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 .
Response to Amendment
Applicant filed a Reply on 03 December 2025 that:
Removed nonce terms and added structural elements to the claims thereby overcoming the 112(f) claim interpretation; and
Amended the claims in an unsuccessful attempt to overcome the prior art of record as explained below that includes new grounds of rejection necessitated by the amendments.
Response to Arguments
Applicant's arguments filed 03 December 2025 have been fully considered but they are not persuasive.
Applicant argues that Hau fails to disclose or suggest “calculating how much variance in the Action Units of the second audio-visual stream is explained by the Action Units of the first audio-visual stream” as recited in claim 1. Applicant contrasts the instant invention’s measure of the strength of the relationship or fit between the first and second audio-visual streams with a Hau’s predictive forecast of future values based on time lags. Hau is characterized as identifying fast and slow mirroring based on time delays while amended claim 1 requires a determination of the degree of dependence based on variance.
Hau is further argued as correlating emotion labels that merely map an emotion to a topic.
In response, Hau is not so limited as Applicant suggests. First of all, Hau is not limited to non-verbal reactions to a topic and more broadly and comprehensively discloses a system for nonverbal micro-expression facial analysis applied to interpret human interpersonal interactions during a dyadic communication (a therapy communication session between two persons--psychotherapist and patient). See abstract, [0002]-[0004], [0009]-[0010], [0017], [0020], [0033], Fig. 1 copied below, Fig. 2 and cites below)}
Although Hau does not explicitly employ Action Units throughout his disclosure, Hau calculates how much variance in the facial expressions (non-verbal cues) of the second audio-visual stream are explained by the facial expressions (non-verbal cues) of the first audio-visual stream. See [0006], [0027]-[0031], [0043]-[0045] including analyzing patterns and degree of dependencies between complex micro-facial expressions of a first person and corresponding affect/response expressions of a second person while they are having a conversation, patterns of different affects being connected to specific micro-facial expressions, non-verbal interaction analysis between two people in a dialogue in terms of, for example the extent (variance) of mirroring behavior patterns based on temporal and contextual relationships between initiating cues and reactive (e.g. mirroring) responses using Granger causality analysis with different time lags which corresponds to the Granger causality analysis employed by the disclosed invention in [0024] of the instant specification.
Further as to “variance” see [0030], [0051] in which human facial analysis for such interactions includes numerical figures identifying the amplitude of and speed of micro-expression changes in the faces of the video-captured subjects and amplitude, speed, harshness and pitch of speech. Note that amplitude and/or speed of micro-expression facial reactions is considered to be within the BRI of calculating of how much variance in the micro-facial expression reactions are explained by the facial micro-expressions within the first person’s audio-visual stream.
Furthermore, the BRI (broadest reasonable interpretation) “calculating how much variance in the Action Units of the second audio-visual stream is explained by the Action Units of the first audio-visual stream” clearly encompasses the temporal alignment/synchronicity of cues and their reactive responses as per [0017]-[0024] of the instant specification in which time lags and time series analysis of the non-verbal cues are used to determine such “variance”. As such, both Hau’s methods discussed above and Borg’s concept of the degree of synchrony between cues and responses are also measures of responsive “variance … explained by” that is well within the BRI of the claimed invention. See revised rejection below which was necessitated by Applicant’s amendments to the independent claims.
Curiously, Applicant fails to present arguments against Borg despite the fact that Borg was applied to teach facial Action Units and the claims have been amended to recite such Action Units.
Claims 1-3, 6-7, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Hau (US 2017/0364741 A1) and Borg (US 20230049168 A1).
