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 .
Status of Claims
The following non-is a final office action.
Claims 1-20 are currently pending and have been examined on their merits.
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.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-18 recites a method (i.e. a series of steps), claim 19 recites a non-transitory computer readable medium, and claim 20 recites a system and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 19, and 20 recite: A method comprising: accessing transcribed text associated with a predetermined source; inputting features of the transcribed text to detect presence of sensitive content of a predetermined category in the input features; inputting the features to detect a neutral emotion score; accessing a plurality of neutral score thresholds associated with the predetermined category of sensitive content whose presence is detected in the input features; applying the accessed plurality of neutral score thresholds to the detected neutral emotion score to determine, for the predetermined source, a risk level associated with the predetermined category of sensitive content; and performing an action when the risk level for the predetermined category of sensitive content for the predetermined source meets a tolerance threshold associated with a user.
The claims recite a certain method of organizing human activity. The claims recite a method for determining a score for detecting the presence of sensitive content of a predetermined category in content and performing an action if a risk level is above a predetermined threshold. Therefore, the claims recite a method for managing personal behavior, relationships, and or interactions between people. As the claims recite a series of steps or rules for monitoring content being shared, evaluating it to determine a risk score of the content contains sensitive content, and performing an action or a response if the score is above a threshold.
Alternatively, the claims also recite a mental process. The claims recite merely a method of receiving and evaluating text or information pertaining to received content, determining a tone and risk level of the content containing sensitive content, and performing an action if the risk level is above a threshold. The examiner finds these limitations to merely recite concepts the courts have identified as being mental processes such as be observations, evaluations, judgements, and opinions. As the claims recite merely observing and evaluating the text of content to determine if it contains sensitive information and performing an action in response. Furthermore, the examiner finds that a user could mentally, or with the aid of a “pen and paper,” perform the steps of monitoring and evaluating the text of content to determine a tone and risk of it containing sensitive information and performing an action or a response if the risk level is above a predetermined threshold.
Therefore, the examiner finds the claims to recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: a classifier and a tonal model that is trained
Claim 19: A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor of a brand integrity system, cause the hardware processor to perform steps comprising: a classifier and a tonal model that is trained.
Claim 20: A brand integrity system, comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: a classifier and a tonal model that is trained.
However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Merely utilizing a generic computer system to perform the claim limitations of receiving and evaluating content to determine a risk level of the content containing sensitive information. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional elements do not integrate the judicial exception into a practical application and do not amount to significantly more.
Claims 2-3 and 7-18 further narrow the abstract idea of receiving and evaluating content to determine a risk level of the content containing sensitive information as disclosed by claims 1, 19, and 20.
Claims 4-6 further narrow the abstract idea of evaluating content and determining a public sentiment score towards the user as disclosed in claims 1, 19, and 20.
The dependent claims recite the following additional elements:
Claim 2: transmitting an application programming interface notification and a digital server.
Claim 7: a topic model
Claim 11: a first machine learning model that is trained.
Claim 12: wherein the tonal model is a second machine learning model.
Claim 15: a user interface.
However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1-20 are rejected under U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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) 1-3 and 7-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ashoori (US 2021/0275928) in view of Li (US 2021/0397793).
Claims 1 and 19-20: Ashoori discloses (Claim 1) a method comprising: (Claim 19) A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor of a brand integrity system, cause the hardware processor to perform steps comprising: (Claim 20) A brand integrity system, comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: accessing transcribed text associated with a predetermined source (Paragraph [0011]; [0014]; [0016-0019]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment);
inputting features of the transcribed text into a classifier that is configured to detect presence of sensitive content of a predetermined category in the input features (Paragraph [0011]; [0014]; [0016-0019]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment);
accessing a plurality of neutral score thresholds associated with the predetermined category of sensitive content whose presence is detected in the input features (Paragraph [0011]; [0014]; [0016-0019]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate);
applying the accessed plurality of neutral score thresholds to the detected neutral emotion score to determine, for the predetermined source, a risk level associated with the predetermined category of sensitive content (Paragraph [0011]; [0014]; [0016-0019]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate);
and performing an action when the risk level for the predetermined category of sensitive content for the predetermined source meets a tolerance threshold associated with a user (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Ashoori discloses a system of detecting inappropriate content or sensitive information in a piece of content. However, Ashoori does not specifically disclose the following claim limitations: inputting the features into a tonal model that is trained to detect a neutral emotion score.
