DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In response to Applicant’s claims filed on August 20, 2025, claims 1, 3, 5-11, 13, 15-22 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 01/20/2026. In this action claim(s) 1, 3, 7-11, 13, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US Pub. No. 20210397610) and 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 Singh et al. reference has been added to address the amendment of compare, using the scalable vector search engine, the generated content representation with the plurality of content representations stored in the vector embedding database to identify one or more candidate matches for the searched content, the comparing including computing vector distance metrics between the generated content representation and the stored plurality of content representations.
Applicant’s arguments:
On Page 9, applicant argues “The Examiner asserts that the following limitations recite mental processes "capable of being performed by the human mind using a computer as a tool:" generating a content representation using an ML model trained with frame-sampling rate inputs, comparing high- dimensional content representations, and identifying candidate content units based on those representations. (See page 6 of the Office Action.) Applicant respectfully submits that this characterization is legally and factually erroneous.”
Examiner’s Reply:
The examiner respectfully disagrees and would like to point out that human mind using a computer as a tool 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 (eg using semantics in indexing, searching, and retrieving content).
Applicant’s arguments:
On Pages 11-12, applicant argues “Applicant respectfully submits that currently amended independent claims 1 and 11 are directed to a specific technological improvement in semantic content search systems, namely, enabling mood and feeling based retrieval of multimedia content, using frame-level image embeddings, and employing a trained ML model that translates between images and text representations. This is not a mere application of an abstract idea using a computer. Instead, currently amended independent claim 1, for example, defines how the system itself operates differently, i.e., by generating and comparing frame-level embedding representations that could not exist absent the claimed ML training and architecture.”
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 according to mood and feelings. 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 14, applicant argues “Applicant notes that under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), whether claim elements are well-understood, routine, or conventional is a factual determination that must be supported by evidence (See MPEP § 2106.05(d)). The Examiner has provided no evidence that the features recited by independent claims 1 and 11 were well-understood, routine, or conventional at the time the present application was filed. Absent such evidence, a Step 2B rejection cannot be sustained. Here, the currently amended independent claims require, among other features, generating image embeddings using an ML model trained with frame sampling rate as a training input, and performing embedding comparison by computing vector distances.”
Examiner’s Reply:
Machine learning is well-understood, routine and conventional and using a model in order to generate data for improved searching WURC. There is nothing novel about representing content using parameters such as mood and feelings.
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.
Claims 1, 3, 5-11, 13, 15-22 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 claims 1 and 11 contain 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. There is no support for “vector embedding database…” and “vector distance metrics…a”.
Dependent claims 3, 5-10, 13, 15-22 is/are also rejected for inheriting the deficiencies of the independent claims from which they depend on.
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, 5-11, 13, 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, 3, 5-11, 13, 15-22 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, 5-11, 13, 15-22 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 scalable vector search engine, the generated content representation with the plurality of content representations stored in the vector embedding database (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 comparing including computing vector distance metrics between the generated content representation and the stored 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 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 units).
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 scalable vector search engine including a vector embedding database 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) 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 content representations 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) 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, 3, 7-11, 13, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US Pub. No. 20210397610) and 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, Singh et al. et al. teaches a system comprising:
a hardware processor (Paragraph 24 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 7 discloses a trained machine learning system for processing queries that improves performance by mapping ill-formed and potentially noisy or ambiguous initial queries to a canonical, i.e. representative, query), and a scalable vector search engine including a vector embedding database populated with a plurality of content representations output by the at least one ML model (Paragraph 8 discloses obtaining the training data comprises obtaining pairs of text data representing queries and responses, and obtaining vector representations of the query data samples comprises converting the pairs of text data to corresponding vector representations and Paragraph 45 discloses query processor 248 may output a response as pre-rendered data (e.g., an audio and/or video sample) or the response interface 250 may comprise a text-to-modality system, such as a text-to-speech or text-to-video system, or other vectorized information representation system, such as Musical Instrument Digital Interface (MIDI) music, Scalable Vector Graphics (SVG) graphics, or Flash animations, for providing the response 230);
the hardware processor configured to execute the software code to:
compare, using the scalable vector search engine, the generated content representation with the plurality of content representations stored in the vector embedding database to identify one or more candidate matches for the searched content, the comparing including computing vector distance metrics between the generated content representation and the stored plurality of content representations (Paragraph 74 discloses comparing a distance measure in vector space between the source and target data samples and removing data sample pairs that are greater than a predefined threshold. It may also comprise generating language parsing metrics for both source and target data samples and then removing data samples based on a comparison of these metrics).
output a query response identifying at least one of the one or more content units (Paragraph 55 discloses the vector representations may comprise vector representations of query data and vector representations of accompanying response data, in this case the vector representations of accompanying response data may be clustered within a first stage and the vector representations of query data may be clustered in a second stage, using the results of the first stage clustering (e.g., a second stage of clustering within groups defined by the first stage clustering). Sing et al. does not explicitly disclose identify one or more content units each corresponding respectively to one of the one or more candidate matches.
