DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 5/14/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Statements (IDS) filed on 8/5/2024 and 1/9/2026 have been accepted and considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
Response to Election
Applicants elect Group II with traverse. In the Restriction/Election requirement of 03/27/2026 the Office notes that the restriction improperly separated independent claims 1, 7, and 13 from Groups II and III. However, it was obvious that Group II includes claims 1, 3-4, 7, 9-10, 13, and 15-16, and Group III includes 1, 5-6, 7, 11-12, 13, and 17-18. Thus, the claims 1, 3-4, 7, 9-10, 13, and 15-16 are examined in this office action.
Claim Objection(s)
Claim 15 recites, “The system of claim 1, further comprising: a first video data source configured to supply first video data, the first video data being representative of detected video images of the first user …” The Examiner believes claim 15 should depends on claim 13 rather than claim 1.
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 of this title, 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, 7, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over REECE (US 2021/0264921 A1), and in further view of LUBOLD (US 2022/0044683 A1).
REGARDING CLAIM 1, REECE discloses a system for detecting artificial entrainment, the system comprising a processing system that is configured to:
extract a plurality of first speech-related features (REECE Par 87 – “Acoustic processing component 508 is configured to analyze acoustic features (e.g., tone, pauses, relative volume). Acoustic processing component 508 includes a neural network system, such as a convolutional neural network. In one implementation, acoustic processing component 508 is configured to identify the number of pauses a speaker takes, identify instances of one speaker interrupting another, and/or determine the relative tone/volume of a speaker. For example, acoustic processing component 508 may determine that a user is speaking at an elevated or reduced volume. As another example, acoustic process component 508 may determine the user is speaking at an accelerated rate, or with a modified pitch.”) and a plurality of first lexical-related features from first audio signals generated in response to speech supplied from a first user (REECE Fig. 5; Par 80 – “For example, utterance 502 may include acoustic and video data of a user captured by a camera (e.g., laptop webcam). The text data corresponds to the words spoken in the acoustic data. In the example implementation, the text data is generated based on the acoustic and/or video data, using a transcription service or software package. In alternate implementations, conversation analytics system 400 may be configured to generate the text data by transcribing the acoustic/video data using algorithmic methods (e.g., neural networks).”; Par 67 – “… identify particular significant phrases or word choices (e.g., mm-hmm, yes, yah, oh my god, huh, uhh, etc.), segment turn length, gaps or delays in speaking, sentence length, topic choice, who is choosing the topics, shared knowledge, mistakes or self-corrections, active listening, use of humor, participant biometrics, etc.”);
extract a plurality of second speech-related features and a plurality of second lexical-related features from second audio signals generated in response to speech supplied from a remote source (REECE Figs.4-5; Par 64 – “For example, conversation analytics system 400 may include recording a web-based videoconference between two or more users, capturing acoustic and video data. In the example implementation, block 402 includes recording a multi-participant coaching conversation (e.g., a coaching conversation between a coach and a mentee) ...”; Par 80 – “For example, utterance 502 may include acoustic and video data of a user captured by a camera (e.g., laptop webcam). The text data corresponds to the words spoken in the acoustic data. … to generate the text data by transcribing the acoustic/video data using algorithmic methods (e.g., neural networks).”; Par 67 – “… identify particular significant phrases or word choices (e.g., mm-hmm, yes, yah, oh my god, huh, uhh, etc.), segment turn length, gaps or delays in speaking, sentence length, topic choice, who is choosing the topics, shared knowledge, mistakes or self-corrections, active listening, use of humor, participant biometrics, etc.”);