In the same field of endeavor of determining sensitive content and changing the content LI teaches inputting the features into a tonal model that is trained to detect a neutral emotion score (Paragraph [0005-0007]; [0020-0021]; [0037]; Fig. 5, the instant application describes a data processing system that functions to receive a request to detect a tone for a content segment, inputting the content segment into a first machine learning model to detect the tone, obtaining the detected tone as a first output, inputting the content segment into a second ML model for modifying the tone, and obtaining one rephrased content segment. The content may be transmitted without direct user request in some applications to enable automatic notifications of improper tone).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if content contains sensitive information based on a target audience as disclosed by Ashoori with the system of inputting the features into a tonal model that is trained to detect a neutral emotion score as taught by Li (Li [0007]). With the motivation of helping to identify inappropriate or sensitive information in content (Li [0003]).
Claim 2: Modified Ashoori discloses the method as per claim 1. However, Ashoori does not disclose wherein performing the action comprises: transmitting an application programming interface notification to a digital item server to perform valuing for a digital item placement associated with the predetermined source.
In the same field of endeavor of determining sensitive content and changing the content LI teaches wherein performing the action comprises: transmitting an application programming interface notification to a digital item server to perform valuing for a digital item placement associated with the predetermined source (Paragraph [0005-0007]; [0020-0021]; [0037]; Fig. 5, the instant application describes a data processing system that functions to receive a request to detect a tone for a content segment, inputting the content segment into a first machine learning model to detect the tone, obtaining the detected tone as a first output, inputting the content segment into a second ML model for modifying the tone, and obtaining one rephrased content segment. The content may be transmitted without direct user request in some applications to enable automatic notifications of improper tone).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if content contains sensitive information based on a target audience as disclosed by Ashoori with the system of inputting the features into a tonal model that is trained to detect a neutral emotion score as taught by Li (Li [0007]). With the motivation of helping to identify inappropriate or sensitive information in content (Li [0003]).
Claim 3: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses wherein performing the action comprises: recommending the predetermined source as a candidate for a digital item placement to the user (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim 7: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses further comprising: applying a topic model to the transcribed text to identify one or more of a plurality of tags associated with a taxonomy of content categories; aggregating the identified one or more of the plurality of tags across multiple instances of transcribed text associated with the predetermined source; and out putting the aggregated tags associated with the predetermined source to the user (Paragraph [0019-0020] a segment determined to have inappropriate content can be flagged. For example, the segment can be tagged. Segments of the media content can be tagged as sensitive or non-sensitive based on the audience profile and the results of the classification).
Claim 8: Modified Ashoori discloses the method as per claim 7. Ashoori further discloses wherein performing the action comprises: determining whether the aggregated tags include one or more tags specified by the user; and removing the predetermined source from a list of candidates for a digital item placement (Paragraph [0019-0020] a segment determined to have inappropriate content can be flagged. For example, the segment can be tagged. Segments of the media content can be tagged as sensitive or non-sensitive based on the audience profile and the results of the classification).
Claim 9: Modified Ashoori discloses the method as per claim 7. Ashoori further discloses wherein the topic model is configured to identify tags having a similarity to the transcribed text higher than a threshold similarity as the one or more of the plurality of tags (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim 10: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses wherein the predetermined source is content selected from a group including text content, audio content, and video content (Paragraph [0011]; [0014]; [0016-0019]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment).
Claim 11: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses wherein the classifier is a first machine learning model that is trained based on empirical text samples including text samples labeled to indicate presence of the sensitive content of the predetermined category and text samples labeled to indicate absence of the sensitive content of the predetermined category (Paragraph [0011]; [0014]; [0016-0019]; [0021-0022]; [0030]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. Training of the model can be done ahead of time. Once the models are trained the generation of the content can happen in real time. The generated text script of the sensitive content can be automatically generated using AI technology trained on historical data and ana analysis of context);
Claim 12: Modified Ashoori discloses the method as per claim 1. However, Ashoori does not disclose wherein the tonal model is a second machine learning model trained based on empirical text samples including text samples labeled to indicate presence of one or more of a plurality of human emotions and a labeled score for each, one of the plurality of human emotions being a neutral emotion.
