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 .
Response to Amendment
This Office Action is in response to applicant’s communication filed 23 December 2025, in response to the Office Action mailed 23 September 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claim(s) 16, 17, 24-26, and 29-32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 9,513,149) in view of Thuries (US 5,693,873 – cited in an IDS) or, alternatively, over Thuries in view of Henry, both as described below.
As per claim 16, Henry teaches a method for determining a fluid density of a fluid in an encapsulated electrical device [sensor signals are used to determine the mass flow rate and density of fluid in a flowtube of an electronic system (figs. 2-4; col. 4, line 54 to col. 5, line 40; col. 10, lines 56-64; etc.)], the method comprising: acquiring measurement data by a sensor unit and deriving from the measurement data measurement values for the fluid density [sensor signals (measurement data) are used to determine the mass flow rate and density of fluid (measurement values) in a flowtube of an electronic system (figs. 2-4; col. 4, line 54 to col. 5, line 40; col. 10, lines 56-64; etc.)]; collecting weather data relating to weather conditions of the electrical device [the system can use additional information, including pressure and temperature measurements from pressure and temperature sensors and other sensors (col. 12, lines 35-61; fig. 11; etc.); where the temperature and pressure sensor measurements are weather data relating to weather conditions of the device]; using machine learning to generate a digital model for an influence of the weather conditions on a measurement deviation of a measurement value from a true fluid density [the system can use additional information, including pressure and temperature measurements (weather conditions) from pressure and temperature sensors and other sensors, as inputs to a neural network (machine learning model) to determine corrections to be made to the mass flow rate and density measurements (col. 12, lines 35-61; etc.); where the neural network (NN) is the digital model generated using machine learning and the corrections determined by the NN are the deviation influenced by the weather conditions]; using the digital model to calculate a correction value for the measurement values as a function of the weather data [the system can use additional information, including pressure and temperature measurements (weather conditions) from pressure and temperature sensors and other sensors, as inputs to a neural network (machine learning model) to determine corrections to be made to the mass flow rate and density measurements (col. 12, lines 35-61; etc.)]; and correcting a measurement value with the correction value [the measurement system uses the determined corrections to correct the measured mass flow rate and density values (col. 12, lines 35-61; etc.)].
While Henry teaches inputting temperature and pressure sensor data (weather conditions) to the neural network to determine the correction (see above), it has not been relied upon for teaching collecting weather data relating to weather conditions in an environment of the electrical device.
Thuries teaches a method for determining a fluid density of a fluid in an encapsulated electrical device [a system for determining the density of an insulating gas in an electrical apparatus (abstract; fig. 1; etc.)], the method comprising: acquiring measurement data by a sensor unit and deriving from the measurement data measurement values for the fluid density [a system for determining the density of an insulating gas in an electrical apparatus (abstract; fig. 1; etc.) from a pressure measuring sensor in the device (col. 3, lines 16-17; fig. 1; etc.)]; collecting weather data relating to weather conditions in an environment of the electrical device [weather condition data is collected by sensors (col. 4, lines 37-58; fig. 1; etc.) including from a reference temperature sensor collecting reference temperature data (fig. 1; col. 3, lines 37-61; etc.)]; using mathematical modelling to generate a digital model for an influence of the weather conditions on a measurement deviation of a measurement value from a true fluid density [a microprocessor of the device may use a mathematical model to determine corrections to the density measurements from the reference temperature changes (weather condition) data (col. 2, lines 53-63; col. 3, lines 52-61; etc.)]; using the digital model to calculate a correction value for the measurement values as a function of the weather data [a microprocessor of the device may use a mathematical model to determine corrections to the density measurements from the reference temperature changes (weather condition) data (col. 2, lines 53-63; col. 3, lines 52-61; etc.)]; and correcting a measurement value with the correction value [the determined correction is used to correct the density measurement data (col. 5, lines 1-4; claim 5; etc.)].
Henry and Thuries are analogous art, as they are within the same field of endeavor, namely using models for correcting fluid density measurements.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include weather condition data from the environment of the device in the inputs to the model for correcting density measurements, as taught by Thuries, in the condition data used as inputs to the neural network model for correcting density measurements in the system taught by Henry.
Thuries provides motivation as [commonly-used pressure sensors require correction based on temperature conditions of the device (col. 5, lines 1-4; etc.) and weather conditions that affect the temperature sensors should be accounted for (col. 4, lines 37-42; claim 3; etc.)].
Alternatively, while Thuries uses a mathematical model to determine temperature and weather dependent corrections to the density measurements, it has not been relied upon for using machine learning to generate the digital model. However, it would also have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the neural network model for determining the temperature-dependent correction to the measured density values, as taught by Henry, as the mathematical model for determining the weather and temperature dependent correction to the measured density values in the system taught by Thuries.
Henry provides motivation as [using a neural network (or other empirical model) to determine the correction information from condition inputs provides for more accurate density and mass flow measurement values (col. 10, lines 56-64; col. 1, lines 53-63; etc.)].
As per claim 17, Henry/Thuries teaches wherein the digital model comprises an artificial neural network having a plurality of layers of networked artificial neurons [the system can use additional sensor information as inputs to a neural network to determine corrections to be made to the mass flow rate and density measurements (Henry: col. 12, lines 35-61; etc.); where a neural network has a plurality of layers of networked artificial neurons].
As per claim 24, Henry/Thuries teaches specifying a calculation period and calculating with the digital model the correction value for the measurement values that are acquired within the calculation period is calculated [calculating the measurement values includes producing predictions for a specified time period of collected data (Henry: col. 7, lines 13-50; Thuries: col. 4, lines 24-62; etc.)].
As per claim 25, Henry/Thuries teaches specifying a period of time for the calculation period [calculating the measurement values includes producing predictions for a specified time period of collected data (Henry: col. 7, lines 13-50; Thuries: col. 4, lines 24-62; etc.)].
While Henry/Thuries teaches specifying a time period for calculation (see above), it has not been relied upon for teaching specifying a 24 hour period.
However, it has been held that discovering an optimum value of a result effective variable (the time period) involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980). It has also been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or working ranges (the time period) involves only routine skill in the art. In re Aller, 105 USPQ 233. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to specify a 24 hour period for the calculation period determined in the system taught by Henry/Thuries, to achieve the predictable result of calculating values for a common day-long time period.
As per claim 26, Henry/Thuries teaches wherein the weather data are selected from the group consisting of a temperature, a wind speed, precipitation, an air humidity, and an air pressure in the environment of the electrical device [the weather condition data can include temperature (Thuries: col. 4, lines 37-58; fig. 1; Henry: col. 12, lines 35-61; fig. 11; etc.)].
As per claim 29, Henry/Thuries teaches feeding only measurement values and weather data as input variables to the digital model [the system can use pressure and temperature measurements as inputs to a neural network (machine learning model) to determine corrections to be made to the mass flow rate and density measurements (Henry: col. 12, lines 35-61; etc.); and a microprocessor of the device may use a mathematical model to determine corrections to the density measurements from the reference temperature changes (weather condition) data (Thuries: col. 2, lines 53-63; col. 3, lines 52-61; etc.)].
As per claim 30, Henry/Thuries teaches feeding measurement values, weather data, and additional data, generated from measurement values and the weather data, as input variables to the digital model [the system can use additional information, including pressure and temperature measurements (weather conditions) from pressure and temperature sensors and other sensors (additional data), as inputs to a neural network (machine learning model) to determine corrections to be made to the mass flow rate and density measurements (Henry: col. 12, lines 35-61; etc.); and a microprocessor of the device may use a mathematical model to determine corrections to the density measurements from the reference temperature changes (weather condition) data (Thuries: col. 2, lines 53-63; col. 3, lines 52-61; etc.)].
As per claim 31, Henry/Thuries teaches a non-transitory computer-readable medium storing computer-executable instructions which, when executed by at least one processor of a control unit or a computing system of a data cloud, cause the processor to implement the method according to claim 16 [the system may be implemented as software code executed by one or more processors (Henry: col. 5, lines 31-57; Thuries: col. 2, lines 43-67; col. 4, lines 43-58; fig. 1; etc.)].
As per claim 32, see the rejection of claim 16, above, wherein Henry/Thuries also teaches an electrical device with encapsulated fluid [sensor signals are used to determine the mass flow rate and density of fluid in a flowtube of an electronic system (Henry: figs. 2-4; col. 4, line 54 to col. 5, line 40; col. 10, lines 56-64; etc.) and a system for determining the density of an insulating gas in an electrical apparatus (Thuries: abstract; fig. 1; etc.)], the electrical device comprising: a sensor unit for acquiring measurement data relating to a fluid density of the fluid [sensor signals are used to determine the mass flow rate and density of fluid in a flowtube of an electronic system (Henry: figs. 2-4; col. 4, line 54 to col. 5, line 40; col. 10, lines 56-64; etc.) and a system for determining the density of an insulating gas in an electrical apparatus from sensor data (Thuries: abstract; fig. 1; etc.)]; a control unit or a connection to a data cloud [the system may be implemented as software code executed by one or more processors (a control unit) (Henry: col. 5, lines 31-57; Thuries: col. 2, lines 43-67; col. 4, lines 43-58; fig. 1; etc.)]; a computer program residing in the control unit or in the data cloud, the computer program being configured to: [perform the method] [the system may be implemented as software code executed by one or more processors (a control unit) (Henry: col. 5, lines 31-57; Thuries: col. 2, lines 43-67; col. 4, lines 43-58; fig. 1; etc.)].
Claim(s) 18-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 9,513,149), in view of Thuries (US 5,693,873 – cited in an IDS), and further in view of Roy (US 2020/0088897).
As per claim 18, Henry/Thuries teaches the method according to claim 17, as described above.
While Henry/Thuries teaches using an artificial neural network (see above), it has not been relied upon for teaching wherein the artificial neural network is a recurrent artificial neural network.
Roy teaches wherein the artificial neural network is a recurrent artificial neural network [a deep learning model is used to predict fluid attributes of a reservoir from received attribute data, where the deep learning model can include Long Short-Term Memory networks (LSTM) and/or a type of Recurrent Neural Networks (RNN) (para. 0013, etc.)].
Henry/Thuries and Roy are analogous art, as they are within the same field of endeavor, namely using a neural network to determine properties of encapsulated/reservoir fluids.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize an LSTM/RNN as the model for predicting fluid properties, as taught by Roy, for the NN model predicting fluid density correction properties in the system taught by Henry/Thuries.
Roy provides motivation as [the LSTM/RNN models can provide more effective and accurate predictions (para. 0013, etc.)].
As per claim 19, Henry/Thuries/Roy teaches wherein the artificial neural network comprises at least one memory-enabled cell [a deep learning model is used to predict fluid attributes of a reservoir from received attribute data, where the deep learning model can include Long Short-Term Memory networks (LSTM) and/or a type of Recurrent Neural Networks (RNN) (Roy: para. 0013, etc.); where an LSTM comprises memory-enabled cells].
Examiner’s Note: the reasoning and motivation for the combination of these references is the same as that provided, above, in the rejection of claim 18.
As per claim 20, Henry/Thuries teaches the method according to claim 16, as described above.
While Henry/Thuries teaches various processors for receiving external data and calculating the correction values (see above), it has not been relied upon for teaching transferring at least one of the measurement data or the measurement values to a data cloud, and/or calculating the correction value with the digital model in a data cloud.
Roy teaches transferring at least one of the measurement data or the measurement values to a data cloud, and/or calculating the correction value with the digital model in a data cloud [the measurement data is received and processed in one or more cloud-computing systems (para. 0043, etc.)].
Henry/Thuries and Roy are analogous art, as they are within the same field of endeavor, namely using a neural network to determine properties of encapsulated/reservoir fluids.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize a cloud-based computing system to receive the measurement data and perform the processing for the neural network model(s), as taught by Roy, for the processor(s) receiving measurement data and performing processing of the neural network model in the system taught by Henry/Thuries.
Roy provides motivation as [cloud-computing systems allows for distributed processing and use of more powerful processing components (para. 0043, etc.)].
As per claim 21, Henry/Thuries teaches the method according to claim 17, as described above.
While Henry/Thuries teaches using an artificial neural network (see above), it has not been relied upon for teaching training the digital model by generating further training values for measurement values and/or weather data from measurement values and/or weather data.
Roy teaches training the digital model by generating further training values for measurement values and/or weather data from measurement values and/or weather data [training the machine learning model can include generating simulated fluid property measurements (paras. 0010-12, 0052-53, etc.)].
Henry/Thuries and Roy are analogous art, as they are within the same field of endeavor, namely using a neural network to determine properties of encapsulated/reservoir fluids.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate simulated measurement data as training samples to train the neural network, as taught by Roy, for training the neural network in the system taught by Henry/Thuries.
Roy provides motivation as [using simulated training data provides for more accurate trained models without having to use large amounts of physical reservoir data, allowing training with less data while providing more data for specific types/wells as desired (paras. 0010-12, etc.)].
As per claim 22, Henry/Thuries/Roy teaches generating the training values by at least one of: temporally shifting weather data relative to measurement values, scaling measurement values and/or weather data, or shifting a value range of the measurement values [training the machine learning model can include generating simulated fluid property measurements based upon actual measurements and ranges (Roy: paras. 0010-12, 0052-53, etc.), including shifting seismic attribute data over time (Roy: paras. 0048, 0065, etc.); for weather condition data timing data of the environment of the electronic device holding the fluid (Thuries: col. 4, lines 37-58; fig. 1; etc.)].
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 9,513,149), in view of Thuries (US 5,693,873 – cited in an IDS), and further in view of Hauge (US 2019/0169982).
As per claim 23, Henry/Thuries teaches the method according to claim 16, as described above.
While Henry/Thuries teaches using an artificial neural network (see above), it has not been relied upon for teaching training the digital model by generating training values for simulated fluid losses.
Hauge teaches training the digital model by generating training values for simulated fluid losses [a neural network can be used to perform the data analysis (para. 0204, etc.) and can be trained using simulated data including fluid leak data (paras. 0301-303, etc.)].
Henry/Thuries and Hauge are analogous art, as they are within the same field of endeavor, namely fluid property predictions using a neural network.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate simulated fluid leaks (losses) for training the neural network, as taught by Hauge, for training the neural network in the system taught by Henry/Thuries.
Hauge provides motivation as [additional properties are used to create a more accurate characterization of the fluid (paras. 0051-54, etc.)].
Claim(s) 27-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 9,513,149), in view of Thuries (US 5,693,873 – cited in an IDS), and further in view of well-known practices in the art.
As per claim 27, Henry/Thuries teaches the method according to claim 16, as described above.
While Henry/Thuries teaches generating a digital model on an electrical device (see above), it has not been relied upon for teaching generating the digital model specifically for a given electrical device.
However, the examiner takes official notice that generating (machine learning) models for a specific device is old and well-known within the art. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate the digital model taught by Henry/Thuries by generating the digital model specifically for a given electrical device, to achieve the predictable result of generating a model that makes better use of the particular resources and limitations of the particular device.
As per claim 28, Henry/Thuries teaches the method according to claim 16, as described above.
While Henry/Thuries teaches generating a digital model on an electrical device (see above), it has not been relied upon for teaching generating the digital model specifically for mutually different electrical devices.
However, the examiner takes official notice that generating (machine learning) models for specific devices is old and well-known within the art. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate the digital model taught by Henry/Thuries by generating the digital model specifically for mutually different electrical devices, to achieve the predictable result of generating a model that makes better use of the particular resources and limitations of the particular devices.
Response to Arguments
The objections to claims 20-25 and 27-30 have withdrawn due to the amendments filed.
The rejections of claims 21, 26, and 31 under 35 U.S.C. 112(b) have been withdrawn due to the amendments filed.
The rejection of claim 31 under 35 U.S.C. 101 has been withdrawn due to the amendments filed.
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive.
In response to applicant's argument that Henry is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Henry teaches a system/method utilizing external temperature and pressure sensor data to determine corrections to be made to mass flow rate and density measurements of a fluid within a device, which is pertinent to the problem of determining fluid density in a device based upon the influence of external weather conditions.
Applicant also argues that the cited art does not teach using weather data to improve measurements.
However, Henry teaches the system can use additional information, including pressure and temperature measurements from pressure and temperature sensors and other sensors (col. 12, lines 35-61; fig. 11; etc.); and Thuries teaches weather condition data is collected by sensors (col. 4, lines 37-58; fig. 1; etc.) including from a reference temperature sensor collecting reference temperature data (fig. 1; col. 3, lines 37-61; etc.); both of which are within the broadest reasonable interpretation of the claimed “collecting weather data relating to weather conditions in an environment of the electrical device.”
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., using additional information for the collection of any data outside the system, where the additional information is 100% independent from the measurement equipment) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., using outside, independent, non-predictable information) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Additionally, Henry teaches the system can use additional information, including pressure and temperature measurements from pressure and temperature sensors and other sensors (col. 12, lines 35-61; fig. 11; etc.); and Thuries teaches weather condition data is collected by sensors (col. 4, lines 37-58; fig. 1; etc.) including from a reference temperature sensor collecting reference temperature data (fig. 1; col. 3, lines 37-61; etc.); both of which are collecting/using outside, independent data (though it is not clear what is meant by “non-predictable” information in this context).
Applicant also argues that the cited art does not teach using “alternative data that is not derived from the measured liquid or the measurement equipment itself” or “[leaving] this measurement equipment and to go outside this system.”
However, Henry teaches the system can use additional information, including pressure and temperature measurements from pressure and temperature sensors and other sensors (col. 12, lines 35-61; fig. 11; etc.); and Thuries teaches weather condition data is collected by sensors (col. 4, lines 37-58; fig. 1; etc.); both of which are within the broadest reasonable interpretation of the claimed “collecting weather data relating to weather conditions in an environment of the electrical device.” Additionally, it is not clear how the claimed invention would collect or utilize data that is not derived from measurement equipment (nor do the claims require such).
Applicant also argues that Thuries does not teach “correction of density data.”
However, Thuries teaches a microprocessor of the device may use a mathematical model to determine corrections to the density measurements from the reference temperature changes (weather condition) data (col. 2, lines 53-63; col. 3, lines 52-61; etc.), where the determined correction is used to correct the density measurement data (col. 5, lines 1-4; claim 5; etc.); which is information about the correction of density data.
Applicant also argues that Thuries discloses “the first option is the case where the reference temperature is that of the case containing the gas, the thermal time period is independent of weather conditions” which applicant argues “teaches away from weather conditions being used.”
However, this is describing one scenario/option using a reference temperature of the case, rather than weather conditions. Thuries also teaches “however, if the reference temperature is not the temperature of the case, then the thermal time period is going to depend on weather conditions” (col. 4, lines 43-45; etc.) in the following paragraph. This is not teaching away from using weather conditions, but rather providing several alternative options, some of which use the weather condition data and some of which do not.
Applicant also argues that Thuries does not teach taking weather data into consideration.
However, Thuries teaches weather condition data is collected by sensors (col. 4, lines 37-58; fig. 1; etc.) including from a reference temperature sensor collecting reference temperature data (fig. 1; col. 3, lines 37-61; etc.), which is taking weather data into consideration. Additionally, Henry teaches the system can use additional information, including pressure and temperature measurements from pressure and temperature sensors and other sensors (col. 12, lines 35-61; fig. 11; etc.), which is weather data to be taken into consideration.
Applicant also argues that the weather data collected and used by Thuries is “not based on weather data analogous to the present application.”
However, as described above, the weather data collected and utilized by Thuries (and Henry) is within the broadest reasonable interpretation of the claimed “collecting weather data relating to weather conditions in an environment of the electrical device.” It is not clear how the “weather data” of the claimed invention does not include such weather data.
In response to applicant's argument that a combination of Henry and Thuries “would have led to an incorporation of a thermal imaging probe being used to measure a temperature of the probe near the casing”, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Even if, arguendo, this would be the only combination of Henry and Thuries possible, “incorporation of a thermal imaging probe being used to measure a temperature of the probe near the casing” appears as if it would be within the broadest reasonable interpretation of the claimed “collecting weather data relating to weather conditions in an environment of the electrical device.”
Applicant also argues that the cited art references temperature collection, but argues that it is “always the temperature of the fluid to be measured or the surrounding of the fluid (enclosure, surrounding of the enclosure) to be measured. None of the documents refers to weather data analogous to the present application.
However, it is not clear how temperature (and pressure) measurements taken from the environment surrounding the enclosure is “not analogous” to the claimed weather data. As described above, temperature and pressure readings from the environment surrounding the device are within the broadest reasonable interpretation of “collecting weather data relating to weather conditions in an environment of the electrical device.”
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., using weather data from a web service without onsite measurement equipment) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-15 are cancelled; claims 16-32 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu (US 2017/0316121) – discloses a system for simulating effects of rupture on fluid expansion in sealed/open annuli, including predicting fluid density.
Nazari (US 2018/0306693) – discloses a neural network predicting fluid density from sensor data, including determining a correction factor.
Chen (US 2017/0270225) – discloses a neural network using fluid attributes as inputs to determine integrated computational element (ICE) optimization.
Elyas (US 2020/0285216) – discloses real time fluid analysis for drilling/reservoir control using machine learning model(s) and fluid attribute values.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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, Omar Fernandez Rivas can be reached at 571-272-2589. 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.
/GEORGE GIROUX/Primary Examiner, Art Unit 2128