embodiments with one or more analytical techniques including but not limited to classification and regression trees (CART), discrete choice models, linear regression models, logistic regression, logit versus probit, multinomial logistic regression, multivariate adaptive regression splines, probit regression, regression techniques, survival or duration analysis, and time series models. The predictive model 1850 is trained in some embodiments with one or more machine learning approaches including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and dimensionality reduction. The predictive model 1850 in some embodiments includes regression analysis including analysis of variance (ANOVA), linear regression, logistic regression, ridge regression, and/or time series. The predictive model 1850 in some embodiments includes classification analysis including decision trees and/or neural networks. In FIG. 18, a depiction of a multi-layer neural network is provided as a non-limiting, exemplary predictive model 1850, the exemplary neural network including an input layer (left side), three hidden layers (middle), and an output layer (right side) with 32 neurons and 192 edges, which is intended to be illustrative, not limiting. The predictive model 1850 is based on data engineering and/or modeling techniques. The data engineering techniques include exploration, cleaning, normalizing, feature engineering, and scaling. The modeling techniques include model selection, training, evaluation, and tuning. The predictive model 1850 is operationalized using registration, deployment, monitoring, and/or retraining techniques.
Applicant’s specification does not teach “wherein the trained model is trained on one or more video frames of the plurality of content items to extract one or more attributes of the plurality of content items”.
Rather the trained model is trained on one or more knowledge graphs (Paragraph 0080) or trained with data, where training data is developed using one or more data techniques mentioned in Paragraph 0197. As such, Applicant’s specifications do not teach “wherein the trained model is trained on one or more video frames of the plurality of content items”.
Although other portions detail trained model, machine vision, deep neural network model, etc. It is unclear whether or not they can perform all the functions of the claimed “trained model” in the claimed invention. However, looking further into Applicant’s specification, it appears that “predictive model 1850” may be the closest to the claimed “trained model” in the claims. But even then, “predictive model 1850” is similarly trained with data, but there is no mention in Applicant’s specifications where it may be trained one or more video frames of the plurality of content items.
Appropriate correction(s) is/are required.
Response to Arguments
Applicant’s arguments with respect to Claim(s) 1, 7, 9, 70, 78, 80-82, 84-85, and 91-100 have been considered but are moot in view of the new ground(s) of rejection.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 7, 70, 85, 93, 95, and 97-100 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), and further in view of Agarwal (US 11,468,675).
Consider claims 1 and 70, Chungapalli teaches a method and system comprising:
circuitry (Fig.3, Paragraph 0035-0039) configured to:
receive a request for content, wherein the request indicates a desired attribute (Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in. Paragraph 0040 teaches user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as voice recognition interface);
access a trained model associated with a plurality of content items (Paragraph 0005 teaches leveraging the importance of the nodes in a semantic graph to train a machine-learning model that will automatically determine the relevance of an entity in a given text string in order to provide better results for users. Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Paragraph 0027 teaches the semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Paragraph 0033 teaches semantic graphs may be used, by the system, for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in FIGS. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of FIGS. 1-2. Paragraph 0068 teaches the system gathers a data set and generates a semantic graph that identifies key entities and their associations. Paragraph 0070 teaches system may pull its data set from the wiki plot sections, synopsis sections, category references in the plot sections, and noun chunks from the plot. Paragraph 0071 teaches the model may be trained on the training dataset. Paragraph 0093 teaches examples of entities and roles extracted by the system, where Fig.8 corresponds to the movie “Pulp Fiction”. Fig.9 corresponds to the movie “Dr. Strangelove”);
determine, based at least in part on the trained model, that a first portion of the plurality of content items comprises the desired attribute indicated in the request (Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. In response, the system has generated for display program recommendation 104. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” In response, the system has generated for display program recommendations 204 and 206. Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in);
generate for output a graphical user interface (GUI) that visually indicates: the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request; a selectable visual indication of the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request (Fig.2, Paragraph 0028 teaches user interface receiving text string and in response to the request, recommending other content. Additionally, the system has generated scores for each of the similar movies. For example, program recommendation 204 includes score 208. Fig.2, includes link 210, which is a link to access the program corresponding to the program recommendation 204. Paragraph 0029 teaches the entities, e.g., program recommendations 204 and 206 are considered as semantic concepts and similarities of entities are used in recommendations. Paragraph 0033 teaches semantic graphs may be used, by the system for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in Figs. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of Figs.1-2; Fig.1, Paragraph 0027 teaches user interface receiving text string and in response to the request, recommending other content. The semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Dataset could comprise any type of data from any data source and/or based on any particular subject matter. Videos that contain user inputted keywords {desired attribute}, along with a score {relationships}, are shown in the recommendations. We see a score 208 for each of the program recommendations 204. The scores signify how closely these semantic entities, which are the recommendations, are to the inputted keywords. The scores also serve to show the relationship between items, as to how close their semantic relationships may be to keywords, where closer scores would indicate them being closer in semantic relationship);
receive, via the GUI, input indicating selection of the selectable visual indication of the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request; and upon receiving the input indicating the selection of the selectable visual indication of the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request generate for output only the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request (Paragraph 0028 teaches Fig.2 includes link 210, which is a link to access the program corresponding to the program recommendation 204. Paragraph 0030 teaches guidance applications also allow users to navigate among and locate content. As referred to herein, the term “multimedia” should be understood to mean content that utilizes at least two different content forms described above, for example, text, audio, images, video, or interactivity content forms. Content may be recorded, played, displayed, or accessed by user equipment devices, but can also be part of a live performance. Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in).