Claim 1
In regards to claim 1, Hau discloses a system for interpreting human interpersonal interaction {see abstract, [0002]-[0004], [0009]-[0010], [0017], [0020], [0033], Fig. 1 copied below, Fig. 2 and cites below disclosing a system for micro-expression analysis applied to interpret human interpersonal interactions during a dyadic communication (a therapy communication session between two persons--psychotherapist and patient)}, comprising:
first and second audio-visual stream generating devices configured to output synchronized audiovisual data streams of a first and a second individual engaged in a real-time or recorded conversation
{[0009]-[0010], [0017], [0020], [0021], [0024], Fig. 1 including cameras 106, 108 that capture respective audio-visual streams of two people during a dyadic conversation/session (interpersonal interaction). See also Fig. 3, steps 304 and 306, [0048]-[0049]. Further as to synchronized see Fig. 1 showing detection of reactions from time-aligned video streams. See also [0027]-[0029], [0038]-[0039], [0049] clearly indicating that emotional reactions of a receiving person are time-aligned (correlated time-wise, aka synchronized) such that the system can detect delays between, for example, the delivering person’s voice or micro-expression and the receiving person’s reaction thereto during a human interpersonal interaction}
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a processor {analysis system 110, Fig. 1, [0038], fig. 4 computer system 400, [0057]-[0059]} configured to:
(i) process each audio-visual stream to identify non-verbal cues
{See Fig. 1 showing detection of micro-expression reactions from temporally-aligned video streams. See also [0027]-[0029], [0038]-[0039], [0049] clearly indicating that emotional reactions of a receiving person are temporally-aligned (correlated time-wise, aka synchronized) such that the system can detect delays between, for example, the delivering person’s voice or micro-expression and the receiving person’s reaction thereto
See also Fig. 2 micro-expression analyzer 218, [0051] that identifies non-verbal cues exhibited by the first individual and corresponding reactive cues exhibited by the second individual [0026]-[0027], [0038]. Further as to defined response window and conversational turn-taking see [0027], [0042]-[0045] in which a time scale/response conversational turn taking window is about 0.5 second to 1 second while micro-expression corresponding reactive cues are identified within a fraction of a section (e.g. “millisecond-scale timing”};
(ii) map identified non-verbal cues in the first one of the audio-visual streams to corresponding non-verbal cues in the second audio-visual stream by performing a time-series analysis on pairs of corresponding
{see Fig. 2 micro-expression analyzer 218, [0051] that identifies non-verbal cues exhibited by the first individual and corresponding reactive cues exhibited by the second individual [0026]-[0027], [0038].
Further as to mapping see time-series analysis see [0006], [0027]-[0031], [0043]-[0045] including analyzing patterns and degree of dependencies between complex micro-facial expressions (akin to action units) of a first person and corresponding affect/response expressions of a second person while they are having a conversation including determining mirroring behavior patterns based on temporal relationships between initiating cues and reactive (e.g. mirroring) responses using Granger causality analysis with different time lags which corresponds to the example of time series analysis in the instant specification which applies Granger causality analysis as per [0023]-[0024] of the instant specification}; and
(iii) identify a non-verbal communication pattern by determining a degree of dependence between the first and second individuals, wherein determining the degree of dependence comprising calculating how much variance in the
{See [0006], [0027]-[0031], [0043]-[0045] including analyzing patterns and degree of dependencies between complex micro-facial expressions of a first person and corresponding affect/response expressions of a second person while they are having a conversation, patterns of different affects being connected to specific micro-facial expressions, non-verbal interaction analysis between two people in a dialogue in terms of, for example the extent (variance) of mirroring behavior patterns based on temporal and contextual relationships between initiating cues and reactive (e.g. mirroring) responses using Granger causality analysis with different time lags which corresponds to the Granger causality analysis employed by the disclosed invention in [0024] of the instant specification.
Further as to “variance” see [0030], [0051] in which human facial analysis for such interactions includes numerical figures identifying the amplitude of and speed of micro-expression changes in the faces of the video-captured subjects and amplitude, speed, harshness and pitch of speech. Note that amplitude and/or speed of micro-expression facial reactions is considered to be within the BRI of calculating of how much variance in the micro-facial expression reactions are explained by the facial micro-expressions within the first person’s audio-visual stream.
