DETAILED ACTION
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental processes) without significantly more. Claim 1 recites:
A non-transitory computer readable storage medium storing an operation improvement assistance program for causing a computer to function as an operation improvement assistance device that assists improvement of an operation status of a device or improvement of an outcome by operation of the device, the operation improvement assistance program causing the computer to execute process procedures including: (this falls within the statutory categories of invention. The computer is recited in a manner equivalent to mere instructions to apply an exception with generic computer components, as per MPEP 2106.05(f). Any link to the operation of a process or factory is merely generally linking the use of the exception to a particular technical field as per MPEP 2106.05(h).)
predicting output data indicating the operation status or the outcome from input data including each value of a plurality of feature amounts related to operation of the device, the input data being data collected during operation of the device; (a person can mentally observe status and evaluate a prediction based on their expertise and judgement)
extracting a target feature amount that is a feature amount whose value is adjustable in prediction of the output data, from among the plurality of feature amounts; (a person can mentally observe a process, then evaluate and judge a number of features that should be considered based on the situation)
simulating the predicted output data by changing a value of the target feature amount; and (a person can mentally predict the results of changing the number of features they have selected by means of mental evaluations and judgements)
presenting a simulation result of the output data. (this is insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated, as per MPEP 2106.05(g), notably similar to example iii. of such, “Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display”)
This judicial exception is not integrated into a practical application. In particular, the claim only recites the following additional elements: 1) mere instructions to apply the exception using generic computer components (the computer), 2) generally linking the use of the exception to the technical field of factory operation, and 3) insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated (presenting output results). The computer is recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of executing instructions and storing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application. The specification that a output is generated is only tangentially linked to the calculation and analysis steps, and does not meaningfully limit the claim. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. The addition of insignificant extra-solution activity does not amount to an inventive concept. The claim is not patent eligible.
The dependent claims 2-7 provide only further details of the abstract analysis operations that can be performed mentally, and further specification of insignificant extra-solution activity in the form of mere data gathering (taking input from a user, see MPEP 2106.05(g) regarding mere data gathering) as well as insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated (outputting results to a screen) as already addressed above. They remain ineligible
Claims 8 and 9 are substantially similar to claim 1, and are rejected under the same grounds.
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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Mizutani (JP 2013145521 A) in view of Nishiki (US 20220121182 A1)
Regarding Claim 1:
Mizutani teaches:
predicting output data indicating the operation status or the outcome from input data including each value of a plurality of feature amounts related to operation of the device, (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product)
extracting a target feature amount that is a feature amount whose value is adjustable in prediction of the output data, from among the plurality of feature amounts; (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶29 classification is performed for each parameter such that the average of the parameters for each product group differs by 10%, and the processing efficiency is calculated. Then, by comparing the processing efficiency of each process for each product group, it is possible to identify the rate-limiting (neck) process in a plurality of processes in series, direct connection, and multiple stages for each product group, and to obtain the inherent consistency efficiency for each product group.)
simulating the predicted output data by changing a value of the target feature amount; and (¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product; ¶36 Once the inherent efficiency of each product group is determined as described above, the overall efficiency of the entire manufacturing process can be determined using the inherent efficiency of each product group according to the composition ratio of the product group in the period to be predicted.; ¶40 As described above, parameters that affect the processing efficiency of each process are appropriately extracted in a manufacturing process that combines multiple types of product groups with series, direct connection, and multiple stages with different processing capabilities. At the same time, by properly defining and classifying product groups with significant differences in these parameters and obtaining unique consistency efficiency for each product group, the consistency efficiency of the entire manufacturing process according to the composition ratio of the product group Can be calculated and predicted with high accuracy and simplicity.)
Mizutani does not teach in particular, but Nishiki teaches:
the input data being data collected during operation of the device; (Abstract, based on input data related to an operation of a factory at the evaluation time using a learned prediction model.; ¶9 the input data may include data related to a running ratio of a facility of the factory and energy consumption of the facility.; ¶31 The measurement system 20 measures a plurality of kinds of data (for example, current values, voltages, power amounts, vibrations) regarding running of the production facilities 10. Data regarding the running of the production facility 10 is an example of data related to an operation of a factory.)
presenting a simulation result of the output data. (¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16). In the display screen of the importance, items of the factory measurement data are arranged and displayed in a descending order of the importance. In each item, a graph indicating the magnitude of the importance and a time-series importance rank for each past learning are displayed in association.; see also Nishiki "Reference numeral 104 denotes an output unit that displays a consistent efficiency of the entire manufacturing process, which is a calculation result of the calculation unit 103, on a display device (not shown). The operator refers to the consistent efficiency of the entire manufacturing process and adjusts the throughput and operating rate of the front and rear processes (adjusts the amount of steel slabs received from the steelmaking process, which is the previous process, Adjustment of the operation rate / processing capacity of each process, etc.) and, if necessary, measures are taken to increase the facility capacity of the process that becomes a bottleneck.")
