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 .
35 USC § 101
Claims 1, 11 and 19 are independent claims directed to a determining appropriateness of audio data. Products and Processes fall within statutory categories of invention (Step 1: YES).
The claims are then analyzed to determine whether it is directed to an exception. The claims were considered to evaluate if the invention is drawn to an abstract idea of a mental process or a concept performed in the human mind (including an observation, evaluation, judgment, opinion). In this case, the use of machine learning models and updating audio data cannot be done mentally. (Step 2A, prong one: NO)
Per the current guidelines, since Step 2A, Prong One is NO, Per Pathway B, the claims qualify as eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, 8-9, 19, 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756) in view of Velasco (US 2015/0279359).
Dave et al. shows all of the limitations of the claims except for specifying that the processing is based at least on one or more machine learning models processing the audio data.
In regards to claims 1 and 19,
A system comprising:
one or more processors to:
obtain audio data generated using one or more microphones of a first client device, the audio data representative of user speech; (figure 1, attendees 142a … 142n)
determine one or more words associated with the user speech that are classified as being inappropriate and one or more timestamps associated with the one or more words; (Figures 17 and 18, paragraphs [0228] and [0229], “a bleep audio button 4153 that may allow the moderator 144 to temporarily disable the audio being broadcast from one or more attendees 142” and “For example, moderator 144 may use the bleep audio button 4153 to prevent certain inappropriate content from being broadcast to the other attendees.”
based at least on the one or more words being classified as inappropriate:
determine, based at least on the one or more timestamps, one or more portions of the audio data that are associated with the one or more words; and (Figure 17, timestamps 4143 identify portions of audio data. One of which is flagged by a language filter.)
update the one or more portions of the audio data to generate updated audio data; and send the updated audio data to one or more second client devices. (The “bleeped” out audio is considered to be an updated portion of audio.)
In regards to claims 2 and 21,
wherein the one or more portions of the audio data are updated by at least one of:
removing the one or more words associated with the one or more portions of the audio data;
causing the one or more words associated with the one or more portions of the audio data to be muted;
causing the one or more words associated with the one or more portions of the audio data to be obscured; (bleeped)
causing the one or more words associated with the one or more portions of the audio data to be obfuscated; or
replacing the one or more words associated with the one or more portions of the audio data with one or more second words.
In regards to claims 5 and 22,
wherein the one or more processors are further to generate a report that references at least one of: the one or more portions of the audio data; the one or more words classified as inappropriate; the one or more timestamps associated with the one or more words; or account information associated with the first client device. (paragraph [0068] recites that Analytics subsystem 154 may generate reports based on a multitude of different data sets including text, audio and/or video content.)
In regards to claims 6 and 23,
wherein the one or more machine learning models comprise at least one of:
one or more first machine learning models that generate one or more characters associated with the user speech;
one or more language models that generate the one or more words and the one or more timestamps based at least on the one or more characters; (Figure 17, timestamps 4143 identify portions of audio data.) or
one or more second machine learning models that determine that the one or more words are classified as inappropriate.
In regards to claims 8 and 24,
wherein the one or more words are classified as inappropriate based at least on the at least the one or more words being one of: profane language, abusive language, taunting language, derogatory language, or harassing language. (paragraph [0105])
In regards to claims 9 and 25,
wherein the system is comprised in at least one of: a system for performing real-time streaming; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for performing light transport simulation; a system for performing deep learning operations; a system implemented using an edge device; a system for performing conversational artificial intelligence operations; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (Server 140 and Analytics subsystem 154 are considered to be a data center.)
In regards to claim 20,
wherein the one or more portions of the audio data are updated by at least one of:
removing the one or more words associated with the one or more portions of the audio data;
causing the one or more words associated with the one or more portions of the audio data to be muted;
causing the one or more words associated with the one or more portions of the audio data to be obscured; (bleeped)
causing the one or more words associated with the one or more portions of the audio data to be obfuscated; or
replacing the one or more words associated with the one or more portions of the audio data with one or more second words.