Furthermore, the BRI (broadest reasonable interpretation) “calculating how much variance in the Action Units of the second audio-visual stream is explained by the Action Units of the first audio-visual stream” clearly encompasses the temporal alignment/synchronicity of cues and their reactive responses as per [0017]-[0024] of the instant specification in which time lags and time series analysis of the non-verbal cues are used to determine such “variance”. As such, both Hau’s methods discussed above and Borg’s concept of the degree of synchrony between cues and responses are also measures of responsive “variance … explained by” that is well within the BRI of the claimed invention.}; and
(iv) output a representation of the non-verbal communication pattern
{the therapist is presented with a display while the dyadic communication (therapy session) is ongoing providing reports in the form of textual descriptions (representations) that indicate, for example, that the communication topic or statements or expressions made by the therapist is making the patient nervous, anxious, angry or any other emotion label that can be applied using micro-expression analysis such that the psychotherapist may immediately correlate the feedback with the topic or expressions made by the therapist that was most recently part of the dyadic communication.
The psychotherapist may then decide to go deeper into a topic if it indicates, for example, that the topic is creating minor discomfort for the patient, and avoid the topic if the analysis indicates that the patient is experiencing extreme discomfort. Other techniques for communicating the patient's micro-expression derived emotional state to the therapist may also be used, including by displaying a representation of the non-verbal communication pattern in the form of graduated colors to the psychotherapist, where, e.g., colors closer to green indicate general patient comfort and
colors closer to red indicate general patient discomfort. Such data may also be captured for later usage and reporting on a display. See [0004]-[0006], [0055], [0059], [0062], [0065]}.
Although Hau does not explicitly employ Action Units such as highly conventional facial Action Units, Hau employs a highly related concept of facial expressions and calculates how much variance in the facial expressions of the second audio-visual stream are explained by the facial expressions of the first audio-visual stream as detailed above.
Borg is an analogous reference from the same field of analyzing dyadic communications to interpret human interpersonal interactions and. See title, abstract, Figs. 1, 2, 7A, 7B, [0002]-[0005] and cites below.
Borg teaches first and second audio-visual stream generating devices each arranged to capture an audio-visual stream relating two people have a dyadic communication during a session or a series of sessions {Fig. 4A, receiving (405) video/audio/biosensor recordings of first and second participants in a social interaction. For hardware, see Figs. 7A, 7B [0109]-[0116] including cameras, microphones}; wherein the first and second audio-visual stream generating devices are synchronized {[see [0136]-[0141]}.
Borg also teaches a processor {Figs. 3, 7a-b including computing device 310, server 320 and computer readable medium [0101]-[0116]} configured to:
process each audio-visual stream to identify non-verbal cues by monitoring a plurality of pred-defined Action Units (AUs), wherein each Action Unit corresponds to a set of coordinates OR a relation between coordinates of a predefined part of a face or body of a respective individual
{See extraction of facial Action Units, voice tone and head movement/pose in [0031], [0039]-[0040], [0049]-[0052], [0064]-[0071], [0093], [0111], [0160]. Note that vocal prosody may also be employed and this term refers to various aspects of speech including rhythm, stress, intonation (tone), variations in pitch, loudness and duration that express emotions, emphasize points}.