Chungapalli does not explicitly teach wherein the trained model is trained on one or more video frames of the plurality of content items to extract one or more attributes of the plurality of content items;
determine, a second portion of the plurality of content items lacks the desired attribute indicated in the request;
generate for output a graphical user interface (GUI) that visually indicates:
the determined second portion of the plurality of content items that lack the desired attribute indicated in the request.
In an analogous art, Martinelli teaches determine, a second portion of a plurality of content items lacks a desired attribute indicated in a request; generate for output a graphical user interface (GUI) that visually indicates: the determined second portion of the plurality of content items that lack the desired attribute indicated in the request (Paragraph 0029 teaches accepting audio input via a microphone. Paragraph 0052 teaches data indicating one or more keywords is received (204). The video platform can provide a user interface that enables the user to enter keywords. Paragraph 0053 teaches a set of candidate video groups is identified (206). Paragraph 0070 teaches when keyword tab 314 is selected, user can enter keywords int the seed entry area 316. Fig.6, Paragraph 0086 teaches a first list 621 of video groups having the highest topicality scores are shown and a second list 628 of video groups having the lowest topicality scores are shown. The candidate video groups shown in this tab can include a portion of the subset of video groups that were identified based on the keywords specified by the user. Paragraph 0087 teaches a topicality score 623 for the video group. Videos that contain user inputted keywords {desired attribute}, along with a topicality score {relationships}, are shown on a first list. A second list displays videos that have the lowest topicality score. From Fig.6, we see on the first list Video Group 478 having a topicality score of 99% and 3 matching keywords {desired attribute} with check marks next to them. For the second list, we see that videos listed lack any matching keywords {desired attribute}, as there are no check marks signifying that there are corresponding matching keywords. Topicality score in this case helps to see the relationship between items that comprise desired attribute, and those that do not, and how closely in topicality they are meeting the inputted keywords).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli to include determine, a second portion of a plurality of content items lacks a desired attribute indicated in a request; generate for output a graphical user interface (GUI) that visually indicates: the determined second portion of the plurality of content items that lack the desired attribute indicated in the request, as taught by Martinelli, for the advantage of allowing users to see the contrast between content that closely matches and does not closely match with user’s desired attributes, allowing them to get a more complete picture on both ends of the request, allowing them to better refine and tailor their requests.
Chungapalli and Martinelli do not explicitly teach wherein the trained model is trained on one or more video frames of the plurality of content items to extract one or more attributes of the plurality of content items.
