DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Remarks and Amendments filed 03/27/2026.
Claims 9-14 remain canceled.
Claims 1, 6, 15, 23 are amended.
Claims 1-8, 15-25 have been examined and are pending.
(AIA ) Examiner Note
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Dependent claim 6 recites, “The method of claim 1, wherein the characteristics of the dynamic time warping path comprises a deviation from a diagonal of a derivative dynamic time warping path of the feature time series pair.”
Respectfully, dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are different algorithms (as attested to by the applicant himself per Specification at [0141]). These different algorithms result in different minimal cost paths through completely different cost matrices. The characteristics of a path produced by DTW (dynamic time warping) does not have any sensible connection in terms of a literal “deviation” to the diagonal of a cost matrix used to calculate a DDTW (derivative dynamic time warping) path. The claim appears to be nonsensical; i.e. although it is always true that the characteristics of a DTW path are different from a diagonal of a DDTW path, it is not clear that this axiom is what applicant is asserting by his “deviation”. If applicant is merely asserting a fact which is inherent and always true, then there is actually no further limitation being placed on the claimed method of parent claim 1 and the claim is rejected for failing to further place a limit on the method as claimed. Furthermore, it is not clear what “Deviation” or how such claimed “deviation” is intended to be measured or what applicant actually intends for this to encompass and therefore it is not clear what the metes and bounds of the claim are intended to encompass and therefore the claim is indefinite for this reason as well.
Examiner notes that to find the alignment between the two signals using DDTW an n by m matrix is constructed where the (i th ,j th ) element of the matrix contains the pair-wise distance d(ri, cj) between the two points ri and cj. The "ideal path", i.e. the path that would exist if there were no warping between the two data signals is the diagonal of this cost matrix. Applicant’s term “diagonal”, as used in this claim, appears to reference this ideal path, i.e. the diagonal of the cost matrix. However, asserting a DTW path has characteristics which comprises a deviation from a diagonal of a derivative dynamic time warping path is analogous to asserting the characteristics of a particular apple comprises a deviation [some type of difference] in characteristics from that of an ideal orange; e.g. their respective characteristic flavor profiles are different and the flavor of a particular apple deviates from the ideal flavor of an orange – which should always be true.
Therefore, it appears the claims draftsman has initially conflated different fundamental ideas regarding DTW and DDTW which appears to stem from a flaw in applicant’s original disclosure in the Specification at [0070] which reiterates the same nonsensical verbiage now recited in the claim.
Although the verbiage of claim 6 is found at Spec [0070], this verbiage is nonsensical. The Specification fails to illuminate this ambiguity. Furthermore, as already noted, Specification at [0141] appears to support the Examiner’s finding. Spec at [0141] notes that DDTW is different than DTW; it is a version of DTW but is not the same algorithm. Note that Applicant has not asserted to have invented an algorithm called DDTW but instead is referencing a known algorithm already widely used and discussed in literature such as first proposed by Eamonn J. Keogh et al. (as referenced as prior art below). Furthermore, this known algorithm is different than the previously known DTW algorithm. Applicant has asserted, e.g. Spec [0153]: “…The inventors hypothesized that DDTW would be better suited for assessing social synchrony than the ordinary DTW…”; however, this is not the same as asserting the inventors invented DDTW but instead they are asserting they are applying DDTW for assessing social synchrony.
For the purpose of compact prosecution, claim 6 is interpreted as follows: The method of claim 1, wherein a derivative dynamic time warping path is determined whose characteristics comprise a distance from a diagonal of a reference path of the feature time series pair.
Nonetheless, correction or clarification is required.
Claim Rejections - 35 USC § 103 (AIA )
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 1-5 and 7-8, 15-21 are rejected under 35 U.S.C. 103 as obvious over Maria Tomprou et al. (NPL: “Speaking out of turn: How video conferencing reduces vocal synchrony and collective intelligence”, Article in Plos One – March 2021; https://www.researchgate.net/publication/350161832; hereinafter, “Tomprou”) in view of Lahiri et al. (U.S. 2021/0358324 A1; hereinafter, "Lahiri").
Claims 1, 15 (currently amended):
Pertaining to claims 1, 15 exemplified in the limitations of method claim 1, Tomprou as shown teaches the following:
A method comprising:
receiving a recording of a social interaction between a first participant and a second participant, the social interaction comprising features exchanged between the first participant and the second participant; (Tomprou, see at least Figs. 1 and 2 and “Method” and “Measures” [pgs. 3-5], teaching: data collection [receiving] of interactions between 99 dyads [first and second participants] of 198 individuals via “the Platform for Online Group Studies (POGS: pogs.mit.edu), a web browser-based platform supporting synchronous multiplayer interaction”. Each frame of each recorded dyad interaction was analyzed to detect facial movements and facial expressions [features], etc…. between members of each dyad [i.e. features exchanged between first and second participants] as they completed a standardized Test of Collective Intelligence (TCI). Note that per “Condition 2, participants could also see each other through a video connection… Collective intelligence was measured using the Test of Collective Intelligence (TCI) completed by dyads working together.”
