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 .
DETAILED ACTION
Claims 1-3 and 5-19 are pending. Claims 1, 11, 17, and 19 are independent.
Claims 2-3 and 5-10 depend from Claim 1.
Claims 12-16 depend from Claim 11.
Claims 18-19 depend from Claim 17.
Claim 4 is cancelled.
This Application was published as U.S. 2024/0412735.
Response to Amendment
Examiner thanks Applicant for the response filed on 8 Apr 2026 which has been correspondingly accepted and considered in this office action. Claims 1-3 and 5-19 are pending.
Response to Arguments
With regards to Claim Rejections - 35 USC § 103, Applicant has provided arguments, see pages 10-13, filed 8 Apr 2026 and amended claims 1-3, 5-13, 17, and 19. As a result, amendments to claims and arguments have been fully 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.
While Applicant arguments are moot, a few comments are warranted. Applicant attempts to provide arguments for claim 4, but then cites “The Office contends that Heigold discloses ''wherein updating the classifier comprises: aggregating parameters of the personalised trained ML model received from a plurality of user devices," citing to para. [0099].” (Applicant arguments page 11) First of all, the cited claim language is for claim 5, whereas the citation to Heigold Par [0099] is to claim 4. From this statement, it is not clear if applicant intends to argue claim 4 or 5. If applicant intends for this statement to be arguments for claim 5, the citation to Heigold Par [0098-99] is irrelevant because the 9 Jan 2026 Office Action cites to Heigold Par [0046] for claim 5.
On the other hand, if Applicant is arguing with respect to claim 4 a few comments are warranted. First, the 9 Jan 2026 Office action admits that Heigold does not teach a classifier so there is no need to argue that Heigold does not teach a classifier. Secondly, other references are used to teach a classifier, such as Kwon (US2021/0134302) and Wang et al. (US2021/0110833) as will be explained in more detail in the office action below.
With regards to objections to drawings, Applicant has amended specification filed 8 Apr 2026. As a result, the objection to the drawings have been withdrawn. As for Priority, Applicant requested receipt of priority on 12 Jul 2024. Yes, priority documents were received, and priority is reflected on BIB data sheet and Office Action summary, see attached.
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 10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 depends on cancelled claim 4. Per MPEP 608.01(n)(V) rejection and objection, If the base claim has been canceled, a claim which is directly or indirectly dependent thereon should be rejected as incomplete. For the sake of examination, it will be assumed that claim 10 will depend on claim 2 which claim 4 originally depended on before it was cancelled.
Claim Rejections - 35 USC § 103
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 1-3, 5-6, 8, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Heigold et al. (US2017/0069327 hereinafter Heigold) in view of Kwon (US2021/0134302 hereinafter Kwon) in further view of Wang et al. (US2021/0110833)
With regards to claim 1, Heigold teaches:
A computer-implemented method, performed by a server, for personalising a trained speaker verification machine learning, ML, model for specific users, the method comprising: [Heigold Fig 1 teaches system of trained speaker verification with ML model (144) for specific users that can be implemented on back end server, middleware application server, or front end component, “or any combination of such back end, middleware, or front end components” (Par [0118])]
obtaining at least one audio sample of the voice of a specific user; [Heigold Fig 1 teaches verification utterance (154) or enrollment utterance (152) as audio sample for a user]
updating the classifier using parameters of the personalised ML model. [Heigold Fig 6 item 608 teaches “speaker model for the enrolled user may then be updated based on the verification utterance” (Par [0099]) where updating the model is updating parameters using the verification utterance provided on the user device]
With regards to claim 1, Heigold fails to teach:
identifying, using the at least one audio sample and a classifier, a group of users that have a similar voice to the voice of the specific user;
selecting, from a database, a set of audio samples of voices corresponding to the identified group of users;
With regards to claim 1, Kwon teaches:
identifying, using the at least one audio sample and a classifier, a group of users that have a similar voice to the voice of the specific user; [Kwon Figs 2-3 teaches using the audio sample from a user (step S210) and then obtaining a plurality of user voice groups (step 220) where “processor 170 of the disclosure may classify user voices having similar utterance characteristics.” (Par [0055]) While Kwon does not specifically state using a classifier, Kwon states the processor uses “at least one of a machine learning, a neural network, or a deep learning algorithm as a rule-based or an artificial intelligence algorithm” (Par [0048]) where classifying data can be done using a neural network classifier (see Wang Par [0092])]
