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 submission filed December 10, 2024. Claims 1-20 are pending.
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 1-17 of U.S. Patent No. 12,198,700 in view of Yurick et al (US Patent Application Publication No. 2007/0011012) and Kumar (US Patent Application Publication No. 2023/0252980). The claims of 12,198,700 fail to teach the metadata associated with the obtained media includes a plot description of the obtained media. Yurick teaches utilizing topic or theme information [para 0036; 0064]. Additionally, Kumar teaches a machine learning model receives as input values various attributes associated with the conversations, such as: participants, semantic content, metadata associated with the conversation [para 0027], where a received conversation messages includes metadata and message [0082] and determining content of the message [para 0061], where the semantic recognition model identifies: words, how the words combine to form phrases, how the phrases combine to form sentences, and the meanings associated with the words, phrases, and sentences, where the conversation attributes provide a form of description. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the concept of utilizing metadata, topic/theme information, and semantic content information in recognizing content, as suggested by Yurick and Kumar, in the system of 12,198,700, to assist in accurately determining and recognizing the words for the captioning system, to improve the recognition accuracy of spoken speech, thereby ensuring the users are reading the correct content of the multi-media.
Claims of 18/975,589
Claims of 12,198,700
1. A method comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and the metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a plot description of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech as a basis to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
2. The method of claim 1, further comprising: extracting from the obtained media, the audio representing speech, wherein using the audio representing speech as the basis to generate the speech text comprises using the extracted audio representing speech as the basis to generate the speech text.
3. The method of claim 1, wherein using at least the audio representing speech as the basis to generate the speech text comprises: using at least (i) the audio representing speech and (ii) mouth movement depictions of the video, as the basis to generate the speech text.
4. The method of claim 1, wherein using at least the audio representing speech to determine starting and ending time points of the speech comprises: providing to a trained model, at least audio data for the audio representing speech; and responsive to the providing, receiving from the trained model, starting and ending time points determined by the trained model.
5. The method of claim 1, wherein the obtained media further includes audio representing a sound effect, and wherein the method further comprises: using at least the audio representing the sound effect as a basis to generate sound effect description text.
6. The method of claim 5, wherein using at least the audio representing the sound effect as the basis to generate the sound effect description text comprises: providing to a trained model, at least audio data for the audio representing the sound effect; and responsive to the providing, receiving from the trained model, sound effect description text generated by the trained model.
7. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein associating the generated closed-captioning data with the obtained media comprises: storing the generated closed-captioning data as metadata associated with the obtained media.
8. The method of claim 7, further comprising: transmitting to a media-presentation device, the obtained media and the generated closed-captioning data as metadata of the media, wherein the media-presentation device is configured to (i) receive the transmitted media and closed-captioning data as metadata of the media, and (ii) present the received media with closed-captioning text overlaid thereon in accordance with the received closed-captioning data.
9. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated subtitle data, and wherein associating the generated subtitle data with the obtained media comprises: modifying the obtained media by overlaying on it subtitle text in accordance with the subtitle data.
10. The method of claim 1, further comprising: transmitting to a media-presentation device, the modified media, wherein the media-presentation device is configured to receive and output for presentation the modified media.
11. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media with closed-captioning text overlaid thereon in accordance with the closed-captioning data.
12. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated subtitle data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media modified to include subtitle text in accordance with the subtitle data.
13. The method of claim 1, further comprising: determining that the speech was spoken by a character associated with a given region within the video; and based on the determining, outputting the speech text in or near that given region.
14. The method of claim 1, further comprising: determining that the speech was spoken by a given character; and based on the determining, outputting the speech text in a font color associated with the given character.
15. A computing system comprising a processor and a non-transitory computer-readable storage medium having stored thereon program instructions that upon execution by the processor, cause the computing system to perform a set of acts comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and the metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a plot description of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
16. The computing system of claim 15, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein associating the generated closed-captioning data with the obtained media comprises: storing the generated closed-captioning data as metadata associated with the obtained media.