map identified non-verbal cues in the first one of the audio-visual streams to corresponding non-verbal cues in the second audio-visual stream by performing a time-series analysis on pairs of corresponding Action Units
{see [0049]-[0052] including analysis of social interactions between clinician and patient, [0064]-[0071] social synchrony indicated by feature set that comprises facial Action Units including analyzing delayed/synchronized reactions visible through the activations of the AU called cheek raise and extracting a feature time series pair comprising a first time series of Action Units of the first participant and a second time series of Action Units of the second participant, [0093], [0111], [0160]}
identify a non-verbal communication pattern by determining a degree of dependence between the first and second individuals, wherein determining the degree of dependence comprising calculating how much variance in the Action Units of the second audio-visual stream is explained by the Action Units of the first audio-visual stream
{see Borg’s concept of the degree of synchrony between cues and responses which are also measures of responsive “variance … explained by” that is well within the BRI of the claimed invention. See [0064]-[0076], [0133]-[0141], [0149]-[0151]. Note that Borg’s degree of social synchrony employs a time-series analysis and maps identified non-verbal cues in the first one of the audio-visual streams to corresponding non-verbal cues in the second audio-visual stream by performing a time-series analysis on pairs of corresponding Action Units}.
It 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 to have modified which already discloses processing each audio-visual stream to identify non-verbal cues by monitoring a plurality of pred-defined facial micro-expressions, mapping identified non-verbal cues in the first one of the audio-visual streams to corresponding non-verbal cues in the second audio-visual stream by performing a time-series analysis on pairs of facial micro-expression; and identifies a non-verbal communication pattern by determining a degree of dependence between the first and second individuals, wherein determining the degree of dependence comprising calculating how much variance in facial micro-expressions of the second audio-visual stream is explained by the facial micro-expressions of the first audio-visual stream such that the identifying non-verbal cue, mapping, and identifying a non-verbal communication pattern are performed using Actions Units as taught by Borg because an Action Unit is a highly related and conventional concept for representing facial micro-expressions, because Borg demonstrates the equivalence of using a variety of time-based relational actions or responses between persons in an interpersonal interaction in [0040], because Hau at least suggests using facial action units to represent micro-expressions in [0038] by using the FaceReader application, because there is a reasonable expectation of success, and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 2
In regards to claim 2, Hau discloses wherein the processor is further configured to assign a directional mapping between initiating and responsive cues, such that influence of one participant's behavior on an other is explicitly represented.
{see [0006], [0044]-[0045], analyzing patterns of complex micro-expressions and affect/response expressions, patterns of different affects being connected to specific micro-expressions, non-verbal interaction analysis between two people in a dialogue in terms of, for example mirroring behavior patterns based on temporal and contextual relationships between initiating cues and reactive (e.g. mirroring) responses including Granger causality analysis with different time lags. Note that the mirroring detection assigns a bi-directional mapping between initiating and response cues such that the influence of one participant’s behavior on another is explicitly represented. See also measuring dominance and emotional attentiveness which are also directional mappings as broadly claimed. Note that the instant specification also employs Granger causality to perform this analysis in [0024] and identifying mirroring, dominance and attentiveness in [0018], [0038]-[0047], [0066], [0151] are disclosed examples that fits well within the BRI (broadest reasonable interpretation) of the term of “directional mapping”}.
Claim 3
In regards to claim 3, Hau discloses wherein a response time window used to identify reactive cues is dynamically adjusted based on detected conversational context or speech turn-taking behavior
{see Fig. 2 micro-expression analyzer 218, [0051] that identifies non-verbal cues exhibited by the first individual and corresponding reactive cues exhibited by the second individual [0026]-[0027], [0038]. Further as to dynamically adjusted response time window see [0027], [0042]-[0045] in which a time scale/response conversational turn taking window is dynamically adjusted to about 0.5 second to 1 second for conversational turn-taking and to fraction of a second time window (e.g. “millisecond-scale timing” window) for detecting micro-expressions corresponding reactive cues are identified within a fraction of a section. In other words, two different time scales/response time windows are used and adaptively selected to detect both micro-expressions and speech turn taking behavior which occur at different time scales}.