However, Ferreira Moreno et al. teaches identify one or more content units each corresponding respectively to one of the one or more candidate matches (Paragraph 55 discloses the vector representations obtained at block 410 are clustered. This may comprise clustering the vector representations within vector space, i.e. identifying clusters based on one or more clustering functions. The clustering functions may be provided by machine learning libraries such as sci-kit learn); and
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Sing et al. with Ferreira Moreno et al. to include identifying one or more content units each corresponding respectively to one of the one or more candidate matches. This would have facilitated improved content searching using semantics and features of video/audio/text.
Sing et al. as modified by 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 Singh et al. and 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.
Singh et al. as modified by Ferreira Moreno et al. and 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 Singh et al. and 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 Singh et al. reference as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 3, Abraham et al. discloses the system of claim 2, wherein the plurality of content representations 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 Singh et al. reference as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 7, Singh 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 77 discloses A named entity tag match may require both that the tag type (e.g., <PLACE>, <TIME>) and the tag values (e.g., “Toronto”, “12:15 pm”) match within input and output text data for the machine learning system).
The Singh et al. reference as modified by Ferreira Moreno et al. and 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 Singh et al. reference as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. teaches all the limitations of claim 1. Regarding claim 9, Singh 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 77 discloses the machine learning system is trained with the original named entity values (e.g., “Toronto”, “12:15 pm”, “Mike”) and is configured to produce outputs that pass these through to the output sequences. A named entity tag match may require both that the tag type (e.g., <PLACE>, <TIME>) and the tag values (e.g., “Toronto”, “12:15 pm”) match within input and output text data for the machine learning system);
wherein the query response includes the each first and second bounding timestamps (Paragraph 77 discloses the machine learning system is trained with the original named entity values (e.g., “Toronto”, “12:15 pm”, “Mike”) and is configured to produce outputs that pass these through to the output sequences. A named entity tag match may require both that the tag type (e.g., <PLACE>, <TIME>) and the tag values (e.g., “Toronto”, “12:15 pm”) match within input and output text data for the machine learning system).
The Singh et al. reference as modified by Ferreira Moreno et al. and 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, using the scalable vector search engine, the generated content representation with the plurality of content representations stored in the vector embedding database to identify one or more candidate matches for the searched content, the comparing including computing vector distance metrics between the generated content representation and the stored plurality of content representations (Paragraph 74 discloses comparing a distance measure in vector space between the source and target data samples and removing data sample pairs that are greater than a predefined threshold. It may also comprise generating language parsing metrics for both source and target data samples and then removing data samples based on a comparison of these metrics).
outputting a query response identifying at least one of the one or more content units (Paragraph 55 discloses the vector representations may comprise vector representations of query data and vector representations of accompanying response data, in this case the vector representations of accompanying response data may be clustered within a first stage and the vector representations of query data may be clustered in a second stage, using the results of the first stage clustering (e.g., a second stage of clustering within groups defined by the first stage clustering). Sing et al. does not explicitly disclose identify one or more content units each corresponding respectively to one of the one or more candidate matches.
However, Ferreira Moreno et al. teaches identifying one or more content units each corresponding respectively to one of the one or more candidate matches (Paragraph 55 discloses the vector representations obtained at block 410 are clustered. This may comprise clustering the vector representations within vector space, i.e. identifying clusters based on one or more clustering functions. The clustering functions may be provided by machine learning libraries such as sci-kit learn); and
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Sing et al. with Ferreira Moreno et al. to include identifying one or more content units each corresponding respectively to one of the one or more candidate matches. This would have facilitated improved content searching using semantics and features of video/audio/text.
Sing et al. as modified by Ferreira Moreno et al. does not explicitly teach receiving 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 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 Singh et al. and 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.
Singh et al. as modified by Ferreira Moreno et al. and Li et al. does not explicitly disclose generating, 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, 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 Singh et al. and 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 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 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 Singh et al. (US Pub. No. 20210397610) and 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 Singh et al. reference as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. teaches all the limitations of claim 1. With respect to claim 5, Singh et al. as modified by Ferreira Moreno et al. and 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 Singh et al. and 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 Singh et al. reference as modified by Ferreira Moreno et al. and 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 Singh et al. (US Pub. No. 20210397610) and 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 Singh et al. reference as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. teaches all the limitations of claim 9. Regarding claim 21, Singh et al. as modified by Ferreira Moreno et al. and Li et al. and Abraham et al. 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 Singh et al. and 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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