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); for each set of features of the features exchanged between the first participant and the second participant, extracting, from the recording, a feature time series pair comprising a first time series of the first participant including a first feature of the set of features and a second time series of the second participant including a second feature of the set of features (Tomprou, see citations noted supra, including again at least the portion of Fig. 2 as shown below which depicts an extracted feature time series pair containing a set of corresponding features, between a pair of participants, Person A and Person B, and each time series contains a feature of the corresponding set of features); The only difference between the prior art and the limitation in question is that Tomprou may not explicitly use the “extracting” terminology/language recited in the claims to express how she obtained the feature time series pairs for which she compares sets of features exchanged between participants. However, as she does teach recordings of participants both via audio and visual interaction, and some mechanism is required to obtain the feature set being compared per Fig. 2, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have extracted, from her recording, her feature times series pair, e.g. as depicted per Fig. 2, for each set of features of the total features which the participants exchanged to enable her disclosure and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.
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for each feature time series pair, determining an individual social synchrony1 level that reflects temporal coordination behavior between the first time series and the second time series, using one or more structural characteristics of a dynamic time warping path that represent temporal linkage of non-verbal behavior between the respective feature time series pair (Tomprou, see again Fig. 1 and at least Fig. 2 depicting point-to-point matches [characteristics] between warped signals which is used to compute a DTW distance2 [social synchrony level using characteristics of a dynamic time warping path of the feature time series pair] – i.e. the “distance” is the minimum cumulative cost through the cost matrix of a Dynamic Time Warping path for a feature time series pair. Here the feature time series pair represents a feature of Person A and a feature of Person B [i.e. for a set of features] over a particular time period of interaction. See also [pg. 5], e.g.: “…Fig 1 illustrates how the raw data of each participant was transformed to derive individual signals [a feature time series] or measures. These individual signals [feature time series] or measures were then used to calculate dyadic synchrony [social synchrony] in facial expressions [non-verbal behavior] and prosodic features, speaking turn inequality, and amount of overall communication. We computed synchrony in… prosodic features between partners for each dyad, using Dynamic Time Warping (DTW). DTW takes two signals [first time series and second time series] and warps them in a nonlinear manner to match them with each other and adjust to different speeds. It then returns the distance [synchrony level using characteristics of a dynamic time warping path of the feature time series pair] between the warped signals. The lower this distance, the higher the synchrony between members of the dyad. Hence, we reversed the signs of the DTW distance measure [measure of synchrony level] to facilitate its interpretation as a measure of synchrony. We use DTW instead of other distance metrics such as the Pearson correlation or simple Euclidean distance because DTW is able to match similar behaviors of different duration that occur a few seconds apart, which better captures the responsive, social nature of these expressions (see comparison in Fig 2). For both facial expressions and prosodic features, we calculated synchrony across the six tasks of the TCI…”; i.e. the calculation of “DTW distance” necessarily uses characteristics, i.e. the specific set of point-to-point matches [characteristics] between warped signals, of a DTW path of the feature time series pair in question.
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Wherein the one or more structural characteristics comprise at least a deviation from a diagonal of the dynamic time warping path and wherein the structural characteristics are distinct from a scalar dynamic time warping distance (Examiner notes applicant’s “diagonal” as regards a DTW path represents an "ideal path", i.e. the path that would exist if there were no warping between the two data signals is the diagonal of the cost matrix of the DTW algorithm. Applicant’s term “diagonal”, as used in this claim, appears to reference this ideal path, i.e. the diagonal of the cost matrix and is interpreted as such. In view of this finding, the Examiner notes that the signals represented by Tomprou e.g. per Fig. 2 depict warping between two signals and therefore the structural characteristic of the minimum cumulative cost through the cost matrix for these signals as computed by DTW will be a deviation from the ideal path between two signals with no warping. This deviation is not a scalar dynamic time warping distance even though such a scalar could be computed and is apparently computed by Tomprou. Applicant’s feature appears to be a statement of an inherent fact for two signals which are warped in relation to each other.)
analyzing the determined individual social synchrony level of every feature time series pair to identify a set of the features of the features exchanged between the first participant and the second participant related to a prediction target (Tomprou, see at least Table 1 and Fig. 3 and [pgs. 7-9], teaching, e.g.: “…We further examined [analyzed] whether synchrony [social synchrony level of a feature time series pair] affects CI (collective intelligence) [a prediction target] … we found [identified] that synchrony in facial expressions [a set of features of the features exchanged] positively predicted [is related to] collective intelligence [prediction target] only in the video condition… This result suggests that when visual cues are available it is important that interaction partners attend to them… We also found [identified] that prosodic synchrony [another individual social synchrony level] improves [is related to] collective intelligence [prediction target] in physically separated collaborators whether or not they had access to video. An important precursor to prosodic synchrony is the equality in speaking turns [another set of features exchanged] that emerges among collaborators, which enhances prosodic synchrony and, in turn, collective intelligence [a prediction target]...”); and