Claim 6
In regards to claim 6, Hau discloses wherein the processor is further configured to determine spontaneity of a reactive cue based on a measured time-lag between an initiating cue and the corresponding response, such that shorter delays indicate higher spontaneity levels, which inform the system's analysis of interpersonal dynamics
{see [0029], [0044] in which metadata indicating emotion action units from the interpersonal interaction are correlated time-wise to measure a time-lag between activations of the corresponding non-verbal cues in to better understand the meaning of the micro-expression. In addition, the same Granger causality test disclosed in the instant application is used to determine spontaneous/consciously controlled micro-expression (conscious cognitive process)}.
Claim 7
In regards to claim 7, Hau discloses wherein the processor is further configured to
based on determined occasions of activations and non-activations of reactive non-verbal cues for one or a plurality of action units, determine whether there is a dynamic in the interaction and/or determined whether any of the first and second individuals has a dynamic behavior {the terms “dynamic” and “dynamic behavior” are broad terms not well defined by the instant specification but having a meaning in the art as indicating behavior that changes over time. As such, Hau’s system continuously tracks actions, reactions, further actions and subsequent reactions in a manner that tracks or determines changing, “dynamic” emotional behavior of the two persons having an interpersonal interaction as per [0020]-[0021], [0026]-[0031]. See also the emotion correlator 222, [0042]-[0045]}.
Claim 9
In regards to claim 9, Hau discloses wherein the processor is further configured to
analyse an identified non-verbal communication pattern to categorize psycho-social states of a respective person of the first and second individuals, said psycho-social states comprising at least one of emotion, attention pro-social, dominance and mirroring, said analyses being performed in a rolling window time series, wherein the time series is from 0.1 s and more, for example in the interval 0.2-10s.
{See the emotion correlator 222, [0042]-[0045], [0029]. See also the mapping for claim 1 including [0042]-[0045] in which a time scale/response conversational turn taking window is about 0.5 second to 1 second while micro-expression corresponding reactive cues are identified within a fraction of a section (e.g. “millisecond-scale timing”}.
Claim 10
In regards to claim 10, Hau discloses a presentation device arranged to present to a user information relating to a non-verbal communication pattern, including at least one of a categorized psycho-social states of at least one of the first and second individuals during a session {see [0004], [0055]-[0056], [0062], [0065] computer display for psychotherapist talking to a patient that displays the patient’s emotional state}.
Claims 11-16 (Cancelled)
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Hau and Borg as applied to claim 2 above, and further in view of Mireles (WO 2021/225682).
Claim 4
In regards to claim 4, Hau discloses wherein the processor is further configured to monitor a plurality of predefined action units wherein the action units comprises
at least one body action unit corresponding to a predetermined part of the body, wherein the body action unit comprises a set of coordinates or a relation between coordinates of the predetermined part of the body, wherein the non-verbal cues are determined based on a temporary change in the set of coordinates or relation between coordinates
{Fig. 2 micro-expression analyzer 218, [0051] that include facial and body non-verbal cues including pre-defined action units with characteristics that are used to identify the non-verbal cues as per [0026]-[0027], [0038] wherein the action units used by the employed FaceReader application unit include facial action units having a set of coordinates or a relation between coordinates of the predetermined part of the face, wherein the non-verbal cues are determined based on a temporary change in the set of coordinates or relation between coordinates.}
Although the human face is part of the human body in which case Hau anticipates claim 4, this rejection assumes in arguendo that the “predetermined part of the body” does not include the face.
