DETAILED ACTION
This Office Action is in response to Applicants’ Application filed on June 6, 2025. Claims 1-20 are pending and presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/16/2026 and 2/12/2026 has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: the status of the Cross-Reference to Related Applications in on page 1, lines 5-15 needs to be updated.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-28 of U.S. Patent No. 12,341,619. Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is fully disclosed in U.S. Pat. No. 12,341,619 and would be covered by any patent granted since the U.S. Pat. No. 12,341,619 and the instant application are claiming common subject matter.
Instant Application 19/231,045
1.A computer-implemented method for moderating online voice content, comprising:
providing a multi-stage voice content analysis system including a pre-moderator stage
with a toxicity scorer configured to generate a toxicity score for a speech segment, the toxicity score being determined as a function of a platform-specific content policy;
generating the toxicity score for the speech segment; and
providing the speech segment to a moderator based on the toxicity score.
2.The method of claim 1, further comprising:
receiving feedback from the moderator indicating whether the speech segment violates the platform content policy.
3.The method of claim 1, further comprising:
setting a toxicity score threshold for automatic moderation action; and
automatically moderating the user when the toxicity score exceeds the threshold.
4.The method of claim 3, further comprising:
providing to the moderator only those speech segments with toxicity scores below the
threshold.
5.The method of claim 4, further comprising:
updating the toxicity score of the provided speech segments based on the moderator
feedback;
determining an accuracy metric for the scoring; and adjusting the automatic moderation threshold based on the accuracy metric.
6.The method of claim 1, wherein the toxicity scorer comprises a machine learning model
trained using a dataset of labeled toxic and non-toxic speech examples.
7.The method of claim 6, wherein the dataset includes labeled data for one or more of:
adult language, audio assault, violent speech, racial hate speech, and gender-based hate speech.
8.The method of claim 7, wherein the toxicity scorer outputs a separate toxicity score for
each of the toxicity categories.
9.The method of claim 7, wherein the toxicity scorer outputs a single aggregated toxicity
score across the toxicity categories.
10.The method of claim 6, wherein the dataset further includes labeled data for emotion,
user demographic characteristics, and contextual information.
11.A multi-stage voice content analysis system, comprising:
a first stage configured to receive speech input and identify first-stage positive and
negative speech content; a pre-moderator stage configured to: receive and analyze at least a portion of the first-stage positive and negative speech content categorize the received content as pre-moderator-stage positive or negative speech content,
generate a toxicity score for pre-moderator-stage positive speech content, and
update a training database using scoring results and moderator feedback; and
a user interface configured to display the toxicity score and associated speech content to a moderator.
12.The system of claim 11, further comprising: an automatic action threshold setter configured to define a toxicity score threshold for automatic moderation actions.
13.The system of claim 12, further comprising: a moderator feedback module configured to receive moderator input confirming or rejecting the toxicity of the displayed speech content.
14.The system of claim 11, wherein the pre-moderator stage is configured to forward to the moderator only speech segments having toxicity scores below the automatic action threshold.
15.The system of claim 14, wherein the threshold setter dynamically adjusts the threshold
based on scoring accuracy derived from moderator feedback.
16.A computer-implemented method for policy-weighted scoring of toxic voice content, comprising: generating raw toxicity scores for a plurality of toxicity categories for a given speech segment; applying platform-specific weighting factors to each raw toxicity score to generate weighted toxicity scores;
determining the maximum weighted toxicity score and its corresponding category; and
providing the speech segment to a moderator along with the maximum weighted score
and its associated category.
17.The method of claim 16, wherein the plurality of toxicity categories comprises one or more of: adult language, audio assault, violent speech, racial/cultural hate speech, gender/sexual hate speech, sexual harassment, misrepresentation, manipulation, and bullying.
18.The method of claim 16, wherein the platform-specific weighting factors are received via manual user input.
19. The method of claim 16, wherein the platform-specific weighting factors are derived from user responses to a content policy configuration questionnaire.
20. The method of claim 16, further comprising: receiving moderator feedback regarding whether the speech segment is correctly identified as toxic and whether the assigned category is appropriate.
U.S. Pat. No. 12/341,619
1. A computer-implemented method for displaying toxicity information within a timeline window of a graphical user interface, the method comprising: calculating a toxicity score for a plurality of speech segments over the course of an audio chat session; displaying a detailed session timeline showing a plurality of users in at least a portion of an audio chat session in a user interface, the detailed session timeline including a time axis having one or more toxicity indicators that represent a severity of toxicity and correspond to a given user.
2. The method of claim 1, wherein each of the plurality of users has a time axis simultaneously displayed in the timeline.
