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 .
This Office Action is in response to the amendment filed August 21, 2025. Claims 1, 7, 10-13, 15-17, and 19-20 have been amended. Claims 2, 8-9, 14, and 18 have been cancelled. Claims 21-25 have been added. Claims 1, 3-7, 10-13, 15-17, and 19-25 remain pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 17, 2025 is being considered by the examiner.
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.
Claims 1, 5, 7, 10-11, 13, 15-17, and 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Garcia et al (US Patent Application Publication No. 2010/0138772), hereinafter Garcia.
Regarding claims 1, 10 and 16, Schmidt teaches a method, apparatus and system comprising [(Abstract; para [0115] - product for filtering data in an audio stream; Audible input 101 may, for example, include conversations on a telephone call, speech dictated into a recording device, conversations using video chat)]: one or more tangible non-transitory media operably connectable to the one or more processors and storing instructions that, when, executed cause the one or more processors to perform operations comprising [(para [0081]-[0083]- computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device)]: sending from a user computer to one or more viewer computers a stream including audio content [(para [0015-0016] – conversation using video chat; [0038]-[0039]- The audible input 101 is received by the least one channel, 102-1, 102- 2, and 102-N. A channel can be any type of transmission medium, such as a wire, cable, optical fiber, etc. In some cases, audible input 101 may be recorded on a single channel 102, and in others, one or more audible Input may be recorded on separate channels 102.)]; receiving from a first viewer computer a first audio comment [(para [0016], [0038]-[0039]- The audible input 101 is received by the least one channel, 102-1, 102- 2, and 102-N. A channel can be any type of transmission medium, such as a wire, cable, optical fiber, etc. In some cases, audible input 101 may be recorded on a single channel 102, and in others, one or more audible Input may be recorded on separate channels 102.)]; converting the audio comment to text [(para [0017]-[0018]- sensitive speech blocking system 100, illustrated in FIG. 1, decodes received speech data 105 using one or more speech-to-text engines 120-1, 120-2, 120-3, 120-4, 120-N and sensitive word detection engine 130)]; processing the first text comment with a machine learning (ML) model to determine whether the text contains first content [(para [0020], [0030], [0041] - system takes the decoded language and runs it through sensitive word-detection engine 130. In some embodiments, sensitive word detection engine 130 compares words and/or phrases provided by speech-to-text engine 120 to a sensitive word list 132. The received text can have. various markers identified by one of the various models (e.g., model 125 and model 135) in speech-to-text engine 120; all available speech-to-text engines 120 may be used to convert speech data 105 into text at step 220. At step 220, different aspects of the speech are analyzed by and may be tagged for use by sensitive word detection engine 130. For example, particular segments of data can be tagged for dialect, accent, identity of a person, slang, etc; system uses. machine learning to analyze words or phrases entered into sensitive word list 132)]; determining the text does not contain the first content and adding the first audio comment to the audio content of the livestream [para 0012 – sensitive information is not provided or detected , the system does not provide blocking of user hearing the audio input]. Schmidt does not teach adding the first text comment to a list of text comments presented in a user interface on a display of the livestream computer, the user interface including a respective selectable icon for each text comment in the list of text comments and receiving an indication of user selection of a selectable icon for the text comment. However, Garcia teaches a content control module 156 that allows the eventcaster to decide whether to share communications as part of a broadcast (livestream) [para 0076]; providing the list of viewer comments on the user interface [Fig 22] where the GUI allows for the hiding or showing of messages with a "hide" or "show" command [Fig 22; para 0076] to allows the eventcaster to decide whether to share those communications as part of a broadcast [para 0076; 0086-0088]. It would have been obvious to one of ordinary skill in the art to combine the audio content analysis and filtering taught by Schmidt with the streaming content comment moderation taught by Garcia since doing so would prevent the presentation of offensive and sensitive content presentation in livestreams and real-time event updates and to allow an eventcaster to disseminate their content in real-time to their viewers while allowing viewers to participate in a moderated manner by the submission of real-time commentary, as suggested by Garcia [para 005].