17. The computing system of claim 15, further comprising: transmitting to a media-presentation device, the obtained media and the generated closed-captioning data as metadata of the media, wherein the media-presentation device is configured to (i) receive the transmitted media and closed-captioning data as metadata of the media, and (ii) present the received media with closed-captioning text overlaid thereon in accordance with the received closed-captioning data.
18. The computing system of claim 15, further comprising: determining that the speech was spoken by a character associated with a given region within the video; and based on the determining, outputting the speech text in or near that given region.
19. The computing system of claim 15, further comprising: determining that the speech was spoken by a given character; and based on the determining, outputting the speech text in a font color associated with the given character.
20. A non-transitory computer-readable storage medium having stored thereon program instructions that upon execution by a processor, cause a computing system to perform a set of acts comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a plot description of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
1. A method comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and the metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a rating of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech as a basis to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
2. The method of claim 1, further comprising: extracting from the obtained media, the audio representing speech, wherein using the audio representing speech as the basis to generate the speech text comprises using the extracted audio representing speech as the basis to generate the speech text.
3. The method of claim 1, wherein using at least the audio representing speech as the basis to generate the speech text comprises: using at least (i) the audio representing speech and (ii) mouth movement depictions of the video, as the basis to generate the speech text.
4. The method of claim 1, wherein using at least the audio representing speech to determine starting and ending time points of the speech comprises: providing to a trained model, at least audio data for the audio representing speech; and responsive to the providing, receiving from the trained model, starting and ending time points determined by the trained model.
5. The method of claim 1, wherein the obtained media further includes audio representing a sound effect, and wherein the method further comprises: using at least the audio representing the sound effect as a basis to generate sound effect description text.
6. The method of claim 5, wherein using at least the audio representing the sound effect as the basis to generate the sound effect description text comprises: providing to a trained model, at least audio data for the audio representing the sound effect; and responsive to the providing, receiving from the trained model, sound effect description text generated by the trained model.
7. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein associating the generated closed-captioning data with the obtained media comprises: storing the generated closed-captioning data as metadata associated with the obtained media.
8. The method of claim 7, further comprising: transmitting to a media-presentation device, the obtained media and the generated closed-captioning data as metadata of the media, wherein the media-presentation device is configured to (i) receive the transmitted media and closed-captioning data as metadata of the media, and (ii) present the received media with closed-captioning text overlaid thereon in accordance with the received closed-captioning data.
9. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated subtitle data, and wherein associating the generated subtitle data with the obtained media comprises: modifying the obtained media by overlaying on it subtitle text in accordance with the subtitle data.
10. The method of claim 9, further comprising: transmitting to a media-presentation device, the modified media, wherein the media-presentation device is configured to receive and output for presentation the modified media.
11. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media with closed-captioning text overlaid thereon in accordance with the closed-captioning data.
12. The method of claim 1, wherein the generated closed-captioning or subtitle data is generated subtitle data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media modified to include subtitle text in accordance with the subtitle data.
13. The method of claim 1, further comprising: determining that the speech was spoken by a character associated with a given region within the video; and based on the determining, outputting the speech text in or near that given region.
14. The method of claim 1, further comprising: determining that the speech was spoken by a given character; and based on the determining, outputting the speech text in a font color associated with the given character.
15. A computing system comprising a processor and a non-transitory computer-readable storage medium having stored thereon program instructions that upon execution by the processor, cause the computing system to perform a set of acts comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and the metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a rating of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
16. The computing system of claim 15, wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein associating the generated closed-captioning data with the obtained media comprises: storing the generated closed-captioning data as metadata associated with the obtained media.
17. A non-transitory computer-readable storage medium having stored thereon program instructions that upon execution by a processor, cause a computing system to perform a set of acts comprising: obtaining media, wherein the obtained media includes (i) audio representing speech, (ii) video, and (iii) metadata associated with the obtained media; using at least the audio representing speech as a basis to generate speech text, wherein using at least the audio representing speech as the basis to generate the speech text comprises: (i) providing to a trained model, at least audio data for the audio representing speech and metadata associated with the obtained media, wherein the metadata associated with the obtained media includes a rating of the obtained media; and (ii) responsive to the providing, receiving from the trained model, generated speech text generated by the trained model; using at least the audio representing speech to determine starting and ending time points of the speech; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yurick et al (US Patent Application Publication No. 2007/0011012), hereinafter, Yurick, in view of Kumar (US Patent Application Publication No. 2023/0252980).