Although Tomprou teaches the above limitations and teaches generating a [finding/conclusion] for the identified set of the features exchanged between the first participant and the second participant related to the prediction target based on the determined individual social synchrony level between the first time series and the second time series; i.e. Tomprou as shown supra per at least [pgs. 7-9] she has found [generates a finding/conclusion] that speaking turns and prosodic features [i.e. the identified set of the features exchanged between the first participant and the second participant] are related to collective intelligence [the prediction target] based on determined synchrony [determined individual social synchrony level] in the interaction/conversation [between the first time series and the second time series]. In view of these teachings, the Examiner finds that the very disclosure of Tomprou’s findings per her paper, itself being a type of notification to the public, implies her findings/conclusions should be shared as a notification to those engaged in such conversation, or prior to engaging in conversation, e.g. as a recommendation to speak or not speak depending on identified equality of speaking turns, etc…, for the purpose of enhancing synchrony and ultimately collective intelligence [i.e. the prediction target]. Therefore, although Tomprou may not explicitly teach generating a notification to share these findings/conclusions, e.g. via a notification, with the interacting partners – i.e. with those engaging in conversation, there is nonetheless motivation to do so. And regarding generating such a notification, Tomprou in view of Lahiri teaches the following:
generating a notification (Lahiri, who is in the same field of endeavor and directed towards techniques and methods of observing social interactions between individuals such as facial expressions categorized as facial action units [features exchanged] between individuals during conversation, present and past, and then making recommendations to such individuals based on predicted social impact of such recommendations, e.g. per at least Abstract, [0002], [0022], [0026]-[0027], and Fig. 1, teaching, e.g.: “a method for monitoring social interactions and generating behavioral recommendations… the analyzing including predicting a probable impact of one or more social interactions that could be performed between the user and the one or more individuals; and generating, by the one or more processors, a behavioral recommendation to be communicated to the user, the behavioral recommendation including a social interaction…”; where per at least [0027]: “…the program code tracks the physical behaviors of individuals (e.g., facial gestures, body language, eye contact, etc.) in the vicinity of the user as part of contextualizing the possible behavioral recommendation (e.g., whether an individual would be receptive to the user initiating a conversation)…”; see also at least [0079]-[0082], and [0088] and Table B.).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Lahiri (directed towards observing features exchanged between individuals, including facial gestures, body language, eye contact, etc… and generating a recommendation [notification] regarding whether to initiate or continue in conversation) which is applicable to a known base device/method of Tomprou (who already teaches determining synchrony between individuals engaged in conversation, where synchrony is based on exchanged features, and then subsequently generating findings/conclusions regarding [i.e. for] various sets of exchanged feature, e.g. equality in speaking turns and prosodic features [an identified set of the features exchanged between the first participant and the second participant] related to increased collective intelligence [the prediction target] based on the measure of synchrony [determined individual social synchrony level] in the interaction/conversation [between the first time series and the second time series], which implies such findings/conclusion should be shared as a notification to those engaged in conversation) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Lahiri to the device/method of Tomprou in order to realize a practitioner of Tomprou’s method/system would benefit by also generating a notification, e.g. as a recommendation to speak or not speak depending on Tomprou’s identified equality of speaking turns [set of exchanged features], for the purpose of enhancing synchrony and ultimately collective intelligence amongst participants in a conversation, and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 16 (previously presented)
Tomprou/Lahiri teach the limitations upon which these claims depend including analyzing the determined individual social synchrony level of all feature time series pairs […] to identify the set of the features of the features exchanged between the first participant and the second participant related to the prediction target as already shown supra per the independent claims. Although Tomprou teaches the aforementioned limitation, Tomprou may not explicitly teach the nuance regarding use of machine learning engine, as recited below, to perform her aforementioned analyzing. However, Tomprou in view of Lahiri teaches the following:
… using a social synchrony prediction engine…, wherein the social synchrony prediction engine comprises a neural network, a machine learning engine, or an artificial intelligence engine (Lahiri, see citations noted supra and at least [0022], e.g.: “..Program code in embodiments of the present invention can utilize these data, after the program code obtains the data, for example, as parameters entered into artificial intelligence (AI) systems, for model training and machine learning…”)
Therefore, the Examiner understands that the use a prediction engine making use of machine learning to identify important features for a model is merely applying a known technique of Lahiri which is applicable to a known base device/method of Tomprou to yield predictable results and therefore the claimed features would be obvious because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 3, 17 (Original)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: …further comprising: analyzing the determined individual social synchrony level of every feature time series pair to determine an overall social synchrony level between the first participant and the second participant (Tomprou, see at least [pg. 5] regarding: “…calculate dyadic synchrony in… amount of overall communication”; i.e. applicant’s an overall social synchrony level reads on Tomprou’s calculation of dyadic synchrony in amount of overall communication. see also at least Table 2)
Regarding generating a notification associated with the overall social synchrony level between the first participant and the second participant, Tomprou may not explicitly teach generation of such a notification in regards to her determined calculation of dyadic synchrony in amount of overall communication [overall social synchrony level]. However, regarding generating such a notification, Tomprou in view of Lahiri teaches the following: generating a notification (Lahiri, who is in the same field of endeavor and directed towards techniques and methods of observing social interactions between individuals such as facial expressions categorized as facial action units [features exchanged] between individuals during conversation, present and past, and then making recommendations to such individuals based on predicted social impact of such recommendations, e.g. per at least Abstract, [0002], [0022], [0026]-[0027], and Fig. 1, teaching, e.g.: “a method for monitoring social interactions and generating behavioral recommendations… the analyzing including predicting a probable impact of one or more social interactions that could be performed between the user and the one or more individuals; and generating, by the one or more processors, a behavioral recommendation to be communicated to the user, the behavioral recommendation including a social interaction…”; where per at least [0027]: “…the program code tracks the physical behaviors of individuals (e.g., facial gestures, body language, eye contact, etc.) in the vicinity of the user as part of contextualizing the possible behavioral recommendation (e.g., whether an individual would be receptive to the user initiating a conversation)…”; see also at least [0079]-[0082], and [0088] and Table B.).