Regarding claims 5, 11, and 19, Schmidt teaches the first characteristic comprises one or more of profanity, hate speech, personally-identifiable information [(para [0015]- blocking sensitive information (e.g., social security numbers, routing numbers, account numbers, health information, and/or credit card information))].
Regarding claim 7, the combination of Schmidt and Garcia teaches the first content to be identified by the ML model is defined by user input received by the livestream computer [Schmidt at para 0011 – system becomes more accurate as it gains more information by dealing with different users; (para [0030]-[0031}- stored in sensitive word list 132); Garcia’s word filter].
Regarding claim 13, Schmidt teaches the first characteristic comprises at least one non-verbal audio feature [(para [0033]- Ambient noises, such as noises from wind or automobiles, are examples of possible interfering features. If an acoustic model 125 is trained to recognize and filter out this noise, sounds)].
Regarding claim 15, Schmidt teaches the audio is spoken by a viewer of the livestream [(para [001 1]- the system can block the listener from hearing the speaker disclose the sensitive information)].
Regarding claim 17, Schmidt teaches the audio comment is spoken by a viewer of the Livestream [(para [0011]- the system can block the listener from hearing the speaker disclose the sensitive information)].
Regarding claims 21 and 25, the combination of Schmidt and Garcia teaches after adding the first audio comment to the audio content of the livestream : receiving an indication of user selection of a second respective selectable icon for a second text comment of the list of text comments [Fig 22; para 0076; Garcia’s GUI with user selection of comments]; and adding a second audio comment corresponding to the second text comment to the audio content of the livestream [Fig 22; para 0076; Garcia’s GUI with user selection of comments; Schmidt’s allowed audio provided to the listener].
Regarding claim 22, the combination of Schmidt and Garcia teaches the second audio comment was received before the first audio comment was received [[Fig 22; para 0076; Garcia’s GUI with user selection of comments – where the user selects which comments are provided and a non-desired comment may be received prior to a desired comment].
Regarding claim 23, the combination of Schmidt and Garcia teaches receiving, from a viewer computer of the one or more viewer computers, a second audio comment [(para [0016], [0038]-[0039]- The audible input 101 is received by the least one channel, 102-1, 102- 2, and 102-N. A channel can be any type of transmission medium, such as a wire, cable, optical fiber, etc. In some cases, audible input 101 may be recorded on a single channel 102, and in others, one or more audible Input may be recorded on separate channels 102.) – multiple users and channels provide for second or subsequent comments]; converting the second audio comment to a second text comment [(para [0017]-[0018]- sensitive speech blocking system 100, illustrated in FIG. 1, decodes received speech data 105 using one or more speech-to-text engines 120-1, 120-2, 120-3, 120-4, 120-N and sensitive word detection engine 130)]; processing the second text comment with the machine learning model to determine whether the second text comment contains first content [(para [0020], [0030], [0041] - system takes the decoded language and runs it through sensitive word-detection engine 130. In some embodiments, sensitive word detection engine 130 compares words and/or phrases provided by speech-to-text engine 120 to a sensitive word list 132. The received text can have. various markers identified by one of the various models (e.g., model 125 and model 135) in speech-to-text engine 120; all available speech-to-text engines 120 may be used to convert speech data 105 into text at step 220. At step 220, different aspects of the speech are analyzed by and may be tagged for use by sensitive word detection engine 130. For example, particular segments of data can be tagged for dialect, accent, identity of a person, slang, etc; system uses. machine learning to analyze words or phrases entered into sensitive word list 132)]; responsive to determining that the second text comment does contain the first content [Schmidt’s sensitive speech detection and blocking; Garcia’s word filter]: generating a filtered second text comment by removing the first content from the second text comment [[Fig 22; para 0076; Garcia’s GUI with user selection of comments; word filter and comment banning]; and adding the filtered second text comment to the list of text comments presented in the user interface [Fig 22 – list of viewer comments]; and responsive to receiving an indication of user selection of a second respective selectable icon for the filtered second text comment [Garcia’s providing the list of viewer comments on the user interface [Fig 22] where the GUI allows for the hiding or showing of messages with a "hide" or "show" command [Fig 22; para 0076]: generating a filtered second audio comment by removing audio corresponding to the removed first content from the second audio comment [Schmidt’s sensitive speech detection and blocking; Garcia’s word filter]; and adding the filtered second audio comment to the audio content of the livestream [Fig 22; para 0076; Garcia’s GUI with user selection of comments; Schmidt’s allowed audio provided to the listener].