Yurick discloses methods, system, and apparatus for facilitating captioning for multi-media content.
Regarding claims 1, 15, and 20 Yurick teaches a [Figs 1-3] method, system, a processor and computer readable storage medium having instructions executed by the processor to achieve the features of: obtaining media [para 0028 – multi-media data is received], wherein the obtained media includes (i) audio representing speech and (ii) video [para 0025 – multi-media data combination of audio and video]; using at least the audio representing speech as a basis to generate speech text [para 0025 – automatic captioning engine using general speech recognition, field-specific speech recognition, speaker-specific speech recognition, timestamping algorithms, and optical character recognition; 0029]; providing to a trained model [para 0030 – speech recognition engine trained for field specific recognition; 0031 – trained speaker-specific speech recognition engines], at least audio data for the audio representing speech; and responsive to the providing, receiving from the trained model, generated speech text generated by the trained model [para 0030 – speech recognition engine trained for field specific recognition used to accurately recognize field terms; 0031 – trained speaker-specific speech recognition engines produces captions for that speaker]. Yurick teaches utilizing topic or theme information [para 0036; 0064]. Yurick fails to teach the obtained media includes metadata or that the metadata is provided to the trained model. In a similar field of endeavor, Kumar teaches a system for conversation processing, where received conversation messages includes metadata and message content [para 0082] and the metadata is input to a recognition model for context in determining content of the message [para 0061], where the semantic recognition model identifies: words, how the words combine to form phrases, how the phrases combine to form sentences, and the meanings associated with the words, phrases, and sentences. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the concept of utilizing metadata information in recognizing content, as suggested by Kumar, in the captioning system of Yurick, to assist in determining and recognizing the words for the captioning system, to improve the recognition accuracy of spoken speech, thereby ensuring the users are reading the correct content of the multi-media.
Yurick fails to specifically teach using at least the audio representing speech as a basis to determine staffing and ending time points of the speech, Yurick teaches generating timestamps and durations of words, where one having ordinary skill in the art at the time of the invention would have recognized the advantages of utilizing the timestamp and duration data to determine the starting and ending time points of speech, for the purpose of making it easier for the corrections operator to easily access minute audio portions to correct or verify the captions and recognized text data [para 0033 – timestamps used to link recognized words to the media and can provide timestamp durations]; and using at least the generated speech text and the determined starting and ending time points of the speech to (i) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and [para 0025 – automatic captioning engine using general speech recognition, field-specific speech recognition, speaker-specific speech recognition, timestamping algorithms, and optical character recognition]; (ii) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time- aligned with the video based on the determined starting and ending time points of the speech [para 0033 – timestamps used to link recognized words to the media and can provide timestamp durations; 0043 – caption text with timestamps can be created; 0049 – text-to-speech alignment –associate each word with playback location].
Regarding claim 2, the combination of Yurick and Kumar teaches extracting from the obtained media, the audio representing speech, wherein using the audio representing speech as the basis to generate the speech text comprises using the extracted audio representing speech as the basis to generate the speech text [Yurick para 0029-0031 — speech recognition, field- specific speech recognition; speaker-specific speech recognition].
Regarding claim 3, the combination of Yurick and Kumar teaches wherein using at least the audio representing speech as the basis to generate the speech text comprises: using at least (i) the audio representing speech and (ii) mouth movement depictions of the video, as the basis to generate the speech text [Yurick para 0044 – face recognition and lip movement can be used to create caption segments; para 0046].
