DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are presented for examination.
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.
Claims 6 and 15 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 applicant regards as the invention.
As to claim 6, line(s) 1, the limitation “at least two of the plurality of timeframes encompasses” renders the claim indefinite, because it is unclear whether the claimed is directed to a singular timeframe or a plurality of "timeframes". Examiner notes that a plurality of timeframes antecedes a singular timeframe. The cardinality of "timeframes" is unclear.
As to claim 15, the same deficiency applies.
Appropriate correction or clarification is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes)
Independent claim 1 Step 2A, Prong One: claim recites:
generating a divided input dataset by dividing an input dataset comprising field data into a plurality of timeframes; restructuring the divided input dataset based on a spatial component associated with a target well of the well field
Claim 1 is substantially drawn to mental concepts: observation, evaluation, judgment, opinion. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power Group1 (Electric Power hereinafter): “Information… is an intangible”.
As to the limitations "generating a divided input dataset by dividing an input dataset comprising field data into a plurality of timeframes", the limitations encompass a user simply dividing/processing data in his/her mind.
As to the limitations "restructuring the divided input dataset based on a spatial component associated with a target well of the well field", the limitations encompass a user simply manipulating/processing data in his/her mind. The specification reads (underline emphasis added):
'[0025]… in operation 306, restructure the dataset in a spatial context to one or more of the wells of the field. In particular, within each epoch 408, spatial relationships of the existing wells may be computed or estimated…
[0026]… The manipulated or restructured dataset may be used as input to a deep learning model generating system'
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes).
Independent claim 1 Step 2A, Prong Two:
As to the limitations "for generating a forecast model of a well field… generating an optimized production forecast model from the plurality of trained production forecast models", these limitations represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "training, based on the input dataset and utilizing a deep learning computing technique, a plurality of production forecast models", they represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e., they fail to recite details of how a solution to a problem is accomplished.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1 Step 2B:
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2). The specification reads (underline emphasis added):
'[0036]… the dynamic waterflood model generation tool 806 may generate an optimized dynamic waterflood model 212 based on transformed or manipulated field data 202. As such, the dynamic waterflood model generation tool 806 may include a waterflood model generation application 812 executed to perform one or more of the operations… the waterflood model generation application 812 may include instructions that may be executed in an operating system environment'
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1). The limitations are so broad that little is known about how the claimed "training… utilizing a deep learning computing technique… production forecast models" is performed. The specification reads (underline emphasis added):
'[0039] The waterflood model generation application 812 may also include a deep learning trainer 816 to generate and/or train one or more waterflood models based on an input dataset 206 received from the training data manager 814. As explained above, the deep learning trainer 816 may include any machine learning or artificial intelligence techniques to generate a waterflood model from the input dataset 206. In one particular implementation, the deep learning trainer 816 may employ a neural network to execute an artificial intelligence algorithm on the dataset 206 to generate one or more waterflood models from the input dataset'
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claim 11 recite substantially the same elements as claim 1 and is rejected for the same reasons above.
Independent claim 11 Step 2A Prong two and 2B: As to the limitation computer-readable storage media storing computer-executable instructions, it is interpreted as drawn to a generic computer. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mental algorithm has not been held by the courts to be enough to qualify as “significantly more”. The implementation on a computing system is described in the specification (underline emphasis added):
"[0046] The computer system 900 may be a conventional computer, a distributed computer, or any other type of computer".
Independent claim 20, Step 1: a system (system = 2019 PEG Step 1 = yes)
Independent claim 20 Step 2A, Prong One: claim recites:
the input dataset divided into a plurality of timeframes and restructured based on a spatial component associated with a target well of the well field
Claim 20 is substantially drawn to mental concepts, but for the recitation of generic computer components.
As to the limitations "the input dataset divided into a plurality of timeframes", the limitations encompass a user simply dividing/processing data in his/her mind.
As to the limitations "input dataset… restructured based on a spatial component associated with a target well", the limitations encompass a user simply manipulating/processing data in his/her mind. (See Independent claim 1, Step 2A, Prong One above).
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes).
Independent claim 20 Step 2A, Prong Two: As to the limitations "a waterflood modeling system having at least one processor configured to", they are interpreted as drawn to a generic computer.
As to the limitations "for generating a forecast model of a well field… generate a plurality of production forecast models… the waterflood modeling system generating an optimized production forecast model from the plurality of trained production forecast models", these limitations represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 20 Step 2B: As discussed with respect to Step 2A, Prong two, the limitations "a waterflood modeling system having at least one processor configured to" are interpreted as drawn to a generic computer. (See Independent claim 11 Step 2A Prong two and 2B above).