In an analogous art, Agarwal teaches wherein a trained model is trained on one or more video frames of a plurality of content items to extract one or more attributes of the plurality of content items (Col 4: lines 24-47 teaches image recognition techniques, e.g. Markov models, Bayesian models, neural networks, and the like that are trained to identify object features within an image/video frame, may be utilized to identify object features of the object depicted in the image, e.g., the video frame. Object features may include any suitable attribute of the object such as color, pattern, shape, size, edges, gradients and any other suitable portion of the object. Col 17: lines 46-49 teaches model to identify one or more sets of object features from images. Col 18: lines 12-37 teaches a model may be trained using a set of example video frames each manually labeled with objects depicted within the video frame. Model may be trained to identify object within subsequent video frames provided as input. As more objects are detected in new video frame instances, the input and output of the model may be added to the training data set and model manager 716 may update and/or retrain the model using the updated training data set. Model manager 716 may similarly train a model to identify object features and objects from a video frame. Model manager 716 may object training data that include sets of object features, each labeled with the specific object to which the features correspond. Model manager 716 may train this model to identify objects from object feature sets provided as input using the training data and a supervised learning algorithm. Model manager 716 may be configured to manage, train, retrain, and/or update any of the machine-learning models).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli and Martinelli to include wherein a trained model is trained on one or more video frames of a plurality of content items to extract one or more attributes of the plurality of content items, as taught by Agarwal, for the advantage of training models using a set of example video frames, as well as allowing for further updating and/or retraining of the model using updated training data added from further object detections, to identify objects and object features (Agarwal – Col 18: lines 17-37), allowing for the model to be exposed to similar data that will be used as input, increasing accuracy.
Consider claim 7, Martinelli further teaches wherein the GUI further comprises one or more options to search for additional content items based at least in part on the desired attribute (Paragraph 0082-0084, 0090).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein the GUI further comprises one or more options to search for additional content items based at least in part on the desired attribute, as further taught by Martinelli, for the advantage of refreshing and updating the set of candidate video groups (Martinelli – Paragraph 0084, 0090), allowing user(s) to see additional items, that may better meet what they are looking for.
Consider claim 85, Martinelli further teaches wherein: the graphical objects corresponding to the first portion comprise a symbol indicating the desired attribute; and the graphical objects corresponding to the second portion do not comprise the symbol (Fig.6 teaches on the first list Video Group 478 having a topicality score of 99% and 3 matching keywords {desired attribute} with check marks {symbol indicating desired attribute} next to them. For the second list, we see that videos listed lack any matching keywords {desired attribute}, as there are no check marks {do not comprise the symbol} signifying that there are corresponding matching keywords).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein: the graphical objects corresponding to the first portion comprise a symbol indicating the desired attribute; and the graphical objects corresponding to the second portion do not comprise the symbol, as further taught by Martinelli, for the advantage of allowing the user to quickly peruse through the user interface to easily ascertain which items contain characteristics they are looking for, and which do not, at a glance.
Consider claim 93, Martinelli further teaches wherein the content analysis comprises at least one of: analysis of closed caption data, video analysis using machine vision (Paragraph 0045), or feature extraction and tagging.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein the content analysis comprises at least one of: analysis of closed caption data, video analysis using machine vision, or feature extraction and tagging, as further taught by Martinelli, for the advantage of providing advanced processing of content, enabling the system to better determine various characteristics and information pertaining to the content.
Consider claim 95, Chungapalli, Martinelli, and Agarwal teach wherein: the one or more attributes comprise one or more depicted objects, the trained model is trained on the one or more video frames of the plurality of content items, and the trained model is trained to extract the one or more depicted objects from the one or more video frames of the plurality of content items (Agarwal - Col 4: lines 24-47 teaches image recognition techniques, e.g. Markov models, Bayesian models, neural networks, and the like that are trained to identify object features within an image/video frame, may be utilized to identify object features of the object depicted in the image, e.g., the video frame. Object features may include any suitable attribute of the object such as color, pattern, shape, size, edges, gradients and any other suitable portion of the object. Col 17: lines 46-49 teaches model to identify one or more sets of object features from images. Col 18: lines 12-37 teaches a model may be trained using a set of example video frames each manually labeled with objects depicted within the video frame. Model may be trained to identify object within subsequent video frames provided as input. As more objects are detected in new video frame instances, the input and output of the model may be added to the training data set and model manager 716 may update and/or retrain the model using the updated training data set. Model manager 716 may similarly train a model to identify object features and objects from a video frame. Model manager 716 may object training data that include sets of object features, each labeled with the specific object to which the features correspond. Model manager 716 may train this model to identify objects from object feature sets provided as input using the training data and a supervised learning algorithm. Model manager 716 may be configured to manage, train, retrain, and/or update any of the machine-learning models).
Consider claim 97, Chungapalli, Martinelli, and Agarwal teach wherein the request is a verbal request (Chungapalli - Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in. Paragraph 0040 teaches user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as voice recognition interface).
