Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. Claims 1-16 are presented for examination.
Claim Objections
2. Claim 13 is objected to because of the following informalities:
As per Claim 13, it recites the limitation “kernal” in line 2 which would be better as “kernel”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 1 and 16, they recite the limitation “a model output that is not part of the data” which is unclear what the limitation refers. In particular, which “data” is it referring to? Previously it recites received “data” relating to a process, cleaned “data” and conditioned “data”. It is interpreted that the limitation “a model output that is not part of the data” is “a model output” while “the data” is interpreted as “a model input”.
As per Claim 5, it recites the limitation “the data” which is unclear what the limitation refers. Is it referring to received “data” relating to a process, cleaned “data” and conditioned “data” claimed in Claim 1.
Regarding claims 7 and 11, the phrase "or the like" renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "or the like"), thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d).
As per Claim 14, it recites the limitation “average R.sup.2, look ahead R.sup.2” which is unclear what the limitation refers. In particular, what is R?
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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claim 1-15 recite steps or acts including outputting the one or more models and the corresponding validation metric; thus, the claims are to a process, which is one of the statutory categories of invention. The claim 16 recites a system comprising: a processor; a memory coupled to the processor and therefore is a machine, which is a statutory category of invention.
(Step 2A – Prong One) For the sake of identifying the abstract ideas, a copy of the claim is provided below. Abstract ideas are bolded.
The claims 1 and 16 recite:
receiving data relating to a process; (insignificant extra-solution activity – data gathering)
cleaning the data to yield cleaned data (under its broadest reasonable interpretation, a mental process and “mathematical concepts” group of abstract ideas), wherein cleaning the data comprises: identifying and removing outlying data points from the data; and conditioning the data (under its broadest reasonable interpretation, a mental process and “mathematical concepts” group of abstract ideas);
identifying inferential model parameters comprising a parameter selected from the group (under its broadest reasonable interpretation, “a mental process” that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) consisting of: a compositional property of the process as a model output that is not part of the data, operational constraints of the process, interactions between process variables of the process, and any combination thereof (insignificant extra-solution activity – data gathering);
building one or more inferential models based on the cleaned data and the inferential model parameters (under its broadest reasonable interpretation, a mental process and “mathematical concepts” group of abstract ideas), wherein building comprises:
identifying input variables from the cleaned data (under its broadest reasonable interpretation, a mental process and “mathematical concepts” group of abstract ideas);
fitting the input variables from a first portion of the cleaned data to a model (under its broadest reasonable interpretation, a mental process and “mathematical concepts” group of abstract ideas);
validating the model using a second portion of the cleaned data to yield a validation metric corresponding to the model (under its broadest reasonable interpretation, a mental process); and
outputting the one or more models and the corresponding validation metric (insignificant extra-solution activity for the act of outputting).
Therefore, the limitations, under the broadest reasonable interpretation, have been identified to recite judicial exceptions, an abstract idea.
(Step 2A – Prong Two: integration into practical application) This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements of “A system comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the method” (Claim 16) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further, the additional elements of “processor” does not (1) improve the functioning of a computer or other technology, (2) is not applied with any particular machine (except for generic computer components), (3) does not effect a transformation of a particular article to a different state, and (4) is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Claims 1 and 16 recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering (see MPEP 2106.05(g)): “receiving data relating to a process; (insignificant extra-solution activity – data gathering) and a compositional property of the process as a model output that is not part of the data, operational constraints of the process, interactions between process variables of the process, and any combination thereof(insignificant extra-solution activity – data gathering);”.
Claims 1 and 16 recite the limitation which insignificant extra-solution activity for the act of outputting itself (see MPEP 2106.05(g)):: “outputting the one or more models and the corresponding validation metric (insignificant extra-solution activity for the act of outputting).”
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
(Step 2B - inventive concept) The claim(s) does/do 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 elements of “A system comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the method” (Claim 16) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)).
