DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission filed on 12/02/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-19 are currently pending and examined below.
Drawings
2. The drawings filed on 12/02/2024 have been accepted and considered by the Examiner.
Information Disclosure Statement
3. The Information Statements (IDSs) filed on 12/02/2024, 03/31/2026 have been accepted/considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
Priority
4. The Applicants priority to United States Provisional Application # 62774743 filed on December 3, 2018, has been accepted and considered in this office action.
Double Patenting
5. The non-statutory 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 time-wise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory obviousness-type 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 Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed e-terminal disclaimer (e-TD) in compliance with 37 CFR 1.321 (c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign an e-terminal disclaimer. An e-terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1-19 of the instant Application are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent # 12159622. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are broader in scope than those of U.S. Patent # 12159622 and hence the claims of U.S. Patent # 12159622 can anticipate those of the present invention. That is, the claims of U.S. Patent # 12159622 contain every limitation of the claims of the present application or the claims of the present application are obvious variants thereof. It should be noted that this is in fact a non-provisional non-statutory obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
As an example; claim 1 of the instant application and claim 1 of U.S. Patent # 12159622 both contain a method implemented by one or more processors, the method comprising receiving, from a client device and via a network, an automated assistant request that includes audio data that captures spoken input of a user, wherein the audio data is captured at one or more microphones of the client device, and a text dependent (TD) user measure generated locally at the client device using a TD speaker recognition model stored locally at the client device and using a TD speaker embedding stored locally at the client device, the TD speaker embedding being for a particular user; processing at least a portion of the audio data using a text independent (TI) speaker recognition model to generate TI output; determining a TI user measure by comparing the TI output with a TI speaker embedding that is associated with the automated assistant request, and that is for the particular user; determining whether the particular user spoke the spoken input using both the TD user measure and the TI user measure; in response to determining the spoken input is spoken by the particular user generating responsive content that is responsive to the spoken input and that is customized for the particular user and transmitting the responsive content to the client device to cause the client device to render output based on the responsive content. One of ordinary skill in the art would recognize that it would have been obvious at the time of the invention to drop narrower limitations in order to have a patent with wider applicability and freedom to operate. In other words, the narrower claim 1 of U.S. Patent # 12159622 anticipate the broader claim 1 of the instant application. Also, removal of the additional steps is obvious: In re Karlson, 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before".
Claim 1-19 of the instant Application are also rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent # 11527235. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are broader in scope than those of U.S. Patent # 11527235 and hence the claims of U.S. Patent # 11527235 can anticipate those of the present invention. That is, the claims of U.S. Patent # 11527235 contain every limitation of the claims of the present application or the claims of the present application are obvious variants thereof. It should be noted that this is in fact a non-provisional non-statutory obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
As an example; claim 8 of the instant application and claim 15 of U.S. Patent # 11527235 both outline a method, implemented by one or more processors, the method comprising receiving, from a client device and via a network, an automated assistant request that includes audio data that captures spoken input, wherein the audio data is captured at one or more microphones of the client device; determining that a first user profile and a second user profile are associated with the automated assistant request; responsive to determining that the first user profile and the second user profile are associated with the automated assistant request initiating generating of first responsive content that is customized for the first user and that is responsive to the spoken input; initiating generating of second responsive content that is customized for a second user and that is responsive to the spoken input; prior to completion of generating the first responsive content and the second responsive content, processing at least a portion of the audio data using a text independent (TI) speaker recognition model to generate TI output; determining that the first user spoke the spoken input by comparing a first user speaker embedding corresponding to the first user profile and the TI output; in response to determining the first user spoke the spoken input transmitting, to the client device, the first responsive content without transmitting the second responsive content to the client device. One of ordinary skill in the art would recognize that it would have been obvious at the time of the invention to drop narrower limitations in order to have a patent with wider applicability and freedom to operate. In other words, the narrower claim 15 of U.S. Patent # 11527235 anticipates the broader claim 8 of the instant application. Also, removal of the additional steps is obvious: In re Karlson, 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before".
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 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.
6. Claims 1-2 and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Falkson (U.S. Patent Application Publication # 2018/0201226 A1). Falkson is of the record, having been disclosed during the prosecution of the parent Applications 18078476 and 17046994.