Consider claim 98, Chungapalli, Martinelli, and Agarwal teach comprising: selecting a plurality of content items based at least in part on the request for content (Chungapalli - Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Paragraph 0027 teaches the semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Paragraph 0033 teaches semantic graphs may be used, by the system, for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in FIGS. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of FIGS. 1-2. Paragraph 0068 teaches the system gathers a data set and generates a semantic graph that identifies key entities and their associations. Paragraph 0070 teaches system may pull its data set from the wiki plot sections, synopsis sections, category references in the plot sections, and noun chunks from the plot. Paragraph 0071 teaches the model may be trained on the training dataset. Paragraph 0093 teaches examples of entities and roles extracted by the system, where Fig.8 corresponds to the movie “Pulp Fiction”. Fig.9 corresponds to the movie “Dr. Strangelove”).
Consider claim 99, Chungapalli, Martinelli, and Agarwal teach wherein one or more additional attributes of the plurality of content items are determined based at least in part on content analysis (Chungapalli - Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Paragraph 0027 teaches the semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Paragraph 0033 teaches semantic graphs may be used, by the system, for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in FIGS. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of FIGS. 1-2. Paragraph 0068 teaches the system gathers a data set and generates a semantic graph that identifies key entities and their associations. Paragraph 0070 teaches system may pull its data set from the wiki plot sections, synopsis sections, category references in the plot sections, and noun chunks from the plot. Paragraph 0071 teaches the model may be trained on the training dataset. Paragraph 0093 teaches examples of entities and roles extracted by the system, where Fig.8 corresponds to the movie “Pulp Fiction”. Fig.9 corresponds to the movie “Dr. Strangelove”; Agarawal - Col 4: lines 24-47 teaches image recognition techniques, e.g. Markov models, Bayesian models, neural networks, and the like that are trained to identify object features within an image/video frame, may be utilized to identify object features of the object depicted in the image, e.g., the video frame. Object features may include any suitable attribute of the object such as color, pattern, shape, size, edges, gradients and any other suitable portion of the object. Col 17: lines 46-49 teaches model to identify one or more sets of object features from images. Col 18: lines 12-37 teaches a model may be trained using a set of example video frames each manually labeled with objects depicted within the video frame. Model may be trained to identify object within subsequent video frames provided as input. As more objects are detected in new video frame instances, the input and output of the model may be added to the training data set and model manager 716 may update and/or retrain the model using the updated training data set. Model manager 716 may similarly train a model to identify object features and objects from a video frame. Model manager 716 may object training data that include sets of object features, each labeled with the specific object to which the features correspond. Model manager 716 may train this model to identify objects from object feature sets provided as input using the training data and a supervised learning algorithm. Model manager 716 may be configured to manage, train, retrain, and/or update any of the machine-learning models).
Consider claim 100, Chungapalli, Martinelli, and Agarwal teach wherein the GUI visually indicates: one or more relationships between the selected plurality of content items; the determined first portion distinctly from the determined second portion; and one or more relationships between the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request (Chungapalli - Fig.2, Paragraph 0028 teaches user interface receiving text string and in response to the request, recommending other content. Additionally, the system has generated scores for each of the similar movies. For example, program recommendation 204 includes score 208. Fig.2, includes link 210, which is a link to access the program corresponding to the program recommendation 204. Paragraph 0029 teaches the entities, e.g., program recommendations 204 and 206 are considered as semantic concepts and similarities of entities are used in recommendations. Paragraph 0033 teaches semantic graphs may be used, by the system for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in Figs. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of Figs.1-2; Fig.1, Paragraph 0027 teaches user interface receiving text string and in response to the request, recommending other content. The semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Dataset could comprise any type of data from any data source and/or based on any particular subject matter. Videos that contain user inputted keywords {desired attribute}, along with a score {relationships}, are shown in the recommendations. We see a score 208 for each of the program recommendations 204. The scores signify how closely these semantic entities, which are the recommendations, are to the inputted keywords. The scores also serve to show the relationship between items, as to how close their semantic relationships may be to keywords, where closer scores would indicate them being closer in semantic relationship; Martinelli - Paragraph 0029 teaches accepting audio input via a microphone. Paragraph 0052 teaches data indicating one or more keywords is received (204). The video platform can provide a user interface that enables the user to enter keywords. Paragraph 0053 teaches a set of candidate video groups is identified (206). Paragraph 0070 teaches when keyword tab 314 is selected, user can enter keywords int the seed entry area 316. Fig.6, Paragraph 0086 teaches a first list 621 of video groups having the highest topicality scores are shown and a second list 628 of video groups having the lowest topicality scores are shown. The candidate video groups shown in this tab can include a portion of the subset of video groups that were identified based on the keywords specified by the user. Paragraph 0087 teaches a topicality score 623 for the video group. Videos that contain user inputted keywords {desired attribute}, along with a topicality score {relationships}, are shown on a first list. A second list displays videos that have the lowest topicality score. From Fig.6, we see on the first list Video Group 478 having a topicality score of 99% and 3 matching keywords {desired attribute} with check marks next to them. For the second list, we see that videos listed lack any matching keywords {desired attribute}, as there are no check marks signifying that there are corresponding matching keywords. Topicality score in this case helps to see the relationship between items that comprise desired attribute, and those that do not, and how closely in topicality they are meeting the inputted keywords).
