Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
Claims 1, 6, 11, and 16 are currently amended.
Claims 1, 3-6, 8-11, 13-16, and 18-20 remain pending in the application.
The amendment filed 01/26/2026 is sufficient to overcome the 101 rejections of
claims 1, 3-6, 8-11, 13-16, and 18-20. The previous rejections have been withdrawn.
Argument 1, regarding the 101 rejections, applicant argues that the 101 rejections should be withdrawn because the claims are directed towards improving the accuracy of recommendation model training based on a user’s interests or hobbies. Examiner agrees and the 101 rejections have been withdrawn.
Argument 2, regarding the prior art rejections, applicant argues that Tan does not teach “a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, without impacts of position information, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object” because the model taught in Tan is trained with user behavior data and position information. Examiner notes that this limitation is taught by Majumder. Majumder teaches a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, without impacts of position information, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object (generative model generates probabilities that the user has bias towards content based on its position, and the probability the user selects the recommended content. C4:L36-39, C5:L34-37. Recommendation model may make prediction for different position arrangements, but does this not necessarily mean position of content must change to make the recommendation, C5:L24-32). The examiner notes that removing position or location bias from the impacts of the model is the inventive concept of this reference. See Abstract.
Applicant argues that Dolhansky does not teach “wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability”. Applicant’s argument has been fully considered but is not persuasive because Applicant does not argue why the difference between the ground truth label and prediction should not be reasonably interpreted as the difference between a sample label and a jointly predicted selection probability. Examiner notes that the broadest reasonable interpretations of the phrases “sample label” and “selection probability” are being considered. Dolhansky teaches wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability (“the model update module 780 can determine whether the prediction generated by the image fake module 770 is correct (in the case of a Boolean prediction) or a difference between the prediction and the ground truth label (in the case of a probability prediction). In either case, the model update module 780 may use differences between the labels and the predictions as a cost function to train the image fake model 770 (e.g., using backpropagation)”, C13:L2-10).
Applicant argues that Menick fails to teach “wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model” because the observations cited in Menick are input data and not output data. Examiner respectfully disagrees because Menick recites “The encoder neural network is configured to process the given observation in accordance with current parameter values of the encoder neural network to generate as output parameters of an encoding probability distribution over the latent space (206). For example, the encoding probability distribution may be a multi-dimensional Normal distribution with a predetermined covariance matrix (e.g., a diagonal covariance matrix with only is on the diagonal) and with a mean defined by the output of the encoder neural network” (See Menick P0082) in addition to the observations being passed as input to the encoder neural network. Thus, Menick teaches output parameters of an encoder neural network being multiplied with current parameter values to produce an encoding probability distribution such as a multi-dimensional Normal distribution.
The full prior art rejections are outlined below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being as being unpatentable over Tan et al (Pub. No.: CN 109345302 A), hereafter Tan in view of Menick et al (US 20210004677 A1), hereafter Menick, Majumder et al (Pub. No.: US 10497012 B1), hereafter Majumder, and Dolhansky et al (US 10810725 B1), hereafter Dolhansky.
Regarding claims 1 and 11, Tan teaches A recommendation model training method implemented by a computer device having at least one processor and a transmitter coupled to the at least one processor, comprising (method includes device with a processor and obtaining module, paragraphs 3-7) : obtaining, by the at least one processor, a training sample, wherein the training sample comprises a sample user behavior log, position information of a sample recommended object, and a sample label, and wherein the sample label indicates whether a user selects the sample recommended object (collecting model training data which includes user sample target behavior data, label information that is positive or negative depending on if the user has behavior data corresponding to the recommended object. Page 9, paragraph 3); …training on … a recommendation model by using the sample user behavior log and the position information of the sample recommended object as input data (recommendation model is trained with user behavior data and position information of the recommended object as input. Page 18, paragraph 7 and page 9, paragraph 6) and using the sample label as a target output value, to obtain a trained recommendation model (“label corresponding to the model training data is the desired output model of model training”, page 4, paragraph 4)… and transmitting, by the transmitter, the trained recommendation model to an execution device (machine learning model training device is used to execute the trained model, receives model data from obtaining module 60, page 23, paragraphs 1-3).
Tan does not appear to explicitly teach performing joint training… wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability, and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model.
Menick teaches performing, by the at least one processor, joint training (joint training is done between two models based on probability distributions of possible observations, including classified observations. Classified observations include class labels. P0050, P0074-P0075)… and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model (calculated probability is multi-dimensional probability, which is calculated with multiplication of observations of the prior and current neural networks jointly modeled, P0077-P0078, P0082).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan and Menick before them, to include Menick’s specific teaching of performing joint training in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of performing joint training (see Menick P0050, P0074-P0075) and training different models to account for differences in capacity or different sample or characteristic data (see Tan page 4, paragraph 4).
