Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The filed information disclosure statement (IDS) is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
3. As provided in 37 CFR 1.77(b), the specification of a utility application should include all sections that apply in order. Given that the current application is a continuation of two Patents, the current specification is missing section (b) known as CROSS-REFERENCES TO RELATED APPLICATIONS (See MPEP § 211 et seq.)
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent 12217736 and claims 1-16 of U.S. Patent No. 11,798,530. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1-20 of the instant application merely broadens the scope of the claims of the Patent by eliminating the elements and their functions of the claims. It has been held that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA). Also note Ex parte Rainu, 168 USPQ 375 (Bd.App.1969); omission of a reference element whose function is not needed would be obvious to one skilled in the art. All sets of claims relate to detecting audio data that captures an acoustic event at multiple assistant devices in an ecosystem that includes a plurality of assistant devices.
Below is a comparison of claim 1 of the current application and claim 1 of the patents.
Current Application
US Patent 12217736
1. A method implemented by one or more processors, the method comprising:
detecting, via one or more microphones of an assistant device located in an ecosystem that includes a plurality of assistant devices, audio data that captures an acoustic event;
processing, using an event detection model that is stored locally at the assistant device, the audio data that captures the acoustic event to generate a measure associated with the acoustic event;
receiving, from an additional assistant device co-located in the ecosystem with the assistant device, additional audio data that also captures the acoustic event, the additional assistant device being in addition to the assistant device, and the additional audio data being generated via one or more additional microphones of the additional assistant device;
processing, using the event detection model that is stored locally at the assistant device, the additional audio data that captures the acoustic event to generate an additional measure associated with the acoustic event;
processing both the measure and the additional measure to determine whether the acoustic event detected by at least both the assistant device and the additional assistant device is an actual acoustic event; and
in response to determining that the acoustic event is the actual acoustic event, causing an action associated with the actual acoustic event to be performed.
1. A method implemented by one or more processors, the method comprising:
detecting, via one or more microphones of an assistant device located in an ecosystem that includes a plurality of assistant devices, audio data that captures an acoustic event, wherein the acoustic event comprises a hotword detection event;
processing, using an event detection model that is stored locally at the assistant device, the audio data that captures the acoustic event to generate a measure associated with the acoustic event, wherein the event detection model that is stored locally at the assistant device comprises a hotword detection model that is trained to detect whether a particular word or phrase is captured in the audio data;
in response to detecting the audio data via the one or more microphones of the assistant device: anticipating detection of additional audio data via one or more additional microphones of an additional assistant device based on a plurality of historical acoustic events being detected at both the assistant device and the additional assistant device, the additional assistant device being in addition to the assistant device, and the additional assistant device being co-located in the ecosystem with the assistant device;
detecting, via the one or more additional microphones of the additional assistant device located in the ecosystem, the additional audio data that also captures the acoustic event; processing, using an additional event detection model that is stored locally at the additional assistant device, the additional audio data that captures the acoustic event to generate an additional measure associated with the acoustic event, wherein the additional event detection model that is stored locally at the additional assistant device comprises an additional hotword detection model that is trained to detect whether the particular word or phrase is captured in the additional audio data;
determining, based on the measure satisfying a threshold indicating that the particular word or phrase is captured in the audio data and based on the additional measure satisfying the threshold indicating that the particular word or phrase is captured in the additional audio data, that the acoustic event detected by at least both the assistant device and the additional assistant device corresponds to an occurrence of an actual acoustic event; and
in response to determining that the acoustic event corresponds to an occurrence of the actual acoustic event, causing one or more components of an automated assistant to be activated at one or more of: the assistant device, the additional assistant device, or a further additional assistant device.
Current Application
US Patent 11798530
1. A method implemented by one or more processors, the method comprising: detecting, via one or more microphones of an assistant device located in an ecosystem that includes a plurality of assistant devices, audio data that captures an acoustic event; processing, using an event detection model that is stored locally at the assistant device, the audio data that captures the acoustic event to generate a measure associated with the acoustic event; receiving, from an additional assistant device co-located in the ecosystem with the assistant device, additional audio data that also captures the acoustic event, the additional assistant device being in addition to the assistant device, and the additional audio data being generated via one or more additional microphones of the additional assistant device; processing, using the event detection model that is stored locally at the assistant device, the additional audio data that captures the acoustic event to generate an additional measure associated with the acoustic event; processing both the measure and the additional measure to determine whether the acoustic event detected by at least both the assistant device and the additional assistant device is an actual acoustic event; and in response to determining that the acoustic event is the actual acoustic event, causing an action associated with the actual acoustic event to be performed.