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Lahiri (directed towards observing synchrony of features exchanged between individuals, including facial gestures, body language, eye contact, etc… and generating a recommendation [notification] regarding whether to initiate or continue in conversation) which is applicable to a known base device/method of Tomprou (who already teaches calculation of dyadic synchrony in amount of overall communication [overall social synchrony level] between individuals engaged in conversation, where synchrony is based on exchanged features, and then subsequently generating findings/conclusions regarding [i.e. for] various sets of exchanged feature, e.g. equality in speaking turns and prosodic features [an identified set of the features exchanged between the first participant and the second participant] related to increased collective intelligence [the prediction target] based on the measure of synchrony [determined individual social synchrony level] in the interaction/conversation [between the first time series and the second time series], which implies such findings/conclusion should be shared as a notification to those engaged in conversation) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Lahiri to the device/method of Tomprou in order to realize a practitioner of Tomprou’s method/system would benefit by also generating a notification, e.g. as a recommendation to speak or not speak depending on Tomprou’s identified equality of speaking turns [set of exchanged features], for the purpose of enhancing synchrony and ultimately collective intelligence amongst participants in a conversation, and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 4, 18 (Original)
Tomprour/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou in view of Lahiri teaches the following: …further comprising: analyzing the identified set of the features exchanged between the first participant and the second participant related to the prediction target using a social synchrony prediction engine to determine a prediction target-specific overall social synchrony level between the first participant and the second participant; and generating a notification associated with the prediction target-specific overall social synchrony level between the first participant and the second participant (Lahiri, again see citations noted supra, e.g. per at least [0088]-[0089], teaching: “…According to one embodiment, training data can be used to train a matrix on which combinations of attributes are likely to have a high correlation value (e.g., v3), which indicate a high perceived value of having a conversation [prediction target specific overall social synchrony level]. Once the matrix is trained for a given individual, the program code can derive the perceived value of having the conversation 770. The perceived value of having the conversation 770 can be included, for example, with other parameters to develop the comprehensive composite profile used by the program code to make a behavioral recommendation…” Also, per at least [0022], e.g.: “..Program code in embodiments of the present invention can utilize these data, after the program code obtains the data, for example, as parameters entered into artificial intelligence (AI) systems, for model training and machine learning…”) Therefore, the Examiner understands that the use a prediction engine making use of machine learning to identify important features for a model is merely applying a known technique of Lahiri which is applicable to a known base device/method of Tomprou to yield predictable results and therefore the claimed features would be obvious because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 5 (previously presented)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: …wherein the recording is a video recording, wherein extracting, from the recording, the feature time series pair comprising the first time series of the first participant and the second time series of the second participant comprises: for each feature of the features exchanged between the first participant and the second participant: extracting the feature from each frame of the recording for the first participant to generate a first frame-by-frame index of the feature, the first frame-by-frame index of the feature being the first time series of the first participant; and extracting the feature from each frame of the recording for the second participant to generate a second frame-by-frame index of the feature, the second frame-by-frame index of the feature being the second time series of the second participant (Tomprou, see at least [pg. 5] teaching, e.g.: “…We used OpenFace [59] to automatically detect [extracting] facial movements [the feature] in each frame [from each frame], based on the Facial Action Coding System (FACS)…. We computed synchrony in facial expressions (coded as positive, negative, and other in each frame)…”; note reference [59] has been incorporated by reference: Amos B, Ludwiczuk B, Satyanarayanan Mea. Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science. 2016.)
Claim 7 (original)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: The method of claim 1, wherein the features exchanged between the first participant and the second participant comprise facial action units, the facial action units being minimal units of facial activity that are anatomically separate and visually distinguishable (Tomprou, see at least [pg. 5], e.g.: “…Facial expressions. We used OpenFace [59] to automatically detect facial movements in each frame, based on the Facial Action Coding System (FACS)….”).
Claim 8 (previously presented)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: The method of claim 1, wherein the individual social synchrony level indicates an extent to which the first feature of the first participant and the second feature of the second participant are coordinated with each other objectively and subjectively over time (Tomprou, see citations noted supra, including again at least [pgs. 2-4]. Tomprou’s calculation of DTW distance is a measure of synchrony. She teaches, e.g. [pg. 4]: “…the lower this distance, the higher the synchrony between members of the dyad. Hence, we reversed the signs of the DTW distance measure to facilitate its interpretation as a measure of synchrony. We use DTW … because DTW is able to match similar behaviors of different duration that occur a few seconds apart, which better captures the responsive, social nature of these expressions (see comparison in Fig 2) For both facial expressions and prosodic features, we calculated synchrony across the six tasks of the TCI….”; Also note Tomprou’s discssion regarding what is meant in the literature to be indicated by the term “synchrony” [individual social synchrony level]; applicant’s social synchrony level reads on this accepted definition; e.g. “…the common view is that synchrony is achieved when two or more nonverbal cues or behaviors are aligned [21, 22]. Social psychology researchers traditionally study synchrony in terms of body movements, such as leg movements [23], body posture sway [24, 25], finger tapping [26] and dancing [27]. These forms of synchrony contribute to interpersonal liking, cohesion, and coordination in relatively simple tasks [28, 29]. Synchrony in facial muscle activity [30] and prosodic cues such as vocal pitch and voice quality [31–33] are of particular importance for the coordination of interacting group members, as these facilitate both communication and interpersonal closeness. For example, synchrony in facial cues has been consistently found to indicate partners’ liking for each other and cohesion [30]…”).