Velasco teaches, paragraph [0054], “Analytics server 1012 performs processing operations on audio such as those described below (referring to FIGS. 6-9), for example to analyze audio according to the invention. Management server 1014 may configure or modify such analysis operations, such as by manual input from a human user received via client user interface 1030, or automated or semi-automated operation such as by rules-based or machine learning operation.”
Based on the teaching of Velasco, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Dave et al. to supplement the manual input with an automated machine learning operation such that the processing of the audio data is based on one or more machine learning models in order to make the processing of the audio data more efficient.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756) and Velasco (US 2015/0279359) in further view of Lavin (US patent 8,554,791).
The combination invention of Dave et al., and Velasco, as applied above, shows all of the limitations of the claims except for specifying that one or more files are hidden when the file directory is shown.
Lavin teaches, column 4, lines 62-67, “Alternatively, a shell or lock filter (not shown) may be provided to hide any files that are stored outside of the fixed directory structure, or to prevent such files from being renamed or deleted. In this way, the file save associations with the predefined or default storage location is maintained, even when the user has previously saved files to another location.
Based on the teaching of Lavin, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dave et al. and Velasco to hide some files when the file directory is shown in order to prevent such files from being renamed or deleted.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756) and Velasco (US 2015/0279359) in further view of Beechum et al. (US PG pub 2014/0114895).
The combination invention of Dave et al., and Velasco, as applied above, shows all of the limitations of the claims except for specifying that the machine learning models are trained using a dictionary.
Beechum et al. teaches, “[0006] Software is often used to filter chat which may be offensive and that may require further scrutiny by a moderator. The software filter most commonly deployed to monitor chat is a Bayesian filter. Bayesian filters work by using straight matching of human created dictionary of tokens to calculate the probability of chat being inappropriate and flagged for further examination by the moderator. These tokens are typically words, phrases or other classifiers.”
Based on the teaching of Beechum et al., it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dave et al. and Velasco to train the machine learning models using a dictionary in order to make the processing of the audio data more efficient.
Claims 11-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756) in view of Velasco (US 2015/0279359) and Shutterstock (NPL).
Dave et al. shows all of the limitations of the claims except for specifying that the processing is based at least on one or more machine learning models processing the audio data and a list of words that are classified as being inappropriate.
In regards to claims 11 and 13,
A method comprising:
obtain audio data generated using one or more microphones of a first client device, the audio data representative of user speech; (figure 1, attendees 142a … 142n)
determine one or more words associated with the user speech that are classified as being inappropriate and one or more timestamps associated with the one or more words; (Figures 17 and 18, paragraphs [0228] and [0229], “a bleep audio button 4153 that may allow the moderator 144 to temporarily disable the audio being broadcast from one or more attendees 142” and “For example, moderator 144 may use the bleep audio button 4153 to prevent certain inappropriate content from being broadcast to the other attendees.”
based at least on the one or more words being classified as inappropriate:
determine, based at least on the one or more timestamps, one or more portions of the audio data that are associated with the one or more words; and (Figure 17, timestamps 4143 identify portions of audio data. One of which is flagged by a language filter.)
update the one or more portions of the audio data to generate updated audio data; and send the updated audio data to one or more second client devices. (The “bleeped” out audio is considered to be an updated portion of audio.)
Each client interaction is considered to be an application (multiple applications)
Velasco teaches, paragraph [0054], “Analytics server 1012 performs processing operations on audio such as those described below (referring to FIGS. 6-9), for example to analyze audio according to the invention. Management server 1014 may configure or modify such analysis operations, such as by manual input from a human user received via client user interface 1030, or automated or semi-automated operation such as by rules-based or machine learning operation.”
Based on the teaching of Velasco, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Dave et al. to supplement the manual input with an automated machine learning operation such that the processing of the audio data is based on one or more machine learning models in order to make the processing of the audio data more efficient.