1. A method implemented by one or more processors, the method comprising:
detecting, via one or more microphones of an assistant device located in an ecosystem that includes a plurality of assistant devices, audio data that captures an acoustic event, wherein the acoustic event comprises one or more of: glass breaking, a dog barking, a cat meowing, a doorbell ringing, a smoke alarm sounding, a carbon monoxide detector sounding, a baby crying, or a door knocking;
processing, using an acoustic event detection model that is stored locally at the assistant device and that is trained to detect whether one or more particular acoustic events are captured in the audio data, the audio data that captures the acoustic event to generate one or more corresponding measures associated with the acoustic event, wherein the one or more corresponding measures associated with the acoustic event comprise one or more corresponding confidence levels associated with whether the audio data is predicted to capture one or more of the particular acoustic events;
detecting, via one or more additional microphones of an additional assistant device located in the ecosystem, additional audio data that also captures the acoustic event, the additional assistant device being in addition to the assistant device, and the additional assistant device being co-located in the ecosystem with the assistant device;
processing, using an additional acoustic event detection model that is stored locally at the additional assistant device and that is trained to detect whether one or more particular acoustic events are captured in the additional audio data, the additional audio data that captures the acoustic event to generate one or more corresponding additional measures associated with the acoustic event, wherein the one or more corresponding additional measures associated with the acoustic event comprise one or more corresponding additional confidence levels associated with whether the additional audio data is predicted to capture one or more of the particular acoustic events; determining, based on comparing the one or more confidence levels to a threshold confidence level and based on comparing the one or more additional confidence levels to the threshold confidence level or an additional threshold confidence level, whether the acoustic event detected by at least both the assistant device and the additional assistant device corresponds to an occurrence of an actual acoustic event from among the one or more particular acoustic events; and in response to determining that the acoustic event corresponds to the occurrence of the actual acoustic event, causing an action associated with the actual acoustic event to be performed.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 11-13, 15, 17-20 are rejected under 35 U.S.C. 103 as being anticipated by Thomsen (EP 3407348).
As per claims 1, 15, Thomsen teaches detecting/receiving, via one or more microphones of an assistant device located in an ecosystem that includes a plurality of assistant devices, audio data that captures an acoustic event ((figure 3 with paragraph [0033], speech recognition engine 204 in a local VRD 104 locally detects a speech event") together with figure 1 and paragraphs [0010] ("VRD network 102 includes multiple VRDs 104(1 )-(N)") and [0011] ("A VRD 104 includes at least one microphone for capturing audio commands"));
processing, using an event detection model that is stored locally at the assistant device, the audio data that captures the acoustic event to generate a measure associated with the acoustic event ([0017], [0033], the analysis of the audio signals is based on acoustic models; [0013], a Voice Recognition Device (VRD) 104 operates in a "listening" state by processing audio signals 108 that are incident on the microphones to identify spoken keywords or key phrases; [0028], [0034], the output quality metrics associated with recognized speech indicates a confidence that the recognized speech is accurate and/or correct relative to the spoken words);
receiving, from an additional assistant device co-located in the ecosystem with the assistant device, additional audio data that also captures the acoustic event, the additional assistant device being in addition to the assistant device, and the additional audio data being generated via one or more additional microphones of the additional assistant device;
processing, using the event detection model that is stored locally at the assistant device, the additional audio data that captures the acoustic event to generate an additional measure associated with the acoustic event ([0035]- [0038], detecting additional audio data by additional assistance devices, i.e. external VRD 104 in view of any of paragraphs [0014] ("At a given time, the audio signals 108 associated with a speech event are incident on the microphones included in several VRDs 104."); [0020] ("other VRDs 104 in the VRD network 102 (referred to herein as "external VRDs 104") that detected the same speech event"));
processing both the measure and the additional measure to determine whether the acoustic event detected by at least both the assistant device and the additional assistant device is an actual acoustic event ([0013], [0017], and [0018], determining based on confidence scores associated with the voice recognition device and the additional voice recognition devices whether the speech event (recognized key word or key phrase as in [0017]) is the correct event. See also [0022], for determining whether the locally detected speech event is the same as an externally detected speech event. See also, [0028], [0033], [0034]); and
in response to determining that the acoustic event is the actual acoustic event, causing an action associated with the actual acoustic event to be performed ([0023]- [0024], [0031], processing the recognized speech event and executing the corresponding command).