Claim 19 (Original)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: The system of claim 15, wherein a first set of the features exchanged between the first participant and the second participant related to a first prediction target is different than a second set of the features exchanged between the first participant and the second participant related to a second prediction target (Tomprou, see at least [pg. 10], e.g.: “…our findings suggest that visual nonverbal cues [first set of exchanged features] may also enable some interacting partners to dominate [first prediction target] the conversation. By contrast, we show that when interacting partners have audio cues only [second set of exchanged features], the lack of video does not hinder them from communicating these rules but instead helps them to regulate [second prediction target different than the first] their conversation more smoothly by engaging in more equal exchange of turns and by establishing
improved prosodic synchrony…”)
Claim 20 (previously presented)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou in view of Lahiri teaches the following: The system of claim 15, wherein the instructions further direct the processing system to provide the notification for the identified set of features to a computing device of the first participant (Lahiri, see also at least [0045], e.g.: … With the guidance from the wearable device [computing device of the first participant], the user can then initiate the conversation with the recommended (by the program code) and selected (by the user based on the recommendation) individual…”).
Claim 21 (previously presented)
Tomprou/Lahiri teach the limitations upon which these claims depend. Furthermore, Tomprou teaches the following: The method of claim 1, wherein the characteristics of the dynamic time warping path of the feature time series pair comprise one or more quantitative characteristics distinct from a scalar dynamic warping path distance value that summarizes similarity between the time series after alignment. (Tomprou, see citations noted supra, e.g. again per at least Fig. 2 depicting point-to-point matches [characteristics] of a DTW path of a feature time series pair; the point-to-point matches [characteristics] are quantitative characteristics which are distinct from a scalar value of the DTW distance which is computed from the set of these point-to-point matches [characteristics] – i.e. the distance is a scalar metric which is distinct from the point-to-point matches [characteristics] which form the path itself. See also [pg. 5], e.g.: “…DTW takes two signals [first time series and second time series] and warps them in a nonlinear manner to match them with each other and adjust to different speeds. It then returns the distance [synchrony level using characteristics of a dynamic time warping path of the feature time series pair] between the warped signals. The lower this distance, the higher the synchrony between members of the dyad. Hence, we reversed the signs of the DTW distance measure [measure of synchrony level] to facilitate its interpretation as a measure of synchrony. …DTW is able to match similar behaviors of different duration that occur a few seconds apart, which better captures the responsive, social nature of these expressions (see comparison in Fig 2). For both facial expressions and prosodic features, we calculated synchrony across the six tasks of the TCI…”)
Claims 6, 22 is rejected under 35 U.S.C. 103 as obvious over Tomprou and Lahiri further in view of Keogh et al. (Keogh, E. & Pazzani, M. (2001). Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining (SDM'2001), Chicago, USA).
Claim 6 (currently amended)
Although Tomprou/Lahiri teach the limitations upon which these claims depend, and Tomprou as shown supra teaches determining a DTW distance [synchrony level] between a first time series and a second time series using point-to-point matches [characteristics] of a dynamic time warping path, she may not explicitly teach her that characteristics of her DTW path comprises a distance from a diagonal of a derivative dynamic time warping path as recited below. However, regarding this feature Tomprou in view of Keogh teaches the following:
The method of claim 1, wherein the characteristics of the dynamic time warping path comprises a deviation from a diagonal of a derivative dynamic time warping path of the feature time series pair (Examiner notes the 112(b) rejection guiding claim interpretation. Keogh, see at least [pgs. 3-6], especially [pg. 5] regarding his new algorithm called “Derivative dynamic time warping (DDTW)” which he notes is a modification of classical DTW; He discloses: “…we propose a modification of DTW that does not consider the Y-values of the datapoints, but rather considers the higher level feature of "shape". We obtain information about shape by considering the first derivative of the sequences, and thus call our algorithm Derivative Dynamic Time Warping (DDTW).” Keogh teaches his DDTW path, as computed by his DDTW algorithm, is a least cost path through a cost matrix, similar to a DTW “warping path” as noted per Fig. 3, and therefore there inherently exists a diagonal [applicant’s diagonal] through this cost matrix which represents an ideal path if the derivatives of the two compared signals are exactly the same. Therefore, a DDTW path will always have a shape with characteristics which may be compared to this diagonal; e.g. by computing a distance or difference from this diagonal.