selecting, from a database, a set of audio samples of voices corresponding to the identified group of users; and [Kwon Fig 2 teaches selecting a plurality of user voice groups (step 220) that correspond to the identified group of users. While Kwon doesn’t specifically state selecting data from a database it is known to select voice data from a database. (See Kwon et al. (US2022/0230648 Par [0011])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold with the speaker verification system using voice groups as taught by Kwon. The motivation to combine the teachings of Heigold and Kwon is because Kwon teaches “speaker recognition is possible only with the user voice accumulated according to the operation of the electronic apparatus 100, thereby improving the user convenience” (Par [0054]) which increases the capabilities of the invention of Heigold by improving user experience]
With regards to claim 1, Heigold in view of Kwon fails to teach:
transmitting the selected set of audio samples to a user device used by the specific user, for personalising the trained ML model for the user using the set of audio samples; and
With regards to claim 1, Wang teaches:
transmitting the selected set of audio samples to a user device used by the specific user, for personalising the trained ML model for the user using the set of audio samples. [Wang Fig 2C teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where the “second model may be deployed in a remote server, cloud, client-side device, etc.” (Par [0045])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold and Kwon with the personalized speaker verification system as taught by Wang. The motivation to combine the teachings of Heigold and Kwon with Wang is because Wang teaches an improvement over traditional speaker verification system that “uses a general speaker recognition model for all users (one-for-all) without any personalized update for target user, and hence lacks robustness and flexibility.” (Par [0004]) which increases the capabilities of the invention of Heigold and Kwon to be more flexible and robust]
With regards to claim 2, Heigold in view of Kwon and Wang teaches: All the limitations of claim 1
wherein identifying a group of users comprises using a classifier to: process the at least one audio sample of the voice of the specific user to determine characteristics of the voice of the specific user, and [Kwon Fig 2 teaches “processor 170 performs an operation corresponding to each user voice by recognizing the plurality of user voices input to the microphone 160 (S210)” (Par [0054]) where a processor can use classifier as previously discussed (Par [0048,92])]
identify, based on the determined characteristics of the voice of the specific user, a group of users, from a plurality of groups, that have a similar voice to the voice of the specific user. [Kwon Fig 2 step 230 (Par [0054])]
With regards to claim 3, Heigold in view of Kwon and Wang teaches: All the limitations of claim 1
wherein selecting a set of audio samples comprises selecting a set of audio samples from the identified group of users which are most similar to the voice of the specific user. [Kwon Fig 3 teaches “processor 170 of the disclosure may classify user voices having similar utterance characteristics based on the utterance characteristics of the plurality of user voices into voice group 1, voice group 2, voice group 3, . . . , and voice group k, and the like (S310)” (Par [0055])]
With regards to claim 5, Heigold in view of Kwon and Wang teaches: All the limitations of claim 4
wherein updating the classifier comprises: aggregating parameters of the personalised trained ML model received from a plurality of user devices. [Heigold teaches “enrolling one or more new users” (Par [0046]) which is aggregating parameters for the model received from the user devices. While training of the model does not have to be done for each new user, “enrollment, verification, or both, may be provided to the computing system 120 and added to the training data so that the neural network (and thus the speaker verification model) may be regularly updated based using newly collected training data.” (Par [046])]
With regards to claim 6, Heigold in view of Kwon and Wang teaches: All the limitations of claim 5
wherein aggregating parameters comprises aggregating parameters received from a plurality of user devices in the identified group of users. [Heigold teaches “enrolling one or more new users” (Par [0046]) which is aggregating parameters for the model received from the user devices in the group of users]
With regards to claim 8, Heigold in view of Kwon and Wang teaches: All the limitations of claim 5
wherein aggregating parameters comprises aggregating parameters received from a plurality of groups of users. [Heigold teaches “enrolling one or more new users” (Par [0046]) which is aggregating parameters for the model received from user devices of a plurality of groups]
With regards to claim 10, Heigold in view of Kwon and Wang teaches: All the limitations of claim 4
wherein updating the classifier comprises: receiving, from at least one user device, an embedding corresponding to a positively-verified audio input and a pseudo-label corresponding to the positively-verified audio input; and [Heigold teaches “a reference vector or other set of values corresponding to the user 102. The reference vector or other set of values may constitute a speaker model that characterizes distinctive features of the user's voice” (Par [0056]) where a vector or other value is an embedding that characterizes the user’s voice or audio input and includes a label for enrolled data]
retraining the classifier using the received embedding and pseudo-label.