As per claim 11, Thomsen teaches wherein the audio data temporally corresponds to the additional audio data ([0014], [0025], timestamps corresponding to the detected speech events… are the same or close in time") that the audio data captured by the VRDs have temporal correspondence).
As per claim 12, Thomsen teaches wherein the assistant device and the additional assistant device historically detect respective audio data that captures the same acoustic event ([0035]- [0038], detecting additional audio data by additional assistance devices, i.e. external VRD 104 in view of any of paragraphs [0014] ("At a given time, the audio signals 108 associated with a speech event are incident on the microphones included in several VRDs 104."); [0020] ("other VRDs 104 in the VRD network 102 (referred to herein as "external VRDs 104") that detected the same speech event")).
As per claim 13, Thomsen teaches wherein processing both the measure and the additional measure to determine whether the acoustic event detected by both the assistant device and the additional assistant device is the actual acoustic event is in response to determining that a timestamp associated with the audio data temporally corresponds to an additional timestamp associated with the additional audio data ([0025], using timing information to determine whether the acoustic event detected by both the assistant device and the additional assistant device…).
As per claim 17, Thomsen teaches wherein the one or more processors are of a remote system that is remote from the ecosystem, and wherein the event detection model is remote from the ecosystem. ([0012], each VRD 104 is connected via a network connection to the processing system 106 that is remote from the VRD network 102. In one embodiment, the VRD 104 operates in conjunction with the processing system 106 to process audio commands captured via the microphones).
As per claims 18-20, system claims 18-20 and method claims 1, 11, 12 are related as apparatus and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 18-20 are similarly rejected under the same rationale as applied above with respect to method claims 1, 11, 12. Furthermore, Thomsen teaches one or more processors; and memory storing thereon instructions, as claimed (Fig. 2).
Claim Rejections - 35 USC § 103
6. 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.
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.
Claims 2-9 are rejected under 35 U.S.C. 103 as being unpatentable over Thomsen (EP 3407348) in view of Strope (US 20180330735).
As per claim 2, Thomsen teaches wherein the acoustic event comprises a hotword detection event, and wherein the event detection model that is stored locally at the assistant device comprises a hotword detection model that is trained to detect whether a particular word or phrase is captured in the audio data and the additional audio data ([0017], [0033], the analysis of the audio signals is based on acoustic models; [0013], a Voice Recognition Device (VRD) 104 operates in a "listening" state by processing audio signals 108 that are incident on the microphones to identify spoken keywords or key phrases; [0028], [0034], the output quality metrics associated with recognized speech indicates a confidence that the recognized speech is accurate and/or correct relative to the spoken words). Thomsen may not explicitly disclose using a trained acoustic model, as claimed. However, Thomsen’s acoustic model ([0017], [0033]) is necessarily trained. Otherwise, it wouldn’t be able to perform the audio signal analysis as in paragraphs [0017] and [0033]. Furthermore, Strope in the same field of endeavor teaches a speech recognition system having multiple acoustic models trained by different training algorithms ([0036]). Therefore, it would have been obvious at the time the application was filed to use the training procedures of Strope to train the acoustic models of Thomsen, in order to increase accuracy of speech recognition results.
As per claim 3, Thomsen teaches wherein the measure associated with the acoustic event comprises a confidence level corresponding to whether the audio data captures the particular word or phrase, and wherein the additional measure associated with the acoustic event comprises an additional confidence level corresponding to whether the additional audio data captures the particular word or phrase ([0018], [0028], generating, by the voice recognition device and the additional device, quality metrics based on confidence that the recognized speech is accurate).