With regards to claim 1, Falkson teaches a method implemented by one or more processors, the method comprising receiving, from a client device and via a network, an automated assistant request that includes: audio data that captures spoken input of a user, wherein the audio data is captured at one or more microphones of the client device (Para 229, teaches that the systems and methods of this disclosure can be implemented in conjunction with a special purpose computer, a programmed microprocessor. Paragraphs 218-219, teach that the vehicle system may receive an audio sample such as a passenger in a backseat may make an oral command to an AI assistant. Such a request may be recorded as an audio sample. The audio sample may be sent via a network connection to a server. This may occur when a voice of a user of the vehicle, including a driver or a passenger, or a user of an application as discussed herein, or a caller calling into an entity associated with the vehicle manufacturer, is received via a microphone associated with the system),
and a text dependent (TD) user measure generated locally at the client device using a TD speaker recognition model stored locally at the client device and using a TD speaker embedding stored locally at the client device (Paragraphs 183 and 194, teach that onboard processor may perform analysis using audio sample received in relation to the voiceprints associated with user profiles stores onboard the vehicle and generate an ‘onboard match score’ e.g. TD speaker recognition model stored locally as per the voice biometric analysis system that may generate a voiceprint file associated with the user),
the TD speaker embedding being for a particular user (Paragraphs 134 and 183, teach that audio sample has been processed and voice biometric analysis system may generate a voiceprint file associated with the user; furthermore, generated from a setup password or passphrase i.e. text-dependent);
processing at least a portion of the audio data using a text independent (TI) speaker recognition model to generate TI output (Paragraphs 134 and 187, teach that voice biometric analysis may be one or more of text-dependent and text-independent, this indicates that a portion of the audio is processed through TI speaker recognition model as per voice biometric analysis system, as per the text, a command is spoken and a verification of the user i.e. TI output is generated);
determining a TI user measure by comparing the TI output with a TI speaker embedding that is associated with the automated assistant request, and that is for the particular user (Para 194, teaches that onboard processor may perform analysis using audio sample received in relation to the voiceprints associated with user profiles stores onboard the vehicle and generate an ‘onboard match score’ e.g. text-independent recognition model as per the voice biometric analysis system generating a voiceprint for a user and storing that voiceprint to verify the request as per “unlock the doors” as corresponding to a particular identity of a user);
determining whether the particular user spoke the spoken input using both the TD user measure and the TI user measure (Paragraphs 133 and 187, teach analyzing voice a speaker using both text-dependent and text-independent analysis measures to identify and verify speaker);
and in response to determining the spoken input is spoken by the particular user generating responsive content that is responsive to the spoken input and that is customized for the particular user and transmitting the responsive content to the client device to cause the client device to render output based on the responsive content. (Paragraphs 158-159 and 187, teach that interior microphones may be used to listen to a command or through a user device such as a smartphone to enter commands and perform command i.e. once a particular user has been verified from their speech/audio command, the command itself is performed, such as, unlocking the doors. All this is achieved by means of transmission of data packets including audio data packets as outlined in paragraphs 177-190).
With regards to claim 2, Falkson teaches the method of claim 1, wherein the automated assistant request received from the client device via the network further includes the TI speaker embedding for the particular user (Paragraphs 213-219 and 194, teach that the audio sample including any request/command from the voice biometric analysis system onboard the vehicle may be sent via a network connection. Furthermore, the voiceprint version e.g. speaker embedding from the voice biometric analysis system that used text-independent speaker verification models, and user ID information may also be sent via a network. Comparison of received speaker embedding is also taught in these sections).
With regards to claims 13-14, these are system claims for the corresponding method claims 1-2. These two sets of claims are related as method and system of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 13-14 are similarly rejected under the same rationale as applied above with respect to method claims 1-2.
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 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.
7. Claims 3-6 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Falkson in view of Huang (U.S. Patent Application Publication # 2019/0027152 A1). Huang is also of the record, having been disclosed during the prosecution of the parent Applications 18078476 and 17046994.
With regards to claim 3, Falkson may not explicitly detail the limitation wherein determining whether the particular user spoke the spoken input using both the
TD user measure and the TI user measure comprises determining a particular user probability measure which indicates the probability the particular user spoken the spoken input by combining the TD user measure and the TI user measure. In a related field of endeavor of speaker verification, Huang teaches this limitation (Para 31, teaches determining a probability for a certain speaker by combing score e.g. measures correspond to text-dependent and text-independent speaker verification scores in combination);
Huang also teaches determining whether the particular user spoke the spoken input by determining whether the user probability measure satisfies a threshold. In a related field of endeavor of speaker verification (Para 33, teaches a verification comparison determining whether the particular user spoke the input by determining whether the score e.g. user probability measure exceeds a threshold e.g. scores in relation to a threshold to determine verification);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Huang to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example, speaker verification. Further, Including Huang’s features would have improved the users of Falkson, with the benefit of obtain a more confident classification so as to improve the confidence in user identity (Huang, paragraphs 22-31).
With regards to claim 4, Falkson may not explicitly detail the limitation wherein combining the TD user measure and the TI user measure comprises utilizing a
first weight for the TD user measure in the combining and utilizing a second weight for the TI user measure in the combining. In a related field of endeavor of speaker verification, Huang teaches this limitation (Para 31, teaches that a text dependent and text-independent user measure e.g. score may differ and be used in combination. A higher weight may be placed on the TD measure versus the TI measure e.g. first weight for TD score and second weight for TI score when combining);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Huang to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example, speaker verification. Further, Including Huang’s features would have improved the users of Falkson, with the benefit of obtain a more confident classification so as to improve the confidence in user identity (Huang, paragraphs 22-31).