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Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Keogh (directed towards a modification of DTW, called DDTW derivative dynamic time warping, which considers the higher level feature of "shape" by considering the first derivative of the sequences, where such warping path inherently has characteristics which comprises a distance from a diagonal of the cost matrix), which is applicable to a known base device/method of Tomprou (already directed towards use of DTW to find alignment of features of two time series pairs and thereby determine individual social synchrony between the first and second time series) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Keogh to the device/method of Tomprou to also calculate a derivative dynamic time warping path for the feature time series pair, where such DDTW path has characteristics which comprises a distance from a diagonal of the cost matrix, at least to take advantage of the benefits of DDTW over DTW noted by Keogh, e.g. per [pg. 6] and because Keogh’s improvement to DTW, by use of DDTW, is pertinent to the method/system of Tomprou which makes use of DTW to align and measure synchrony between feature time series pairs and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 22 (previously presented)
Although Tomprou/Lahiri teach the limitations upon which these claims depend, Tomprou may not explicitly teach a characteristic comprises a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line. However, regarding this feature Tomprou in view of Keogh teaches the following:
The method of claim 1, wherein the characteristics of the dynamic time warping path of the feature time series pair comprise: a median deviation of points of the dynamic time warping path from a diagonal reference line representing no warping; or, a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line (Keogh, see at least Fig. 3 showing an example warping path of DTW where portions of the dynamic time warping path are positioned above and below a diagonal through the cost matrix from 1,1 to m,n; the diagonal represents an ideal path which would be taken if the two signals were identical and perfectly synchronous. Furthermore, per Keogh [pg. 4] “2.1 Constraining the classic dynamic time warping algorithm”, there are at least three considered constraints where constraint (1) “windowing” requires determination of whether portions of the dynamic time warping path are positioned above or below diagonal reference lines used as constraints, e.g. dashed lines as depicted in Fig. 3; and constraint (2) “slope weighting” requires determination of distance from diagonal to bias the warping towards the diagonal of the matrix as X gets larger. Examiner notes that applicant’s claim language is so broad as to read on all of these teachings from Keogh.
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Keogh [pg. 4]:
“…1) Windowing: (Berndt & Clifford 1994) Allowable elements of the matrix can be restricted to those that fall into a warping window, |i-(n/(m/j))| < R, where R is a positive integer window width. This effectively means that the corners of the matrix are pruned from consideration, as shown by the dashed lines in Figure 3. Others have experimented with various other shaped warping windows (Rabiner et al 1978, Tappert & Das 1978, Myers et. al. 1980). This approach constrains the maximum size of a singularity, but does not prevent them from occurring. 2) Slope Weighting: (Kruskall & Liberman 1983,Sakoe, & Chiba 1978) If equation 5 is replaced with g(i,j) = d(i,j) + min{ g(i-1,j-1) , X g(i-1,j ) , X g(i,j-1) } where X is a positive real number, we can constrain the warping by changing the value of X. As X gets larger, the warping path is increasing biased toward the diagonal.”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Keogh (directed towards techniques such as implementing constraints on a DTW path to make a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line), which is applicable to a known base device/method of Tomprou (already directed towards use of DTW to find alignment of features of two time series pairs and thereby determine individual social synchrony between the first and second time series) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Keogh to the device/method of Tomprou because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 23-24 are rejected under 35 U.S.C. 103 as obvious over Maria Tomprou et al. (NPL: “Speaking out of turn: How video conferencing reduces vocal synchrony and collective intelligence”, Article in Plos One – March 2021; https://www.researchgate.net/publication/350161832; hereinafter, “Tomprou”)
Claim 23 (currently amended):
Pertaining to claim 23, Tomprou as shown teaches the following:
A method comprising:
receiving a recording of a social interaction between a first participant and a second participant, the social interaction comprising features exchanged between the first participant and the second participant; (Tomprou, see at least Figs. 1 and 2 and “Method” and “Measures” [pgs. 3-5], teaching: data collection [receiving] of interactions between 99 dyads [first and second participants] of 198 individuals via “the Platform for Online Group Studies (POGS: pogs.mit.edu), a web browser-based platform supporting synchronous multiplayer interaction”. Each frame of each recorded dyad interaction was analyzed to detect facial movements and facial expressions [features], etc…. between members of each dyad [i.e. features exchanged between first and second participants] as they completed a standardized Test of Collective Intelligence (TCI). Note that per “Condition 2, participants could also see each other through a video connection… Collective intelligence was measured using the Test of Collective Intelligence (TCI) completed by dyads working together.”
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); for each set of features of the features exchanged between the first participant and the second participant, extracting, from the recording, a feature time series pair comprising a first time series of the first participant including a first feature of the set of features and a second time series of the second participant including a second feature of the set of features (Tomprou, see citations noted supra, including again at least the portion of Fig. 2 as shown below which depicts an extracted feature time series pair containing a set of corresponding features, between a pair of participants, Person A and Person B, and each time series contains a feature of the corresponding set of features); The only difference between the prior art and the limitation in question is that Tomprou may not explicitly use the “extracting” terminology/language recited in the claims to express how she obtained the feature time series pairs for which she compares sets of features exchanged between participants. However, as she does teach recordings of participants both via audio and visual interaction, and some mechanism is required to obtain the feature set being compared per Fig. 2, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have extracted, from her recording, her feature times series pair, e.g. as depicted per Fig. 2, for each set of features of the total features which the participants exchanged to enable her disclosure and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.
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for each feature time series pair, determining an individual social synchrony3 level that reflects temporal coordination behavior between the first time series and the second time series based on one or more quantitative characteristics of a dynamic time warping (DTW) path corresponding to the feature time series pair that reflect how alignment between the first time series and the second time series varies over time, distinct from a scalar DTW distance value that summarizes similarity between the time series after alignment (Tomprou, see again [pg. 5] and Fig. 1 and at least Fig. 2 depicting point-to-point matches [quantitative characteristics] between warped signals which is used to compute a DTW distance4 [social synchrony level] which is a reflection of temporal coordination of behavior; e.g. [pg. 5]: “…DTW is able to match similar behaviors of different duration that occur a few seconds apart, which better captures the responsive, social nature of these expressions (see comparison in Fig 2). For both facial expressions and prosodic features, we calculated synchrony across the six tasks of the TCI…”; Note the point-to-point matches [characteristics] are quantitative characteristics which are distinct from a scalar value of the DTW distance which is computed from the set of these point-to-point matches [characteristics] – i.e. the distance may be a scalar metric which summarizes similarity after alignment but it is distinct from the point-to-point matches [characteristics] which form the path itself. See also [pg. 5], e.g.: “…DTW takes two signals [first time series and second time series] and warps them in a nonlinear manner to match them with each other and adjust to different speeds. It then returns the distance [synchrony level using characteristics of a dynamic time warping path of the feature time series pair] between the warped signals. The lower this distance, the higher the synchrony between members of the dyad. Hence, we reversed the signs of the DTW distance measure [measure of synchrony level] to facilitate its interpretation as a measure of synchrony….”)