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 2:
Mizutani does not teach in particular, but Nishiki teaches:
presenting an item of the target feature amount and a range of a changeable value of the target feature amount, and (¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16). In the display screen of the importance, items of the factory measurement data are arranged and displayed in a descending order of the importance. In each item, a graph indicating the magnitude of the importance and a time-series importance rank for each past learning are displayed in association.)
receiving an operation of changing the value of the target feature amount in the range, wherein (¶68; ¶69 That is, the user can change a present value of the factory measurement data and predict the evaluation value using the prediction model based on the changed value of the factory measurement data;)
in presenting the simulation result, a prediction result of the output data indicated by the simulation result is changed in accordance with the operation. (¶69 The changing unit 310 accepts the change in the value of the factory measurement data acquired in step S1 from the user (step S17). The simulation unit 311 predicts the time-series overall facility efficiencies and the time-series energy basic units at a time point after the given time passes by inputting the changed factory measurement data to the prediction model (step S18). The overall evaluation unit 304 calculates the changed overall evaluation value based on the overall facility efficiency and the energy basic unit at each unit time predicted in step S18 (step S19).; ¶70 FIG. 6 is a diagram illustrating an example of a display screen of the changed overall evaluation value according to the first embodiment.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 3:
Mizutani does not teach in particular, but Nishiki teaches:
in predicting the output data and simulating the output data, the output data is obtained by inputting the input data to a learned model generated by machine learning. (¶16 based on input data related to the operation of the factory at the evaluation time using a prediction model which is a learned model learned so that the evaluation value at a time point after the given time passes from one time point is output by inputting a plurality of kinds of data related to the operation of the factory at one time point)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 4:
Mizutani does not teach in particular, but Nishiki teaches:
acquiring information on an importance degree of each of the plurality of feature amounts from the learned model, and extracting the target feature amount in accordance with the importance degree. (¶12 an importance specifying unit configured to specify importance by a kind of the input data in the prediction model. The evaluation value output unit may output the importance by the kind of input data.; ¶48 The importance specifying unit 307 can specify importance in accordance with, for example, a scheme such as permutation importance. The permutation importance is a scheme of specifying a demand of evaluation target data by observing a change in an output at the time of inputting of a value shuffled from a value of evaluation target data of the importance in the data set to the prediction model and a change in an output at the time of inputting of a changed data set to the prediction model.; see also Mizutani ¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 5:
Mizutani teaches:
in extracting the target feature amount, the target feature amount is extracted from among selected feature amounts. (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶29 the processing efficiency of each process, that is, parameters that affect how much processing can be performed in a unit time (number of passes, rolling temperature, temperature waiting time, etc.) are extracted, and these Are classified into product groups having significant differences, and the processing efficiency is statistically calculated for each product group. For example, classification is performed for each parameter such that the average of the parameters for each product group differs by 10%, and the processing efficiency is calculated. )
Mizutani does not teach in particular, but Nishiki teaches:
receiving an operation for selecting a feature amount whose value is changeable from the plurality of feature amounts; wherein (¶68; ¶69 That is, the user can change a present value of the factory measurement data and predict the evaluation value using the prediction model based on the changed value of the factory measurement data;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 6:
Mizutani does not teach in particular, but Nishiki teaches:
in presenting a simulation result of the output data, the simulation result is displayed on a screen, and (¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16). In the display screen of the importance, items of the factory measurement data are arranged and displayed in a descending order of the importance. In each item, a graph indicating the magnitude of the importance and a time-series importance rank for each past learning are displayed in association.)
a target value of the output data is inputted by an operation on the screen. (¶48 The permutation importance is a scheme of specifying a demand of evaluation target data by observing a change in an output at the time of inputting of a value shuffled from a value of evaluation target data of the importance in the data set to the prediction model and a change in an output at the time of inputting of a changed data set to the prediction model. The shuffling of the values of the data is performed for a purpose of eliminating correlation of the evaluation target data.; ¶69 after viewing the display screen of the importance of the factory measurement data. That is, the user can change a present value of the factory measurement data and predict the evaluation value using the prediction mode)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 7:
Mizutani does not teach in particular, but Nishiki teaches:
outputting the simulation result and a value of the target feature amount corresponding to the simulation result, to an outside of the operation improvement assistance device. (¶62 The evaluation value output unit 309 generates and outputs a display screen of the overall evaluation values illustrated in FIG. 4 (step S15).; ¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16).; ¶71 The evaluation value output unit 309 generates and outputs a display screen of the changed overall evaluation value illustrated in FIG. 6 ... Thus, the user can verify validity of the change in the driving plan.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 8:
Mizutani teaches:
target feature amount extraction circuitry to extract a target feature amount that is a feature amount whose value is adjustable from among a plurality of feature amounts (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶29 classification is performed for each parameter such that the average of the parameters for each product group differs by 10%, and the processing efficiency is calculated. Then, by comparing the processing efficiency of each process for each product group, it is possible to identify the rate-limiting (neck) process in a plurality of processes in series, direct connection, and multiple stages for each product group, and to obtain the inherent consistency efficiency for each product group.)
in order to simulate output data indicating the operation status or the outcome when the device is operated in accordance with input data, from the input data including each value of the plurality of feature amounts related to operation of the device, (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product)
simulation circuitry to obtain, by simulation, the output data corresponding to a changed value of the target feature amount; and (¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product; ¶36 Once the inherent efficiency of each product group is determined as described above, the overall efficiency of the entire manufacturing process can be determined using the inherent efficiency of each product group according to the composition ratio of the product group in the period to be predicted.; ¶40 As described above, parameters that affect the processing efficiency of each process are appropriately extracted in a manufacturing process that combines multiple types of product groups with series, direct connection, and multiple stages with different processing capabilities. At the same time, by properly defining and classifying product groups with significant differences in these parameters and obtaining unique consistency efficiency for each product group, the consistency efficiency of the entire manufacturing process according to the composition ratio of the product group Can be calculated and predicted with high accuracy and simplicity.)