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. (See Independent claim 1, Step 2B above).
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. (See Independent claim 1, Step 2B above).
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Dependent claims Step 2A, Prong One: Dependent claims limitations further the mental concepts of their independent claims. (See Independent claim 1, Step 2A, Prong One above).
As to the limitations "4/14… wherein restructuring the divided input dataset comprises: establishing one or more distance rings defining a distance from the target well; and determining one or more wells of the well field are located within the one or more distance rings" and "5… wherein a first defined distance of a first distance ring of the one or more distance rings is larger than a second defined distance of a second distance ring of the one or more distance rings", distance definitions and determinations are mental in nature. These limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing deciding/determining (judgments, opinions), that can be performed in the human mind or by a human using a pen and paper. Examiner notes that “establish*” is not mentioned in the Specification.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes).
Dependent claims, Step 2A Prong two: As to the limitations "2/12… extracting the input dataset from a database of raw field data comprising production data from a plurality of wells of the well field“ and "3/13… of claim 2/12, wherein the plurality of wells comprise at least one oil well and at least one injector well", these limitations describe the concept of “mere data gathering” , which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. As to the limitations "9… displaying… the generated production forecast of the well of the well field", "10/19… displaying Electric Power.
As to the limitations "9… displaying, on a user interface" and "18… for display on a user interface", they are recited as a GUI performing generic computer functions routinely used in computer applications.
As to the limitations "7/16… recursively executing the optimized production forecast model to generate a production prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data" and "8/17… recursively executing the optimized production forecast model to generate a production forecast of a well of the well field, the optimized production forecast model receiving assumed production data from the field data"; these limitations represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "10… generating… a map of infill locations associated with the well field, the infill locations generated by the optimized production forecast model" and "19… generating… a map of infill locations associated with the well field, the infill locations generated by the optimized production forecast model", they represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e., they fail to recite details of how a solution to a problem is accomplished.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims, Step 2B: As discussed with respect to Step 2A, claims recite data gathering and displaying at a high level of generality; and therefore, these limitations remain insignificant extra-solution activity even upon reconsideration. See MPEP § 2106.05(g).
As discussed with respect to Step 2A, Prong two, the GUI limitations have been found by the courts as not adding an inventive component/concept to claims to render them patentable. A GUI is a well-known graphical modeling means, and it is well-understood, routine, and conventional in the art. A GUI is a well-known graphical modeling means, and it is well-understood, routine, and conventional in the art. See MPEP 2106.04(a)(2), 2106.05(a), 2106.05(f).
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations – simply adding a general purpose computer or computer components after the fact to an abstract idea. See MPEP 2106.05 Well-Understood, Routine, Conventional Activity [R-07.2022] (d)(II): 'Performing repetitive calculations, Flook2… (recomputing or readjusting alarm limit values)'.
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1). The limitations are so broad that little is known about how the claimed map of infill locations is generated. Examiner notes that the specification is mute about generating a map of infill locations.
In the dependent claims, their additional elements do not provide an inventive concept in Step 2B. Therefore, the claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sun et al., (Sun hereinafter), U.S. Pre–Grant publication 20210010351 (see IDS dated 09/12/2025), taken in view of Nunez et al., (Nunez hereinafter), U.S. Patent 8306801 (see IDS dated 09/12/2025).
As to claim 1, Sun discloses a method for generating a forecast model of a well field (see "[0050] The model development engine… to… generate… models… to forecast well production"), the method comprising: (see "[0067]… an input layer 602 receives input data at each time stamp or each time period. The input data may include temporal time series data… model may receive input data at a number of time stamps or number of periods of time as an input sequence"); restructuring the divided input dataset (see "[0042] For a single well, the dataset may be divided into a training part, a validation part, and a test part and a window interval may move from a beginning of the time series until an end of the time series"); training, based on the input dataset and utilizing a deep learning computing technique, a plurality of production forecast models (see "[0072]… forward model prediction of the well productivity system… input layer 802 illustrates that extracted features from a first time stamp to a sixth time stamp and well constraints from a fourth time stamp to a ninth time stamp may be used to estimate well production responses from the seventh time stamp to the ninth time stamp. The hidden layer 804 applies machine learning such as deep learning neural networks and techniques including LSTM or GRU based on previous input data"); and generating an optimized production forecast model from the plurality of trained (see "[0053]… model development engine 204 may use the training data subset, the validation data subset, and the test data subset to gradually improve the accuracy of the model") production forecast models (see "[0050]… generate… models… to forecast well production").
While Sun discloses a divided input dataset, restructuring the divided input dataset, and a spatial component associated with a target well of the well field; Sun fails to disclose generating
Nunez discloses generating (see “generate model input in a generation block 150. For example, the generation block 150 may adjust one or more parameters of a mathematical model” in col. 3, lines 9-12) and based on (see “ECLIPSE® software relies on a finite difference technique, which is a numerical technique that discretizes a physical space into blocks defined by a multidimensional grid. Numerical techniques (e.g., finite difference, finite element, etc.) typically use transforms or mappings to map a physical space to a computational or model space, for example, to facilitate computing… a 3D grid feature to discretize a physical space and a solver to solve reservoir models” in col. 3, lines 21-35).
Sun and Nunez are analogous art because they are related to developing forecasts and models of oil wells.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Nunez with Sun, because Nunez points out that "a model can include… virtual sensors" (see col. 4, lines 21-43), and as a result, Nunez reports that "[a]s shown in FIG. 8, the method 840 includes a position block 844 for initial positioning of… virtual sensors. A simulation block 848 runs simulations at a current time and a future time. A decision block 852 decides whether breakthrough has occurred at the future time. If not, the method 840 continues to a wait block 856, which waits for a time until proceeding back to the simulation block 848. However, if the decision block 852 decides that breakthrough has occurred at the future time, the method 800 continues at an adjustment block 860 that adjusts… virtual sensors (e.g., as shown in the scenario 810). Such an approach can provide for more robust and timely management of material recovery from a reservoir" (see col. 7, lines 26-43).
As to claim 2, Sun discloses extracting the input dataset from a database (see "[0051] The model development engine 204 can receive input data associated with one well or more than one well. The input data may be retrieved from an on-premise database") of raw field data comprising production data from a plurality of wells of the well field (see "[0035] The temporal data may include well production response information such as oil rates, gas rates, and water rates. The temporal data also may include well operation constraint information such as wellhead or bottomhole pressure information and choke size information. Other input data may include drilling and well completion design").
As to claim 3, Sun discloses wherein the plurality of wells comprise at least one oil well (see "[0035] The temporal data may include well production response information such as oil rates, gas rates, and water rates. The temporal data also may include well operation constraint information such as wellhead or bottomhole pressure information and choke size information. Other input data may include drilling and well completion design") and at least one injector well (see "[0058]… forecast engine 206 may be used to modify input constraints associated with the… wells such as a pumping schedule of hydraulic fracture treatment, a number of stages, and types of fluid systems (i.e., slick water with friction reducer, linear gel), among other input constraints. As another example, a water flooding field development plan can be optimized by modifying input constraints by the forecast engine 206 such as a water injection period or a water amount").
As to claim 4, Nunez discloses wherein restructuring the divided input dataset comprises: establishing one or more distance rings defining a distance from the target well; and determining one or more wells of the well field are located within the one or more distance rings (see "distance rings" as "dashed lines that represent virtual sensor rings and a virtual sensor arc", "sensed information is communicated to a modeling loop 204 that relies on virtual sensor analysis using the virtual sensor module… a model can include… virtual sensors. In the example of FIG. 2, a virtual sensor is defined with respect to a model of the reservoir (see, e.g., dashed lines that represent virtual sensor rings and a virtual sensor arc)… the virtual sensor module 290 may be configured as add-on software for receiving input from… modules of the system 100 and for providing output" in col. 4, lines 21-43; "virtual sensors may be updated responsive to future time results (e.g., forecast results), for example, according to the method 840. As shown in FIG. 8, the method 840 includes a position block 844 for initial positioning of… virtual sensors… if the decision block 852 decides that breakthrough has occurred at the future time, the method 800 continues at an adjustment block 860 that adjusts… virtual sensors (e.g., as shown in the scenario 810). Such an approach can provide for more robust and timely management of material recovery from a reservoir" in col. 7, lines 26-43).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Nunez with Sun, (see supra).
As to claim 5, Nunez discloses wherein a first defined distance of a first distance ring of the one or more distance rings is larger than a second defined distance of a second distance ring of the one or more distance rings (see "adjustment block 860 that adjusts… virtual sensors (e.g., as shown in the scenario 810)" in col. 7, lines 26-43).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Nunez with Sun, (see supra).
As to claim 6, Sun discloses wherein at least two of the plurality of timeframes encompasses a different duration (see "[0067]… an input layer 602 receives input data at each time stamp or each time period. The input data may include temporal time series data… model may receive input data at a number of time stamps or number of periods of time as an input sequence").
As to claim 7, Sun discloses recursively executing the optimized production forecast model to generate a production prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data (see "[0071]… hidden layer 704 applies machine learning such as deep learning neural networks and techniques including LSTM or GRU based on previous input data. FIG. 7 then shows that new features are extracted for the seventh time stamp at 706 and may be used in the input sequence 708 to the deep learning techniques in 710 to predict well production responses at an eighth time stamp at 712. As an example, input features may include well production rates for the well and well operation constraints for the well at each time stamp").
As to claim 8, Sun discloses recursively executing the optimized production forecast model to generate a production forecast of a well of the well field, the optimized production forecast model receiving assumed production data from the field data (see "[0081] At step 1008, the well productivity system 201 can generate a forecast for the well for a future period of time using the well production model. The forecast may include a future well production rate for the well including at least one of an oil rate, a gas rate, and a water rate. The forecast can be generated for one well or one or more wells using the well production model").
As to claim 9, Sun discloses displaying, on a user interface (see "[0061] FIG. 3 illustrates graphs of… output data for the well productivity system"; "[0059]… storage 208 can store… outputs or results generated by the well productivity system 201 (for example, data and/or calculations from the model development engine 204, the forecast engine 206, etc.)… GUI content, and/or any other data and content"), the generated production forecast of the well of the well field (see "[0081] At step 1008, the well productivity system 201 can generate a forecast for the well for a future period of time using the well production model. The forecast may include a future well production rate for the well including at least one of an oil rate, a gas rate, and a water rate. The forecast can be generated for one well or one or more wells using the well production model").
As to claim 10, Sun discloses generating and displaying (see "[0059]… storage 208 can store… outputs or results generated by the well productivity system 201 (for example, data and/or calculations from the model development engine 204, the forecast engine 206, etc.)… GUI content, and/or any other data and content") a map of infill locations associated with the well field, the infill locations generated by the optimized production forecast model (see "infill" in "parent-child well relationship information", "[0036] After the new database is created with the input data, the input data may be engineered and transformed. This may include performing exploratory data analysis and extracting preliminary insights from the input data… [0040] The spatial features may include… multiple well spacing information, and parent-child well relationship information. In addition, the spatial features may include geological map information").
As to claims 11-19, these claims recite a computer-readable storage media storing computer-executable instructions for performing the method of claims 1-4 and 6-10. Sun discloses "[0048] [t]he well productivity system 201 can be part of, or implemented by, one or more computing devices" for performing a method that teaches claims 1-4 and 6-10. Therefore, claims 11-19 are rejected for the same reasons given above.
As to claim 20, Sun discloses a system (see "[0048] [t]he well productivity system 201 can be part of, or implemented by, one or more computing devices") for generating a forecast model of a well field (see "[0050] The model development engine… to… generate… models… to forecast well production"), the system comprising: a waterflood (see "[0059]… water flooding field development plan can be optimized by modifying input constraints by the forecast engine 206 such as a water injection period or a water amount") modeling system having at least one processor configured to generate a plurality of production forecast models trained based on an input dataset and using a deep learning computing technique (see "[0072]… forward model prediction of the well productivity system… input layer 802 illustrates that extracted features from a first time stamp to a sixth time stamp and well constraints from a fourth time stamp to a ninth time stamp may be used to estimate well production responses from the seventh time stamp to the ninth time stamp. The hidden layer 804 applies machine learning such as deep learning neural networks and techniques including LSTM or GRU based on previous input data"), the input dataset divided into a plurality of timeframes (see "[0067]… an input layer 602 receives input data at each time stamp or each time period. The input data may include temporal time series data… model may receive input data at a number of time stamps or number of periods of time as an input sequence") and restructured (see "[0042] For a single well, the dataset may be divided into a training part, a validation part, and a test part and a window interval may move from a beginning of the time series until an end of the time series"), the waterflood modeling system generating an optimized production forecast model from the plurality of trained (see "[0053]… model development engine 204 may use the training data subset, the validation data subset, and the test data subset to gradually improve the accuracy of the model") production forecast models (see "[0050]… generate… models… to forecast well production").
While Sun discloses restructured and a spatial component associated with a target well of the well field; Sun fails to disclose based on
Nunez discloses based on (see “ECLIPSE® software relies on a finite difference technique, which is a numerical technique that discretizes a physical space into blocks defined by a multidimensional grid. Numerical techniques (e.g., finite difference, finite element, etc.) typically use transforms or mappings to map a physical space to a computational or model space, for example, to facilitate computing… a 3D grid feature to discretize a physical space and a solver to solve reservoir models” in col. 3, lines 21-35).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Nunez with Sun, (see supra).
Conclusion
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 8:00 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, Renee Chavez can be reached at 571-270-1104. 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.
/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
2 Flook, 437 U.S. at 594, 198 USPQ2d at 199
Prosecution Insights