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 .
Claim Interpretation
Claim 20 cites a computer program production comprising a computer-readable storage medium. The examiner interprets the computer readable storage medium to not include transitory media as para. [0026] of instant application cites “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it repeats information given in the title. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huffman et al. (US 2023/0321546 A1 – hereinafter Huffman) and further in view of in view of Rose et al. (US 2022/0139383 A1 – hereinafter Rose).
In regards to claim 1, Huffman discloses a computer-implement method, comprising:
processing real-time communication data; (Huffman para. [0122 and 0125] teaches receiving an electronic speech signal of a verbal communication comprising a first portion at a first time and processes the electronic speech signal using artificial intelligence.)
predicting, based on selected data included in the real-time communications data, that subsequent data to be received in the communication data will include objectionable content; (Huffman para. [0125] teaches processing a first portion of speech to predict a subsequent portion, wherein the first portion is the real-time communication data and subsequent data is the next time window or subsequent portion. Huffman para. [0067] teaches that the target speech includes objectionable content such as profanity, epithets, insults, and sensitive personal information.)
executing a user interface (UI) action to mitigate an impact of subsequent data including the objectionable content. (Huffman para. [0120] teaches after processing the first portion of speech data, it produces the redacted verbal communication signal. Also para. [0126] teaches redacting target speech subsequent time windows wherein it cites “Step 350 includes taking action in response to the prediction of the target speech. For example, in some embodiments, at step 350 the artificial intelligence 240 is configured to, and does, redact said target speech from said electronic speech signal 140 during said time window to produce a redacted verbal communication signal 150, 150b.” and “The target speech includes a pre-defined set of terms to be redacted.” Huffman para. [0111 and 0118] teaches a user interface to specify objectional language and other parameters of the system. Also para. [0190] teaches an output interface that provides the redact communication data.)
However, Huffman does not explicitly discloses wherein the communication data is associated with a virtual meeting and execute an action in the virtual meeting application.
Rose discloses communication data associated with a virtual meeting (Rose para. [0014-0018] teaches receiving communication data in a virtual meeting wherein it cites “In some embodiments, a chat management system may receive a data stream (e.g., audio data, video data, metadata, etc.) associated with an application, such as from a host device during an online video conference, video stream (or playback), game stream, and/or the like.”; processing the real time communication data wherein it cites “According to some examples, as a user is speaking or presenting content during an online video conference, the data stream may be processed to generate a transcript of the audio and/or information relating to the video.”; and filtering the communication if objectionable content is founds wherein it cites “Additionally or alternatively, the neural network may output data to indicate whether the comment is offensive harassing profane or otherwise inappropriate for the topic of discussion.” And “deleted/removed such that the comment is not displayed, de-emphasized, or otherwise filtered. In some embodiments, a filter may be applied only to a portion of the comment. For example, an irrelevant portion of a comment may be deleted or obscured, while the relevant portion of the comment may be displayed within the chat feature. As a result, irrelevant comments may be blocked from being displayed to the users, which may allow the users to focus on the material being discussed in the current instance of the application.”) and execute an action in the virtual meeting application. (Rose fig. 2 shows a user interface (UI) during a virtual meeting and communication data, this also taught in para. [0037] wherein it cites “Turning to FIG. 2, FIG. 2 is an example user interface 200 for a video conference application…”. and para. [0038] teaches blocking or filtering comments that are offensive or not on topic from appearing in the UI application while posting comments that are relevant, which is executing an action in the virtual meeting application.)
It would have been obvious to one of ordinary skill in the art before the earlier effective filing date of the claimed invention to modify the teachings of the Huffman with that of the Rose in order allow for using communication data and performing actions in virtual meetings as both reference deal with detecting utterances or communication data. It would provide the benefit of keeping virtual meetings orderly by redacting or blocking speech that is prohibited thus keeping meetings focused.
In regards to claim 10, it is the computer system embodiment of claim 1 with similar limitations and thus is rejected using the same reasoning found in claim 1. The only difference being claim 10 cites a computer system with one or more processors and memory, which is disclosed figure 2 of Huffman wherein the computer system is element 200, the processor is element 230 and the memory is element 220.
Claims 2-4 and 6, 11-13, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huffman et al. (US 2023/0321546 A1 – hereinafter Huffman) in view of Rose et al. (US 2022/0139383 A1 – hereinafter Rose) in view of Nouri et al. (US 2023/0110274 A1 – hereinafter Nouri) and further in view of Schmidt et al. (US 2020/0005773 A1 – hereinafter Schmidt).
In regards to claim 2, Huffman in view of Rose discloses the computer-implemented method of claim 1, further comprising:
generating a remark prediction model comprises a linguistic model; and (Huffman para. [0089] teaches using a phoneme extraction model to create an ordered sequence of phoneme probability distributions. Huffman para. [0090] teaches using a 4-gram world language model that uses the phoneme sequences to predict the next likely words or sequence of the words to be used.