process the first and second [speech-related] features to determine when the first user and the remote source begin to exhibit vocal entrainment (REECE; Par 161 – “Additional conversation features may be extracted from data modalities (e.g., video, audio, text, etc.). Examples of conversation features from the video data modality 1602 may include smiles, nods, laughter, posture (e.g., slanted, forward, backward, open, closed, expanded, deflated), head position, gestures, etc. Examples of conversation features from the audio data modality 1604 may include listener feedback (e.g., “ah-ha,” “um?”, “uh-huh,” etc.), paralanguage, vocal traffic signals (e.g., “go on,” “um . . . ,” “but!”, etc.), turn length, conversation percentage (e.g., percent of total conversation during which a speaker was active), etc. Examples of conversation features from the text data modality 1610 may include number of topics, topic keywords, repetition of words/phrases, question asking, speaker mimicry, indirect speech, vocabulary convergence, etc. Examples of mixed modality conversation features 1516 can include warmth, intelligence, arousal, engagement, enthusiasm, passion, emotional suppression, conversation repair, question asking, complementing, speech convergence, mimicry, indirect speech, egocentrism, comfort, conflict, dominance, prestige, humor, etc. Conversation features may be automatically generated by a machine learning system (e.g., as shown in FIG. 6), and/or may be manually determined by an annotator (e.g., as shown in FIG. 11).”);
process the first and second lexical-related features to determine when the first user and the remote source begin to exhibit lexical entrainment (REECE Par 161 – “Examples of conversation features from the text data modality 1610 may include number of topics, topic keywords, repetition of words/phrases, question asking, speaker mimicry, indirect speech, vocabulary convergence, etc. Examples of mixed modality conversation features 1516 can include warmth, intelligence, arousal, engagement, enthusiasm, passion, emotional suppression, conversation repair, question asking, complementing, speech convergence, mimicry, indirect speech, egocentrism, comfort, conflict, dominance, prestige, humor, etc. Conversation features may be automatically generated by a machine learning system (e.g., as shown in FIG. 6), and/or may be manually determined by an annotator (e.g., as shown in FIG. 11).”); and
determine, using a plurality of algorithms, metrics, and features, when the vocal entrainment and or the lexical entrainment exhibits artificial speech entrainment, wherein artificial speech entrainment is purposeful manipulation of the speech supplied from the remote source to increase rapport with the first user, decrease rapport with the first user, or keep rapport with the first user neutral (REECE Fig. 6; Par 94 – “In some implementations, conversation analysis indicators 612 further include synthesized conversation features. Synthesized conversation features leverage features from multiple data modalities (from utterance output 512) to generate higher-level features. These higher-level features may not be directly observable in individual utterances. For example, synthesized conversation features can include an engagement score, and/or an active listening score. Synthesized conversation features are further described at FIG. 15.”; Par 104 – “In some implementations, conversation analysis indicators 612 include complex indicators, such as emotional suppression (e.g., neutral affect combined with low genuineness), uncertainty reduction (e.g., reassuring language and visual cues), and nonconscious mimicry (e.g., adopting the speaking style of a second speaker).”; Par 161 – “Examples of mixed modality conversation features 1516 can include warmth, intelligence, arousal, engagement, enthusiasm, passion, emotional suppression, conversation repair, question asking, complementing, speech convergence, mimicry, indirect speech, egocentrism, comfort, conflict, dominance, prestige, humor, etc. Conversation features may be automatically generated by a machine learning system (e.g., as shown in FIG. 6), and/or may be manually determined by an annotator (e.g., as shown in FIG. 11).”; Par 198 – “Conversation score 2202 is an overall rating of a particular conversation. In some implementations, conversation score 2202 is calculated by combining multiple conversation analysis indicators, such as an openness score, an engagement score, and an enthusiasm score. Conversation score 2202 and/or multiple sub-scores are generated by a conversation analysis system, such as the system shown in FIG. 4. More specifically, conversation score 2202 and/or multiple sub-scores (e.g., an engagement score, an ownership score, a goal score, an interruptions score, a “time spent listening” score, an openness score, etc.) are generated by one or more machine learning systems based on identified conversation features (e.g., audio and video from a conversation, facial expressions, voice tone, key phrases, etc.) of the recorded conversation. In various implementations, the user interface 2200 can include indications of such sub-scores. These can be shown as sub-scores for the conversation or as sub-progress scores in each category for the sub-score, generated from combinations of sub-scores in that category from the conversation and previous conversations.”).
REECE does not explicitly teach the [square-bracketed] limitation. In other words, REECE teaches detecting entrainment using lexical data (e.g., detecting vocabulary convergence using lexical features) and detecting speech convergence/mimicry using mixed modality features, but does not explicitly using the [speech-related] features, separated from the lexical and visual features, to determine vocal entrainment.