Further as discussed above Claims 1 and 16 recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering/outputting (see MPEP 2106.05(g)) which is the element that the courts have recognized as well-understood, routine, conventional activity (see MPEP 2106.05(d) II. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93): “receiving data relating to a process; (insignificant extra-solution activity – data gathering) and a compositional property of the process as a model output that is not part of the data, operational constraints of the process, interactions between process variables of the process, and any combination thereof(insignificant extra-solution activity – data gathering);” and “outputting the one or more models and the corresponding validation metric (insignificant extra-solution activity for the act of outputting)”.
Further dependent claims 2-15 recite:
2. The method of claim 1 further comprising: selecting a preferred model from the one or more models based on the corresponding validation metric. (a mental process)
3. The method of claim 2 further comprising: implementing the preferred model relative to the process or a related process. (insignificant extra-solution activity – field of use)
4. The method of claim 1, wherein the one or more models is one model, and wherein the method further comprises: implementing the one model relative to the process or a related process. (insignificant extra-solution activity – field of use)
5. The method of claim 1, wherein the data is selected from the group consisting of: upstream data, process data, downstream data, and any combination thereof. (insignificant extra-solution activity – data gathering)
6. The method of claim 1, wherein the identifying of the outlying data points comprises a method selected from the group consisting of: slicing methods, conditional methods, statistical methods, and any combination thereof. (insignificant extra-solution activity for the act of outputting itself and/or “apply it”)
7. The method of claim 1, wherein the conditioning of the data comprises a method selected from the group consisting of: synchronizing variables, noise filtering, and the like, and any combination thereof. (insignificant extra-solution activity for the act of outputting itself and/or “apply it”)
8. The method of claim 1, wherein the compositional property is selected from the group consisting of: concentration of a component in the composition, flash point, freezing point, boiling point, cloud point, melt flow index, density, and any combination thereof. (insignificant extra-solution activity – data gathering)
9. The method of claim 1, wherein operational constraints comprise a constraint of an operation parameter selected from the group consisting of: temperature, pressure, pressure compensated temperature, chemical species concentration, feed quality, contaminant concentrations, bed height, density, specific gravity, API gravity, draw rate, feed rate, flow rate, space velocity, and any combination thereof. (insignificant extra-solution activity – data gathering)
10. The method of claim 1, wherein the interactions between process variables comprise an interaction selected from the group consisting of: reaction rates, heat balance, correlations between temperature and pressure, operational parameter deltas, operational parameter ratios, and any combination thereof. (insignificant extra-solution activity – data gathering)
11. The method of claim 1, wherein the input variables comprise a variable selected from the group consisting of: temperature, pressure, pressure compensated temperature, chemical species concentration, feed quality, contaminant concentrations, bed height, density, specific gravity, API gravity, draw rate, feed rate, flow rate, space velocity, and the like, and any combination thereof. (insignificant extra-solution activity – data gathering)
12. The method of claim 1, wherein the identifying the input variables include a method selected from the group consisting of: cross correlation matrix methods, relief ranking methods, statistical variable reduction methods, latent variable methods, and any combination thereof. (insignificant extra-solution activity for the act of outputting itself and/or “apply it”)
13. The method of claim 1, wherein the models are selected from the group consisting of: neural networks, decision trees/random forest methods, kernal methods, reinforcement learning methods, and any ensemble thereof. (insignificant extra-solution activity – field of use and/or and “apply it”)
14. The method of claim 1, wherein the validation metric comprises a metric selected from the group consisting of: average R.sup.2, look ahead R.sup.2, average error, standard deviation of error, number of data points, and any combination thereof. (insignificant extra-solution activity – field of use and/or “apply it”)
15. The method of claim 1, wherein the process relates to the production, refining, manufacture, formulation, blending, and/or storage of chemicals. (insignificant extra-solution activity – field of use)
Considering the claim both individually and in combination, there is no element or combination of elements recited contains any “inventive concept” or adds “significantly more” to transform the abstract concept into a patent-eligible application.
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.