Huffman para. [0018] teaches the prediction model using previous conversations, player history information and other context data. Thus Huffman discloses a prediction model includes a linguistic model use for subsequent remark/communication data.)
performing the predicting on the selected data using the prediction model. (Huffman para. [0091] teaches using predictions by the model to determine the recently spoken words and predict words that will be spoken. Also Huffman para. [0125] also teaches using an AI to process first spoken words to generate/predict subsequent spoken words.)
However Huffman in view of Rose does not explicitly disclose generating a custom remark prediction model for each meeting participant, wherein each custom remark prediction model comprises both a linguistic model and a hidden Markov model; and performing the predicting using the custom remark prediction model corresponding to the participant that sent the selected data.
Nouri discloses generating a custom model for each participant of a virtual meeting. (Nouri para. [0026] teaches that the models may include individual models for participants of the virtual meeting and the model is trained using meeting data associated with participants including prior meeting data. Nouri para. [0046 and 0060] teaches determining the context of host or participant using individual participation model that are specific to the host or a particular participant. Thus, Nouri teaches generating a custom model for each meeting participant and using that model for data corresponding to that individual.)
However Huffman in view of Rose in view of Nouri does not explicitly disclose that the customer remark prediction model comprises a hidden Markov model.
Schmidt discloses a remark prediction model comprising a linguistic model and a hidden Markov model. (Schmidt para. [0011] teaches training a system using real conversation streams to identify or predict when sensitive information is about to spoken, it also teaches a customizing the model to individual speakers and each person may have different keywords and patterns that indicate sensitive information is about to spoken. Para. [0018] teaches a speech-to-text engine that includes a acoustic model and a language mode, which is also shown in fig. 1. Para. [0035] teaches language model (element 135 fig. 1) provides linguistic context, estimates probability of words appearing next, and may be a hidden Markov model. It also teaches the hidden Markov model uses observed words and phrases to predict unobserved words and phrases that are likely to be spoken.
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Huffman in view of Rose with that of the individual models of Nouri and the Hidden Markov Models of the Schmidt as all the reference deal with speech detection. Huffman uses a language model to predict soon to be spoken words/phrases, including objectionable data, Nouri teaches applying an individual model associated with a participate and Schmidt teaches using a hidden Markov model in the language model to predict subsequent/unobserved words and phrases. These combined provide the benefit of improving the accuracy of prediction next words/phrases by accounting for different linguistic patterns, keyworks, and speech histories of respective participants.
In regards to claim 3, Huffman in view of Rose in view of Nouri in view of Schmidt disclose the computer-implemented method of claim 2, further comprising: performing corpus linguistics analysis on historical communications data from each meeting participant to generate the linguistic model of the custom remark prediction model, wherein the linguistic model indicates respective linguistic patterns for each meeting participant. (Huffman para. [0090 and 0118] teaches using a 4-gram word language model to analyze phonemes and generate/predict subsequent sequences of spoken words and using previous speech. Nouri para. [0026 and 0046] teaches individual participant models may be trained using meeting data associated with the respective participant wherein the model is specific to the person. Nouri para. [0060] teaches each individual participant model may learn a particular participants usage pattern based on meeting data associated with that user. Schmidt para. [0011] teaches training the prediction model using real conversation streams such as historical call data and each model is customized based on different keyword patterns. Also, Schmidt para. [00035-0037] teaches the language model using linguistic data and statistical models to predict next words/phrases. Thus, the reference teach using linguistic patterns of each meeting participant is disclosed.)
In regards to claim 4, Huffman in view of Rose in view of Nouri in view of Schmidt discloses the computer-implemented method of claim 3, further comprising: performing discrete sequence analysis on the historical communications data from each meeting participant to generate the hidden Markov model of the custom remark prediction model, wherein the hidden Markov model includes transition data and emission data determined based on the linguistic model associated with each meeting participant. (Schmidt para. [0035] teaches a language model that estimates the probability of a word appearing in a particular linguistic context and using a HMM to predict unobserved (subsequent) words/phrases on the observed sequence of words. Because the HMM generates probabilistic prediction from observed linguistic sequences with a probability, it implicitly includes the transition data data and emission data to predict next probability of unobserved word/phrase based observed words/phrases as both transition matrix and emission are inherent to HMM.)
In regards to claim 6, Huffman in view of Rose in view of Nouri in view of Schmidt disclose the computer-implemented method of claim 4, wherein the transition data further comprises a transition matrix that includes user-specific transition probabilities based on the respective linguistic patterns for each meeting participant. (Nouri para. [0026, 0046, and 0060] teaches individual participant models trained using the respective participant’s prior communication and usage patterns. Schmidt para. [0035] teaches a participant customized language model that uses HMM to estimate word probabilities and predict subsequent words/phrases while Schmidt para. [0037] teaches training using words, sentences, and grammatical conventions. This teaches user specification models based on linguistic patterns of the participant. Additionally transition matrix are inherent to HMM.)