LUBOLD teaches the [square-bracketed] limitations. LUBOLD discloses a method/system for analyzing multimodal data for determining entrainment comprising:
extract a plurality of first speech-related features (Note REECE already teaches the lexical-related features) from first audio signals generated in response to speech supplied from a first user (LUBOLD Par 30 – “The example process 400 includes processing first audio signals to extract a plurality of first speech-related features (402), and processing first EEG signals to extract a plurality of first brain activity features (404). The example process 400 also includes processing second audio signals to extract a plurality of second speech-related features (406), and processing second EEG signals to extract a plurality of second brain activity features (408).”);
extract a plurality of second speech-related features (Note REECE already teaches the lexical-related features) from second audio signals generated in response to speech supplied from a remote source (LUBOLD Par 30 – “The example process 400 includes processing first audio signals to extract a plurality of first speech-related features (402), and processing first EEG signals to extract a plurality of first brain activity features (404). The example process 400 also includes processing second audio signals to extract a plurality of second speech-related features (406), and processing second EEG signals to extract a plurality of second brain activity features (408).”; Par 7 – “In yet another embodiment, a system for providing real-time feedback of remote collaborative communication between a first user and a second user includes a first microphone, a plurality of first electroencephalogram (EEG) sensors, a second microphone, a plurality of second EEG sensors, and a processing system. The first microphone is configured to receive speech supplied from the first user and, in response thereto, supply first audio signals. The first EEG sensors are disposed on the first user and are configured to supply first EEG signals in response to brain activity of the first user. The second microphone is configured to receive speech supplied from the second user and, in response thereto, supply second audio signals.”);
process the first and second [speech-related features] to determine when the first user and the remote source begin to exhibit vocal entrainment (LUBOLD Par 31 – “The example process 400 includes processing the first and second speech-related features to determine if the speech from the first and second users exhibits positive or negative vocal entrainment (412), and processing the first and second brain activity features to determine if the brain activity of the first and second users is aligned or misaligned (414).”);
(Note REECE already teaches the limitation); and
determine, using a plurality of algorithms, metrics, and features, when the vocal entrainment and or the lexical entrainment exhibits artificial speech entrainment, wherein artificial speech entrainment is purposeful manipulation of the speech supplied from the remote source to increase rapport with the first user, decrease rapport with the first user, or keep rapport with the first user neutral (LUBOLD Par 21 – “Before proceeding further, it is noted that vocal entrainment is a known temporal phenomenon that has been shown to be one of several critical factors that impacts conversational success, including task success, rapport, and trust. Vocal entrainment can be positive, where the speakers are aligning and adapting to one another to become more similar over the course of a conversation, or it can negative, where the opposite is occurring.”; Par 24 – “For example, the processing system 116 may determine if the speech from the first and second users exhibits positive or negative vocal entrainment by evaluating turn-by-turn feature similarity and increasing or decreasing alignment in terms of change over time across multiple speech features. Likewise, the processing system 116 may determine if the brain activity of the first and second users is aligned or misaligned by comparing the brain activity features via autocorrelation, analysis of short sequences, and comparison to established patterns.”).
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 method/system of REECE to include determining a vocal entrainment by processing the speech-related features, as taught by LUBOLD.
One of ordinary skill would have been motivated to include determining a vocal entrainment by processing the speech-related features, in order to accurately determine communication entrainment so that remote communication can be improved (Par 3).
Claim 7 is similar to claim 1; thus, it is rejected under the same rationale.