[Wang Fig 2 teaches deploying the second model on the user device (Par [0045]) where the “positive/negative sample vectors are output from the embedding layer 208 of the first model” (Par [0098]) which is used to retrain the first model to create a second model]
With regards to claim 19, Heigold teaches:
A system for personalising a trained speaker verification machine learning, ML, model for specific users, the system comprising: [Heigold Fig 1 teaches system of trained speaker verification with ML model (144) for specific users that can be implemented on back end server, middleware application server, or front end component, “or any combination of such back end, middleware, or front end components” (Par [0118])]
a central server comprising at least one processor coupled to memory for: [Heigold teaches computing device (102) that may “include one or more processors (e.g., a digital processor, an analog processor … and one or more memories (e.g., permanent memory, temporary memory, non-transitory computer-readable storage medium)” (Par [0040]) where the computing system (102) can be on a central server (Par [0038])]
obtaining at least one audio sample of the voice of each specific user of a plurality of user devices; [Heigold Fig 1 teaches verification utterance (154) or enrollment utterance (152) as audio sample for a specific user and the model may be used on “many different client devices” (Par [0055])]
updating the classifier using parameters of the personalised ML model; and [Heigold Fig 6 item 608 teaches “speaker model for the enrolled user may then be updated based on the verification utterance” (Par [0099]) where updating the model is updating parameters using the verification utterance provided on the user device]
a plurality of user devices, each user device comprising at least one processor coupled to memory for: [Heigold Fig 1 teaches the model may be used on “many different client devices” (Par [0055]) where the “client device 110 can be, for example, a desktop computer, laptop computer, a tablet computer, a watch, a wearable computer, a cellular phone, a smart phone, a music player, an e-book reader, a navigation system, or any other appropriate computing device that a user may interact with”(Par [0045])]
obtaining, from the central server, and storing the trained speaker verification ML model; [Heigold Fig 1 teaches obtaining speaker verification model (144) from computing device (120)
With regards to claim 19, Heigold fails to teach:
identifying, using the at least one audio sample and a classifier, a group of users that have a similar voice to the voice of each specific user;
selecting, from a database, a set of audio samples of voices corresponding to the identified group of users;
With regards to claim 19, Kwon teaches:
identifying, using the at least one audio sample and a classifier, a group of users that have a similar voice to the voice of each specific user; [Kwon Figs 2-3 teaches using the audio sample from a user (step S210) and then obtaining a plurality of user voice groups (step 220) where “processor 170 of the disclosure may classify user voices having similar utterance characteristics.” (Par [0055]) While Kwon does not specifically state using a classifier, Kwon states the processor uses “at least one of a machine learning, a neural network, or a deep learning algorithm as a rule-based or an artificial intelligence algorithm” (Par [0048]) where classifying data can be done using a neural network classifier (see Wang Par [0092])]
selecting, from a database, a set of audio samples of voices corresponding to the identified group of users; [Kwon Fig 2 teaches selecting a plurality of user voice groups (step 220) that correspond to the identified group of users. While Kwon doesn’t specifically state selecting data from a database it is known to select voice data from a database. (See Kwon et al. (US2022/0230648 Par [0011])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold with the speaker verification system using voice groups as taught by Kwon. The motivation to combine the teachings of Heigold and Kwon is because Kwon teaches “speaker recognition is possible only with the user voice accumulated according to the operation of the electronic apparatus 100, thereby improving the user convenience” (Par [0054]) which increases the capabilities of the invention of Heigold by improving user experience]
With regards to claim 19, Heigold in view of Kwon fails to teach:
transmitting the selected set of audio samples to a user device used by the specific user, for personalising the trained ML model for the user using the set of audio samples; and
receiving and storing the selected set of audio samples, the set of audio samples comprising voices that are similar to the voice of the specific user of the user device; and
personalising the trained speaker verification ML model using at least one reference audio sample comprising the voice of the specific user and the obtained selected set of audio samples.
