Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14, and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims recite “determining a prediction for the energy consumption…” which represents an action performable fully within the human mind. This judicial exception is not integrated into a practical application because the additional elements listed, “obtaining respective values…”, represent mere extra-solution activity and are therefore not sufficient to integrate the judicial exception into a practical application. The claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements do not provide meaningful limitations on the solution presented in the claims therefore do not cause the claims to be directed away from the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-8, 14-19, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Baglino (US 2019/0265057).
As per Claim 1:
Baglino discloses all of the following limitations:
“A method of predicting an energy consumption of a vehicle for its travel along a defined route between a given starting point and a given destination point, the method comprising: Obtaining respective values for a set of one or more energy consumption impact parameters of an energy consumption model for the vehicle, the set of energy consumption impact parameters representing in the energy consumption model one or more of the following impact factors on the energy consumption of the vehicle along the route: a surface impact factor (21) defining a road-surface-dependent impact on the energy consumption of the vehicle; a curvature impact factor (23) defining a road-curvature-dependent impact on the energy consumption of the vehicle; a wind impact factor (25) defining a wind-dependent impact on the energy consumption of the vehicle; a driving style impact factor (34) defining a driving style-dependent impact on the energy consumption of the vehicle; a tire pressure impact factor (31) defining a tire pressure-dependent impact for a selected driver on the energy consumption of the vehicle; a temperature-related battery consumption impact factor (32) defining an ambient temperature-dependent impact on the energy supply capability of a traction battery of the vehicle and/or on the power consumption of an active cooling and/or heating system of the traction battery of the vehicle;”
Baglino Paragraphs [0059]-[0060] disclose assigning value to an expected driving style and how such a style would affect the energy consumption prediction model for a trip.
“determining a prediction for the energy consumption of the vehicle for the route based on the route the energy consumption model, and the obtained one or more values of the set of energy consumption impact parameters.”
Baglino Paragraph [0060] discloses doing an energy prediction model for a trip based on intended driving style influences.
With regards to Claim 2, Baglino discloses all of the limitations of Claim 1 and further discloses the following limitations:
“ the set of energy consumption impact parameters of the energy consumption model further comprises one or more of the following additional energy consumption impact parameters; a mass impact factor defining a total vehicle mass-dependent impact on the energy consumption of the vehicle; a temperature impact factor defining an ambient temperature-dependent impact on the energy consumption of the vehicle; a route topology impact factor defining an elevation profile-dependent impact on the energy consumption of the vehicle; obtaining the respective values for the set of energy consumption impact parameters comprises obtaining respective values for these one or more additional energy consumption impact parameters; and determining a prediction for the energy consumption of the vehicle for the route is further based on the obtained one or more values of said additional energy consumption impact parameters.”
Baglino Paragraph [0070] discloses including a variety of other parameters into the prediction model including at least road topology. Paragraph [0070] discloses making energy estimates about consumption based on impact parameters.
With regards to Claim 3, Baglino discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein one or more of the energy consumption impact parameters are each defined as a respective numerical parameter referring as a factor to a related pre-defined reference parameter.”
Baglino Paragraph [0062] discloses explicitly assigning a numerical value to a parameter, however, it is clear from the reference that all parameters are assigned some form of numerical value based on their used in mathematical prediction models.
With regards to Claim 4, Baglino discloses all of the limitations of Claim 1 and further discloses the following limitations:
“the route is partitioned into a set of route segments; obtaining respective values for a set of one or more energy consumption impact parameters comprises obtaining for at least one of said energy consumption impact parameters respective segment-specific values on a per route segment basis; and determining a prediction for the energy consumption of the vehicle for the route comprises calculating a respective segment-specific energy consumption of the vehicle for each of the route segments based on the obtained one or more values of the energy consumption impact parameters including said segment-specific values and integrating the calculated segment-specific energy consumptions to obtain the energy consumption of the vehicle for the whole route”
Baglino Paragraphs [0020]-[0021] disclose identifying parameters related to specific segments of a proposed journey for energy consumption predictions.