In the same field of endeavor of determining sensitive content and changing the content LI teaches wherein the tonal model is a second machine learning model trained based on empirical text samples including text samples labeled to indicate presence of one or more of a plurality of human emotions and a labeled score for each, one of the plurality of human emotions being a neutral emotion (Paragraph [0005-0007]; [0020-0021]; [0037]; Fig. 5, the instant application describes a data processing system that functions to receive a request to detect a tone for a content segment, inputting the content segment into a first machine learning model to detect the tone, obtaining the detected tone as a first output, inputting the content segment into a second ML model for modifying the tone, and obtaining one rephrased content segment. The content may be transmitted without direct user request in some applications to enable automatic notifications of improper tone).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if content contains sensitive information based on a target audience as disclosed by Ashoori with the system of inputting the features into a tonal model that is trained to detect a neutral emotion score as taught by Li (Li [0007]). With the motivation of helping to identify inappropriate or sensitive information in content (Li [0003]).
Claim 13: Modified Ashoori discloses the method as per claim 12. However, Ashoori does not disclose wherein the neutral emotion score is based on a predicted probability of the input features expressing the neutral emotion.
In the same field of endeavor of determining sensitive content and changing the content LI teaches wherein the neutral emotion score is based on a predicted probability of the input features expressing the neutral emotion (Paragraph [0005-0007]; [0020-0021]; [0037]; Fig. 5, the instant application describes a data processing system that functions to receive a request to detect a tone for a content segment, inputting the content segment into a first machine learning model to detect the tone, obtaining the detected tone as a first output, inputting the content segment into a second ML model for modifying the tone, and obtaining one rephrased content segment. The content may be transmitted without direct user request in some applications to enable automatic notifications of improper tone).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if content contains sensitive information based on a target audience as disclosed by Ashoori with the system of inputting the features into a tonal model that is trained to detect a neutral emotion score as taught by Li (Li [0007]). With the motivation of helping to identify inappropriate or sensitive information in content (Li [0003]).
Claim 14: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses wherein the predetermined category of sensitive content is one of a plurality of predetermined categories of sensitive content, and wherein the classifier is one of a plurality of classifiers configured to respectively detect the presence of sensitive content of a corresponding one of the plurality of predetermined categories (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim 15: Modified Ashoori discloses the method as per claim 14. Ashoori further discloses further comprising: receiving, from the user at a user interface, input to selectively set a tolerance threshold for one or more of the plurality of predetermined categories of sensitive content; wherein the action is performed when the risk level for each of the plurality of predetermined categories of sensitive content for the predetermined source meets respective tolerance thresholds selectively set by the user (Paragraph [0019-0020]; [0040] the method may dynamically replace the audio and video depending upon the target audience settings and can be applicable for any type of streaming and/or multimedia files. The audience profile may indicate a threshold score for an inappropriate content for that particular audience).
Claim 16: Modified Ashoori discloses the method as per claim 14. Ashoori further discloses further comprising: periodically redetermining for the predetermined source a risk level associated with each of the plurality of predetermined categories of sensitive content (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim 17: Modified Ashoori discloses the method as per claim 14. Ashoori further discloses wherein at least some of the plurality of predetermined categories of sensitive content are custom categories defined by the user (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim 18: Modified Ashoori discloses the method as per claim 1. Ashoori further discloses wherein the determined risk level is one of a low risk level, a medium risk level, and a high-risk level (Paragraph [0011]; [0014]; [0016-0019]; [0024]; Fig. 1, a system, method, and technique which generates an alternative audience appropriate multi-media segment in real time. The system may use generative models and deep learning technology to generate such content. The system flags sensitive, inappropriate segments of content, for example, taking into account the targeted audience. The system performs an analysis to identify the level of sensitivity of the content for a given audience. Multimedia content is received, which is triggered to be played on a device can be intercepted. The content can be handled as a whole or by segments. The audio script and/or imagery of a segment of a content is extracted. The extracted segments including text and/or images can be transmitted to a classifier. One or more classifiers are applied to the script and/or image of the segment to detect and flag a segment with potential sensitive content. A text classifier or classification algorithm can be used to classifier inappropriate language or audio in the segment. A classifier may output a score indicating how close the content is for appropriateness corresponding to the requested profile. A segment content which has a score that exceeds a general threshold score can be flagged as being inappropriate. An embodiment of an implementation can include replacing the offensive or sensitive word with an accepted word).