In regards to claim 11, it the computer system embodiment of claim 2 with similar limitations and thus is rejected using the same reasoning found in claim 2.
In regards to claim 12, it the computer system embodiment of claim 3 with similar limitations and thus is rejected using the same reasoning found in claim 3.
In regards to claim 13, it the computer system embodiment of claim 4 with similar limitations and thus is rejected using the same reasoning found in claim 4.
In regards to claim 15, it the computer system embodiment of claim 6 with similar limitations and thus is rejected using the same reasoning found in claim 6.
Claims 7-9 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Huffman et al. (US 2023/0321546 A1 – hereinafter Huffman) in view of Rose et al. (US 2022/0139383 A1 – hereinafter Rose) in view of Nouri et al. (US 2023/0110274 A1 – hereinafter Nouri).
In regards to claim 7, Huffman in view of Rose disclose the computer-implemented method of claim 1, but does not explicitly disclose wherein processing the real-time communications data associated with the virtual meeting application further comprises: determining a meeting participant that sent the selected data; and identifying, in a model repository, a custom remark prediction model associated with the meeting participant, wherein the custom remark prediction model is generated based on historical communications data that is specific to the meeting participant.
Nouri discloses determining a meeting participant that sent the selected data; (Nouri para. [0006] teaches determining/identifying individuals in received meeting data including audio and utterances.) and identifying, in a model repository, a custom remark prediction model associated with the meeting participant, (Nouri para. [0029] teaches a meeting server containing participant-indication models including a set of individual participant models and para. [0037] teaches using the individual model specific to the identified host or participant.) wherein the custom remark prediction model is generated based on historical communications data that is specific to the meeting participant. (Nouri para. [0026] teaches training individual participant models using meeting data for respective participants and data from prior meetings and conversations.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date to modify the teachings of Huffman predictive speech modeling with Nouri participant specific modeling as both reference deal with speech/utterance detection and the benefit of doing so it allow for better accuracy in prediction by using models that are specific to the person instead of a general model.
In regards to claim 8, Huffman in view of Rose in view of Nouri disclose the computer-implemented method of claim 7, further comprising: applying the custom remark prediction model associated with the meeting participant to the selected data associated with the meeting participant to perform the predicting. (Nouri para. [0037] teaches applying receiving meeting data to the individual participant model specific to the identified host or participant. Huffman para. [0090-0091 and 0125] teaches applying a language model to received speech data to predict likely subsequent spoken words.)
In regards to claim 9, Huffman in view of Rose disclose the computer-implemented method of claim 1, but does not explicitly disclose further comprising: identifying a plurality of meeting participants communicating using the virtual meeting application, wherein the real-time communications data includes respective utterances from the plurality of meeting participants; selecting a custom remark prediction model for each meeting participant of the plurality of meeting participants, wherein the custom remark prediction model selected for each meeting participant is generated based on historical communications data that is specific to each meeting participant; and applying the custom remark prediction model to analyze the respective utterances from each meeting participant of the plurality of meeting participants.
Nouri discloses identifying a plurality of meeting participants communicating using the virtual meeting application, wherein the real-time communications data includes respective utterances from the plurality of meeting participants; (Nouri para. [0006] teaches identifying individual participants in received meeting data and analyzing their utterances.) selecting a custom remark prediction model for each meeting participant of the plurality of meeting participants, wherein the custom remark prediction model selected for each meeting participant is generated based on historical communications data that is specific to each meeting participant; (Nouri para. [0037 and 0044] teaches selecting from a set of individual models that the model specific to a particular host or participant; Nouri para. [0026] teaches training the individual models using data for respective participants and conversations from prior meetings.) and applying the custom remark prediction model to analyze the respective utterances from each meeting participant of the plurality of meeting participants. (Nouri para. [0037] teaches applying received meeting data to the individual model specific to the identified participant and using natural language processing to analyze words spoken by the host and participants.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date to modify the teachings of Huffman predictive speech modeling with Nouri participant specific modeling as both reference deal with speech/utterance detection and the benefit of doing so it allow for better accuracy in prediction by using models that are specific to the person instead of a general model.
In regards to claim 16, it is the computer system embodiment of claim 7 with similar limitations and thus is rejected using the same reasoning found in claim 7.
In regards to claim 17, it is the computer system embodiment of claim 8 with similar limitations and thus is rejected using the same reasoning found in claim 8.
In regards to claim 18, it is the computer system embodiment of claim 9 with similar limitations and thus is rejected using the same reasoning found in claim 9.
Allowable Subject Matter
Claims 5 and 14 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/ Primary Examiner, Art Unit 2127
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