wherein the individual social synchrony level is analyzed to provide an output associated with the social interaction (Tomprou, see at least Table 1 and Fig. 3 and [pgs. 7-9], teaching, e.g.: “…We further examined [analyzed] whether synchrony [social synchrony level of a feature time series pair] affects CI (collective intelligence)… This result [an output associated with the social interaction] suggests that when visual cues are available it is important that interaction partners attend to them…)
Claim 24 (previously presented):
Tomprou teaches the limitations upon which this claim depends. Furthermore, as shown, Tomprou teaches the following:
The method of claim 23, wherein the characteristics of the dynamic time warping path of the feature time series pair that is used to determine the individual social synchrony level comprises a warping characteristic of a path taken to align points of the first time series with the second time series (Tomprou, see citations noted supra, e.g. again per Fig. 2 depicting point-to-point matches [characteristics] of the DTW path of the feature time series pair is a warping characteristic of a path taken to align points of the first time series with the second time series; this DTW path is used to compute “DTW distance” [determined social synchrony level]. Again, note Trompou teaches [pg. 5]: “…DTW takes two signals and warps them in a nonlinear manner to match them with each other and adjust to different speeds. It then returns the distance between the warped signals. The lower this distance, the higher the
synchrony between members of the dyad. Hence, we reversed the signs of the DTW distance
measure to facilitate its interpretation as a measure of synchrony.”
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Claim 25 is rejected under 35 U.S.C. 103 as obvious over Tomprou in view of Keogh et al. (Keogh, E. & Pazzani, M. (2001). Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining (SDM'2001), Chicago, USA).
Claim 25 (previously presented)
Although Tomprou teach the limitations upon which these claims depend, he may not explicitly teach a characteristic of her DTW path comprises a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line. However, regarding this feature Tomprou in view of Keogh teaches the following:
The method of claim 23, wherein the characteristics of the dynamic time warping path of the feature time series pair comprise: a median deviation of points of the dynamic time warping path from a diagonal reference line representing no warping; or, a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line (Keogh, see at least Fig. 3 showing an example warping path of DTW where portions of the dynamic time warping path are positioned above and below a diagonal through the cost matrix from 1,1 to m,n; the diagonal represents an ideal path which would be taken if the two signals were identical and perfectly synchronous. Furthermore, per Keogh [pg. 4] “2.1 Constraining the classic dynamic time warping algorithm”, there are at least three considered constraints where constraint (1) “windowing” requires determination of whether portions of the dynamic time warping path are positioned above or below diagonal reference lines used as constraints, e.g. dashed lines as depicted in Fig. 3; and constraint (2) “slope weighting” requires determination of distance from diagonal to bias the warping towards the diagonal of the matrix as X gets larger. Examiner notes that applicant’s claim language is so broad as to read on all of these teachings from Keogh.
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Keogh [pg. 4]:
“…1) Windowing: (Berndt & Clifford 1994) Allowable elements of the matrix can be restricted to those that fall into a warping window, |i-(n/(m/j))| < R, where R is a positive integer window width. This effectively means that the corners of the matrix are pruned from consideration, as shown by the dashed lines in Figure 3. Others have experimented with various other shaped warping windows (Rabiner et al 1978, Tappert & Das 1978, Myers et. al. 1980). This approach constrains the maximum size of a singularity, but does not prevent them from occurring. 2) Slope Weighting: (Kruskall & Liberman 1983,Sakoe, & Chiba 1978) If equation 5 is replaced with g(i,j) = d(i,j) + min{ g(i-1,j-1) , X g(i-1,j ) , X g(i,j-1) } where X is a positive real number, we can constrain the warping by changing the value of X. As X gets larger, the warping path is increasing biased toward the diagonal.”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Keogh (directed towards techniques such as implementing constraints on a DTW path to make a determination of whether portions of the dynamic time warping path are positioned above or below a diagonal reference line), which is applicable to a known base device/method of Tomprou (already directed towards use of DTW to find alignment of features of two time series pairs and thereby determine individual social synchrony between the first and second time series) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Keogh to the device/method of Tomprou because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Response to Arguments
Respectfully, Applicant’s arguments have been fully considered but are not convincing; Tomprou fully teaches applicant’s amended and argued features as noted per the rejections provided supra.