3. The method of claim 2, wherein each of the independent time axes is a horizontal axis.
4. The method of claim 2, wherein each of the independent time axes displays a speech indicator that represents a time when the corresponding user is speaking.
5. The method of claim 2, wherein each of the independent time axes has a session start time indicator and a session end time indicator for the user.
6. The method of claim 2, wherein each of the independent time axes has a user indicator.
7. The method of claim 1, wherein the toxicity indicator includes a toxicity score for the speech segment.
8. The method of claim 1, wherein the toxicity indicators provide a heatmap for toxicity scores.
9. The method of claim 1, wherein a moderator may select a user view for a particular user.
10. The method of claim 1, wherein the length of the one or more vertical toxicity indicators is a function of a toxicity score.
11. The method of claim 1, wherein selecting a toxicity indicator displays a transcript and audio file for the associated speech segment within the user interface.
12. The method of claim 1, further comprising display an entire session timeline window showing the entire audio chat session.
13. The method of claim 12, further comprising selecting a portion for the detailed session timeline window from the entire session timeline window; and displaying the selected portion in the detailed session timeline window.
14. The method of claim 13, further comprising selecting a different portion for the detailed session timeline window from the entire session timeline window; and displaying the different portion in the detailed session timeline window.
15. The method of claim 1, further comprising displaying a session details window, the session details window including a speaker identification, a number of speaker offenses for the speaker, a max toxicity score for the speaker, and a classification of offenses for the speaker during the session.
16. The method of claim 15, wherein a moderator selection of the speaker identification causes the graphical user interface to display a user view with a window with showing all of the selected user's activity.
17. The method of claim 16, wherein the user view displays the length of each session for the user, the maximum toxicity for the user for each session, and the offense category for each session.
18. The method of claim 1, further comprising selected a portion of the detailed timeline view.
19. The method of claim 1, further comprising displaying toxicity indicators for toxicity that meets a toxicity score threshold.
20. The method of claim 1, further comprising receiving an input in the detailed session timeline window, the input corresponding to an interval of time; and displaying a transcription of toxic speech that meets a toxicity score threshold for the corresponding interval of time.
21. The method of claim 20, further comprising displaying a link to audio for the toxic speech for the corresponding interval of time.
22. The method of claim 1, further comprising displaying toxicity indicators for a plurality of users.
23. The method of claim 1, wherein the horizontal time axis has a thickness along its length that is a function of an amount of speech.
24. The method of claim 1, further comprising display a moderator action window.
25. The method of claim 24, wherein the moderator action window includes options for muting, suspending, or banning the speaker.
26. A multi-stage content analysis system comprising: a first stage trained using a database having training data with positive and/or negative examples of training content for the first stage, the first stage configured to: receive speech content, analyze the speech content to categorize the speech content as having first-stage positive speech content and/or first-stage negative speech content; a subsequent stage configured to receive at least a portion, but less than all, of the first-stage negative speech content, the subsequent stage further configured to analyze the first-stage positive speech content to categorize the first-stage positive speech content as having subsequent-stage positive speech content and/or subsequent-stage negative speech content, the subsequent stage further configured to update the database using the subsequent-stage positive speech content and/or the subsequent-stage negative speech content, the subsequent stage including a toxicity scorer configured to provide a toxicity score for subsequent-stage positive speech content; a user interface configured to display the toxicity score for the subsequent-stage positive speech content.
27. The multi-stage content analysis system of claim 26, wherein the user interface is configured to display the toxicity score for multiple users within a timeline.
28. The multi-stage content analysis system of claim 27, wherein each of the toxicity scores for the multiple users includes a toxicity indicator and an associated user indicator within the timeline.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,996,117. Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is fully disclosed in U.S. Pat. No. 11,996,117and would be covered by any patent granted since the U.S. Pat. No. 11,996,117 and the instant application are claiming common subject matter.
Instant Application 19/231,045
1.A computer-implemented method for moderating online voice content, comprising:
providing a multi-stage voice content analysis system including a pre-moderator stage
with a toxicity scorer configured to generate a toxicity score for a speech segment, the toxicity score being determined as a function of a platform-specific content policy;
generating the toxicity score for the speech segment; and
providing the speech segment to a moderator based on the toxicity score.
2.The method of claim 1, further comprising:
receiving feedback from the moderator indicating whether the speech segment violates the platform content policy.
3.The method of claim 1, further comprising:
setting a toxicity score threshold for automatic moderation action; and
automatically moderating the user when the toxicity score exceeds the threshold.
4.The method of claim 3, further comprising:
providing to the moderator only those speech segments with toxicity scores below the
threshold.