Regarding claim 4, the combination of Yurick and Kumar teaches wherein using at least the audio representing speech to determine starting and ending time points of the speech comprises: providing to a trained model, at least audio data for the audio representing speech [Yurick para 0043 – timestamps created by the speech recognition engine]; and responsive to the providing, receiving from the trained model, starting and ending time points determined by the trained model [Yurick para 0043 – timestamps created by the speech recognition engine – where the trained model is a part of the speech recognition engine (para 0030-0031)].
Regarding claim 5, the combination of Yurick and Kumar teaches wherein the obtained media further includes audio representing a sound effect, and wherein the method further comprises: using at least the audio representing the sound effect as a basis to generate sound effect description text [Yurick para 0040 – auto-formatting where laughter and crying are included in the caption]
Regarding claim 6, the combination of Yurick and Kumar teaches wherein using at least the audio representing the sound effect as the basis to generate the sound effect description text comprises: providing to a trained model, at least audio data for the audio representing the sound effect [Yurick para 0042 – acoustic classification]; and responsive to the providing, receiving from the trained model, sound effect description text generated by the trained model [Yurick para 0045 – acoustic classification – detects laughter or music playing; para 0040 – laughter/crying including in caption].
Regarding claims 7 and 16, the combination of Yurick and Kumar teaches wherein the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein associating the generated closed-captioning data with the obtained media comprises: storing the generated closed-captioning data as metadata associated with the obtained media [Yurick para 0027 – caption publication engine made available with caption and subtitle format possibilities for a plurality of media players; 0041-0042 – output caption words, metadata –timestamps, formatting info].
Regarding claims 8 and 17, the combination of Yurick and Kumar teaches transmitting to a media-presentation device [Yurick para 0065 – published to a multi-media search tool], the obtained media and the generated closed- captioning data as metadata of the media [Yurick para 0041], wherein the media-presentation device is configured to (i) receive the transmitted media and closed-captioning data as metadata of the media, and (ii) present the received media with closed-captioning text overlaid thereon in accordance with the received closed-captioning data [Yurick para 0066 – users use search tool to view and access captions in the form of closed captions; para 0027 – caption data encoded in line 21 of vertical blanking interval of the video signal].
Regarding claim 9, the combination of Yurick and Kumar teaches wherein the generated closed-captioning or subtitle data is generated subtitle data, and wherein associating the generated subtitle data with the obtained media comprises: modifying the obtained media by overlaying on it subtitle text in accordance with the subtitle data [Yurick para 0027 – use of SCC file format to produce subtitles].
Regarding claim 10, the combination of Yurick and Kumar teaches transmitting to a media-presentation device, the modified media, wherein the media- presentation device is configured to receive and output for presentation the modified media [Yurick para 0027 – caption publication engine made available with caption and subtitle format possibilities for a plurality of media players; 0041-0042 – output caption words, metadata –timestamps, formatting info].
Regarding claim 11, the combination of Yurick and Kumar teaches the generated closed-captioning or subtitle data is generated closed-captioning data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media with closed-captioning text overlaid thereon in accordance with the closed-captioning data [Yurick para 0066 – users use search tool to view and access captions in the form of closed captions; para 0027 – caption data encoded in line 21 of vertical blanking interval of the video signal].
Regarding claim 12, the combination of Yurick and Kumar teaches the generated closed-captioning or subtitle data is generated subtitle data, and wherein the method further comprises: outputting for presentation, by a media presentation device, media modified to include subtitle text in accordance with the subtitle data [Yurick para 0027 – caption publication engine made available with caption and subtitle format possibilities for a plurality of media players; use of SCC file format to produce subtitles].
Regarding claims 13 and 18, the combination of Yurick and Kumar teaches determining that the speech was spoken by a character associated with a given region within the video; and based on the determining, outputting the speech text in or near that given region [Yurick para 0046 – captions are placed near the speaking person].
Regarding claims 14 and 19, the combination of Yurick and Kumar teaches determining that the speech was spoken by a given character [Yurick 0031 – trained speaker-specific speech recognition engines produces captions for that speaker]; and based on the determining, outputting the speech text in a font color associated with the given character [Yurick para 0010 – captions formatting in color].
Conclusion
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
Prosecution Insights