7DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 20, 2025 has been entered.
In response to Applicant’s claims filed on August 20, 2025, claims 1-3, 5-12, 14-22 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 08/20/2025. In this action claim(s) 1-3, 5-12, 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US Pub. No. 20220198211) and Li et al. (US Pub. No. 20200193288) in further view of Abraham et al. (US Pub. No. 20220398274). The Abraham et al. reference has been added to address the amendment of receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content.
Applicant’s arguments:
On Page 14, applicant argues “Applicant respectfully asserts that, at least by the same token, currently amended independent claim 1 does not recite a judicial exception, because currently amended independent claim 1 does not claim or recite a mental process.”
Examiner’s Reply:
The examiner respectfully disagrees and would like to point out that human mind is fully capable of generating, comparing, and identifying and analyzing content for its corresponding mood A human would be able to iteratively follow these steps along with any needed additional elements while using a computer as a tool (eg using semantics in indexing, searching, and retrieving content).
Applicant’s arguments:
On Pages 16-17, applicant argues “Applicant respectfully submits that the introduction of a machine learning model trained using frame sampling rate as a training input to translate between images and text, based at least in part on mood or feelings described by the text, coupled with provision of a search engine populated with content representations output by the trained ML model so as to provide users with enhanced content search capability, achieves a technological improvement resulting in integration of the recited processing steps into a practical application. Thus, for this additional reason, Applicant respectfully submits that currently amended independent claim 1 is directed to patent eligible subject matter.”
Examiner’s Reply:
Applicant argues that the amended claims comprises statutory subject matter. Examiner
respectfully disagrees. The examiner notes that the computer (being used as a generic tool) as recited in the claims is being used for content searching. The use of semantics and mood in searching for query results does not improve the functioning of a computer. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical
application.
Applicant’s arguments:
On Pages 16-17, applicant argues “Applicant respectfully submits the aforementioned limitations recited by currently amended independent claim 1 amount to significantly more than a judicial exception because they recite use of a machine learning model trained using frame sampling rate as a training input to generate image embeddings enabling translation between images and text, based on subjective descriptors of mood, feelings, or mood and feelings described by a user, to enhance the accuracy and speed with which relevant content desirable to the user can be identified and surfaced..”
Examiner’s Reply:
The examiner respectfully disagrees and would like to point out that human mind is fully capable of generating, comparing, and identifying and analyzing content for its corresponding mood. Accordingly, these elements of the recited abstract idea could not possibly be more than themselves. Also, there is nothing significant about the generic computer function of receiving and outputting media content. The argument, therefore, is not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-3, 5-12, 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1-3, 5-12, 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the system, method, and portable device of claims 1-3, 5-12, 14-22 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1 and 11 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1 and 11 recites the following limitations directed towards a Mental Processes:
generate, using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content, wherein the at least one ML model is trained using frame sampling rate as a training input (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a content representation);
compare, using the search engine, the generated content representation with the plurality of content representations (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to compare the content representation)
to identify one or more candidate matches for the searched content (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to identify candidate matches);
identify one or more content units each corresponding respectively to one of the one or more candidate matches (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to identify content units1).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 13, and 20:
a hardware processor (i.e., as a generic processor performing a generic computer function);
a system memory storing a software code configured to support semantic content searching, at least one machine learning (ML) model trained to translate between images and text (i.e., as a generic processor performing a generic computer function), and a search engine populated with a plurality of content representations output by the at least one ML model (recites merely applying the abstract idea on a computer);
receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content (recites insignificant extra solution activity that amounts to mere data gathering);
output a query response identifying at least one of the one or more content units (recites insignificant extra solution activity such as transmission and presentation of collected data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1 and 11 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2 and 12:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the search engine comprises a scalable vector search engine, and wherein the plurality of content representations comprises a plurality of vector embeddings (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3 and 13:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the plurality of vector embeddings comprise a first plurality of image embeddings and a second plurality of text embeddings (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4 and 14:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the content representation corresponding to the searched content comprises an image embedding generated based on the semantic content search query (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5 and 15:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the at least one ML model comprises a trained zero-shot neural network (NN) (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6 and 16:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the trained zero-shot NN comprises a text encoder and an image encoder (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 17:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the one or more content units each corresponding respectively to one of the one or more candidate matches comprise a plurality of content units having differing time durations (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8 and 18:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the one or more content units each corresponding respectively to one of the one or more candidate matches comprise at least one of a frame, a shot, or a scene of video (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9 and 19:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the hardware processor is further configured to execute the software code to:
obtain, for each of the at least one of the one or more content units identified in the query response, first and second bounding timestamps for a respective content segment including that at least one content unit (recites insignificant extrasolution activity);
wherein the query response includes the each first and second bounding timestamps (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10 and 20:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein each respective content segment comprises at least one of a shot or scene from an episode of episodic entertainment content, a shot or scene from a movie, the episode, or the movie (recites insignificant extrasolution activity).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 7-14 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US Pub. No. 20220198211) and Li et al. (US Pub. No. 20200193288) in further view of Abraham et al. (US Pub. No. 20220398274).