With regards to claim 5, Falkson may not explicitly detail the limitation further comprising determining the first weight and the second weight based on the length of the audio data or the spoken input. Huang teaches this limitation (Para 47, teaches the processor can combine the TI SV score and the TD SV score using any suitable technique to generate a combined SV score. For example, a simple average or a weighted average may be used. In some examples, as in the case of weighted average, the weighting can be determined by duration e.g. length of speech);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Huang to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example, speaker verification. Further, Including Huang’s features would have improved the users of Falkson, with the benefit of obtain a more confident classification so as to improve the confidence in user identity (Huang, paragraphs 22-31).
With regards to claim 6, Falkson may not explicitly detail the limitation further comprising determining the first weight and the second weight based on a magnitude of the TD user measure. Huang teaches this limitation (Paragraphs 31 and 47, teach that a higher weight can be given to the TD portion score in the combination of scores, this indicates that due to its higher weight indicative of the magnitude of the TD score and second weight is provided for the TI score as to form a composite score. Furthermore, these magnitudes of the TD user measure may be determined by weighted averages, SNR, duration, Phonetic richness of the segments, or any combination thereof);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Huang to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example, speaker verification. Further, Including Huang’s features would have improved the users of Falkson, with the benefit of obtain a more confident classification so as to improve the confidence in user identity (Huang, paragraphs 22-31).
With regards to claims 15-18, these are system claims for the corresponding method claims 3-6. These two sets of claims are related as method and system of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 15-18 are similarly rejected under the same rationale as applied above with respect to method claims 3-6.
8. Claims 3-6 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Falkson in view of Koishida (U.S. Patent Application Publication # 2018/0233142 A1). Koishida is also of the record, having been disclosed during the prosecution of the parent Applications 18078476 and 17046994.
With regards to claim 8, Falkson teaches a method implemented by one or more processors the method comprising receiving, from a client device and via a network, an automated assistant request that includes audio data that captures spoken input, wherein the audio data is captured at one or more microphones of the client device (Paragraphs 227-229, teach that the systems and methods of this disclosure can be implemented in conjunction with a special purpose computer, a programmed microprocessor. Changes, additions, and omissions to this sequence can occur without materially affecting the operation of the disclosed embodiments, configuration, and aspects. Paragraphs 142-144 and figures 20A, 21A, and 22, teach collection of voice samples and to identify the user either in parallel or in series as seen by the figures where scenarios are given on when to update between the client device and the server where either or both are allowed to have a biometric analysis system and receive audio samples to identify a user and update voiceprints at an indicated period of time. Paragraphs 218-219, teach that the vehicle system may receive an audio sample such as a passenger in a backseat may make an oral command to an AI assistant. Such a request may be recorded as an audio sample. The audio sample may be sent via a network connection to a server. This may occur when a voice of a user of the vehicle, including a driver or a passenger, or a user of an application as discussed herein, or a caller calling into an entity associated with the vehicle manufacturer, is received via a microphone associated with the system);
determining that a first user profile and a second user profile are associated with the automated assistant request (Paragraphs 143 and 193, teach that after receiving the audio sample, the vehicle system may transmit, or attempt to transmit, the audio sample to a network connected server via an onboard communication system. Such a network connected server may be enabled to perform a voice biometric analysis of the audio sample and compare the analysis with voiceprints for any users associated with the vehicle or other voiceprints registered with user profiles in the system. Two profiles are determined from the automated assistant request, “give me directions to mother’s” or “unlock the car for mother” i.e. user profile for user giving the command and mother user profile);
responsive to determining that the first user profile and the second user profile are associated with the automated assistant request initiating generating of first responsive content that is customized for the first user and that is responsive to the spoken input (Para 187, teaches that a user may approach a car and from ten yards away may speak “unlock the doors,” and the vehicle may respond by detecting the audio command, collecting an audio sample of the voice, sending the audio sample to a network connected server, processing the audio sample to identify and verify the speaker, receive a match score from the network, determine based on the results whether the doors should be unlocked, and/or unlock the doors. The vehicle may also greet the user by name and change a number of vehicle settings based on the identity of the speaker. In terms with example of “unlock the car for mother”, processing the audio sample to identify and verify the speaker, receive a match score from the network, determine based on the results whether the doors should be unlocked, and/or unlock the doors. i.e. responsive in customization from the first user profile and the spoken input to unlock the doors);
initiating generating of second responsive content that is customized for a second user and that is responsive to the spoken input (Para 187, teaches “unlock the car for mother” command wherein unlocking the door for second profile i.e. mother and determining their permissions in regards to their user profile i.e. the driver seat may move to a correct position and the mirrors may move accordingly);