Claim(s) 9, 78, 82, and 84 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), in view of Agarwal (US 11,468,675), and further in view of Snibbe et al. (US 2014/0306987).
Consider claims 9 and 78, Chungapalli, Martinelli, and Agarwal teach the lack of the desired attribute is mapped to visually indicate one or more gaps in the desired attribute (Martinelli - Figs.5-7, Paragraph 0082-0084 teaches keywords associated with videos and user being able to view an updated set of candidate video groups that have the highest and lowest co-interaction scores. Paragraph 0090 teaches user interface can also display candidate videos having the highest and lowest topicality scores. As seen in the Figures, the candidate videos having lowest topicality lack the desired attributes, and can be visually seen not having the desired attributes).
Chungapalli, Martinelli, and Agarwal do not explicitly teach wherein:
the GUI further comprises a timeline referencing the plurality of content items,
the timeline is a series of occurrences in the plurality of content items that form a plot or part of a plot of the plurality of content items,
the desired attribute is mapped along the timeline, and
mapped is mapped along the timeline.
In an analogous art, Snibbe teaches wherein: the GUI further comprises a timeline referencing the plurality of content items, the timeline is a series of occurrences in the plurality of content items that form a plot or part of a plot of the plurality of content items, and the desired attribute is mapped along the timeline; mapped is mapped along the timeline (Paragraph 0039, 0049, 0061, 0066, 0076; Paragraph 0034, 0041; Paragraph 0051, 0053).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein: the GUI further comprises a timeline referencing the plurality of content items, the timeline is a series of occurrences in the plurality of content items that form a plot or part of a plot of the plurality of content items, and the desired attribute is mapped along the timeline; mapped is mapped along the timeline, as taught by Snibbe, for the advantage of organizing media stored on a plurality of nodes, where media being contextually connected (Snibbe – Paragraph 0009), allowing user(s) to easily see the contextual connections through the flow of events, and ascertain when they occur.
Consider claim 82, Chungapalli, Martinelli, and Agarwal teach wherein:
the plurality of content items is associated with a movie (Chungapalli – Paragraph 0027-0028, 0066)
the desired attribute is a character of the movie (Chungapalli – Paragraph 0034), and
Chungapalli, Martinelli, and Agarwal do not explicitly teach wherein: each of the plurality of content items comprises a scene of the movie,
the first portion of content items comprises scenes of the movie in which the character appears.
In an analogous art, Snibbe teaches wherein: each of a plurality of content items comprises a scene of the movie (Paragraph 0033),
a first portion of content items comprises scenes of the movie in which the character appears (Paragraph 0061, 0064, 0066, 0076).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein: each of a plurality of content items comprises a scene of the movie, a first portion of content items comprises scenes of the movie in which the character appears, as taught by Snibbe, for the advantage of organizing media stored on a plurality of nodes, where media being contextually connected (Snibbe – Paragraph 0009), allowing user(s) to easily follow along the contextual connections through the flow of events, pertaining to their desired character of interest.
Consider claim 84, Chungapalli, Martinelli, and Agarwal do not explicitly teach wherein the plurality of content items is associated with a series and each of the plurality of content items comprises an episode of the series.
In an analogous art, Snibbe teaches wherein a plurality of content items is associated with a series and each of the plurality of content items comprises an episode of the series (Snibbe – Paragraph 0033, 0039).
character appears (Paragraph 0061, 0064, 0066, 0076).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein a plurality of content items is associated with a series and each of the plurality of content items comprises an episode of the series, as taught by Snibbe, for the advantage of organizing media stored on a plurality of nodes, where media being contextually connected (Snibbe – Paragraph 0009), allowing user(s) to easily follow along the contextual connections through the flow of events, pertaining to episodic events, maintain continuity of storyline.