5.The method of claim 4, further comprising:
updating the toxicity score of the provided speech segments based on the moderator
feedback;
determining an accuracy metric for the scoring; and adjusting the automatic moderation threshold based on the accuracy metric.
6.The method of claim 1, wherein the toxicity scorer comprises a machine learning model
trained using a dataset of labeled toxic and non-toxic speech examples.
7.The method of claim 6, wherein the dataset includes labeled data for one or more of:
adult language, audio assault, violent speech, racial hate speech, and gender-based hate speech.
8.The method of claim 7, wherein the toxicity scorer outputs a separate toxicity score for
each of the toxicity categories.
9.The method of claim 7, wherein the toxicity scorer outputs a single aggregated toxicity
score across the toxicity categories.
10.The method of claim 6, wherein the dataset further includes labeled data for emotion,
user demographic characteristics, and contextual information.
11.A multi-stage voice content analysis system, comprising:
a first stage configured to receive speech input and identify first-stage positive and
negative speech content; a pre-moderator stage configured to: receive and analyze at least a portion of the first-stage positive and negative speech content categorize the received content as pre-moderator-stage positive or negative speech content,
generate a toxicity score for pre-moderator-stage positive speech content, and
update a training database using scoring results and moderator feedback; and
a user interface configured to display the toxicity score and associated speech content to a moderator.
12.The system of claim 11, further comprising: an automatic action threshold setter configured to define a toxicity score threshold for automatic moderation actions.
13.The system of claim 12, further comprising: a moderator feedback module configured to receive moderator input confirming or rejecting the toxicity of the displayed speech content.
14.The system of claim 11, wherein the pre-moderator stage is configured to forward to the moderator only speech segments having toxicity scores below the automatic action threshold.
15.The system of claim 14, wherein the threshold setter dynamically adjusts the threshold
based on scoring accuracy derived from moderator feedback.
16.A computer-implemented method for policy-weighted scoring of toxic voice content, comprising: generating raw toxicity scores for a plurality of toxicity categories for a given speech segment; applying platform-specific weighting factors to each raw toxicity score to generate weighted toxicity scores;
determining the maximum weighted toxicity score and its corresponding category; and
providing the speech segment to a moderator along with the maximum weighted score
and its associated category.
17.The method of claim 16, wherein the plurality of toxicity categories comprises one or more of: adult language, audio assault, violent speech, racial/cultural hate speech, gender/sexual hate speech, sexual harassment, misrepresentation, manipulation, and bullying.
18.The method of claim 16, wherein the platform-specific weighting factors are received via manual user input.
19. The method of claim 16, wherein the platform-specific weighting factors are derived from user responses to a content policy configuration questionnaire.
20. The method of claim 16, further comprising: receiving moderator feedback regarding whether the speech segment is correctly identified as toxic and whether the assigned category is appropriate.
U.S. Pat. No. 11,996,117
1. A toxicity moderation system, the system comprising an input configured to receive speech from a speaker; a multi-stage toxicity machine learning system including a first stage and a second stage, wherein the first stage is trained to analyze the received speech to determine whether a toxicity level of the speech meets a toxicity threshold, the first stage configured to filter-through, to the second stage, speech that meets the toxicity threshold, and further configured to filter-out speech that does not meet the toxicity threshold.
2. The toxicity moderation system of claim 1, wherein the first stage is trained using a database having training data with positive and/or negative examples of training content for the first stage.
3. The toxicity moderation system of claim 2, wherein the first stage is trained using a feedback process comprising: receiving speech content; analyzing the speech content using the first stage to categorize the speech content as having first-stage positive speech content and/or first-stage negative speech content; analyzing the first-stage positive speech content using the second stage to categorize the first-stage positive speech content as having second-stage positive speech content and/or second-stage negative speech content; and updating the database using the second-stage positive speech content and/or the second-stage negative speech content.
4. The toxicity moderation system of claim 3, wherein the first stage discards at least a portion of the first-stage negative speech content.
5. The toxicity moderation system of claim 3, wherein the first stage is trained using the feedback process further comprising: analyzing less than all of the first-stage negative speech content using the second stage to categorize the first-stage negative speech content as having second-stage positive speech content and/or second-stage negative speech content, further updating the database using the second-stage positive speech content and/or the second-stage negative speech content.
6. The toxicity moderation system of claim 1, further comprising a random uploaded configured to upload portions of the speech that did not meet the toxicity threshold to the subsequent stage or a human moderator.
7. The toxicity moderation system of claim 1, further comprising a session context flagger configured to receive an indication that the speaker previously met the toxicity threshold within a pre-determined amount of time, and to: (a) adjust the toxicity threshold, or (b) upload portions of the speech that did not meet the toxicity threshold to the subsequent stage or a human moderator.