Tan in view of Menick does not appear to explicitly teach a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object.
Majumder teaches a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, without impacts of position information, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object (generative model generates probabilities that the user has bias towards content based on its position, and the probability the user selects the recommended content. Position bias is then removed. C4:L36-39, C5:L34-37. Recommendation model may make prediction for different position arrangements, but this does not necessarily mean position of content must change to make the recommendation, C5:L24-32).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, and Majumder before them, to include Majumder’s specific teaching of a position aware model in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of a position aware model (see Majumder C4:L36-39, C5:L34-37) and evaluating position information to better understand users’ preference (see Tan page 9, paragraph 6).
Tan in view of Menick and Majumder does not appear to explicitly teach “wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability”.
Dolhansky teaches wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability (“the model update module 780 can determine whether the prediction generated by the image fake module 770 is correct (in the case of a Boolean prediction) or a difference between the prediction and the ground truth label (in the case of a probability prediction). In either case, the model update module 780 may use differences between the labels and the predictions as a cost function to train the image fake model 770 (e.g., using backpropagation)”, C13:L2-10).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, Majumder, and Dolhansky before them, to include Dolhansky’s specific teaching of model training with the difference between labels and probability in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of model training with the difference between labels and probability (see Dolhansky C13:L2-10) and analyzing user action prediction probability to better understand users’ preference (see Tan page 6, paragraph 3).
Regarding claim 16, Tan teaches a recommendation apparatus, comprising: at least one processor, a memory coupled to the at least one processor, wherein the at least one processor is configured to read and execute instructions in the memory, to cause the recommendation apparatus to perform steps of: (method includes device with a processor, memory, and obtaining module, paragraphs 3-7) obtaining user characteristic information of a to-be-processed user, context information (context of characteristic data is considered, page 3, paragraph 7), and a candidate recommended object set (“acquiring model training data and corresponding tag, the model training data includes characteristic data of the characteristic data and the recommended object sample of user sample” page 2, paragraph 5); inputting the user characteristic information, the context information, and the candidate recommended object set into a pre-trained recommendation model to obtain a probability that the to-be-processed user selects a candidate recommended object in the candidate recommended object set, wherein the pre-trained recommendation model is used to predict, when the user pays attention to a target recommended object, a probability that the user selects the target recommended object (“characteristic data and the characteristic data of the recommended object samples user sample are used as model training data to train the user machine learning model can predict a user action result of the recommended object”, page 3, paragraph 7); and obtaining a recommendation result of the candidate recommended object based on the probability that the to-be-processed user selects the candidate recommended object (“user action prediction probability” measures the recommended object of each candidate there is a user action prediction result. Page 6, paragraph 3 and page 18, paragraph 5),… the recommendation model by using a sample user behavior log and position information of a sample recommended object as input data (recommendation model is trained with user behavior data and position information of the recommended object as input. Page 18, paragraph 7 and page 9, paragraph 6) and using a sample label as a target output value (“label corresponding to the model training data is the desired output model of model training”, page 4, paragraph 4).
Tan does not appear to explicitly teach performing joint training… wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability, and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model.
Menick teaches performing joint training (joint training is done between two models based on probability distributions of possible observations, including classified observations. Classified observations include class labels. P0050, P0074-P0075)… and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model (calculated probability is multi-dimensional probability, which is calculated with multiplication of observations of the prior and current neural networks jointly modeled, P0077-P0078, P0082).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan and Menick before them, to include Menick’s specific teaching of performing joint training in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of performing joint training (see Menick P0050, P0074-P0075) and training different models to account for differences in capacity or different sample or characteristic data (see Tan page 4, paragraph 4).
Tan in view of Menick does not appear to explicitly teach a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object.
Majumder teaches a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, without impacts of position information, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object (generative model generates probabilities that the user has bias towards content based on its position, and the probability the user selects the recommended content. C4:L36-39, C5:L34-37. Recommendation model may make prediction for different position arrangements, but does this not necessarily mean position of content must change to make the recommendation, C5:L24-32).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, and Majumder before them, to include Majumder’s specific teaching of a position aware model in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of a position aware model (see Majumder C4:L36-39, C5:L34-37) and evaluating position information to better understand users’ preference (see Tan page 9, paragraph 6).
Tan in view of Menick and Majumder does not appear to explicitly teach “wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability”
Dolhansky teaches wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability (“the model update module 780 can determine whether the prediction generated by the image fake module 770 is correct (in the case of a Boolean prediction) or a difference between the prediction and the ground truth label (in the case of a probability prediction). In either case, the model update module 780 may use differences between the labels and the predictions as a cost function to train the image fake model 770 (e.g., using backpropagation)”, C13:L2-10).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, Majumder, and Dolhansky before them, to include Dolhansky’s specific teaching of model training with the difference between labels and probability in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of model training with the difference between labels and probability (see Dolhansky C13:L2-10) and analyzing user action prediction probability to better understand users’ preference (see Tan page 6, paragraph 3).
Regarding claims 3, 13, and 18, Tan in view of Menick, Majumder, and Dolhansky teaches the limitations of claims 1, 11, and 16 as outlined above. Tan further teaches inputting the sample user behavior log into the recommendation model to obtain the probability that the user selects the target recommended object (“wherein the user action prediction result specifically is user action prediction probability. Specifically, when the machine learning model is one, then there is one user action prediction result for each model input data”, page 18, paragraph 5. User behavior data is used to obtain the prediction of the candidate object being selected by the user, page 18, paragraph 6/page 19 paragraph 1);
Menick further teaches obtaining the jointly predicted selection probability by multiplying the probability that the user pays attention to the target recommended object by the probability that the user selects the target recommended object (calculated probability is multi-dimensional probability, which is calculated with multiplication of observations of the prior and current neural networks jointly modeled, P0077-P0078, P0082).
Regarding claims 4, 14, and 19, Tan in view of Menick, Majumder, and Dolhansky teaches the limitations of claims 1, 11, and 16 as outlined above. Tan further teaches wherein the sample user behavior log comprises one or more of sample user profile information, characteristic information of the sample recommended object, or sample context information (obtaining model data includes collecting characteristic data of the recommended object sample. Page 9, paragraph 3).
Regarding claims 5, 15, and 20, Tan in view of Menick, Majumder, and Dolhansky teaches the limitations of claims 1, 11, and 16 as outlined above. Majumder further teaches wherein the position information of the sample recommended object is recommendation position information of the sample recommended object in different types of recommended objects, or the position information of the sample recommended object is recommendation position information of the sample recommended object in a same type of recommended object, or the position information of the sample recommended object is recommendation position information of the sample recommended object in recommended objects in different top lists (position information includes position of recommended objects in top lists including but not limited to lists of search results, deals, or recommendations. C5:L13-15, C5:L24-30).
Claims 6 and 8-10 are rejected under 35 U.S.C. 103 as being as being unpatentable over Tan view of Menick, Majumder, Dolhansky and Mishra et al (Pub. No.: US 20190012602 A1), hereafter Mishra.
Regarding claim 6, Tan teaches a selection probability prediction method implemented by a computer device having at least one processor and a transmitter coupled to the at least one processor, comprising (method includes device with a processor and obtaining module, paragraphs 3-7): obtaining, by the at least one processor, user characteristic information of a to-be-processed user, context information (context of characteristic data is considered, page 3, paragraph 7), and a candidate recommended object set (“acquiring model training data and corresponding tag, the model training data includes characteristic data of the characteristic data and the recommended object sample of user sample” page 2, paragraph 5); inputting, by the at least one processor, the user characteristic information, the context information, and the candidate recommended object set into a pre-trained recommendation model to obtain a probability that the to-be-processed user selects a candidate recommended object in the candidate recommended object set, wherein the pre-trained recommendation model is used to predict, when the user pays attention to a target recommended object, a probability that the user selects the target recommended object (“characteristic data and the characteristic data of the recommended object samples user sample are used as model training data to train the user machine learning model can predict a user action result of the recommended object”, page 3, paragraph 7); and obtaining, by the at least one processor, a recommendation result of the candidate recommended object based on the probability that the to-be-processed user selects the candidate recommended object (“user action prediction probability” measures the recommended object of each candidate there is a user action prediction result. Page 6, paragraph 3 and page 18, paragraph 5),… the recommendation model by using a sample user behavior log and position information of a sample recommended object as input data (recommendation model is trained with user behavior data and position information of the recommended object as input. Page 18, paragraph 7 and page 9, paragraph 6) and using a sample label as a target output value (“label corresponding to the model training data is the desired output model of model training”, page 4, paragraph 4)… and transmitting, by the transmitter, the trained recommendation model to an execution device (machine learning model training device is used to execute the trained model, receives model data from obtaining module 60, page 23, paragraphs 1-3).
Tan does not appear to explicitly teach performing joint training… wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability, and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model.
Menick teaches performing joint training (joint training is done between two models based on probability distributions of possible observations, including classified observations. Classified observations include class labels. P0050, P0074-P0075)… and wherein the jointly predicted selection probability is obtained by multiplying output data of the position aware model and output data of the recommendation model (calculated probability is multi-dimensional probability, which is calculated with multiplication of observations of the prior and current neural networks jointly modeled, P0077-P0078, P0082).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan and Menick before them, to include Menick’s specific teaching of performing joint training in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of performing joint training (see Menick P0050, P0074-P0075) and training different models to account for differences in capacity or different sample or characteristic data (see Tan page 4, paragraph 4).
Tan in view of Menick does not appear to explicitly teach a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object.
Majumder teaches a position aware model… wherein the position aware model predicts probabilities that the user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, without impacts of position information, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object (generative model generates probabilities that the user has bias towards content based on its position, and the probability the user selects the recommended content. C4:L36-39, C5:L34-37. Recommendation model may make prediction for different position arrangements, but does this not necessarily mean position of content must change to make the recommendation, C5:L24-32).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, and Majumder before them, to include Majumder’s specific teaching of a position aware model in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of a position aware model (see Majumder C4:L36-39, C5:L34-37) and evaluating position information to better understand users’ preference (see Tan page 9, paragraph 6).
Tan in view of Menick and Majumder does not appear to explicitly teach “wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability”
Dolhansky teaches wherein the joint training is training model parameters of the position aware model and the recommendation model based on a difference between the sample label and a jointly predicted selection probability (“the model update module 780 can determine whether the prediction generated by the image fake module 770 is correct (in the case of a Boolean prediction) or a difference between the prediction and the ground truth label (in the case of a probability prediction). In either case, the model update module 780 may use differences between the labels and the predictions as a cost function to train the image fake model 770 (e.g., using backpropagation)”, C13:L2-10).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, Majumder, and Dolhansky before them, to include Dolhansky’s specific teaching of model training with the difference between labels and probability in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of model training with the difference between labels and probability (see Dolhansky C13:L2-10) and analyzing user action prediction probability to better understand users’ preference (see Tan page 6, paragraph 3).
Tan in view of Menick, Majumder, and Dolhansky does not appear to explicitly teach wherein the context information includes current download information.
Mishra teaches wherein the context information includes current download information (current network information may be used to train more accurate models, P0088. Network information includes download histories of user devices, P0070).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Tan, Menick, and Majumder, Dolhansky and Mishra before them, to include Mishra’s specific teaching of using current download information associated with user devices to train models in Tan’s system of Machine Learning Model Training. One would have been motivated to make such a combination of using current download information associated with user devices to train models (see Mishra P0088, P0070) and evaluating user attribute data to better understand users’ preference (see Tan page 9, paragraph 6).
Regarding claim 8, Tan in view of Menick, Majumder, Dolhansky, and Mishra teaches the limitations of claim 6 as outlined above. Tan further teaches inputting the sample user behavior log into the recommendation model to obtain the probability that the user selects the target recommended object (“wherein the user action prediction result specifically is user action prediction probability. Specifically, when the machine learning model is one, then there is one user action prediction result for each model input data”, page 18, paragraph 5. User behavior data is used to obtain the prediction of the candidate object being selected by the user, page 18, paragraph 6/page 19 paragraph 1);
Menick further teaches obtaining the jointly predicted selection probability by multiplying the probability that the user pays attention to the target recommended object by the probability that the user selects the target recommended object (calculated probability is multi-dimensional probability, which is calculated with multiplication of observations of the prior and current neural networks jointly modeled, P0077-P0078, P0082).
Regarding claim 9, Tan in view of Menick, Majumder, Dolhansky, and Mishra teaches the limitations of claim 6 as outlined above. Tan further teaches wherein the sample user behavior log comprises one or more of sample user profile information, characteristic information of the sample recommended object, or sample context information (obtaining model data includes collecting characteristic data of the recommended object sample. Page 9, paragraph 3).
Regarding claim 10, Tan in view of Menick, Majumder, Dolhansky, and Mishra teaches the limitations of claim 6 as outlined above. Majumder further teaches wherein the position information of the sample recommended object is recommendation position information of the sample recommended object in different types of recommended objects, or the position information of the sample recommended object is recommendation position information of the sample recommended object in a same type of recommended object, or the position information of the sample recommended object is recommendation position information of the sample recommended object in recommended objects in different top lists (position information includes position of recommended objects in top lists including but not limited to lists of search results, deals, or recommendations. C5:L13-15, C5:L24-30).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/I.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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