Regarding claim 24, the combination of Schmidt and Garcia teaches the list of text comments include a plurality of text comments that do not contain the first content, the plurality of text comments being presented in the user interface in a sequence corresponding to an order in which corresponding audio comments were received [Garcia’s providing the list of viewer comments on the user interface [Fig 22] where the GUI allows for the hiding or showing of messages with a "hide" or "show" command [Fig 22; para 0076].
Claims 4, 6, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Garcia and in view of Maycock (US Patent Application Publication No. 2018/0349502).
Regarding claim 4, neither Schmidt nor Garcia teaches the first content comprises hate speech. However, Maycock teaches the first content comprises hate speech [(para [0049]- bullying)]. It would have been obvious to one of ordinary skill in the art to modify the combination of Schmidt and Garcia with those of Maycock since doing so would prevent the presentation of offensive and sensitive content presentation in livestreams. .
Regarding claim 6, neither Schmidt nor Garcia teaches the first content comprises a topic different from a topic being discussed in the livestream. However, Maycock teaches the first content comprises a topic different from a topic being discussed in the livestream [(para [0021], [0091]- the user may select a group of words associated with a topic that the user would like to filter. Groups of words are associated with topics, subjects or themes. For example, a parent may wish that their young child does not see topics that require parental guidance or “adult” content while using an iPad; For example, “Syria” 210, “North Korea” 220, and “War” 230 are listed as topics to filter. The words listed in FIG. 6A may be predetermined and saved in the filter application or they are created by the user through input at the graphical user interface. In other examples, the user may enter these terms to be used as filter criteria, or may enter additional topics or groups to be added under “Add Groups’ 208.)]. It would have been obvious to one of ordinary skill in the art to modify the combination of Schmidt and Garcia with those of Maycock since doing so would prevent the presentation of offensive and sensitive content presentation in livestreams.
Regarding claim 12, Schmidt does not teach the first characteristic comprises off-topic content. However, Maycock teaches the first characteristic comprises off-topic content [(para [0021], [0091]- the user may select a group of words associated with a topic that the user would like to filter. Groups of words are associated with topics, subjects or themes. For example, a parent may wish that their young child does not see topics that require parental guidance or “adult” content while using an iPad; For example, “Syria" 210, “North Korea” 220, and “War” 230 are listed as topics to filter. The words listed in FIG. 6A may be predetermined and saved in the filter application or they are created by the user through input at the graphical user interface. In other examples, the user may enter these terms to be used as filter criteria, or may enter additional topics or groups to be added under “Add Groups” 208.)]. It would have been obvious to one of ordinary skill in the art to combine the audio content analysis and filtering taught by Schmidt with the topic based filtering taught by Maycock since doing so would prevent the presentation of offensive and sensitive content presentation in livestreams.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in in view of Garcia and further in view of Kim et al (US Patent Application Publication No. 2019/0028362), hereinafter Kim.
Regarding claim 20, Schmidt does not teach the content changes segment to segment in the livestream. However, Kim teaches the livestream changes segment to segment in the livestream [(para [0018]-[0019]- list of comments registered to a section of the content corresponding to the highlighted section in response to the user selecting the highlighted section or the displayed comment)]. It would have been obvious to one of ordinary skill in the art to combine the audio content analysis and filtering taught by Schmidt with those of Kim since doing so would contextually present the comments.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Garcia and further in view of Kulavik et al (US Patent Application Publication No. 2015/0117662), hereinafter Kulavik.
Regarding claim 3, neither Schmidt nor Garcia teaches the first content comprises profanity. However, Kulavik teaches the first content comprises profanity [(para [0019]-[0020]- profanity filter)]. It would have been obvious to one of ordinary skill in the art to modify the combination of Schmidt and Garcia with those of Kulavik since doing so would prevent the presentation of offensive and sensitive content presentation in livestreams.
Response to Arguments
Applicant’s arguments with respect to claims 1, 3-7, 10-13, 15-17, and 19-25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659