With regards to claim 19, Wang teaches:
transmitting the selected set of audio samples to a user device used by the specific user, for personalising the trained ML model for the user using the set of audio samples; and [Wang teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where the “second model may be deployed in a remote server, cloud, client-side device, etc.” (Par [0045])]
receiving and storing the selected set of audio samples, the set of audio samples comprising voices that are similar to the voice of the specific user of the user device; and [Wang teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where positive sample is “(e.g., speech data of a target speaker for personalizing speaker verification” (Par [0024]) which are similar to the voice of the specific user)]
personalising the trained speaker verification ML model using at least one reference audio sample comprising the voice of the specific user and the obtained selected set of audio samples. [Wang teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where the “second model may be deployed in a remote server, cloud, client-side device, etc.” (Par [0045])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold and Kwon with the personalized speaker verification system as taught by Wang. The motivation to combine the teachings of Heigold and Kwon with Wang is because Wang teaches an improvement over traditional speaker verification system that “uses a general speaker recognition model for all users (one-for-all) without any personalized update for target user, and hence lacks robustness and flexibility.” (Par [0004]) which increases the capabilities of the invention of Heigold and Kwon to be more flexible and robust]
Claims 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Heigold et al. (US2017/0069327) in view of Wang et al. (US2021/0110833)
With regards to claim 11, Heigold teaches:
A computer-implemented method, performed by a user device, for personalising a trained speaker verification machine learning, ML, model for a specific user of the user device, the method comprising: [Heigold Fig 1 teaches trained speaker verification system that can be implemented on back end server, middleware application server, or “a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here)” (Par [0118])]
obtaining and storing a trained speaker verification ML model; [Heigold teaches “speaker verification model 144 based on the trained neural network 140 is transmitted from the computing system 120 to the client device 110” (Par [0053]) where the ML model is stored]
With regards to claim 11, Heigold fails to teach:
obtaining and storing a selected set of audio samples, the set of audio samples comprising voices that are similar to the voice of the specific user, wherein the set of audio samples were selected using a classifier; and
personalising the trained speaker verification ML model using at least one reference audio sample comprising the voice of the specific user and the obtained selected set of audio samples, wherein parameters of the personalized ML model are used for updating the classifier.
With regards to claim 11, Wang teaches:
obtaining and storing a selected set of audio samples, the set of audio samples comprising voices that are similar to the voice of the specific user, wherein the set of audio samples were selected using a classifier; and [Wang Fig 2C and 3 teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where positive sample is “(e.g., speech data of a target speaker for personalizing speaker verification)” (Par [0024], step 320) which are similar to the voice of the specific user and using neural network classifier (Par [0092]) to select samples based on gradient descent (step 330)]
personalising the trained speaker verification ML model using at least one reference audio sample comprising the voice of the specific user and the obtained selected set of audio samples, wherein parameters of the personalized ML model are used for updating the classifier. [Wang Fig 3 teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification where the “second model may be deployed in a remote server, cloud, client-side device, etc.” (Par [0045]) and gradient descent or parameters are used to update the classifier. (step 340)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold with the personalized speaker verification system as taught by Wang. The motivation to combine the teachings of Heigold with Wang is because Wang teaches an improvement over traditional speaker verification system that “uses a general speaker recognition model for all users (one-for-all) without any personalized update for target user, and hence lacks robustness and flexibility.” (Par [0004]) which increases the capabilities of the invention of Heigold to be more flexible and robust]
With regards to claim 12, Heigold in view of Wang teaches:
All the limitations of claim 11
wherein personalising the trained speaker verification ML model comprises optimising a contrastive loss by: minimising a distance between the at least one audio sample and the at least one reference audio sample; and maximising a distance between the set of audio samples and the at least one reference audio sample. [Heigold Fig 2 teaches adjusting the weight values or other parameters of the neural network (206) by optimizing the loss by “maximize the similarity score for matching speakers samples or to optimize a score output by the logistic regression, and the neural network 206 may also be optimized so as to minimize the similarity score for non-matching speakers samples or to optimize the score output by the logistic regression” (Par [0072])
With regards to claim 13, Heigold in view of Wang teaches:
All the limitations of claim 11