5. Claims 1-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by John et al. (US 20220266166 A1)
As per Claim 1 and 16, John et al. discloses a method (Abstract, Fig. 2A-2B & 6)/ system (Fig. 1A) comprising: a processor (Fig. 1A); a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the method (Fig. 1A) comprising:
receiving data relating to a process ([0083] “the data corresponding to the plant measurements or parameters of the CDU 102 and plant are received by the processing unit 108”);
cleaning the data to yield cleaned data ([0083]-[0084] “the data receiving and pre-processing unit 120 can cleanse and reconcile any bad data by filtering and restricting the use of the set of inputs parameters having any or a combination of a negative value, zero value, and non-numerical value, for further processing or computation.”), wherein cleaning the data comprises:
identifying and removing outlying data points from the data ([0084] “the method for cleansing and reconciling can include a step to remove unwanted noise using filters such as Savitzky-Golay filter or Moving Point Averaging (post elimination of outlier data) filter. Outlier data having variation more than 3 times the standard deviation is removed. Subsequently, the filter smoothens the data and increases the precision of the data without distorting the measured signal tendency.”); and
conditioning the data ([0083]-[0085] “ the method for cleansing and reconciling involves the selection of plant data or parameters with average mass balance closure in the range of 98-102%, for a given period of time. The data reconciliation may be used for conditioning the training data sets from the plant to ensure that the data is consistent. ”, “use the pre-processed/conditioned data from CDU 102”);
identifying inferential model parameters comprising a parameter selected from the group ([0087]-[0088] “ this autoregressive exogenous model includes the current input, previous input and output to define the unit performance as defined in function y(t)=ƒ.sup.NN(x(t), x(t−1), . . . x(t−n), y(t−1), y(t−2) . . . y(t−n),), where ƒ.sup.NN—non-linear function described by a Neural network or machine learning model, y(t)—output variables at time t, y(t−n)−output variables at time t−n time, x(t)—input variables at time t, and x(t−n)—input variables at time t−n time. ”) consisting of: a compositional property of the process as a model output that is not part of the data, operational constraints of the process, interactions between process variables of the process, and any combination thereof ([0081] “set of output parameters comprises any or a combination of composition, characteristics, and flow rate of one or more products of the CDU, and other other operating conditions ”);
building one or more inferential models based on the cleaned data and the inferential model parameters (Fig. 2B element 222-> 228, [0087]-[0088] “autoregressive exogenous model… by a Neural network or machine learning model”), wherein building comprises:
identifying input variables from the cleaned data ([0087]-[0088] “ this autoregressive exogenous model includes the current input, previous input and output to define the unit performance as defined in function y(t)=ƒ.sup.NN(x(t), x(t−1), . . . x(t−n), y(t−1), y(t−2) . . . y(t−n),), where ƒ.sup.NN—non-linear function described by a Neural network or machine learning model, y(t)—output variables at time t, y(t−n)−output variables at time t−n time, x(t)—input variables at time t, and x(t−n)—input variables at time t−n time. ”);
fitting the input variables from a first portion of the cleaned data to a model ([0086]-[0089],“the pre-processed transient data can be divided into training, testing, and validation sets”; “the model configuration is defined model parameters viz. weights and biases are trained to minimize the mean square error between plant/measured output and the model predicted output. The algorithm used for training the model is known to those skilled in the art. In order to avoid overfitting or underfitting, model accuracy is tested against the validation set which is not a part of the training data set.”,” the model is fitted”);
validating the model using a second portion of the cleaned data to yield a validation metric corresponding to the model ([0086] “the pre-processed transient data can be divided into training, testing, and validation sets”; [0089] “Once the model is fitted its accuracy is evaluated w.r.t test data set (step 230), if the model accuracy does not improve despite varying weights and biases, it indicates a need for change in the model hyper parameters (step 232). If the test case accuracy is acceptable, then the error autocorrelation test (step 234) is used to check if whether there is a significant correlation. In step 234, autocorrelation in the current model is compared with another having one lower time dependence as illustrated in FIG. 3. The autocorrelation can be checked if they are within the confidence limit, and if not found satisfactory one need to revise the delay steps used in the model (step 236). If the error correlation is within the confidence limit, the model can be used for prediction purposes. This approach can be used for training parameters for plant and/or property prediction units. In order to make future predictions, the open-loop model is transformed into a closed-loop recurrent model as illustrated in FIG. 4. This allows making forecasting predictions even when external feedback is missing, by using internal feedback.”; [0091] “the model performance can be evaluated (step 612) with current data.”); and
outputting the one or more models and the corresponding validation metric ([0089] “The autocorrelation can be checked if they are within the confidence limit, and if not found satisfactory one need to revise the delay steps used in the model (step 236). If the error correlation is within the confidence limit, the model can be used for prediction purposes. This approach can be used for training parameters for plant and/or property prediction units.”).