Regarding Claim 13, REECE in view of LUBOLD discloses a system for detecting artificial entrainment, the system comprising a processing system that is configured to:
a first audio signal source configured to receive speech supplied from a first user and operable, in response thereto, to supply first audio signals (REECE Fig. 5; Fig. 6 – “User 602; Utterance Output 512”; Par 80 – “For example, utterance 502 may include acoustic and video data of a user captured by a camera (e.g., laptop webcam). The text data corresponds to the words spoken in the acoustic data. In the example implementation, the text data is generated based on the acoustic and/or video data, using a transcription service or software package. In alternate implementations, conversation analytics system 400 may be configured to generate the text data by transcribing the acoustic/video data using algorithmic methods (e.g., neural networks).”; Par 67 – “… identify particular significant phrases or word choices (e.g., mm-hmm, yes, yah, oh my god, huh, uhh, etc.), segment turn length, gaps or delays in speaking, sentence length, topic choice, who is choosing the topics, shared knowledge, mistakes or self-corrections, active listening, use of humor, participant biometrics, etc.”);
a second audio signal source configured to receive speech supplied from a remote source and operable, in response thereto, to supply second audio signals (REECE Fig. 5; Fig.6 – “User 604; Utterance Output 606”; Par 80 – “For example, utterance 502 may include acoustic and video data of a user captured by a camera (e.g., laptop webcam). The text data corresponds to the words spoken in the acoustic data. In the example implementation, the text data is generated based on the acoustic and/or video data, using a transcription service or software package. In alternate implementations, conversation analytics system 400 may be configured to generate the text data by transcribing the acoustic/video data using algorithmic methods (e.g., neural networks).”; Par 67 – “… identify particular significant phrases or word choices (e.g., mm-hmm, yes, yah, oh my god, huh, uhh, etc.), segment turn length, gaps or delays in speaking, sentence length, topic choice, who is choosing the topics, shared knowledge, mistakes or self-corrections, active listening, use of humor, participant biometrics, etc.”); and
a processing system coupled to receive the first and second audio signals and configured to: perform the steps of claim 1; thus, it is rejected under the same rationale.
Claims 3-4, 9-10, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over REECE (US 2021/0264921 A1) in view of LUBOLD (US 2022/0044683 A1), and in further view of SCHMIDT (Schmidt RC, Morr S, Fitzpatrick P, Richardson MJ. Measuring the dynamics of interactional synchrony. Journal of Nonverbal Behavior. 2012 Dec;36(4):263-79.).
REGARDING CLAIM 3, REECE in view of LUBOLD discloses the system of claim 1, wherein processing system is further configured to:
extract a plurality of first physical features from first video data supplied from a first video source, the first video data being representative of detected video images of the first user (REECE Par 67 – “… physical reactions or movements (e.g., eye gaze directions, participant postures, participant gestures, participant head positions, laughter, nodding, facial expressions, etc.)”; Par 101 – “For example, engagement scores may be influenced by the number of questions asked and the length of statements made. Enthusiasm scores may be influenced by changes in pitch, changes in voice volume, and excited facial expressions. Attention scores may be defined by gaze detection (e.g., on-screen gaze, eye contact gaze) and body pose detection from the video data.”; Par 112 – “For example, the video data may be labeled with facial expressions (e.g., smiling, grimacing, crying, nodding) and/or emotional labels (happy, sad, aggressive, surprised, disappointed). These labels may further each have confidence scores, indicating the relative confidence of the video processing part in that particular label (e.g., decimal score between zero and one).”; Par 113 – “Additionally or alternatively, the video processing part may be configured to identify poses, hand gestures, and/or body language in the video data. For example, the frequency with which a user performs hand gestures may be determined. The relative arm position of the user may also be determined.”);
extract a plurality of second physical features from remote video data supplied from a remote video source, the remote video data being representative of detected video images of the remote source (REECE Figs.4-5; Par 64 – “For example, conversation analytics system 400 may include recording a web-based videoconference between two or more users, capturing acoustic and video data. In the example implementation, block 402 includes recording a multi-participant coaching conversation (e.g., a coaching conversation between a coach and a mentee) ...”; Par 67 – “… physical reactions or movements (e.g., eye gaze directions, participant postures, participant gestures, participant head positions, laughter, nodding, facial expressions, etc.)”; Par 101 – “For example, engagement scores may be influenced by the number of questions asked and the length of statements made. Enthusiasm scores may be influenced by changes in pitch, changes in voice volume, and excited facial expressions. Attention scores may be defined by gaze detection (e.g., on-screen gaze, eye contact gaze) and body pose detection from the video data.”; Par 112 – “For example, the video data may be labeled with facial expressions (e.g., smiling, grimacing, crying, nodding) and/or emotional labels (happy, sad, aggressive, surprised, disappointed). These labels may further each have confidence scores, indicating the relative confidence of the video processing part in that particular label (e.g., decimal score between zero and one).”; Par 113 – “Additionally or alternatively, the video processing part may be configured to identify poses, hand gestures, and/or body language in the video data. For example, the frequency with which a user performs hand gestures may be determined. The relative arm position of the user may also be determined.”);