Claim(s) 80-81 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), in view of Agarwal (US 11,468,675), and further in view of Florin et al. (US 5,594,509).
Consider claim 80, Chungapalli, Martinelli, and Agarwalteach the request (Chungapalli - Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in. Paragraph 0040 teaches user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as voice recognition interface).
Chungapalli, Martinelli, and Agarwaldo not explicitly teach wherein: the request is received while generating for display a content item;
generating for display the GUI comprises generating for display the content item in a picture-in-picture portion of the GUI.
In an analogous art, Florin teaches wherein: a request is received while generating for display a content item; generating for display a GUI comprises generating for display the content item in a picture-in-picture portion of the GUI (Col 13: lines 10-14 teaches a microphone 179 that allows user to speak into the microphone, thereby providing input to the A/V system through voice. Col 15: lines 27-48 teaches a function that when activated by the user, displays a GUI and a picture-in-picture window continues to display the currently viewed program which the user was last viewing. Col 22: lines 42-52 teaches user may use the talk function to issue spoken commands into the microphone in lieu of or in addition to pressing buttons. Through the use of voice recognition hardware and software, the present invention can be made to interpret the spoken commands requested by the user, and invoke the corresponding functions).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwalto include wherein: a request is received while generating for display a content item; generating for display a GUI comprises generating for display the content item in a picture-in-picture portion of the GUI, as taught by Florin, for the advantage of providing users with a hands-free option in issuing commands to the audiovisual system, providing greater convenience, and also allowing users to go through further system functions, while still being able to continue to keep up with desired content.
Consider claim 81, Chungapalli, Martinelli, and Florin teach wherein selecting the plurality of content items is based at least in part on a relevance of the request for content to the content item (Chungapalli - Paragraph 0024, 0033; Paragraph 0027-0028, 0034).
Claim(s) 91 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), in view of Agarwal (US 11,468,675), and further in view of Decrop et al. (US 11,297,383).
Consider claim 91, Chungapalli, Martinelli, and Agarwaldo not explicitly teach generating for output a clarifying response.
In an analogous art, Decrop teaches generating for output a clarifying response (Col 9: lines 1-12 teaches confirmation may be requested via a question that is played up by a speaker, and a confirmation response may be picked up by a microphone. Audio confirmation may also be performed via a smart speaker which includes a microphone for capturing audio responses that are spoken by the user. Via a voice response the user may give or deny confirmation).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwalto include generating for output a clarifying response, as taught by Decrop, for the advantage of quickly bringing user’s attention to the task at hand, better prompting a response from the user in a user-friendly and convenient way.
Claim(s) 92 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), in view of Agarwal (US 11,468,675), and further in view of Venkataraman et al. (US 2020/0167386).
Consider claim 92, Chungapalli, Martinelli, and Agarwalteach the desired attribute indicated in the request (Chungapalli - Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in. Paragraph 0040 teaches user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as voice recognition interface).
Chungapalli, Martinelli, and Agarwaldo not explicitly teach receiving, via the GUI, input indicating selection of one or more values or weights of the desired attribute; and
dynamically updating the display of the GUI based at least in part on the received input indicating selections of one or more values or weights of the desired attribute.
In an analogous art, Venkataraman teaches receiving, via a GUI, input indicating selection of one or more values or weights of a desired attribute; and dynamically updating a display of the GUI based at least in part on the received input indicating selections of one or more values or weights of the desired attribute (Abstract, Paragraph 0051-0052, 0123).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwalto include receiving, via a GUI, input indicating selection of one or more values or weights of a desired attribute; and dynamically updating a display of the GUI based at least in part on the received input indicating selections of one or more values or weights of the desired attribute, as taught by Venkataraman, for the advantage of giving user(s) greater control over what keywords, hold more influence in the system search, providing finer granularity to better aid in giving the most relevant results.
