DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 13 is a method claim. Claim 21 is a method claim. Claim 22 is a device claim. Claim 23 is a CRM claim. Therefore, claims 13, 21, 22, and 23 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 13:
Step 2A Prong 1:
assigning a respective difficulty value to each training data set of the training data sets, the difficulty values each specifying a consistency of the time indications for the training data set (mental process – user can manually assign a respective difficulty value to each training data set of the training data sets, the difficulty values each specifying a consistency of the time indications for the training data set)
classifying the training data sets into a number of difficulty classes corresponding to their respective difficulty value (mental process – user can manually classify the training data sets into a number of difficulty classes corresponding to their respective difficulty value)
ascertaining new training data sets as a function of the training data sets assigned to each of the difficulty classes (mental process – user can manually ascertain new training data sets as a function of the training data sets assigned to each of the difficulty classes)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
providing a set of training data sets from a measurement of the internal combustion engine by scanning the sensor signal of a sensor of the injection valve on a test stand, the training data sets assigning a time indication of the opening point in time or of the closing point in time to an evaluation point time series (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
training the data-based point in time determination model with a set of the new training data sets (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
providing a set of training data sets from a measurement of the internal combustion engine by scanning the sensor signal of a sensor of the injection valve on a test stand, the training data sets assigning a time indication of the opening point in time or of the closing point in time to an evaluation point time series (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer)
training the data-based point in time determination model with a set of the new training data sets (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
Conclusion: The claim is not patent eligible.
Additionally for claims 21, 22, and 23:
Claim 21 has the additional limitation of ascertaining an opening point in time or closing point in time of the injection valve, based on a sensor signal and on a data-based point in time determination model which a person can do manually, and has the additional elements of operating the injection valve as a function of the opening point in time and/or of the closing point in time, the operation of the injection valve being carried out in such a way that an opening duration of the injection valve, which is determined by the ascertained opening point in time and/or closing point in time, is set to a predefined setpoint opening duration.
Under Step 2A prong 2, this additional element is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Under Step 2B, this additional element is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Claim 22 has the additional element of a device and a hardware processor. This element is mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Claim 23 has the additional element of a non-transitory machine-readable memory medium. This element is mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claims 14-20: These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. That is, nothing in the claim limitation precludes the step from practically being performed in the mind.
For claim 14: the limitation encompasses the user manually use wherein for determining the respective difficulty value for each training data set, a predetermined number of closest neighbors is ascertained as neighboring training data sets with respect to the evaluation point time series of the training data sets, the training data set being assigned the respective difficulty value as a function of time indications of the neighboring training data sets.
For claim 15: the limitation encompasses the user manually use wherein the data-based point in time determination model is a classification model, a number of output classes being defined, which are assigned to one opening point in time or to one closing point in time each, so that the training data sets assign one evaluation point time series each to one of the output classes, the difficulty value of each of the training data sets corresponding to or being a function of a number of the different class assignments of the neighboring training data sets.
For claim 16: the limitation encompasses the user manually use wherein a number of difficulty classes corresponds to the number of the output classes.
For claim 17: the limitation encompasses the user manually use wherein the data-based point in time determination model is a regression model, the training data sets assigning one evaluation point time series each to an opening point in time or to a closing point in time, the difficulty value of each of the training data sets corresponding to or being a function of a variance of the opening points in time or of the closing points in time of the neighboring training data sets.
For claim 18: the limitation encompasses the user manually use wherein the ascertainment of the new training data sets is carried out for each of the difficulty classes by ascertaining a predefined number of training data sets for each of the difficulty classes.
For claim 19: the limitation encompasses the user manually use wherein the predefined number for each of the difficulty classes is identical or differs from one another by not more than 10%.
For claim 20: the limitation encompasses the user manually use wherein the predefined number of training data sets are ascertained for each of the difficulty classes by selecting from the training data sets assigned to the difficulty class or by generating training data sets as a function of the training data sets assigned to the difficulty class using a data augmentation method by bucket sampling or by applying a noise model.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims 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, no additional elements are cited. Accordingly, the claim is not patent eligible.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 13-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pouyan et al. (hereinafter Pouyan), U.S. Patent Application Publication 2021/0216813 in view of Russe et al. (hereinafter Russe), U.S. Patent Application Publication 2016/0053704, further in view of Svitla, Regression vs Classification in Machine Learning.
