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
In response to the amendment filed 9/15/2025; claims 8-14, 21, and 22 are pending; claims 1 – 7, 15 – 20 have been cancelled.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8, 21 – 22 are rejected under 35 U.S.C. 103 as being unpatentable over Wallack et al. (US 2022/0364829 A1) in view of Souden et al. (US 11508388 B1).
Re claim 8:
Wallack teaches 8. A method of identifying events and language during an interaction (Wallack, Abstract) comprising:
in response to identifying the officer (Wallack, [0017]; [0032]): transcribing the portion of the audio signal associated with the officer to text through a body camera analytics platform, and providing the text as input to a classifier to identify at least one of negative language or positive language in the interaction (Wallack, [0091], “natural language processing”; fig. 1; fig. 5).
Wallack does not explicitly disclose identifying an officer speaking to a civilian, from an audio signal and based on a quality metric value for a portion of the audio signal associated with the officer being higher than a quality metric value for a portion of the audio signal associated with the civilian.
Souden et al. (US 11508388 B1) teaches a device for processing audio signals in a time-domain includes a processor configured to receive multiple audio signals corresponding to respective microphones of at least two or more microphones of the device, at least one of the multiple audio signals comprising speech of a user of the device (Souden, Abstract). Souden teaches identifying an officer speaking to a civilian, from an audio signal and based on a quality metric value for a portion of the audio signal associated with the officer being higher than a quality metric value for a portion of the audio signal associated with the civilian (Souden, col. 8, lines 10 – 25, “The machine learning model 302 may have been further trained by providing different speech signals mixed with different noise signals, and/or different interfering talkers, as well as different environments (e.g., different room configurations) … The cost functions may be configured to maximize various speech signal metrics including … signal-to-distortion ratio (SDR), signal-to-interference ratio (SIR), signal-to-noise ratio enhancement (SNRE), signal-to-artifacts ratio (SAR), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ)”; col. 8, lines 53 – 64; col. 7, lines 16 – 33, “the machine learning model 302 may efficiently separate the input audio signals 301 in a manner to output an output audio signal 303 that is optimized for that application's cost function of the machine learning model 302”; Souden teaches a system capable of separating input audio signals with machine learning model / cost function such as STOI / quality metric value). Therefore, in view of Souden, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Wallack, by providing machine learning model 302 may have been further trained by providing different speech signals based on different user as taught by Souden, in order to provide the machine learning model based at least in part on an expected position of a user of the electronic device (e.g., the speaker of interest) and expected positions of the respective microphones on the electronic device (Souden, col. 15, lines 3 - 17).
Re claim 21:
21. The method of claim 8, wherein:
the quality metric value for the portion of the audio signal associated with the officer and the quality metric value for the portion of the audio signal associated with the civilian are each associated with at least one of a short time intelligibility measure, a time domain segmental signal to noise ratio, a frequency weighted segmental signal to noise ratio, or a normalized covariance metric (Souden, col. 8, lines 10 – 25).
Re claim 22:
22. The method of claim 8, wherein:
the classifier is trained based on officer-specific language patterns (Souden, col. 8, lines 10 – 25, “The machine learning model 302 may have been further trained by providing different speech signals mixed with different noise signals).
Claims 9 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wallack and Souden as applied to claim 8 above, and further in view of Iyengar et al. (US 2024/0177524 A1).
Re claims 9 – 11:
9. The method of claim 8 wherein the officer is alerted of a negative interaction (Wallack, fig. 5; [0025], “provide feedback (e.g., immediate feedback and/or delayed feedback) based on the user's selections and actions during the simulation”; [0061], “220. The feedback may be responsive to a user input received via user interface 228. For example, when a physical input is applied to a trigger of equipment 220, an audible output (e.g., sound) and visual output (e.g., light) may be provided via user interface 228 in accordance with the physical input”).
Wallack does not explicitly disclose provided training suggestions to improve the negative interaction.