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ashoori (US 2021/0275928) in view of Li (US 2021/0397793) further in view of Huang (US 2016/0098738).
Claim 4: Modified Ashoori discloses the method as per claim 1. However, Ashoori does not disclose further comprising: identifying an entity associated with the predetermined source; and obtaining public sentiment scores for the entity over time.
In the same field of endeavor of evaluating content Huang teaches further comprising: identifying an entity associated with the predetermined source; and obtaining public sentiment scores for the entity over time (Paragraph [0007-0008]; [0013]; [0022-0023]; Fig. 1, the invention is to provide an issue manage style internet public opinion information evaluation management system and method. It is able to collect and structure information, It is able to combine text analysis and community action analysis. It is able to continuously observe the development of a particular issue and trend. It is also able to monitor public opinion information. A public opinion information collection module. Each issue, based on each public opinion information evaluation score, would decrease its influence as time goes by. The results is reported to the issue process and public opinion information tracking information display module).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of monitoring content for the presence of sensitive information for a target audience as disclosed by Ashoori with the system of further comprising: identifying an entity associated with the predetermined source; and obtaining public sentiment scores for the entity over time as taught by Huang (Huang [00022]). With the motivation of helping to monitor a target audience’s opinions towards an entity and the content being presented (Huang [0004]).
Claim 5: Modified Ashoori discloses the method as per claim 4. However, Ashoori does not disclose wherein performing the action comprises presenting the public sentiment scores over time to the user.
In the same field of endeavor of evaluating content Huang teaches wherein performing the action comprises presenting the public sentiment scores over time to the user (Paragraph [0007-0008]; [0013]; [0022-0023]; Fig. 1, the invention is to provide an issue manage style internet public opinion information evaluation management system and method. It is able to collect and structure information, It is able to combine text analysis and community action analysis. It is able to continuously observe the development of a particular issue and trend. It is also able to monitor public opinion information. A public opinion information collection module. Each issue, based on each public opinion information evaluation score, would decrease its influence as time goes by. The results is reported to the issue process and public opinion information tracking information display module).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of monitoring content for the presence of sensitive information for a target audience as disclosed by Ashoori with the system of further comprising: identifying an entity associated with the predetermined source; and obtaining public sentiment scores for the entity over time as taught by Huang (Huang [00022]). With the motivation of helping to monitor a target audience’s opinions towards an entity and the content being presented (Huang [0004]).
Claim 6: Modified Ashoori discloses the method as per claim 4. However, Ashoori does not disclose wherein performing the action comprises: determining whether the public sentiment scores over time meet a predetermined condition; and transmitting an application programming interface notification to a digital item server to perform valuing for a digital item placement associated with the predetermined source in response to the determination.
In the same field of endeavor of evaluating content Huang teaches wherein performing the action comprises: determining whether the public sentiment scores over time meet a predetermined condition; and transmitting an application programming interface notification to a digital item server to perform valuing for a digital item placement associated with the predetermined source in response to the determination (Paragraph [0007-0008]; [0013]; [0022-0023]; Fig. 1, the invention is to provide an issue manage style internet public opinion information evaluation management system and method. It is able to collect and structure information, It is able to combine text analysis and community action analysis. It is able to continuously observe the development of a particular issue and trend. It is also able to monitor public opinion information. A public opinion information collection module. Each issue, based on each public opinion information evaluation score, would decrease its influence as time goes by. The results is reported to the issue process and public opinion information tracking information display module).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of monitoring content for the presence of sensitive information for a target audience as disclosed by Ashoori with the system of further comprising: identifying an entity associated with the predetermined source; and obtaining public sentiment scores for the entity over time as taught by Huang (Huang [00022]). With the motivation of helping to monitor a target audience’s opinions towards an entity and the content being presented (Huang [0004]).
Therefore, claim 1-20 are rejected under U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Antonatos (US 2019/0236305) Detecting unauthorized use of sensitive information in content communicated over a network.
Mejia (US 2023/0259979) Facilitating identification of sensitive content.
Ganguly (US 2022/0084045) Computer application content detection and feedback.
Kwok (US 2024/0061952) Identifying sensitive data using redacted data.
Newton (US 2016/0012454) Database systems for measuring impact on the internet.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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