Regarding Claim 13, Pouyan discloses a computer-implemented method for training a data-based point in time determination model for ascertaining an opening point in time or closing point in time of an injection valve of an internal combustion engine, based on a sensor signal, the method comprising the following steps:
providing a set of training data sets [“system 100 receives unlabeled training 102 that includes a series of vectors 104, or datapoints” ¶15]
assigning a respective difficulty value to each training data set of the training data sets, the difficulty values each specifying a consistency …for the training data set [“each datapoint by assigning that point to its K nearest neighbor using Euclidean distance metric” ¶17];
classifying the training data sets into a number of difficulty classes corresponding to their respective difficulty value [“Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19];
ascertaining new training data sets as a function of the training data sets assigned to each of the difficulty classes [“The system 100 stores the labels for the training data 102 in the first stage labeled training data 120.” ¶22].
However, Pouyan fails to explicitly disclose from a measurement of the internal combustion engine by scanning the sensor signal of a sensor of the injection valve on a test stand, the training data sets assigning a time indication of the opening point in time or of the closing point in time to an evaluation point time series;
of the time indications.
Russe discloses from a measurement of the internal combustion engine [“an injection system having at least one injection valve for injecting fuel into an internal combustion engine” ¶36] by scanning the sensor signal of a sensor of the injection valve on a test stand [“signal is triggered by means of a sensor element of the injection valve” ¶36], the training data sets assigning a time indication of the opening point in time or of the closing point in time to an evaluation point time series [“The closure element impacts against an upper stop of the injection valve at an actual opening time and/or impacts in a closed position (against a lower stop) of the injection valve at an actual closing time.” ¶36; “a signal profile over time of the sensor element is detected” ¶37];
of the time indications [“contains an expected opening time at which the impacting of the closure element against the stop is expected, and/or an expected closing time at which the impacting of the closure element in the closed position is expected” ¶37].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan and Russe before him before the effective filing date of the claimed invention, to modify the data training generation method of Pouyan to incorporate the application to fuel injection timing of Russe.
Given the advantage of improving the reliability and the robustness of control methods of fuel injection by adding machine learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Pouyan fails to explicitly disclose training the data-based point in time determination model with a set of the new training data sets.
Svitla discloses training the data-based point in time determination model with a set of the new training data sets [“we train the model, then we input the test data which was not in the training dataset” pg. 3 ¶4].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan, Russe, and Svitla before him before the effective filing date of the claimed invention, to modify the combination to incorporate using the training data to train a model for making a determination on the inputted data of Svitla.
Given the advantage of using the training data in order to train a learning model to produce a desired outcome, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 14, Pouyan, Russe, and Svitla disclose the method as recited in claim 13. Pouyan further discloses wherein for determining the respective difficulty value for each training data set, a predetermined number of closest neighbors is ascertained as neighboring training data sets with respect to the evaluation point time series of the training data sets [“data clusterer 106 constructs a k-nearest neighbor graph to model the Euclidean structure of a manifold of data” ¶17], the training data set being assigned the respective difficulty value as a function …of the neighboring training data sets [“To assign labels, the system 100 provides the unlabeled training data 102 to a data clusterer 106. The data clusterer 106 utilizes a graph-based data clustering approach.” ¶17].
However, Pouyan fails to explicitly disclose of time indications.
Russe discloses of time indications [“contains an expected opening time at which the impacting of the closure element against the stop is expected, and/or an expected closing time at which the impacting of the closure element in the closed position is expected” ¶37].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan and Russe before him before the effective filing date of the claimed invention, to modify the data training generation method of Pouyan to incorporate the application to fuel injection timing of Russe.
Given the advantage of improving the reliability and the robustness of control methods of fuel injection by adding machine learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 15, Pouyan, Russe, and Svitla disclose the method as recited in claim 14. Pouyan further discloses a number of output classes being defined [“Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19], …so that the training data sets assign one evaluation point time series each to one of the output classes, the difficulty value of each of the training data sets corresponding to or being a function of a number of the different class assignments of the neighboring training data sets [“Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19].
However, Pouyan fails to explicitly disclose which are assigned to one opening point in time or to one closing point in time each.
Russe discloses which are assigned to one opening point in time or to one closing point in time each [“The closure element impacts against an upper stop of the injection valve at an actual opening time and/or impacts in a closed position (against a lower stop) of the injection valve at an actual closing time.” ¶36].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan and Russe before him before the effective filing date of the claimed invention, to modify the combination to incorporate the application to fuel injection timing of Russe.
Given the advantage of improving the reliability and the robustness of control methods of fuel injection by adding machine learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Pouyan fails to explicitly disclose wherein the data-based point in time determination model is a classification model.