Shutterstock teaches, List of Dirty, Naughty, Obscene, and Otherwise Bad Words, “This repo, published in 2019, contains a list of words that we use to filter results from our autocomplete server and recommendation engine. The dataset encompasses offensive terms in multiple languages.”
In regards to claim 14, “It is open for contributions, allowing users to add or refine entries, particularly in non-English languages, enhancing its comprehensiveness and applicability across diverse cultural contexts.” (add or refine entries is considered to be adding and removing words.)
Based on the teaching of Shutterstock, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Dave et al. to supplement the manual input with a comprehensiveness list of inappropriate words in order to make the processing of the audio data more efficient.
In regards to claim 12,
wherein the one or more portions of the audio data are updated by at least one of:
removing the one or more words associated with the one or more portions of the audio data;
causing the one or more words associated with the one or more portions of the audio data to be muted;
causing the one or more words associated with the one or more portions of the audio data to be obscured; (bleeped)
causing the one or more words associated with the one or more portions of the audio data to be obfuscated; or
replacing the one or more words associated with the one or more portions of the audio data with one or more second words.
In regards to claim 16,
wherein the one or more processors are further to generate a report that references at least one of: the one or more portions of the audio data; the one or more words classified as inappropriate; the one or more timestamps associated with the one or more words; or account information associated with the first client device. (paragraph [0068] recites that Analytics subsystem 154 may generate reports based on a multitude of different data sets including text, audio and/or video content.)
In regards to claim 18,
the audio data is obtained during a session associated with a gaming application;
and the updating the one or more portions of the audio data to generate the updated
audio data is performed by at least one of:
the first client device;
the second client device; or
one or more servers that are hosting the session associated with the gaming
application. (paragraph [0073], “Similar resources or techniques may be used to personalize the artwork associated with an electronic musical file, or a movie, or digital objects associated with a video game or social networking virtual world, by means of signature and/or dedication as described.”)
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756), Velasco (US 2015/0279359) and Shutterstock in further view of Rodriguez et al. (US PG pub 2013/0150117).
The combination invention of Dave et al., Velasco, and Shutterstock, as applied above for claim 11, but also includes claim 1, shows all of the limitations of the claims except for specifying a transcript.
Rodriguez et al. teaches, paragraphs [0040] and [0047], producing final transcripts of talk radio and work meeting in order to retrieve what was said at a later date.
Based on the teaching of Rodriguez et al., it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dave et al., Velasco, and Shutterstock to produce a final transcripts of talk stream in order to retrieve what was said at a later date.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dave et al. (US PG pub 2018/0351756), Velasco (US 2015/0279359) and Shutterstock in further view of Sundararaman et al. (US Patent 11,120,799).
The combination invention of Dave et al., Velasco, and Shutterstock, as applied above, shows all of the limitations of the claims except for specifying a first and a second machine learning model.
Sundararaman et al. teaches, column 6, lines 14-15, “Additionally, or alternatively, feedback data may be received from customer devices and/or devices associated with the applications. The feedback data may indicate an accuracy of the determinations of policy violations and/or the presence of multiple violative phrases and/or pronouns and/or proper nouns in the content. This feedback data may be utilized to adjust and/or train one or more models utilized by the phrase-management component, the phrase evaluator, the permissive deviation component, and/or the application-management component. Additional details on the various models utilized by some or all of these components are provided below.”
Based on the teaching of Sundararaman et al., it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dave et al., Velasco, and Shutterstock to use multiple machine learning model with many sources of feedback data in order to better adjust and/or train one or more models.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-25 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,121,823. Although the claims at issue are not identical, they are not patentably distinct from each other because the current claims contain similar subject matter to the parent patent with minor rewording.
Conclusion
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/MICHAEL A CUFF/ Primary Examiner, Art Unit 3715
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