Claim(s) 94 and 96 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chungapalli et al. (US 2020/0074321), in view of Martinelli et al. (US 2023/0081938), in view of Agarwal (US 11,468,675), and further in view of Lentzitzky et al. (US 2019/0200098),
Consider claim 94, Chungapalli teaches a method comprising:
receiving a request specifying a desired attribute (Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in. Paragraph 0040 teaches user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as voice recognition interface);
accessing a trained model analysis (Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Paragraph 0027 teaches the semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Paragraph 0033 teaches semantic graphs may be used, by the system, for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in FIGS. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of FIGS. 1-2. Paragraph 0068 teaches the system gathers a data set and generates a semantic graph that identifies key entities and their associations. Paragraph 0070 teaches system may pull its data set from the wiki plot sections, synopsis sections, category references in the plot sections, and noun chunks from the plot. Paragraph 0071 teaches the model may be trained on the training dataset. Paragraph 0093 teaches examples of entities and roles extracted by the system, where Fig.8 corresponds to the movie “Pulp Fiction”. Fig.9 corresponds to the movie “Dr. Strangelove”);
identifying, using the trained model, a first group of the plurality of content items that comprise the desired attribute (Paragraph 0024 teaches using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections generating more relevant search results and recommendations. Fig.1, Paragraph 0027 teaches user interface 100 has received text string 102, e.g., via a user input into a user input interface. In response, the system has generated for display program recommendation 104. Fig.2, Paragraph 0028 teaches user interface 200 has received text string 202, e.g., via a user input into a user input interface, which corresponds to the movie “Argo.” In response, the system has generated for display program recommendations 204 and 206. Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in);
generating for output a graphical user interface (GUI) that visually indicates: the first group; and a selectable visual indication of the first group (Fig.2, Paragraph 0028 teaches user interface receiving text string and in response to the request, recommending other content. Additionally, the system has generated scores for each of the similar movies. For example, program recommendation 204 includes score 208. Paragraph 0029 teaches the entities, e.g., program recommendations 204 and 206 are considered as semantic concepts and similarities of entities are used in recommendations. Paragraph 0033 teaches semantic graphs may be used, by the system for role importance, which is the classification of important and unimportant cast members and roles in content based on the node score from the semantic graph. For example, in Figs. 8 and 9, important roles determined to achieve a high score are shown. These important roles may be displayed in the displays of Figs.1-2; Fig.1, Paragraph 0027 teaches user interface receiving text string and in response to the request, recommending other content. The semantic graph is built based on a dataset comprising keywords and descriptions from plot details of media content. Dataset could comprise any type of data from any data source and/or based on any particular subject matter. Videos that contain user inputted keywords {desired attribute}, along with a score {relationships}, are shown in the recommendations. We see a score 208 for each of the program recommendations 204. The scores signify how closely these semantic entities, which are the recommendations, are to the inputted keywords. The scores also serve to show the relationship between items, as to how close their semantic relationships may be to keywords, where closer scores would indicate them being closer in semantic relationship);
receiving user input selecting the selectable visual indication of the first group; and generating for output based at least in part on the received user input (Paragraph 0028 teaches Fig.2 includes link 210, which is a link to access the program corresponding to the program recommendation 204. Paragraph 0030 teaches guidance applications also allow users to navigate among and locate content. As referred to herein, the term “multimedia” should be understood to mean content that utilizes at least two different content forms described above, for example, text, audio, images, video, or interactivity content forms. Content may be recorded, played, displayed, or accessed by user equipment devices, but can also be part of a live performance. Paragraph 0034 teaches voice-powered search, where viewers can use natural language to find the content they are interested in).
Chungapalli does not explicitly teach wherein the trained model is trained on one or more video frames of a plurality of content items to extract attributes of the plurality of content items;
identifying, a second group of the plurality of content items that do not comprise the desired attribute;
generating for output a graphical user interface (GUI) that visually indicates:
second group;
generating for output only the first group of the plurality of content items based at least in part on the received user input.
In an analogous art, Martinelli teaches identifying, a second group of a plurality of content items that do not comprise a desired attribute; generating for output a graphical user interface (GUI) that visually indicates: second group. (Paragraph 0029 teaches accepting audio input via a microphone. Paragraph 0052 teaches data indicating one or more keywords is received (204). The video platform can provide a user interface that enables the user to enter keywords. Paragraph 0053 teaches a set of candidate video groups is identified (206). Paragraph 0070 teaches when keyword tab 314 is selected, user can enter keywords int the seed entry area 316. Fig.6, Paragraph 0086 teaches a first list 621 of video groups having the highest topicality scores are shown and a second list 628 of video groups having the lowest topicality scores are shown. The candidate video groups shown in this tab can include a portion of the subset of video groups that were identified based on the keywords specified by the user. Paragraph 0087 teaches a topicality score 623 for the video group. Videos that contain user inputted keywords {desired attribute}, along with a topicality score {relationships}, are shown on a first list. A second list displays videos that have the lowest topicality score. From Fig.6, we see on the first list Video Group 478 having a topicality score of 99% and 3 matching keywords {desired attribute} with check marks next to them. For the second list, we see that videos listed lack any matching keywords {desired attribute}, as there are no check marks signifying that there are corresponding matching keywords. Topicality score in this case helps to see the relationship between items that comprise desired attribute, and those that do not, and how closely in topicality they are meeting the inputted keywords).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli to include identifying, a second group of a plurality of content items that do not comprise a desired attribute; generating for output a graphical user interface (GUI) that visually indicates: second group, as taught by Martinelli, for the advantage of allowing users to see the contrast between content that closely matches and does not closely match with user’s desired attributes, allowing them to get a more complete picture on both ends of the request, allowing them to better refine and tailor their requests.