Svitla discloses wherein the data-based point in time determination model is a classification model [“Classification is used to determine whether an entity with certain input parameters belongs to a class within the existing characteristics of the input values.” pg. 1 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan, Russe, and Svitla before him before the effective filing date of the claimed invention, to modify the combination to incorporate a classification model of Svitla.
Given the advantage of classifying data into classes, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding claim 16, Pouyan, Russe, and Svitla disclose the method as recited in claim 15. Pouyan further discloses wherein a number of difficulty classes corresponds to the number of the output classes [“each datapoint by assigning that point to its K nearest neighbor using Euclidean distance metric” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19].
Regarding Claim 17, Pouyan, Russe, and Svitla disclose the method as recited in claim 14. Pouyan further discloses the training data sets assigning one evaluation point time series each [“each datapoint by assigning that point to its K nearest neighbor using Euclidean distance metric” ¶17; “Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19]…, the difficulty value of each of the training data sets corresponding to or being a function of a variance of …of the neighboring training data sets [“each datapoint by assigning that point to its K nearest neighbor using Euclidean distance metric” ¶17; “Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19].
However, Pouyan fails to explicitly disclose to an opening point in time or to a closing point in time,
the opening points in time or of the closing points in time.
Russe discloses to an opening point in time or to a closing point in time [“The closure element impacts against an upper stop of the injection valve at an actual opening time and/or impacts in a closed position (against a lower stop) of the injection valve at an actual closing time.” ¶36],
the opening points in time or of the closing points in time [“The closure element impacts against an upper stop of the injection valve at an actual opening time and/or impacts in a closed position (against a lower stop) of the injection valve at an actual closing time.” ¶36].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan and Russe before him before the effective filing date of the claimed invention, to modify the combination to incorporate the application to fuel injection timing of Russe.
Given the advantage of improving the reliability and the robustness of control methods of fuel injection by adding machine learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Pouyan fails to explicitly disclose wherein the data-based point in time determination model is a regression model.
Svitla discloses wherein the data-based point in time determination model is a regression model [“Regression is used to determine the output predicted value from the input parameters. Linear and logistic regression are the most common types of regression used to solve practical problems.” pg. 1 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan, Russe, and Svitla before him before the effective filing date of the claimed invention, to modify the combination to incorporate a regression model of Svitla.
Given the advantage of predicting future values, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 18, Pouyan, Russe, and Svitla disclose the method as recited in claim 13. Pouyan further discloses wherein the ascertainment of the new training data sets is carried out for each of the difficulty classes by ascertaining a predefined number of training data sets for each of the difficulty classes [“constructs a k-nearest neighbor graph to model the Euclidean structure of a manifold of data” ¶17].
Regarding Claim 19, Pouyan, Russe, and Svitla disclose the method as recited in claim 18. Pouyan further discloses wherein the predefined number for each of the difficulty classes is identical or differs from one another by not more than 10% [“constructs a k-nearest neighbor graph to model the Euclidean structure of a manifold of data” ¶17].
Regarding Claim 20, Pouyan, Russe, and Svitla disclose the method as recited in claim 18. Pouyan further discloses wherein the predefined number of training data sets are ascertained for each of the difficulty classes by selecting from the training data sets assigned to the difficulty class or by generating training data sets as a function of the training data sets assigned to the difficulty class using a data augmentation method by bucket sampling or by applying a noise model [“constructs a k-nearest neighbor graph to model the Euclidean structure of a manifold of data” ¶17; “Each community of the graph G is a population of nodes that are connected to each other” ¶17; “system 100 may estimate the labels based on the identified clusters” ¶19].
Claim 21 is rejected on the same grounds as claim 13.
However, Pouyan fails to explicitly disclose ascertaining an opening point in time or closing point in time of the injection valve, based on a sensor signal and
operating the injection valve as a function of the opening point in time and/or of the closing point in time, the operation of the injection valve being carried out in such a way that an opening duration of the injection valve, which is determined by the ascertained opening point in time and/or closing point in time, is set to a predefined setpoint opening duration.
Russe discloses ascertaining an opening point in time or closing point in time of the injection valve [“closure element impacts against an upper stop of the injection valve at an actual opening time and/or impacts in a closed position (against a lower stop) of the injection valve at an actual closing time.” ¶36], based on a sensor signal [“signal is triggered by means of a sensor element of the injection valve” ¶36] and
operating the injection valve as a function of the opening point in time and/or of the closing point in time [“recurring injection cycles” ¶36], the operation of the injection valve being carried out in such a way that an opening duration of the injection valve, which is determined by the ascertained opening point in time and/or closing point in time, is set to a predefined setpoint opening duration [“fuel is injected into the internal combustion engine as precisely as possible with a previously determined profile over time of an injection rate” ¶3; “the setpoint opening time” ¶40; “the setpoint closing time” ¶40].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan, Russe, and Svitla before him before the effective filing date of the claimed invention, to modify the combination to incorporate fuel injection timing of Russe.