As per claim 4, Thomsen teaches wherein determining that the acoustic event is the actual acoustic event comprises determining the particular word or phrase is captured in both the audio data and the additional audio data based on the confidence level and the additional confidence level ([0021]- [0022], determining whether the locally detected speech event is the same as an externally detected speech event based on characteristics/metadata extracted by the voice recognition device and external voice recognition device 104. The voice recognition device and the additional device generate quality metrics based on confidence that the recognized words or phrases is accurate, [0018], [0028]).
As per claim 5, Thomsen teaches wherein causing the action associated with the actual acoustic event to be performed comprises activating one or more components of an automated assistant, at the assistant device or the additional assistant device, in response to determining the acoustic event data indicates the audio data and the additional audio data captures the particular word or phrase ([0023]- [0024], [0031], processing the recognized speech event and executing the corresponding command).
As per claim 6, Thomsen teaches wherein the acoustic event comprises a sound detection event, and wherein the event detection model that is stored locally at the assistant device comprises a sound detection model that is trained to detect whether a particular sound is captured in the audio data and the additional audio data ([0013], Fig. 3 with paragraph [0033], speech recognition engine 204 in a local VRD 104 locally detects a speech event, together with Fig. 1 and paragraphs [0010]- [0015], wherein VRD network 102 includes multiple VRDs 104(1 )-(N)") and [0011] ("A VRD 104 includes at least one microphone for capturing audio commands")).
As per claim 7, Thomsen teaches wherein the measure associated with the acoustic event comprises a confidence level corresponding to whether the audio data captures the particular sound, and wherein the additional measure associated with the acoustic event comprises an additional confidence level corresponding to whether the additional audio data captures the particular sound ([0028], [0034], the output quality metrics associated with recognized speech indicates a confidence that the recognized speech is accurate and/or correct relative to the spoken words. See also, [0013], [0017], [0028]- [0034], and claim 6 on page 7,, using the additional/external VRDs 104 to detect whether one or more particular acoustic events are captured in the additional audio data, and determining output quality metrics corresponding to a confidence score associated with spoken content recognized from the speech event detected locally, and the second set of characteristics includes a second confidence score associated with spoken content recognized from the speech event detected by the external device).
As per claim 8, Thomsen teaches wherein determining that the acoustic event is the actual acoustic event comprises determining the particular sound is captured in both the audio data and the additional audio data based on the confidence level and the additional confidence level ([0014], [0018], determining whether each of the VRDs detect the same speech event based on associated confidence levels).
As per claim 9, Thomsen teaches wherein causing the action associated with the actual acoustic event to be performed comprises: generating a notification that indicates an occurrence of the sound detection event; and causing the notification to be presented to a user that is associated with the ecosystem via a computing device of the user ([0013], [0031], notifying the user of the result of processing the audio commands and any associated actions. The notification can be visual and/or audio-based).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Thomsen (EP 3407348) in view of Strope (US 20180330735), and further in view of Horling (US 2018/0330589).
As per claim 10, Thomsen does not explicitly disclose wherein the particular sound comprises one or more of: glass breaking, a dog barking, a cat meowing, a doorbell ringing, a smoke alarm sounding, a carbon monoxide detector sounding, a baby crying, or a door knocking.
Horling in the same field of endeavor teaches, at paragraph [0167], the example of Fig. 7B, wherein the audio event has been classified as glass breaking in the kitchen. Therefore, it would have been obvious at the time the application was filed to use the sound detection feature of Horling with the system of Thomsen in view of Strope, in order to enhance audible activity detection and provide better assistance to the user.
Claim 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Thomsen (EP 3407348)
As per claims 14, 16, Thomsen may not explicitly disclose wherein the assistant device is a first-party assistant device manufactured by a first-party, wherein the additional assistant device is a third-party assistant device manufactured by a third-party, and wherein the third-party is a distinct party from the first-party. However, using a third-party device is well known in the art; and Thomsen teaches, at figures 1 and 2 and paragraphs [0013] and [0015], VRDs 104 communicate with one another over the network connection to co-ordinate one or more actions performed by the VRDs 104; and further, each VRD 104 is connected via a network connection to the processing system 106 that is remote from the VRD network 102. Therefore, it would have been obvious at the application was filed for the system of Thomsen to communicate with a remote third party device to perform the claimed steps. This would save on costs and enhance functionality.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/ABDELALI SERROU/ Primary Examiner, Art Unit 2659
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