With regards to Claim 5, Baglino discloses all of the limitations of Claim 1 and further discloses the following limitations:
“further comprising updating (S13) one or more of the obtained values of the set of energy consumption impact parameters as a function of obtained consumption data representing a measured actual energy consumption and/or measured actual respective values for one or more of the energy consumption impact parameters of the set of energy consumption impact parameters for a travel of one or more reference vehicles along the route as a whole or said least one road segment thereof”
Baglino Paragraph [0088] discloses performing continuous updates to the energy consumption prediction whilst the vehicle travels along a journey.
With regards to Claim 7, Baglino discloses all of the limitations of Claim 5 and further discloses the following limitations:
“determining whether and if so, to what extent the consumption data has been impaired by traffic; and selectively using only such consumption data or components thereof as a basis for the updating of one or more of the values of the set of energy consumption impact parameters and/or as a basis for determining the prediction for the energy consumption of the vehicle for the route, for which consumption data or components thereof, respectively, no such impairment has been determined”
Baglino Paragraph [021] discloses including traffic data into account when making energy predictions.
With regards to Claim 8, Baglino discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein obtaining (S11) the respective values for a set of one or more energy consumption impact parameters of the energy consumption model for the vehicle comprises one or more of the following: pre-calculating and/or storing respective values for one or more of the surface impact factor, the curvature impact factor and the route topology factor either for the route as a whole or, if the route is partitioned into a set of route segments, for each of the route segments individually; determining a respective value for the wind impact factor based on a respective linearly approximated wind direction determined for the route as a whole or for each of the route segments individually; determining a respective value for the wind impact factor based on classifying the wind directions according to a discrete set of classes of different wind directions and the vehicle travel directions according to a discrete set of classes of different vehicle travel directions, and by obtaining the value for the wind impact factor through reading from a pre-defined data structure storing for each combination of a class of wind directions and a class of vehicle travel directions a respective pre-set value for the wind impact factor; predicting, based on weather data relating to a geographical region through which the route leads, whether any significant wind or temperature-related impacts on the energy consumption of the vehicle are to be expected during its travel along the route, and using or ignoring one or more of the wind impact factor, the temperature-related battery consumption factor, and the temperature impact factor as a function of the result of said prediction”
Baglino Paragraph [0021] discloses pre-storing at least road topology data.
“representing the respective obtained value of at least two of the impact factors used for determining the prediction for the energy consumption of the vehicle for the route by a discrete value from a respective discrete set of allowed values, encoding the discrete values of said at least two impact factors to obtain a code representing all of the discrete values, and obtaining a value of the combined impact of said at least two impact factors on the energy consumption of the vehicle through reading from a pre-defined data structure storing for each possible variation of the code a respective pre-set value representing a combined impact of said at least two impact factors on the energy consumption of the vehicle.”
Baglino Paragraph [0021] discloses including road parameters that are measured in discrete values such as length. Additionally, a multitude of the disclosed potential parameters are measured in discrete values such as average speed.
As per Claim 14:
Baglino discloses all of the following limitations:
“A method of determining a surface condition of a road, the method comprising: obtaining a reference value of an energy consumption of a vehicle for its travel along a defined route between a given starting point and a given destination point: obtaining respective values for a set of one or more energy consumption impact parameters of an energy consumption model for the vehicle for its travel along the route, the set of energy consumption impact parameters representing in the energy consumption model one or more of the following impact factors on the energy consumption of the vehicle along the route: [[-]] a curvature impact factor defining a road-curvature-dependent impact on the energy consumption of the vehicle; [[-]] a wind impact factor defining a wind-dependent impact on the energy consumption of the vehicle; [[-]] a driving style impact factor defining a driving style-dependent impact on the energy consumption of the vehicle; [[-]] a tire pressure impact factor defining a tire pressure-dependent impact for a selected driver on the energy consumption of the vehicle; [[-]]a temperature-related battery consumption impact factor defining an ambient temperature-dependent impact on the energy supply capability of a traction battery of the vehicle and/or on the power consumption of an active cooling and/or heating system of the traction battery of the vehicle; [[-]]a mass impact factor defining a total vehicle mass-dependent impact on the energy consumption of the vehicle; [[-]] a temperature impact factor defining an ambient temperature-dependent impact on the energy consumption of the vehicle; [[-] a route topology impact factor defining an elevation profile-dependent impact on the energy consumption of the vehicle; estimating a value of a surface impact factor defining in the energy consumption model a road-surface-dependent impact on the energy consumption of the vehicle; and determining a surface condition of a road based on the route, the energy consumption model, and the obtained one or more values of the set of energy consumption impact parameters.”