Given the advantage of improving the reliability and the robustness of control methods of fuel injection by adding machine learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Pouyan fails to explicitly disclose on a data-based point in time determination model, which has been trained by.
Svitla discloses on a data-based point in time determination model, which has been trained by [“we train the model, then we input the test data which was not in the training dataset” pg. 3 ¶4].
It would have been obvious to one having ordinary skill in the art, having the teachings of Pouyan, Russe, and Svitla before him before the effective filing date of the claimed invention, to modify the combination to incorporate using the training data to train a model for making a determination on the inputted data of Svitla.
Given the advantage of using the training data in order to train a learning model to produce a desired outcome, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 22 is rejected on the same grounds as claim 13. Pouyan further discloses a device and a hardware processor [“The computing device 300 includes a processor 302, a memory 304, a storage device 306, a high-speed interface 308 connecting to the memory 304 and multiple high-speed expansion ports 310, and a low-speed interface 312 connecting to a low-speed expansion port 314 and the storage device 306.” ¶42].
Claim 23 is rejected on the same grounds as claim 13. Pouyan further discloses a non-transitory machine-readable memory device [“The computing device 300 includes a processor 302, a memory 304, a storage device 306, a high-speed interface 308 connecting to the memory 304 and multiple high-speed expansion ports 310, and a low-speed interface 312 connecting to a low-speed expansion port 314 and the storage device 306.” ¶42].
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the 112(f) interpretation and 112(b) rejections related to it, those rejections are moot due to amendments adding a hardware processor to claim 22.
Regarding the §101 rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) the limitation training the data-based point in time determination model with a set of the new training data sets represents a technological improvement based on the specification, and 2) the claim recites significantly more than the judicial exception. Examiner disagrees for at least the following reasons.
First, exemplary claim 1 does not reflect a technological improvement and therefore does not provide a practical application. Applicant alleges that the claimed invention is directed to an improved way of training the model (Remarks pg. 10 line 1). However, the training is unchanged, just the data with which the model is trained is changed. No limitation in claim 1 alters the actual training of the model. Additionally, in regards to the Applicant’s claim that the improved training of the model improves the operation of the internal combustion engine, the examiner disagrees. The claim never recites any actual implementation of the engine based on the model. The claim is required to reflect any alleged improvement, which the current claim language does not. Applicant’s citation of nonprecedential PTAB decisions is not persuasive. Since the claim as a whole, and the elements individually and in combination do not recite an improvement, the additional elements do not integrate the abstract idea into a practical application.
Second, exemplary claim 1 does not recite significantly more than the judicial exception. Examiner has provided proper Berkheimer evidence for the additional elements. Namely, MPEP 2106.05(d)(II) which indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim); and MPEP 2106.05(f) which indicates adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer
For at least these reasons, the rejections are maintained.
Regarding the prior art rejections, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues that 1) the references fail to disclose a data-based point in time determination model for ascertaining an opening point in time or closing point in time of an injection valve of an internal combustion engine, based on a sensor signal, and 2) the references fail to disclose classifying the training data sets into a number of difficulty classes corresponding to their respective difficulty value. Examiner disagrees for at least the following reasons.
First, the cited language from claim 1 is in the preamble of the claim and is viewed as intended use unless the claim breaths life into the preamble (MPEP 2111.02). In this case, the preamble does not recite limitations of the claim, and is not necessary to give life, meaning, and vitality to the claim. It merely recites the intended use of the method. Furthermore, any element recited in the preamble that does appear in the body of the claim is rejected in the body of the claim by one or more references as shown in the rejection. Additionally, 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).
Second, Pouyan discloses the classification of the data into clusters (i.e., classes) based on their value. The term “difficulty” in the limitation is not defined or limited and is given little patentable weight. It is reasonably interpreted as merely a label for the classes and data.
For at least these reasons, the rejections are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Brandt et al. (US20140012485) discloses a method for determining the opening point in time of a control valve having a coil drive of an indirectly driven fuel injector for an internal combustion engine of a motor vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Michelle T. Bechtold can be reached at (571) 431-0762. 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.
/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148