As per Claim 2, John et al. discloses further comprising: selecting a preferred model from the one or more models based on the corresponding validation metric ([0089] “The autocorrelation can be checked if they are within the confidence limit, and if not found satisfactory one need to revise the delay steps used in the model (step 236). If the error correlation is within the confidence limit, the model can be used for prediction purposes. This approach can be used for training parameters for plant and/or property prediction units.”).
As per Claim 3, John et al. discloses further comprising: implementing the preferred model relative to the process or a related process ([0085]-[0086] “development of a deployable model”, [0089] “If the error correlation is within the confidence limit, the model can be used for prediction purposes. This approach can be used for training parameters for plant and/or property prediction units.”).
As per Claim 4, John et al. discloses wherein the one or more models is one model (Fig. 2B element 238, [0089]), and wherein the method further comprises: implementing the one model relative to the process or a related process ([0085]-[0086] “development of a deployable model”, [0089] “If the error correlation is within the confidence limit, the model can be used for prediction purposes. This approach can be used for training parameters for plant and/or property prediction units.”).
As per Claim 5, John et al. discloses wherein the data is selected from the group consisting of: upstream data, process data, downstream data, and any combination thereof ([0083] “the data corresponding to the plant measurements”).
As per Claim 6, John et al. discloses wherein the identifying of the outlying data points comprises a method selected from the group consisting of: slicing methods, conditional methods, statistical methods, and any combination thereof ([0084] “the method for cleansing and reconciling can include a step to remove unwanted noise using filters such as Savitzky-Golay filter or Moving Point Averaging (post elimination of outlier data) filter. Outlier data having variation more than 3 times the standard deviation is removed. Subsequently, the filter smoothens the data and increases the precision of the data without distorting the measured signal tendency.The cleansing and reconciling method make use of least-squares-fit convolution for smoothing and computing derivatives of a set of consecutive values from the dynamic plant data. Furthermore, the method for cleansing and reconciling involves the selection of plant data or parameters with average mass balance closure in the range of 98-102%, for a given period of time.”).
As per Claim 7, John et al. discloses wherein the conditioning of the data comprises a method selected from the group consisting of: synchronizing variables, noise filtering, and the like, and any combination thereof ([0084] “the method for cleansing and reconciling can include a step to remove unwanted noise using filters such as Savitzky-Golay filter or Moving Point Averaging (post elimination of outlier data) filter. Outlier data having variation more than 3 times the standard deviation is removed. Subsequently, the filter smoothens the data and increases the precision of the data without distorting the measured signal tendency.The cleansing and reconciling method make use of least-squares-fit convolution for smoothing and computing derivatives of a set of consecutive values from the dynamic plant data. Furthermore, the method for cleansing and reconciling involves the selection of plant data or parameters with average mass balance closure in the range of 98-102%, for a given period of time.”).
As per Claim 8, John et al. discloses wherein the compositional property is selected from the group consisting of: concentration of a component in the composition, flash point, freezing point, boiling point, cloud point, melt flow index, density, and any combination thereof ([0092]- [0094], [0111] “ boiling point (T.sub.b) ”, “Flash Point, Freeze Point, etc.)”).
As per Claim 9, John et al. discloses wherein operational constraints comprise a constraint of an operation parameter selected from the group consisting of: temperature, pressure, pressure compensated temperature, chemical species concentration, feed quality, contaminant concentrations, bed height, density, specific gravity, API gravity, draw rate, feed rate, flow rate, space velocity, and any combination thereof ([0092]- [0094], [0111] “equipment operability aspects (such as maximum flow, temperature, duty limits, etc.). The decision variables are those typically varied by operators in order to meet specifications—for example, product draw rates, product draw temperature and stripping steam rates, reflux and pumparounds.”).