Note also the following: Applicant's recited "characteristics of a dynamic time warping path of the feature time series pair" reads on Tomprou's teachings as shown supra; e.g. see again Tomprou Fig. 2 where the time series pair at the top of Fig. 2 depicts point-to-point matches [characteristics] between warped signals which is used to compute a DTW distance5 [social synchrony level]; i.e. the distance is computed using characteristics of a dynamic time warping path of the feature time series pair – i.e. the “distance” [measure of social synchrony level] is the minimum cumulative cost through the cost matrix of a Dynamic Time Warping path for a feature time series pair and is computed according to the point-to-point matches [characteristics] between warped signals via a cost matrix. Here the feature time series pair represents a feature of Person A and a feature of Person B [i.e. for a set of features] over a particular time period of interaction. In classic DTW “a warping path” is a contiguous set of matrix elements, according to certain restrictions, that defines a mapping between elements of the matrix, e.g. Q and C; there are exponentially many warping paths, and hence characteristics, for two different time series signals which are being compared.
Examiner notes it is always true that there exists a "path" [i.e. applicant's dynamic time warping path] for two dynamic time warped signals and the shape of a path has characteristics which may be measured in any number of numerous manners. Furthermore, the point-to-point matches [characteristics] between warped signals which form a “path”, e.g. as taught by Tomprou, are merely one set of "characteristics" of such DTW path; another characteristic may be the DTW distance of the path which is a characteristic of the DTW path and a measure of synchrony between the signals, and another characteristic of the path may be its shape which may be measured in reference to an ideal shape, etc…
Applicant’s “characteristics” is interpreted in view of Applicant’s own Specification, e.g. note at least Specification at [0070]: "…for each feature time series pair, determining (415) an individual social synchrony level between the feature time series pair using characteristics of a dynamic time warping path…”. Notice, therein, applicant doesn’t specify any particular characteristics except they are “of a dynamic time warping path”. Indeed, there are numerous possible characteristics of a dynamic time warping path (DTW). The specification leaves the term “characteristics” open-ended; and its use in the claims is open to being interpreted per its plain meaning. Applicant does not require a limited definition of this term “characteristics” as recited in the claims. Instead, the applicant has chosen to use broad claim language and therefore the meaning of “characteristics” as recited in the independent claims includes any such “characteristics” of DTW paths which include characteristics other than the particular examples given in the specification. Therefore, although applicant argues the claims should be inferred to correspond to a very particular exemplary “characteristic” of such DTW path, the Examiner finds that such assertion is tenuous and instead, the independent claims are broad and not so limited to any particular characteristic, except that such characteristics be of the DTW path, and therefore the features being argued indeed read on Tomprou’s teachings.
It is clear each DTW path has many "characteristics" which may define such path. Furthermore, the set of possible paths as determined by DTW are different from those as determined by other warping alignment techniques such as DDTW and each path will have their own shape and defining characteristics. Applicant appears to acknowledge these in his own specification, e.g. per at [0025], [0029], and [0154] where at [0154] Applicant notes: "...the warping paths obtained via DDTW vs DTW…." This is a clear indication there are many ways by which to obtain (i.e. measure or calculate) a "warping path", one of which is via classic DTW, another of which is by DDTW and the two are distinct and resultant paths each have their own characteristics.
Conclusion
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).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
1 Specification at [0028]: “The terms "coordination", "interactional synchrony", and "social synchrony" can be used interchangeably herein. As used herein, the term "coordination" can be defined in more than one aspect. Historically in human studies, coordination has sometimes referred to the degree of temporal alignment between subjects in a specific manner… Other times, coordination has been used more subjectively to refer to how well subjects are perceived to cooperate and/or relate to one another (e.g., being "on the same wavelength"). Combining these previous uses, social synchrony can indicate the extent to which two people are coordinated objectively and subjectively over time.”
2 Examiner notes: It is understood by a person of ordinary skill in the art that “distance” as measured by DTW is the cumulative cost (sum of local distances) along an alignment path (aka, a “warping path”), between two time series as represented in a cost matrix; the “DTW distance” is the minimum cost out of all possible warping path costs; the “warping path” which corresponds to the “DTW distance” is the specific set of point-to-point matches [characteristics] between warped signals that yields the smallest total distance – i.e. the minimum cumulative cost through the cost matrix.
3 Specification at [0028]: “The terms "coordination", "interactional synchrony", and "social synchrony" can be used interchangeably herein. As used herein, the term "coordination" can be defined in more than one aspect. Historically in human studies, coordination has sometimes referred to the degree of temporal alignment between subjects in a specific manner… Other times, coordination has been used more subjectively to refer to how well subjects are perceived to cooperate and/or relate to one another (e.g., being "on the same wavelength"). Combining these previous uses, social synchrony can indicate the extent to which two people are coordinated objectively and subjectively over time.”
4 Examiner notes: It is understood by a person of ordinary skill in the art that “distance” as measured by DTW is the cumulative cost (sum of local distances) along an alignment path (aka, a “warping path”), between two time series as represented in a cost matrix; the “DTW distance” is the minimum cost out of all possible warping path costs; the “warping path” which corresponds to the “DTW distance” is the specific set of point-to-point matches [characteristics] between warped signals that yields the smallest total distance – i.e. the minimum cumulative cost through the cost matrix.
5 Examiner notes: It is understood by a person of ordinary skill in the art that “distance” as measured by DTW is the cumulative cost (sum of local distances) along an alignment path (aka, a “warping path”), between two time series as represented in a cost matrix; the “DTW distance” is the minimum cost out of all possible warping path costs; the “warping path” which corresponds to the “DTW distance” is the specific set of point-to-point matches [characteristics] between warped signals that yields the smallest total distance – i.e. the minimum cumulative cost through the cost matrix.
Prosecution Insights