Mizutani does not teach in particular, but Nishiki teaches:
the input data being data collected during operation of the device; (Abstract, based on input data related to an operation of a factory at the evaluation time using a learned prediction model.; ¶9 the input data may include data related to a running ratio of a facility of the factory and energy consumption of the facility.; ¶31 The measurement system 20 measures a plurality of kinds of data (for example, current values, voltages, power amounts, vibrations) regarding running of the production facilities 10. Data regarding the running of the production facility 10 is an example of data related to an operation of a factory.)
presentation circuitry to present a result of the simulation. (¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16). In the display screen of the importance, items of the factory measurement data are arranged and displayed in a descending order of the importance. In each item, a graph indicating the magnitude of the importance and a time-series importance rank for each past learning are displayed in association.; see also Nishiki "Reference numeral 104 denotes an output unit that displays a consistent efficiency of the entire manufacturing process, which is a calculation result of the calculation unit 103, on a display device (not shown). The operator refers to the consistent efficiency of the entire manufacturing process and adjusts the throughput and operating rate of the front and rear processes (adjusts the amount of steel slabs received from the steelmaking process, which is the previous process, Adjustment of the operation rate / processing capacity of each process, etc.) and, if necessary, measures are taken to increase the facility capacity of the process that becomes a bottleneck.")
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Regarding Claim 9:
Mizutani teaches:
predicting output data indicating the operation status or the outcome from input data including each value of a plurality of feature amounts related to operation of the device, (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product)
extracting a target feature amount that is a feature amount whose value is adjustable in prediction of the output data, from among the plurality of feature amounts; (¶19 Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.; ¶29 classification is performed for each parameter such that the average of the parameters for each product group differs by 10%, and the processing efficiency is calculated. Then, by comparing the processing efficiency of each process for each product group, it is possible to identify the rate-limiting (neck) process in a plurality of processes in series, direct connection, and multiple stages for each product group, and to obtain the inherent consistency efficiency for each product group.)
simulating the predicted output data by changing a value of the target feature amount; and (¶22 predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product; ¶36 Once the inherent efficiency of each product group is determined as described above, the overall efficiency of the entire manufacturing process can be determined using the inherent efficiency of each product group according to the composition ratio of the product group in the period to be predicted.; ¶40 As described above, parameters that affect the processing efficiency of each process are appropriately extracted in a manufacturing process that combines multiple types of product groups with series, direct connection, and multiple stages with different processing capabilities. At the same time, by properly defining and classifying product groups with significant differences in these parameters and obtaining unique consistency efficiency for each product group, the consistency efficiency of the entire manufacturing process according to the composition ratio of the product group Can be calculated and predicted with high accuracy and simplicity.)
Mizutani does not teach in particular, but Nishiki teaches:
the input data being data collected during operation of the device; (Abstract, based on input data related to an operation of a factory at the evaluation time using a learned prediction model.; ¶9 the input data may include data related to a running ratio of a facility of the factory and energy consumption of the facility.; ¶31 The measurement system 20 measures a plurality of kinds of data (for example, current values, voltages, power amounts, vibrations) regarding running of the production facilities 10. Data regarding the running of the production facility 10 is an example of data related to an operation of a factory.)
presenting a simulation result of the output data. (¶67 The evaluation value output unit 309 generates and outputs a display screen of the importance of the factory measurement data illustrated in FIG. 5 (step S16). In the display screen of the importance, items of the factory measurement data are arranged and displayed in a descending order of the importance. In each item, a graph indicating the magnitude of the importance and a time-series importance rank for each past learning are displayed in association.; see also Nishiki "Reference numeral 104 denotes an output unit that displays a consistent efficiency of the entire manufacturing process, which is a calculation result of the calculation unit 103, on a display device (not shown). The operator refers to the consistent efficiency of the entire manufacturing process and adjusts the throughput and operating rate of the front and rear processes (adjusts the amount of steel slabs received from the steelmaking process, which is the previous process, Adjustment of the operation rate / processing capacity of each process, etc.) and, if necessary, measures are taken to increase the facility capacity of the process that becomes a bottleneck.")
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the simulation system features, including measurement methodology and display features, of Nishiki to the analysis system of Mizutani, in order to allow the system to predict evaluation values related to a future operation of a factory, noted to be advantangeous in effect (Nishiki, ¶17).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BIJAN MAPAR whose telephone number is (571)270-3674. The examiner can normally be reached Monday - Thursday, 11:00-8:30.
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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189
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