8. The toxicity moderation system of claim 1, further comprising a user context analyzer, the user context analyzer configured to adjust the toxicity threshold and/or the toxicity confidence based on the speaker's age, a listener's age, the speaker's geographic region, the speaker's friends list, history of recently interacted listeners, speaker's gameplay time, length of speaker's game, time at beginning of game and end of game, and/or gameplay history.
9. The toxicity moderation system of claim 1, further comprising an emotion analyzer trained to determine an emotion of the speaker.
10. The toxicity moderation system of claim 1, further comprising an age analyzer trained to determine an age of the speaker.
11. The toxicity moderation system of claim 1, further comprising a temporal receptive field configured to divide speech into time segments that can be received by at least one stage.
12. The toxicity moderation system of claim 1, further comprising a speech segmenter configured to divide speech into time segments that can be analyzed by at least one stage.
13. The toxicity moderation system of claim 1, wherein the first stage is more efficient than the second stage.
14. A multi-stage content analysis system comprising: a first stage trained using a database having training data with positive and/or negative examples of training content for the first stage, the first stage configured to: receive speech content, analyze the speech content to categorize the speech content as having first-stage positive speech content and/or first-stage negative speech content; a second stage configured to receive at least a portion, but less than all, of the first-stage negative speech content, the second stage further configured to analyze the first-stage positive speech content to categorize the first-stage positive speech content as having second-stage positive speech content and/or second-stage negative speech content, the second stage further configured to update the database using the second-stage positive speech content and/or the second-stage negative speech content.
15. The multi-stage content analysis system of claim 14, wherein: the second stage is configured to analyze the received first-stage negative speech content to categorize the first-stage negative speech content as having second-stage positive speech content and/or second-stage negative speech content.
16. The multi-stage content analysis system of claim 15, wherein: the second stage is configured to update the database using the second-stage positive speech content and/or the second-stage negative speech content.
17. A method of training a multi-stage content analysis system, the method comprising: providing a multi-stage content analysis system, the system having a first stage and a second stage; training the first stage using a database having training data with positive and/or negative examples of training content for the first stage; receiving speech content; analyzing the speech content using the first stage to categorize the speech content as having first-stage positive speech content and/or first-stage negative speech content; analyzing the first-stage positive speech content using the second stage to categorize the first-stage positive speech content as having second-stage positive speech content and/or second-stage negative speech content; updating the database using the second-stage positive speech content and/or the second-stage negative speech content; discarding at least a portion of the first-stage negative speech content.
18. The method of claim 17, the method comprising: analyzing less than all of the first-stage negative speech content using the second stage to categorize the first-stage negative speech content as having second-stage positive speech content and/or second-stage negative speech content, further updating the database using the second-stage positive speech content and/or the second-stage negative speech content.
19. The method of claim 18, further comprising: using a database having training data with positive and/or negative examples of training content for the first stage; producing first-stage positive determinations (“S1-positive determinations”) associated with a portion of the speech content, and/or first-stage negative determinations (“S1-negative determinations”); analyzing the speech associated with the S1-positive determinations.
20. The method of claim 19, wherein the positive and/or negative examples relate to particular categories of toxicity.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al (U.S. Pat. No. 11,829,717) discloses context-based abusive language detection and responses. A method may include identifying text associated with first video content, and determining that a first word in the text matches a first keyword indicative of abusive language; determining a first label associated with the first word, the first label indicating that the first word is ambiguous; identifying a first sentence of the text, the first sentence including the first word; determining first and second context of the first word and the first sentence and determining, based on the first and second context, using a machine learning model, a second label associated with the first sentence, the second label indicating a probability that the first sentence includes abusive language.
Newman et al (U.S. Pat. No. 11,126,797) discloses methods, systems, and devices for language mapping are described. Some machine learning models may be trained to support multiple languages. However, word embedding alignments may be too general to accurately capture the meaning of certain words when mapping different languages into a single reference vector space. To improve the accuracy of vector mapping, a system may implement a supervised learning layer to refine the cross-lingual alignment of particular vectors corresponding to a vocabulary of interest (e.g., toxic language). This supervised learning layer may be trained using a dictionary of toxic words or phrases across the different supported languages in order to learn how to weight an initial vector alignment to more accurately map the meanings behind insults, threats, or other toxic words or phrases between languages. The vector output from this weighted mapping can be sent to supervised models, trained on the reference vector space, to determine toxicity scores.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LASHONDA T JACOBS-BURTON whose telephone number is (571)272-4004. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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/LASHONDA JACOBS-BURTON/Primary Examiner, Art Unit 2457
ljb
September 2, 2026
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