Chungapalli and Martinelli do not explicitly teach wherein the trained model is trained on one or more video frames of a plurality of content items to extract attributes of the plurality of content items;
generating for output only the first group of the plurality of content items based at least in part on the received user input.
In an analogous art, Agarwal teaches wherein a trained model is trained on one or more video frames of a plurality of content items to extract attributes of a plurality of content items (Col 4: lines 24-47 teaches image recognition techniques, e.g. Markov models, Bayesian models, neural networks, and the like that are trained to identify object features within an image/video frame, may be utilized to identify object features of the object depicted in the image, e.g., the video frame. Object features may include any suitable attribute of the object such as color, pattern, shape, size, edges, gradients and any other suitable portion of the object. Col 17: lines 46-49 teaches model to identify one or more sets of object features from images. Col 18: lines 12-37 teaches a model may be trained using a set of example video frames each manually labeled with objects depicted within the video frame. Model may be trained to identify object within subsequent video frames provided as input. As more objects are detected in new video frame instances, the input and output of the model may be added to the training data set and model manager 716 may update and/or retrain the model using the updated training data set. Model manager 716 may similarly train a model to identify object features and objects from a video frame. Model manager 716 may object training data that include sets of object features, each labeled with the specific object to which the features correspond. Model manager 716 may train this model to identify objects from object feature sets provided as input using the training data and a supervised learning algorithm. Model manager 716 may be configured to manage, train, retrain, and/or update any of the machine-learning models).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli and Martinelli to include wherein a trained model is trained on one or more video frames of a plurality of content items to extract attributes of a plurality of content items, as taught by Agarwal, for the advantage of training models using a set of example video frames, as well as allowing for further updating and/or retraining of the model using updated training data added from further object detections, to identify objects and object features (Agarwal – Col 18: lines 17-37), allowing for the model to be exposed to similar data that will be used as input, increasing accuracy.
Chungapalli, Martinelli, and Agarwal do not explicitly teach generating for output only the first group of the plurality of content items based at least in part on the received user input.
In an analogous art, Lentzitzky teaches generating for output only a first group of the plurality of content items based at least in part on received user input (Paragraph 0081-0082, 0086).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include generating for output only a first group of the plurality of content items based at least in part on received user input, as taught by Lentzitzky, for the advantage of playing all recommended items as a continuous sequence that does not require user input until the end of the last included content item (Lentzitzky – Paragraph 0081), providing continuous displaying of content units of a sequence selected by a user automatically, without user intervention (Lentzitzky – Paragraph 0086), providing user convenience.
Consider claim 96, Chungapalli, Martinelli, and Agarwal do not explicitly teach wherein the generating for output only the determined first portion of the plurality of content items that comprise the desired attribute indicated in the request occurs as a continuous sequence.
In an analogous art, Lentzitzky teaches wherein generating for output only a determined first portion of a plurality of content items that comprise a desired attribute indicated in a request occurs as a continuous sequence (Paragraph 0081-0082, 0086).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Chungapalli, Martinelli, and Agarwal to include wherein generating for output only a determined first portion of a plurality of content items that comprise a desired attribute indicated in a request occurs as a continuous sequence, as taught by Lentzitzky, for the advantage of playing all recommended items as a continuous sequence that does not require user input until the end of the last included content item (Lentzitzky – Paragraph 0081), providing continuous displaying of content units of a sequence selected by a user automatically, without user intervention (Lentzitzky – Paragraph 0086), providing user convenience.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON K LIN whose telephone number is (571)270-1446. The examiner can normally be reached on Monday-Friday 9AM-5PM.
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/JASON K LIN/Primary Examiner, Art Unit 2425
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