process the first and second physical features to determine when the first user and the remote source begin to exhibit [physical entrainment] (REECE Par 168 – “Conversation synthesis ML system 1844 holistically can analyze features from one or more data modalities (e.g., audio, video, and text) to generate higher-level synthesized conversation features, which may not be directly observable in individual utterances. For example, conversation synthesis ML system 1844 may generate an active listening score based on the acoustic and video data modalities. As another example, conversation synthesis ML system 1844 may generate an engagement metric based on data modalities including biometrics and video. Additionally, conversation synthesis ML system 1844 can leverage previously generated synthesized conversation features to generate higher level (e.g., 3rd order, 4th order, etc.) conversation features.”; Par 173 – “At block 1910, process 1900 extracts conversation features from the data modalities using algorithms and/or machine learning applicable to the structure of each data modality. For example, process 1900 can apply gaze tracking and facial expression recognition algorithms to video data;”;); and
determine, using the plurality of algorithms, metrics, and features, when the physical entrainment exhibits artificial physical entrainment, wherein artificial physical entrainment is purposeful manipulation of the second physical features supplied from the remote source to increase rapport with the first user, decrease rapport with the first user, or keep rapport with the first user neutral (REECE Fig. 6; Par 94 – “In some implementations, conversation analysis indicators 612 further include synthesized conversation features. Synthesized conversation features leverage features from multiple data modalities (from utterance output 512) to generate higher-level features. These higher-level features may not be directly observable in individual utterances. For example, synthesized conversation features can include an engagement score, and/or an active listening score. Synthesized conversation features are further described at FIG. 15.”; Par 104 – “In some implementations, conversation analysis indicators 612 include complex indicators, such as emotional suppression (e.g., neutral affect combined with low genuineness), uncertainty reduction (e.g., reassuring language and visual cues), and nonconscious mimicry (e.g., adopting the speaking style of a second speaker).”; Par 161 – “Examples of mixed modality conversation features 1516 can include warmth, intelligence, arousal, engagement, enthusiasm, passion, emotional suppression, conversation repair, question asking, complementing, speech convergence, mimicry, indirect speech, egocentrism, comfort, conflict, dominance, prestige, humor, etc. Conversation features may be automatically generated by a machine learning system (e.g., as shown in FIG. 6), and/or may be manually determined by an annotator (e.g., as shown in FIG. 11).”; Par 198 – “Conversation score 2202 is an overall rating of a particular conversation. In some implementations, conversation score 2202 is calculated by combining multiple conversation analysis indicators, such as an openness score, an engagement score, and an enthusiasm score. Conversation score 2202 and/or multiple sub-scores are generated by a conversation analysis system, such as the system shown in FIG. 4. More specifically, conversation score 2202 and/or multiple sub-scores (e.g., an engagement score, an ownership score, a goal score, an interruptions score, a “time spent listening” score, an openness score, etc.) are generated by one or more machine learning systems based on identified conversation features (e.g., audio and video from a conversation, facial expressions, voice tone, key phrases, etc.) of the recorded conversation. In various implementations, the user interface 2200 can include indications of such sub-scores. These can be shown as sub-scores for the conversation or as sub-progress scores in each category for the sub-score, generated from combinations of sub-scores in that category from the conversation and previous conversations.”; LUBOLD also teaches the limitations: Par 21 – “Before proceeding further, it is noted that vocal entrainment is a known temporal phenomenon that has been shown to be one of several critical factors that impacts conversational success, including task success, rapport, and trust. Vocal entrainment can be positive, where the speakers are aligning and adapting to one another to become more similar over the course of a conversation, or it can negative, where the opposite is occurring.”; Par 24 – “For example, the processing system 116 may determine if the speech from the first and second users exhibits positive or negative vocal entrainment by evaluating turn-by-turn feature similarity and increasing or decreasing alignment in terms of change over time across multiple speech features. Likewise, the processing system 116 may determine if the brain activity of the first and second users is aligned or misaligned by comparing the brain activity features via autocorrelation, analysis of short sequences, and comparison to established patterns.”).