With respect to claim 1, Ferreira Moreno et al. teaches a system comprising:
a hardware processor (Fig. 7 discloses a processor);
a system memory storing a software code configured to support semantic content searching, at least one machine learning (ML) model trained to translate between images and text (Paragraph 9 discloses The information can be extracted by a machine learning model that classifies labels corresponding to a plurality of fragments of the digital media content), and a search engine populated with a plurality of content representations output by the at least one ML model (Paragraph 9 discloses structuring at least one of spatial and temporal representation of the digital media content in a knowledge graph structure);
the hardware processor configured to execute the software code to:
compare, using the search engine, the generated content representation with the plurality of content representations to identify one or more candidate matches for the searched content (Paragraph 66 discloses process the query, finding in the knowledge graph all fragments that match the query specification);
identify one or more content units each corresponding respectively to one of the one or more candidate matches (Paragraph 71 discloses identify, structure and retrieve spatiotemporal sequences of digital media content according to semantic specification); and
output a query response identifying at least one of the one or more content units (Paragraph 66 discloses New digital media content can be composed, which includes, fragments of the digital media content meeting the search query request). Ferreira Moreno et al. does not explicitly teach receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content.
However, Li et al. teaches receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content (Paragraph 37 discloses a user may search: “show me funny programmes” in which case the query is one type of mood, e.g., “funny”, instead of specific titles of TV programmes).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. with Li et al. to include receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content. This would have facilitated improved content searching using semantics and features of video/audio/text.
Ferreira Moreno et al. as modified by Li et al. does not explicitly disclose generate, using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input.
However, Abraham et al. generate, using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input (Paragraph 39 discloses the embedding generator 114 generates a record or a storage of attribute signals associated with corresponding entity instances. This record may include any number of outputs generated by the models of the attribute model manager 108 that are trained to analyze digital content items and generate various outputs and Paragraph 23 discloses a digital content item refers to a video file, a deep-learned or otherwise complex attribute may refer to predicted features of the video content, such as a mood, an observation of day or night-time, a prediction of slow or fast cuts within various scenes, and any other wide variety of signals that may be predicted or estimated from the content of the video files).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. and Li et al. with Abraham et al. to include generate, using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input. This would have facilitated improved content searching using semantics and features of video/audio/text.