Mireles is an analogous reference from the same field of interpreting non-verbal cues including facial and boy language. See [0033], [0035]-[0050]. Indeed, Mireles extends the vast amount of research on Facial Action Coding System (FACS) to body action units to recognize emotional body language and also teaches wherein the action units comprises
at least one body action unit corresponding to a predetermined part of the body, wherein the body action unit comprises a set of coordinates or a relation between coordinates of the predetermined part of the body, wherein the non-verbal cues are determined based on a temporary change in the set of coordinates or relation between coordinates {see [0033], [00105]. See also [0035]-[0050]}
It 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 to have modified Hau’s system for interpreting human interpersonal interaction that already employs facial action units or at least facial micro-expressions such that the action units also include at least one body action unit corresponding to a predetermined part of the body, wherein the body action unit comprises a set of coordinates or a relation between coordinates of the predetermined part of the body, wherein the non-verbal cues are determined based on a temporary change in the set of coordinates or relation between coordinates as taught by Mirelese because Hau suggests including body language to determine emotion reactions in [0027], because there is a reasonable expectation of success, and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 5
In regards to claim 5, Hau discloses a multi-modal feature analysis to detect non-verbal emotion cues from facial, body language and voice characteristics using a multi-modal micro-expression analysis that employs action units that comprises
at least one voice characteristic including at least one of
- a rate of loudness peaks, i.e., the number of loudness peaks per second,
- a mean length and standard deviation of continuously voiced regions,
- a mean length and standard deviation of unvoiced regions,
- a number of continuous voiced regions per second,
wherein the non - verbal cues are determined based on a temporary change the voice characteristics {[0030] including amplitude, harshness of the speech, speed of the speech and the pitch of the speed are analyzed to identify emotions which are considered “voice characteristics” consistent with the listed characteristics and also with the rate of loudness peaks (amplitude and speed).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hau and Borg as applied to claim 1 above, and further in view of Divakaran (US 2014/0212853 A1).
Claim 8
In regards to claim 8, Hau is not relied upon to disclose an AI model recited therein.
Divakaran is analogous art from the same field of emotion detection and solves a similar problem of detecting emotions during an interpersonal interaction.
Divakaran also teaches multi-modal feature analysis to detect non-verbal emotion cues in a dyadic communication using facial, body (pose or gesture), gaze, and voice characteristics using a multi-model feature analyzer 112. Fig. 1, wherein the multi-model feature analyzer 112 employs action units that comprises at least one voice characteristic, wherein the non - verbal cues are determined based on a temporary change the voice characteristics {Fig. 1, vocalic feature recognizer 144 of multi-modal feature analyzer 112, [0027]-0030] including paralinguistics such as voice pitch, speech tone, energy level and OpenEars platform features which are considered voice characteristics consistent with the listed characteristics.
Divarkaran also teaches wherein an AI model is trained to detect the non-verbal communication pattern by identifying statistically significant sequences of initiating and reactive cues across multiple recorded interactions, wherein the model incorporates temporal alignment, cue directionality, and spontaneity metrics to enhance a classification or prediction of interpersonal dynamics {see Fig. 6, [0070]-[0077]}.
It 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 to have modified Hau’s system for interpreting human interpersonal interaction such that wherein an AI model is trained to detect the non-verbal communication pattern by identifying statistically significant sequences of initiating and reactive cues across multiple recorded interactions, wherein the model incorporates temporal alignment, cue directionality, and spontaneity metrics to enhance a classification or prediction of interpersonal dynamics as taught by Divarkaran because AI algorithms learn thereby increasing the accuracy of the emotion detection as well as making it more robust and adaptable to different applications, because there is a reasonable expectation of success, and/or because doing so merely combines prior art elements according to known methods to yield predictable results
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
In regards to claim 5 and in case Applicant wishes to clarify and narrow to include the voice characteristics listed then see Sobol-Shikler (US 20080147413 A1) including Tables 2 and 3 as well as the NPL prior art listed in the Background.
Further teachings on vocal prosody may be found in Pettinelli US 20050246165 A1 including dyadic discourse characteristics associated with a combination of turn-taking, interruptions, percent airtime, voice shakiness, mutual prosodic harmony, voice pitch, voice energy, voice volume, speaking rate, voiced speech statistics, unvoiced speech statistics, response time, accent, speech intonations, voice fundamental frequency, voice phonemes, vocal stress, voice nasalization, suprasegmental voice features, and subsegmental voice features.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL ROBERT CAMMARATA/Primary Examiner, Art Unit 2667