further comprising: sharing parameters of the personalised ML model with a central server. [Heigold teaches trained speaker verification system that can be implemented on “back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server) … or any combination of such back end, middleware, or front end components.” (Par [0118])]
With regards to claim 14, Heigold in view of Wang teaches:
All the limitations of claim 11
wherein the user device is part of a group of user devices, and the method further comprises: sharing parameters of the personalised ML model with a second user device of the group of user devices, wherein the second user device aggregates the parameters received from user devices in the group and transmits the aggregated parameters to a central server. [Heigold teaches “enrolling one or more new users” (Par [0046]) and their associated devices where the parameters of the speaker verification model including “utterances of a given user that are provided for enrollment, verification, or both, may be provided to the computing system 120 and added to the training data” (Par [0046]) are aggregated for the central server]
With regards to claim 15, Heigold in view of Wang teaches:
All the limitations of claim 11
wherein the user device is part of a group of user devices, and the method further comprises: receiving ML model parameters of the personalised ML model from a plurality of user devices in the group; aggregating the received parameters; and transmitting the aggregated parameters to a central server. [Heigold teaches “enrolling one or more new users” (Par [0046]) and their associated devices where the parameters of the speaker verification model including “utterances of a given user that are provided for enrollment, verification, or both, may be provided to the computing system 120 and added to the training data” (Par [0046]) are aggregated and transmitted to the central server]
With regards to claim 16, Heigold in view of Wang teaches:
All the limitations of claim 11
further comprising: transmitting, to a central server, an embedding corresponding to a positively-verified audio input and a pseudo-label corresponding to the positively-identified audio input. [Wang Fig 2B teaches training the speaker verification system with embeddings (Par [0035,56]) where the “method may be performed by one or more components of the system 100, such as the computing system 102 and/or the computing device 10” (Par [0054]) and where the computing system (102) can be on a central server (Par [0038])]
With regards to claim 17, Heigold teaches:
A computer-implemented method, performed by a user device, for performing speaker verification for a user of the user device, the method comprising: [Heigold Fig 1 teaches trained speaker verification system that can be implemented on back end server, middleware application server, or “a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here)” (Par [0118])]
receiving a request to access a function or service which requires speaker verification; [Heigold Fig 1 teaches “stage (E), the user 102 attempts to gain access to the client device 110 using voice authentication” (Par [0057])]
receiving an audio input containing a voice; [Heigold Fig 1 teaches verification utterance (154) (Par [0057])]
granting access to the function or service to the user when the ML model verifies that the voice in the audio input is the voice of the user of the client device. [Heigold Fig 6 teaches using ML model (604) to verify the voice of the user and take action (612) or grant access]
With regards to claim 17, Heigold fails to teach:
processing, using a personalised trained speaker verification machine learning, ML, model, the received audio input; and
With regards to claim 17, Wang teaches:
processing, using a personalised trained speaker verification machine learning, ML, model, the received audio input; and [Wang Fig 1 teaches transmitting the first model which includes positive and negative samples to create a second model for personalized speaker verification (Par [0045]) where voice input (126) is the received audio input.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the speaker verification system as taught by Heigold with the personalized speaker verification system as taught by Wang. The motivation to combine the teachings of Heigold with Wang is because Wang teaches an improvement over traditional speaker verification system that “uses a general speaker recognition model for all users (one-for-all) without any personalized update for target user, and hence lacks robustness and flexibility.” (Par [0004]) which increases the capabilities of the invention of Heigold to be more flexible and robust]
With regards to claim 18, Wang teaches:
All the limitations of claim 17
wherein when the ML model verifies that the voice is the voice of the user, the method further comprises: generating, using the ML model, an embedding and a pseudo-label for the received audio input; and [Heigold teaches “a reference vector or other set of values corresponding to the user 102. The reference vector or other set of values may constitute a speaker model that characterizes distinctive features of the user's voice” (Par [0056]) where a vector or other value is an embedding that characterizes the user’s voice or audio input and includes a label for enrolled data]
transmitting, to a central server, the generated embedding and pseudo-label. [Wang Fig 2B teaches training the speaker verification system with embeddings (Par [0035,56]) where the “method may be performed by one or more components of the system 100, such as the computing system 102 and/or the computing device 10” (Par [0054]) and where the computing system (102) can be on a central server (Par [0038])]
Allowable Subject Matter
Claims 7 and 9 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights