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 .
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3 and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cachapuz Santos Fontoura (hereinafter “CSF”) United States Patent Application Publication US 2024/0289839.
Regarding claim 1, CSF discloses a computer-implemented method comprising:
generating an initial version of an analytics prediction machine learning model for predicting an analytics metric by learning parameters of the analytics prediction machine learning model utilizing model training data (CSF, para [0083], with regards to fig 5, element 506, training one or more machine learning models 115 on the one or more training datasets 220 via one or more LTR algorithms 226 based on one or more feature vectors (training feature vectors 304));
determining expected data channel contributions for the analytics metric according to prior observed data (CSF, para [0084], loss metric based on a comparison of the resultant ranking and known ranking and/or relevancy scores; CSF, para [0026], ranking based on input data provided by one or more data pipelines. Data pipeline interpreted as a channel)
indicating, for a plurality of data channels comprising channels for distributing or collecting data regarding digital content of a digital content campaign, what contributions to the analytics metric to expect from each data channel of the plurality of data channels (CSF, para [0061] The quality of the advertisement ranking can be measured in accordance with the loss function, and one or more parameters of the machine learning model can again be tuned to reduce and/or minimize the computed loss… For example, the parameters can be optimized to perform ranking operations that prioritize advertisement campaign objects based on how the scoring function and/or loss function are defined); and
generating a modified analytics prediction machine learning model by iteratively updating the parameters until the parameters, as used in a data channel contribution function, produce predicted data channel contributions that are within a threshold similarity of the expected data channel contributions (CSF, para [0084], the computer-implemented method 500 can include tuning (e.g., via the machine learning engine), by the system 100, one or more parameters of the one or more machine learning models 115 to minimize and/or reduce a loss metric, as defined by one or more loss functions, the tuning can be implemented across multiple iterations of the ranking operations).
Regarding claim 2, CSF discloses the computer-implemented method of claim 1. CSF additionally discloses wherein generating the initial version of the analytics prediction machine learning model comprises learning the parameters from the model training data that includes digital content campaign data indicating content distribution and resulting analytics metrics for one or more digital content campaigns (CSF, para [0027], advertisement analysis data including context information on the flight departure/destination locations, the aircraft, behavior data, etc.).
Regarding claim 3, CSF discloses the computer-implemented method of claim 1. CSF additionally discloses wherein determining the expected data channel contributions comprises accessing a database storing the prior observed data indicating respective contributions on impacting analytics metrics for a plurality of data channels (CSF, para [0026], ranking based on input data provided by one or more data pipelines; CSF, para [0027], rankings stored in index 104).
Regarding claim 15, CSF discloses a system comprising:
one or more memory devices comprising an analytics prediction machine learning model comprising parameters learned from an iterative training process that includes updating the parameters over multiple iterations until the parameters, as used in a data channel contribution function, produce predicted data channel contributions that are within a threshold similarity of expected data channel contributions (CSF, para [0084], to minimize and/or reduce a loss metric, as defined by one or more loss functions, the tuning can be implemented across multiple iterations of the ranking operations)
for an analytics metric according to prior observed data indicating what contributions to the analytics metric to expect from each data channel of a plurality of data channels (CSF, para [0061] The quality of the subsequent advertisement ranking 306 (e.g., of the third subset) can be measured in accordance with the loss function 308, and one or more parameters of the machine learning model 115 can again be tuned to reduce and/or minimize the computed loss… For example, the parameters can be optimized to perform ranking operations that prioritize advertisement campaign objects based on how the scoring function and/or loss function are defined.); and
one or more processors configured to cause the system to:
access content distribution data for a digital content campaign (CSF, para [0027], accesses data stored in index 104);
determine a target analytics metric for the digital content campaign (CSF, para [0084], loss metric based on a comparison of the resultant ranking and known ranking and/or relevancy scores; CSF, para [0026], ranking based on input data provided by one or more data pipelines. Data pipeline interpreted as a channel); and
generate an analytics prediction for the target analytics metric utilizing the analytics prediction machine learning model to process the content distribution data according to the parameters learned from the iterative training process (CSF, para [0027], As shown in FIG. 1, the one or more data pipelines 112 can be operably coupled to the one or more advertisement analysis devices 102, which can apply one or more machine learning engines 114 to rank available advertisements… advertisement analysis data including context information on the flight departure/destination locations, the aircraft, behavior data, etc. … the data supplied by the one or more data pipelines 112 can be used as input data for the trained machine learning model 115 to implement one or more advertisement ranking operations… the one or more advertisement analysis devices 102 can generate one or more electronic advertisement rankings based on the advertisements' relevance to one or more feature vectors extracted from the input data).
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.
Claim(s) 4-5, 8-12, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cachapuz Santos Fontoura (hereinafter “CSF”) United States Patent Application Publication US 2024/0289839 in view of Zagayevskiy United States Patent Application Publication US 2021/0133375.
Regarding claim 4, CSF discloses the computer-implemented method of claim 1. CSF discloses wherein generating the modified analytics prediction machine learning model by iteratively updating the parameters comprises, for a number of iterations:
comparing a point with an additional point representing the expected data channel contributions (CSF, para [0084], to minimize and/or reduce a loss metric, as defined by one or more loss functions, the tuning can be implemented across multiple iterations of the ranking operations).
CSF does not disclose:
generating updated parameters from the expected data channel contributions;
generating a point in parameter space representing the updated parameters utilizing the data channel contribution function; and
comparing the point in the parameter space with an additional point in the parameter space.
Zagayevskiy discloses:
generating updated parameters from the expected data channel contributions;
generating a point in parameter space representing the updated parameters utilizing the data channel contribution function; and
comparing the point in the parameter space with an additional point in the parameter space (Zagayevskiy, para [0030, 32], with regards to fig 3, illustrated is a diagram of an algorithm 160 for the flow simulator module 48 of the analytics platform 12 for generating reservoir management workflows and forecasts from a high-dimensional parameter data space; fig. 3, element 170, updates parameters with output variables and algorithmic models until the criteria is satisfied).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 5, CSF discloses the computer-implemented method of claim 1. CSF does not disclose the additional limitations of the present claim.
Zagayevskiy discloses wherein generating the modified analytics prediction machine learning model comprises iteratively updating the parameters according to an objective function that incorporates the expected data channel contributions, the predicted data channel contributions, an observed analytics metric, and a predicted analytics metric generated according to the parameters (Zagayevskiy, para [0015, 17, 25], The machine learning and training and validation module 74 can iteratively generate reduced parameter space algorithmic models from the computationally complex algorithmic model using at least one training dataset and at least one validation dataset, i.e. reservoir models, and at least one selected from a group of: input variables 56, output variables 58, updated reservoir model 76, history matching input variables 78, the optimized reservoir model 84, and optimization input variables 86).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 8, CSF discloses a non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising:
generating an initial version of an analytics prediction machine learning model for predicting an analytics metric by learning parameters of the analytics prediction machine learning model utilizing model training data (CSF, para [0083], with regards to fig 5, element 506, training one or more machine learning models 115 on the one or more training datasets 220 via one or more LTR algorithms 226 based on one or more feature vectors (training feature vectors 304)); and
determining expected data channel contributions for the analytics metric according to prior observed data (CSF, para [0084], loss metric based on a comparison of the resultant ranking and known ranking and/or relevancy scores; CSF, para [0026], ranking based on input data provided by one or more data pipelines. Data pipeline interpreted as a channel)
indicating, for a plurality of data channels comprising channels for distributing or collecting data regarding digital content of a digital content campaign, what contributions to the analytics metric to expect from each data channel of the plurality of data channels (CSF, para [0061] The quality of the subsequent advertisement ranking 306 (e.g., of the third subset) can be measured in accordance with the loss function 308, and one or more parameters of the machine learning model 115 can again be tuned to reduce and/or minimize the computed loss… For example, the parameters can be optimized to perform ranking operations that prioritize advertisement campaign objects based on how the scoring function and/or loss function are defined.); and
generating a modified analytics prediction machine learning model by iteratively updating the parameters (CSF, para [0084], to minimize and/or reduce a loss metric, as defined by one or more loss functions, the tuning can be implemented across multiple iterations of the ranking operations).
CSF does not disclose:
generating a modified analytics prediction machine learning model by iteratively: generating updated parameters from the expected data channel contributions; generating a point in parameter space representing the updated parameters utilizing a data channel contribution function; and comparing the point in parameter space with an additional point in the parameter space representing the expected data channel contributions.
Zagayevskiy discloses:
generating a modified analytics prediction machine learning model by iteratively: generating updated parameters from the expected data channel contributions; generating a point in parameter space representing the updated parameters utilizing a data channel contribution function; and comparing the point in parameter space with an additional point in the parameter space representing the expected data channel contributions (Zagayevskiy, para [0030, 32], with regards to fig 3, illustrated is a diagram of an algorithm 160 for the flow simulator module 48 of the analytics platform 12 for generating reservoir management workflows and forecasts from a high-dimensional parameter data space; fig. 3, element 170, updates parameters with output variables and algorithmic models until the criteria is satisfied).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 9, CSF in view of Zagayevskiy discloses the non-transitory computer readable medium of claim 8. CSF additionally discloses wherein generating the initial version of the analytics prediction machine learning model comprises learning the parameters from the model training data that includes digital content campaign data indicating content distribution and corresponding analytics metrics for one or more digital content campaigns (CSF, para [0027], advertisement analysis data including context information on the flight departure/destination locations, the aircraft, behavior data, etc.).
Regarding claim 10, CSF in view of Zagayevskiy discloses the non-transitory computer readable medium of claim 8. CSF additionally discloses wherein determining the expected data channel contributions comprises determining the expected data channel contributions from the prior observed data indicating, for a plurality of data channels, respective contributions on impacting the analytics metric (CSF, para [0026], ranking based on input data provided by one or more data pipelines; CSF, para [0027], rankings stored in index 104).
Regarding claim 11, CSF in view of Zagayevskiy discloses the non-transitory computer readable medium of claim 8. CSF additionally discloses wherein generating the modified analytics prediction machine learning model comprises iteratively updating the parameters, generating the point in the parameter space, and comparing the point in the parameter space with the additional point until the point and the additional point are within a threshold distance of each other in the parameter space (CSF, para [0084], to minimize and/or reduce a loss metric, as defined by one or more loss functions, the tuning can be implemented across multiple iterations of the ranking operations).
Regarding claim 12, CSF in view of Zagayevskiy discloses the non-transitory computer readable medium of claim 8. Zagayevskiy additionally discloses wherein generating the modified analytics prediction machine learning model comprises iteratively updating the parameters according to an objective function that incorporates the expected data channel contributions, predicted data channel contributions, an observed analytics metric, and a predicted analytics metric generated by a previous version of the analytics prediction machine learning model according to a previous version of the parameters (Zagayevskiy, para [0015, 17, 25], The machine learning and training and validation module 74 can iteratively generate reduced parameter space algorithmic models from the computationally complex algorithmic model using at least one training dataset and at least one validation dataset, i.e. reservoir models, and at least one selected from a group of: input variables 56, output variables 58, updated reservoir model 76, history matching input variables 78, the optimized reservoir model 84, and optimization input variables 86).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 17, CSF discloses the system of claim 15. CSF does not disclose the additional limitations of the present claim.
Zagayevskiy discloses wherein the analytics prediction machine learning model comprises parameters learned by iteratively: generating updated parameters from the expected data channel contributions; generating a point in parameter space representing the updated parameters utilizing the data channel contribution function; and comparing the point in the parameter space with an additional point in the parameter space representing the expected data channel contributions (Zagayevskiy, para [0030, 32], with regards to fig 3, illustrated is a diagram of an algorithm 160 for the flow simulator module 48 of the analytics platform 12 for generating reservoir management workflows and forecasts from a high-dimensional parameter data space; fig. 3, element 170, updates parameters with output variables and algorithmic models until the criteria is satisfied).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 18, CSF discloses the system of claim 15. CSF does not disclose the additional limitations of the present claim.
Zagayevskiy discloses wherein the analytics prediction machine learning model comprises parameters learned according to an objective function that incorporates the expected data channel contributions, predicted data channel contributions, an observed analytics metric, and a predicted analytics metric (Zagayevskiy, para [0015, 17, 25], The machine learning and training and validation module 74 can iteratively generate reduced parameter space algorithmic models from the computationally complex algorithmic model using at least one training dataset and at least one validation dataset, i.e. reservoir models, and at least one selected from a group of: input variables 56, output variables 58, updated reservoir model 76, history matching input variables 78, the optimized reservoir model 84, and optimization input variables 86).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Regarding claim 19, CSF in view of Zagayevskiy discloses the system of claim 18. CSF does not disclose the additional limitations of the present claim.
Zagayevskiy discloses wherein the objective function produces parameters for the analytics prediction machine learning model that reduce a difference between predicted data channel contributions and the expected data channel contributions (Zagayevskiy, para [0030, 32], with regards to fig 3, illustrated is a diagram of an algorithm 160 for the flow simulator module 48 of the analytics platform 12 for generating reservoir management workflows and forecasts from a high-dimensional parameter data space; fig. 3, element 170, updates parameters with output variables and algorithmic models until the criteria is satisfied).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the updating of parameters to include the steps of Zagayevskiy. The motivation for doing so would have been to provide less processing with an acceptable degree of accuracy (Zagayevskiy, para [0002]).
Claim Rejections - 35 USC § 103
Claim(s) 6-7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cachapuz Santos Fontoura (hereinafter “CSF”) United States Patent Application Publication US 2024/0289839 in view of Pyzer-Knapp United States Patent Application Publication US 2022/0128972.
Regarding claim 6, CSF discloses the computer-implemented method of claim 1. CSF does not disclose the additional limitations of the present claim.
Pyzer-Knapp discloses wherein generating the modified analytics prediction machine learning model comprises utilizing a surrogate function to modify terms of the data channel contribution function for iteratively updating the parameters (Pyzer-Knapp, para [0017, 24, 29], iteratively updates surrogate model).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the steps to include using a surrogate function based on the teachings of Pyzer-Knapp. The motivation for doing so would have been to implement a Bayesian optimization because of its strong performance of optimization (Pyzer-Knapp, para [0002]).
Regarding claim 7, CSF in view of Pyzer-Knapp discloses the computer-implemented method of claim 6. Pyzer-Knapp additionally discloses wherein utilizing the surrogate function as part of updating the parameters of the analytics prediction machine learning model comprises replacing predicted analytics metrics of the data channel contribution function with observed analytics metrics (Pyzer-Knapp, para [0017, 33], new sample and the corresponding output (observation) is then used to update the surrogate model).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the steps to include using a surrogate function based on the teachings of Pyzer-Knapp. The motivation for doing so would have been to implement a Bayesian optimization because of its strong performance of optimization (Pyzer-Knapp, para [0002]).
Regarding claim 20, CSF discloses the system of claim 18. CSF does not disclose the additional limitations of the present claim.
Pyzer-Knapp discloses wherein the objective function comprises a surrogate function that substitutes observed analytics metrics for predicted analytics metrics as a component of the objective function (Pyzer-Knapp, para [0017, 24, 29], iteratively updates surrogate model).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the steps to include using a surrogate function based on the teachings of Pyzer-Knapp. The motivation for doing so would have been to implement a Bayesian optimization because of its strong performance of optimization (Pyzer-Knapp, para [0002]).
Claim Rejections - 35 USC § 103
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cachapuz Santos Fontoura (hereinafter “CSF”) United States Patent Application Publication US 2024/0289839 in view of Zagayevskiy United States Patent Application Publication US 2021/0133375 in further view of Pyzer-Knapp United States Patent Application Publication US 2022/0128972.
Regarding claim 13, CSF in view of Zagayevskiy discloses the non-transitory computer readable medium of claim 8. CSF in view of Zagayevskiy does not disclose the additional limitations of the present claim.
Pyzer-Knapp discloses wherein generating the modified analytics prediction machine learning model comprises utilizing a surrogate function to iteratively update the parameters (Pyzer-Knapp, para [0017, 24, 29], iteratively updates surrogate model).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the steps to include using a surrogate function based on the teachings of Pyzer-Knapp. The motivation for doing so would have been to implement a Bayesian optimization because of its strong performance of optimization (Pyzer-Knapp, para [0002]).
Regarding claim 14, CSF in view of Zagayevskiy in further view of Pyzer-Knapp discloses tThe non-transitory computer readable medium of claim 13. Pyzer-Knapp additionally discloses wherein utilizing the surrogate function as part of updating the parameters of the analytics prediction machine learning model comprises: determining a modified data channel contribution function by replacing predicted analytics metrics within the data channel contribution function with observed analytics metrics; and generating the surrogate function to substitute for an objective function designed for updating the parameters of the analytics prediction machine learning model by utilizing the modified data channel contribution function (Pyzer-Knapp, para [0017, 33], new sample and the corresponding output (observation) is then used to update the surrogate model).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the steps to include using a surrogate function based on the teachings of Pyzer-Knapp. The motivation for doing so would have been to implement a Bayesian optimization because of its strong performance of optimization (Pyzer-Knapp, para [0002]).
Claim Rejections - 35 USC § 103
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Cachapuz Santos Fontoura (hereinafter “CSF”) United States Patent Application Publication US 2024/0289839 in view of Chang United States Patent US 10,346,870.
Regarding claim 16, CSF discloses the system of claim 15. CSF does not disclose the additional limitations of the present claim.
Chang discloses wherein generating the analytics prediction for the target analytics metric comprises utilizing the analytics prediction machine learning model to generate a predicted conversion rate for the digital content campaign from the content distribution data (Chang, col 3, rows 47-61, in addition text corresponding to fig 3 element 307, fig 7A element 706, fig 7B element 714-715, probability that a consumer will accept an offer for the promotion program represents a conversion rate. Generates prediction based on collected performance data for a time).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the system to include the promotional analytics of Chang. The motivation for doing so would have been to have a higher likelihood of acceptance for the consumer (Chang, col 1, rows 18-26).
Response to Arguments
With respect to the arguments regarding 35 USC 101, the examiner finds these persuasive. This rejection has been overcome.
Applicant's arguments regarding the 35 USC 102 rejection are moot in view of the new grounds of rejection necessitated by applicant's amendments.
For claims 1 and 15, CSF teaches indicating for a plurality of data channels comprising channels for distributing or collecting data regarding digital content of a digital content campaign, what contributions to the analytics metric to expect from each data channel according to prior observed data in paragraphs 0061 and 0084, where the loss metric defined from a loss function from paragraph 0084 is the expected contribution of the data channel metrics, and the loss function’s results define the parameters in machine learning model 115 in paragraph 0061. The machine learning model’s parameters are the indication of the expected contributions from each data channel. The digital content is the electronic advertisements mentioned in paragraph 0061 and are ranked and used as input for the loss function’s loss metric in paragraph 0084. The prior observed data is the known ranking and/or relevancy scores.
For claim 1, CSF also teaches that a modified analytics prediction machine learning model is generated by tuning one or more parameters in paragraph 0084. The tuned model represents the modified portion of the machine learning model while the one or more parameters that are tuned represent the iteratively updated parameters. The loss functions are the data channel contribution functions that produce predicted contributions, and the minimization or reduction of a loss function’s loss metric is what produces predicted contributions that are within a threshold similarity of expected data channel contributions.
For claim 15, CSF also teaches utilizing the analytics prediction ML model in paragraph 0027. The advertisement analysis devices, which can apply one or more machine learning engines, analyze the analytics metrics by ranking them. The data supplied by the pipelines, which are extracted for their feature vectors, are the parameters learned from the training process to assist with ranking.
For claim 8, CSF teaches indicating for a plurality of data channels comprising channels for distributing or collecting data regarding digital content of a digital content campaign, what contributions to the analytics metric to expect from each data channel according to prior observed data in paragraphs 0061 and 0084, where the loss metric defined from a loss function from paragraph 0084 is the expected contribution of the data channel metrics, and the loss function’s results define the parameters in machine learning model 115 in paragraph 0061. The machine learning model’s parameters are the indication of the expected contributions from each data channel. The digital content is the electronic advertisements mentioned in paragraph 0061 and are ranked and used as input for the loss function’s loss metric in paragraph 0084. The prior observed data is the known ranking and/or relevancy scores.
With respect to argument 1 at bottom of pg. 11 that CSF does not teach the newly amended limitations because "CSF generally relates to ranking electronic advertisements...", the examiner disagrees. As explained in the rejection above, CSF at [0061] describes: The quality of the advertisement ranking can be measured in accordance with the loss function, and one or more parameters of the machine learning model can again be tuned to reduce and/or minimize the computed loss… For example, the parameters can be optimized to perform ranking operations that prioritize advertisement campaign objects based on how the scoring function and/or loss function are defined.
With respect to argument 2 regarding the "generating a modified analytics prediction machine learning model" step, applicant points to CSF’s abstract and [0083], however, what is cited in the rejection is [0084]. Given this, and further that applicant has not made any real argument other than a conclusory statement that the limitation is not taught, this argument is not persuasive for the reasons given above in the rejection.
The same rationale applies to argument 3 on pg. 13.
Further, the same rationale applies to the arguments regarding the 103 rejection of claim 8. The newly amended limitations are taught by CSF as discussed above. The arguments regarding Zagayevskiy are not relevant as Zagayevskiy is not being used for the newly added limitations.
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 DAVID KIM whose telephone number is (571)272-4331. The examiner can normally be reached 7:30 AM - 4:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.K./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
Prosecution Insights