Iyengar et al. (US 2024/0177524 A1) teaches a system and method for automatically classifying an activity of a user during a proposal by an agent to a user based on micro-expression and emotion of the user that provides a succeeding response to the agent such that the proposal becomes successful using an artificial intelligence model (Iyengar, Abstract). Iyengar teaches 9. provided training suggestions to improve the negative interaction (Iyengar, [0045], “used by the police and investigative agencies while questioning the suspect”; [0006], “(ii) record a voice of the agent and a voice of the user … (c) … the intensity of the at least one emotion to classify an activity into at least one of positive events or negative events … provide a successful response to the agent … the determined intensity of the at least one emotion of the user during the proposal in real-time, or (2) the response is detected if a question is from the standard training record”; [0053], “questions from a standard training record”). 10. The method of claim 9 wherein the suggestions compare a response of the officer to peers of the officer. 11. The method of claim 10 wherein the suggestions compare a civilian response of the civilian to interactions of the peers of the officer (Iyengar, [0014], “The method includes training the artificial intelligence model using historical interactive sequences of audio-visual information between the user and the agent, historical activities of the user, historical proposals, historical users, historical agents, historical succeeding responses”; the AI model is trained based on historical users and agents (or peers of the officer)). Therefore, in view of Iyengar, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Wallack, by providing a successful response to the agent record as taught by Iyengar, in order to provide fast user interaction to improve user’s conversational experience.
Claims 12 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wallack, Souden and Lyengar as applied to claim 11 above, and further in view of Divine et al. (US 2019/0050774 A1).
Re claims 12 – 13:
Wallack does not explicitly disclose providing response with less negative language.
Divine (US 2019/0050774 A1) teaches methods, systems, and apparatuses of output suggestions to users based on current or upcoming inter-personal interactions. Divine further teaches 12. The method of claim 11 wherein the suggestions assert that the officer could achieve less civilian negative response by using less negative language. 13. The method of claim 12 wherein the method surfaces interactions where the officer failed to use explanation and received high civilian noncompliance (Divine, [0015], “subset of emotions and generate at least one suggestion for the user with respect to the participant and the interaction by matching one or more of the emotions from the subset of emotions”; [0055], “the emotional intelligence engine 120 to refine/adjust future communication/interaction suggestions from the engine 120.”; [0120], “help improve police interaction with one or more participants … provide the officer with helpful (and legally useful) suggestions via the output generator 130. Such suggestions can be useful to help ensure the police officer asks the right questions to determine admissible evidence … provide respectful suggestions in interacting with users. The officer's body camera can record the interactions to provide feedback 140”). Therefore, in view of Divine, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Wallack, by providing respectful suggestions (positive response) as taught by Divine, since such suggestions can be useful to help ensure the police officer asks the right questions to determine admissible evidence, for example. The system may know more than the officer could ever know and can provide specific suggestions to solve crimes quicker and provide respectful suggestions in interacting with users (Divine, [0120]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wallack, Souden and Lyengar as applied to claim 8 above, and further in view of Day (US 2023/0070179 A1)
Re claim 14:
Wallack does not explicitly disclose verification interface.
Day teaches a health status recording and reporting system includes a digital framework that includes a pattern recognition module configured to record and report a health status of a user (Day, Abstract). Day further teaches 4. The method of claim 8 further comprising a verification interface that utilizes the method and improves accuracy of predictions by the verification interface by analyzing "yes" and "no" clicks (Day, [0131]; [0132]; [0134]; fig. 7, 740, “Yes”, “No”). Therefore, in view of Day, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Wallack, by providing the verification interface as taught by Day, since the yes and no interpreted answers can be used by the machine learning engine by comparing the other user's personal information with AI rules training to predict the accuracy of the answer (Day, [0132]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 8-14, 21, and 22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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).
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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/JACK YIP/ Primary Examiner, Art Unit 3715