prior to completion of generating the first responsive content and the second responsive content, processing at least a portion of the audio data using a text independent (TI) speaker recognition model to generate TI output (Paragraphs 131-134 and 194, teach that the voice biometric analysis may be one or more of text-dependent and/or text-independent and a voiceprint is a model wherein the vehicle's onboard processor may begin processing the audio sample onboard the vehicle, performing a voice biometric analysis to output analysis results i.e. TI output. Furthermore, in order to determine how to generate the first and second responsive content it must process using the voice biometric analysis may be one or more of text-dependent and/or text-independent, where voiceprint created may be a spectrogram e.g. a measure of signal strength over time. A voice print may be used by a voice recognition software program, either onboard the vehicle, on a network connected server, or both, to one or more of identify, verify, or authenticate a user's voice);
determining that the first user spoke the spoken input by comparing a first user speaker embedding corresponding to the first user profile and the TI output (Paragraphs 131 and 194, teach that the onboard processor may compare the analysis results with voiceprints associated with user profiles stored onboard the vehicle and generate an onboard match score wherein such users may be registered with a user profile comprising a voice print i.e. user profiles contain a voice print);
in response to determining the first user spoke the spoken input (Paragraphs 143 and 221, teach that if the voiceprint was updated, the master database stored on the server may be updated reflecting the new voiceprint and a new voiceprint version ID may be generated. At this point, the server may optionally send an updated voiceprint to all vehicles associated with the associated User ID. The method may end responsive content to user assistant request determined from first user profile in spoken input):
However, Falkson fails to explicitly detail transmitting, to the client device, the first responsive content without transmitting the second responsive content to the client device. In a related field of endeavor of entity tracker and providing responsive content through interactions, Koishida discloses this limitation (Para 138, teaches an example, wherein the device setting alternatively or additionally may authorize the first user to receive high-value information based on an organizational relationship between the first user and the high value information, and may authorize the other user to receive filtered content that does not include high-value information based on a different organizational relationship between the other user and the high-value information. In such an example, the instructions alternatively or additionally may be further executable to receive a request from another user in the environment, identify content that the other user is authorized to receive from the intelligent assistant computer, and responsive to identifying the content, lower the blocking threshold. In such an example, the instructions alternatively or additionally may be executable to stop blocking subsequent responses to another user responsive to receiving a command from the first user instructing the intelligent assistant computer to respond to the other user);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Koishida to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example both generating responses according to identification of spoken inputs. Further, doing so would have provided the users of Falkson, with the added benefits of enabling natural user interface experiences where privacy settings are included as to determine how to output selected interpersonal content and with permissions indicated on users allowed to access interpersonal content with added benefits of security in relation to privacy (Koishida, paragraphs 2 and 99).
With regards to claim 9, Falkson may not explicitly detail the limitation wherein determining that the first user spoke the spoken input occurs prior to completion of generating of the second responsive content customized for the second user, and further comprising: in response to determining the first user spoke the spoken input: halting generating of the second responsive content customized for the second user. However, Koishida discloses this aspect (Para 138, teaches an example, wherein the device setting alternatively or additionally may authorize the first user to receive high-value information based on an organizational relationship between the first user and the high value information, and may authorize the other user to receive filtered content that does not include high-value information based on a different organizational relationship between the other user and the high-value information. In such an example, the instructions alternatively or additionally may be further executable to receive a request from another user in the environment, identify content that the other user is authorized to receive from the intelligent assistant computer, and responsive to identifying the content, lower the blocking threshold. In such an example, the instructions alternatively or additionally may be executable to stop blocking subsequent responses to another user responsive to receiving a command from the first user instructing the intelligent assistant computer to respond to the other user);
It would have been obvious to one of ordinary skill in the art to apply the teachings of Koishida to the method of Falkson. Doing so would have been predictable to one of ordinary skill in the art given the similar nature between the two disclosures, for example both generating responses according to identification of spoken inputs. Further, doing so would have provided the users of Falkson, with the added benefits of enabling natural user interface experiences where privacy settings are included as to determine how to output selected interpersonal content and with permissions indicated on users allowed to access interpersonal content with added benefits of security in relation to privacy (Koishida, paragraphs 2 and 99).
Allowable Subject Matter
9. Claims 7, 10-12 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the double patenting rejections are overcome. The prior art of record, alone or in combination, does not currently suggest or teach the invention as outlined in these claims. More detailed reasons for allowance will be outlined as and when the Application proceeds to allowability.
Conclusion
10. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Viswanathan (U.S. Patent Application Publication # 2019/0189132 A1), Thomsen (U.S. Patent Application Publication # 2018/0342244 A1). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)
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