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 2, Abraham et al. discloses the system of claim 1, wherein the search engine comprises a scalable vector search engine, and wherein the plurality of content representations comprises a plurality of vector embeddings (Paragraph 87 discloses an embedding includes a multi-dimensional vector having values that are calculated or otherwise determined based on attributes output from a plurality of attribute models). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ferreira Moreno et al. reference and the Abraham et al. reference is applicable to dependent claim 2).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 2. Regarding claim 3, Abraham et al. discloses the system of claim 2, wherein the plurality of vector embeddings comprise a first plurality of image embeddings and a second plurality of text embeddings (Paragraph 24 discloses the embeddings may include any number of numeric values representative of specific attributes or combinations of multiple attributes as determined by one or more embedding models that are trained to output numeric values representative of the attribute(s) and generate the respective embeddings corresponding to the specific instances). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ferreira Moreno et al. reference and the Abraham et al. reference is applicable to dependent claim 3).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 2. Regarding claim 4, Ferreira Moreno et al. discloses the system of claim 2, wherein the content representation corresponding to the searched content comprises an image embedding generated based on the semantic content search query (Paragraph 66 discloses New digital media content can be composed, which includes, fragments of the digital media content meeting the search query request).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 7, Ferreira Moreno et al. discloses the system of claim 1, wherein the one or more content units each corresponding respectively to one of the one or more candidate matches comprise a plurality of content units having differing time durations (Paragraph 3 discloses the amount of UGC that is uploaded to media content sharing platforms continues to grow, both in terms of the number of instances of uploaded UGC and in the duration or length of each instance of UGC).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 8, Ferreira Moreno et al. discloses the system of claim 1, wherein the one or more content units each corresponding respectively to one of the one or more candidate matches comprise at least one of a frame, a shot, or a scene of video (Paragraph 67 discloses the knowledge graph structure can be traversed (e.g., shown at 618) to find the specified scenes requested in the search query).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 9, Cheng et al. discloses the system of claim 1, wherein the hardware processor is further configured to execute the software code to:
obtain, for each of the at least one of the one or more content units identified in the query response, first and second bounding timestamps for a respective content segment including that at least one content unit (Paragraph 28 discloses a detection event, such as an event text query (i.e. just the event title like “birthday party” or “feeding an animal”), to retrieve a list of video(s) and or video clips (i.e., a ranked list of video clips), frames, segments, and the like based on their content; a segment must be timestamped);
wherein the query response includes the each first and second bounding timestamps (Paragraph 28 discloses a detection event, such as an event text query (i.e. just the event title like “birthday party” or “feeding an animal”), to retrieve a list of video(s) and or video clips (i.e., a ranked list of video clips), frames, segments, and the like based on their content; a segment must be timestamped). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Ferreira Moreno et al. reference and the Cheng et al. reference is applicable to dependent claim 9).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 9. Regarding claim 10, Ferreira Moreno et al. discloses the system of claim 9, wherein each respective content segment comprises at least one of a shot or scene from an episode of episodic entertainment content, a shot or scene from a movie, the episode, or the movie (Paragraph 67 discloses the knowledge graph structure can be traversed (e.g., shown at 618) to find the specified scenes requested in the search query).
With respect to claim 11, Ferreira Moreno et al. teaches a method for use by a system including a hardware processor and a system memory storing a software code configured to support semantic content searching, at least one machine learning (ML) model trained to translate between images and text, and a search engine populated with a plurality of content representations output by the at least one ML 20 model, the method comprising:
comparing, by the software code executed by the hardware processor and using the search engine, the generated content representation with the plurality of content representations to identify one or more candidate matches for the searched content (Paragraph 66 discloses process the query, finding in the knowledge graph all fragments that match the query specification);
identifying, by the software code executed by the hardware processor, one or more content units each corresponding respectively to one of the one or more candidate matches (Paragraph 71 discloses identify, structure and retrieve spatiotemporal sequences of digital media content according to semantic specification); and
outputting, by the software code executed by the hardware processor, a query response identifying at least one of the one or more content units (Paragraph 66 discloses New digital media content can be composed, which includes, fragments of the digital media content meeting the search query request). Ferreira Moreno et al. does not explicitly teach receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content.
However, Li et al. teaches receiving, by the software code executed by the hardware processor, a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content (Paragraph 37 discloses a user may search: “show me funny programmes” in which case the query is one type of mood, e.g., “funny”, instead of specific titles of TV programmes).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. with Li et al. to include receive a semantic content search query describing a searched content, the semantic content search query describing at least one of a mood or a feeling characteristic of the searched content. This would have facilitated improved content searching using semantics and features of video/audio/text.
Ferreira Moreno et al. as modified by Li et al. does not explicitly disclose generating, , by the software code executed by the hardware processor and using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input.