As per Claim 10, John et al. discloses wherein the interactions between process variables comprise an interaction selected from the group consisting of: reaction rates, heat balance, correlations between temperature and pressure, operational parameter deltas, operational parameter ratios, and any combination thereof ([0071] [0092]- [0094], [0100], [0111] “the molecular heat of vaporization and boiling point”, “the thermal property can be calculated (606) and used for heat balance”).
As per Claim 11, John et al. discloses wherein the input variables comprise a variable selected from the group consisting of: temperature, pressure, pressure compensated temperature, chemical species concentration, feed quality, contaminant concentrations, bed height, density, specific gravity, API gravity, draw rate, feed rate, flow rate, space velocity, and the like, and any combination thereof ([0057] “ the set of input parameters can include a first set of input parameters selected from temperature of draw furnace outlet, pumparound return temperature, temperature profile of a distillation column of the CDU, pressure profile of the distillation column, and temperature and pressure of flash zone; and a second set of input parameters selected from steam flow rate, stream draw temperature, pumparound flow rate, crude flow rate, crude density, crude specific gravity (S.G), volatility of crude, and fraction of crude vaporized (Vf), but not limited to the likes.”, [0081] “a first set of input parameters selected from the temperature of draw furnace outlet, pumparound return temperature, temperature profile of a distillation column of the CDU, pressure profile of the distillation column, and temperature and pressure of flash zone; and a second set of input parameters selected from steam flow rate, stream draw temperature, pumparound flow rate, crude flow rate, crude density, crude specific gravity (S.G), volatility of crude, and fraction of crude vaporized (Vf), and set of output parameters comprises any or a combination of composition, characteristics, and flow rate of one or more products of the CDU, and other other operating conditions which could be directly obtained online from a DCS.”, [0100]).
As per Claim 12, John et al. discloses wherein the identifying the input variables include a method selected from the group consisting of: cross correlation matrix methods, relief ranking methods, statistical variable reduction methods, latent variable methods, and any combination thereof ([0084] “the method for cleansing and reconciling can include a step to remove unwanted noise using filters such as Savitzky-Golay filter or Moving Point Averaging (post elimination of outlier data) filter. Outlier data having variation more than 3 times the standard deviation is removed. Subsequently, the filter smoothens the data and increases the precision of the data without distorting the measured signal tendency. The cleansing and reconciling method make use of least-squares-fit convolution for smoothing and computing derivatives of a set of consecutive values from the dynamic plant data. Furthermore, the method for cleansing and reconciling involves the selection of plant data or parameters with average mass balance closure in the range of 98-102%, for a given period of time. The data reconciliation may be used for conditioning the training data sets from the plant to ensure that the data is consistent. The data reconciliation technique exploits the existing system redundancy from measured data to adjust and estimate relevant process data (flow/temperatures) that satisfy energy and mass balance constraints”).
As per Claim 13, John et al. discloses wherein the models are selected from the group consisting of: neural networks, decision trees/random forest methods, kernal methods, reinforcement learning methods, and any ensemble thereof ([0087] “Neural network or machine learning model,”).
As per Claim 14, John et al. discloses wherein the validation metric comprises a metric selected from the group consisting of: average R.sup.2, look ahead R.sup.2, average error, standard deviation of error, number of data points, and any combination thereof ([0088]-[0089] “Once the model configuration is defined model parameters viz. weights and biases are trained to minimize the mean square error between plant/measured output and the model predicted output. ”, “the error autocorrelation test ”; [0118] “error distribution curve for the model prediction”… Error is within ±1% for more than 95% of cases. … good predictions the error was within ±2%”).
As per Claim 15, John et al. discloses wherein the process relates to the production, refining, manufacture, formulation, blending, and/or storage of chemicals ([0023] “The proposed system may further comprise a processing unit in communication with the set of sensors and a control unit associated with the CDU and the plant.”).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wegerich et al. (US 20070005311 A1)
Emigholz (US 20120330631 A1)
Chen et al. (US 8346525 B2)
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/ Primary Examiner, Art Unit 2188