REECE in view of LUBORD does not explicitly teach the [square-bracketed] limitation. In other words, REECE teaches speech convergence using mixed-modality features, and LUBORD teaches determining a vocal entrainment and a EEG-related entrainment/alignment, but they fail to teach [physical] entrainment.
SCHMIDT discloses a method/system for analyzing conversational interaction between two speakers comprising: process the first and second physical features to determine when the first user and the remote source begin to exhibit [physical entrainment] (SCHMIDT Pg. 269 Analysis – “The video recordings of the joke telling interactions were down sampled from 30 to 2 Hz and analyzed in two ways to obtain activity time series (i.e., measurement of activity every 0.5 s) for each of the participants.”; Pg. 270 – “Different dependent measures were used to evaluate the strength and the pattern of synchronization in the activity time series. First, a spectral analysis, a technique used to decompose a complex time series into its component frequencies (rhythms), was performed to determine whether there were indeed periodicities apparent in the activity of the two individuals. A spectral analysis partitions variance of a time series into the amounts accounted for by rhythms of different cycle lengths. The process is much like a best-fitting regression line but fits data to sinusoids of different frequencies/cycle lengths. In the top left of Fig. 4, the x-axis represents the frequencies of the candidate sinusoids whereas the spectral power on the y-axis represents how well the given sinusoids fit the data (see Warner 1998). Then a cross-spectral analysis, which allows one to determine the relationship between two time series at each component frequency, was performed and the average coherence at the dominant rhythms (peak frequencies) of the two participants was calculated to measure the strength of synchronization between the activity of the two participants. This average coherence is a measure of the correlation (actually an r2 value) at the dominant frequencies (i.e., of the dominant rhythms) of the two time series (see Fig. 4, top right) and ranges on a scale from 0 to 1. A coherence of 1 reflects perfect correlation of the movements (absolute synchrony) and 0 reflects no correlation (no synchrony) (Richardson et al. 2005; Schmidt and O’Brien 1997). Coherence values were standardized using a Fisher-z transformation before statistical analyses were performed.”).
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 method/system of REECE in view of LUBORD to include determining a physical entrainment, as taught by SCHMIDT.
One of ordinary skill would have been motivated to include a physical entrainment, in order to accurately determine communication entrainment so that the interspeaker communication can be improved.
REGARDING CLAIM 4, REECE in view of LUBOLD and SCHMDIT discloses the system of claim 3, wherein the processing system is further configured, upon determining that the first user and the remote source begin to exhibit physical entrainment (determining the physical entrainment is taught by SHCMDIT as explained in the rejection of claim 3), to generate the commands that cause the at least one feedback device to supply feedback to the first user that indicates potential artificial physical entrainment between the first user and the remote source (REECE Fig. 22; Par 183 – “The user interfaces implemented by interface and mapping system 2008 can include mobile applications, web applications, chat bots, forms and templates, certificates, printed documents, and so on. In some implementations, interface and mapping system 2008 includes a first user interface for reviewing data (e.g., conversation analysis indicators, conversation scores, conversation analysis indicators) associated with a particular conversation, and a second user interface for reviewing multiple conversations (e.g., a coaching relationship, user progress). For example, an interface can include a conversation impact interface (e.g., with a conversation score visualization, as discussed below in relation to FIG. 22). In some cases the interface can include links to conversation highlights, a conversation score breakdown, and indicators of key events (e.g., interruptions, questions, facial expressions) in the conversation (as discussed below in relation to FIG. 23). As another example, a progress user interface may include a progress score chart, a coach match score, sub-scores (e.g., openness, engagement), and a progress score benchmark visualization, etc.”; LUBOLD also discloses the limitations: Par 32 – “The example process 400 includes generating feedback on at least one display device (416). As noted above, this feedback indicates if the speech from the first and second users exhibits positive or negative vocal entrainment and if the brain activity of the first and second users is aligned or misaligned.”).
Claim 9 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 10 is similar to claim 4; thus, it is rejected under the same rationale.
Claim 15 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 16 is similar to claim 4; thus, it is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C KIM whose telephone number is (571)272-3327. The examiner can normally be reached Monday to Friday 8:00 AM thru 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C Flanders can be reached at 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
Prosecution Insights