However, Abraham et al. generating, , by the software code executed by the hardware processor and using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input (Paragraph 39 discloses the embedding generator 114 generates a record or a storage of attribute signals associated with corresponding entity instances. This record may include any number of outputs generated by the models of the attribute model manager 108 that are trained to analyze digital content items and generate various outputs and Paragraph 23 discloses a digital content item refers to a video file, a deep-learned or otherwise complex attribute may refer to predicted features of the video content, such as a mood, an observation of day or night-time, a prediction of slow or fast cuts within various scenes, and any other wide variety of signals that may be predicted or estimated from the content of the video files).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. and Li et al. with Abraham et al. to include generate, using the at least one ML model and the semantic content search query describing the at least one of the mood or the feeling, a content representation corresponding to the searched content and comprising a plurality of image embeddings, wherein the at least one ML model is trained to generate the plurality of image embeddings at frame level using frame sampling rate as a training input. This would have facilitated improved content searching using semantics and features of video/audio/text.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 2, because claim 12 is substantially equivalent to claim 2.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 3, because claim 13 is substantially equivalent to claim 3.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 4, because claim 14 is substantially equivalent to claim 4.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 7, because claim 17 is substantially equivalent to claim 7.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 8, because claim 18 is substantially equivalent to claim 8.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 9, because claim 19 is substantially equivalent to claim 9.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 10, because claim 20 is substantially equivalent to claim 10.
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US Pub. No. 20220198211) and Li et al. (US Pub. No. 20200193288) and Abraham et al. (US Pub. No. 20220398274) in further view of Yin et al. (US Pub. No. 20220284613).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 1. With respect to claim 5, Ferreira Moreno et al. as modified by Li et al. and Abraham et al. does not disclose a trained zero-shot neural network (NN).
However, Yin et al. teaches the system of claim 1, wherein the at least one ML model comprises a trained zero-shot neural network (NN) (Paragraph 92 discloses neural network layers and Paragraph 162 discloses multiple eight zero-shot datasets).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. and Li et al. and Abraham et al. with Yin et al. to include wherein the at least one ML model comprises a trained zero-shot neural network. This would have facilitated improved content searching using semantics and features of video/audio/text. In addition, all references teach features that are directed to analogous art and they are directed to the same field of endeavor: content querying.
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. and Yin et al. teaches all the limitations of claim 5. With respect to claim 6, Yin et al. teaches the system of claim 5, wherein the trained zero-shot NN comprises a text encoder and an image encoder (Paragraph 65 discloses the encoder 322 of the depth prediction model 300 encodes input images into latent object feature maps or latent object feature vectors (e.g., image depth feature vectors)). The motivation to combine statement previously provided in the rejection of independent claim 5 provided above, combining the Ferreira Moreno et al. reference and the Yin et al. reference is applicable to dependent claim 6).
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 5, because claim 15 is substantially equivalent to claim 5.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 6, because claim 16 is substantially equivalent to claim 6.
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US Pub. No. 20220198211) and Li et al. (US Pub. No. 20200193288) and Abraham et al. (US Pub. No. 20220398274) in further view of Knight et al. (US Patent No. 9788777).
The Ferreira Moreno et al. reference as modified by Li et al. and Abraham et al. teaches all the limitations of claim 9. Regarding claim 21, does not disclose wherein each respective content segment comprises an episode of episodic entertainment content or a movie.
However, Knight et al. teaches the system of claim 9, wherein each respective content segment comprises an episode of episodic entertainment content or a movie (Column 7 Lines 28-45 discloses identify emotions of segments of media and/or an overall mood of the media. The example mood identification system of FIG. 1 includes an audio receiver 115, a sample generator 120, a feature extractor 125, a classification engine 130, a mood model database 140, a mood model validator 145, and a recommendation engine 155 and Column 7 Lines 28-45 and Column 50 Lines 48-60 discloses presenting or requesting to present primary media. (block 2810). In examples disclosed herein, the primary media requested by a user can include music, videos, documentaries, how-to videos, news reports, movies, television shows, or any other media).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Ferreira Moreno et al. and Li et al. and Abraham et al. with Knight et al. to include wherein each respective content segment comprises an episode of episodic entertainment content or a movie. This would have facilitated improved content searching using semantics and features of video/audio/text.
With respect to claim 22, it is rejected on grounds corresponding to above rejected claim 21, because claim 22 is substantially equivalent to claim 21.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20220391426 is directed to MULTI-SYSTEM-BASED INTELLIGENT QUESTION ANSWERING METHOD AND APPARATUS, AND DEVICE [0096] after obtaining the original document, the text content in the original document is analyzed, specifically, it can be a content structure analysis processing, extracting the connective word in the original document to obtain a short segment with a questioning characteristic, then inputting the short segment and the original paragraph where the short segment is located into the neural network model to determine the question text in the second database. The advantage of this setting is to provide as much question text from the second database as possible for the subsequent query of question information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
Prosecution Insights