Baglino Paragraph [0070] discloses including a variety of other parameters into the prediction model including at least road topology. The road topology corresponds to a "surface condition".
With regards to Claim 17, Baglino discloses all of the limitations of Claim 2 and further discloses the following limitations:
“wherein one or more of the energy consumption impact parameters are each defined as a respective numerical parameter referring as a factor to a related pre-defined reference parameter.”
Baglino Paragraph [0070] discloses identifying road slope which is measure in discrete, numerical, units (i.e. degrees).
With regards to Claim 18, Baglino discloses all of the limitations of Claim 2 and further discloses the following limitations:
“wherein: the route is partitioned into a set of route segments; obtaining respective values for a set of one or more energy consumption impact parameters comprises obtaining for at least one of said energy consumption impact parameters respective segment-specific values on a per route segment basis; and determining a prediction for the energy consumption of the vehicle for the route comprises calculating a respective segment-specific energy consumption of the vehicle for each of the route segments based on the obtained one or more values of the energy consumption impact parameters including said segment-specific values and integrating the calculated segment-specific energy consumptions to obtain the energy consumption of the vehicle for the whole route.”
Baglino Paragraphs [0020]-[0021] disclose identifying parameters related to specific segments of a proposed journey for energy consumption predictions.
With regards to Claim 19, Baglino discloses all of the limitations of Claim 2 and further discloses the following limitations:
“further comprising updating one or more of the obtained values of the set of energy consumption impact parameters as a function of obtained consumption data representing a measured actual energy consumption and/or measured actual respective values for one or more of the energy consumption impact parameters of the set of energy consumption impact parameters for a travel of one or more reference vehicles along the route as a whole or said least one road segment thereof.”
Baglino Paragraph [0088] discloses performing continuous updates to the energy consumption prediction whilst the vehicle travels along a journey.
With regards to Claim 21, Baglino discloses all of the limitations of Claim 2 and further discloses the following limitations:
“wherein obtaining the respective values for a set of one or more energy consumption impact parameters of the energy consumption model for the vehicle comprises one or more of the following: pre-calculating and/or storing respective values for one or more of the surface impact factor, the curvature impact factor and the route topology factor either for the route as a whole or, if the route is partitioned into a set of route segments, for each of the route segments individually; determining a respective value for the wind impact factor based on a respective linearly approximated wind direction determined for the route as a whole or for each of the route segments individually; determining a respective value for the wind impact factor based on classifying the wind directions according to a discrete set of classes of different wind directions and the vehicle travel directions according to a discrete set of classes of different vehicle travel directions, and by obtaining the value for the wind impact factor through reading from a pre-defined data structure storing for each combination of a class of wind directions and a class of vehicle travel directions a respective pre-set value for the wind impact factor; predicting, based on weather data relating to a geographical region through which the route leads, whether any significant wind or temperature-related impacts on the energy consumption of the vehicle are to be expected during its travel along the route, and using or ignoring one or more of the wind impact factor, the temperature-related battery consumption factor, and the temperature impact factor as a function of the result of said prediction; representing the respective obtained value of at least two of the impact factors used for determining the prediction for the energy consumption of the vehicle for the route by a discrete value from a respective discrete set of allowed values, encoding the discrete values of said at least two impact factors to obtain a code representing all of the discrete values, and obtaining a value of the combined impact of said at least two impact factors on the energy consumption of the vehicle through reading from a pre-defined data structure storing for each possible variation of the code a respective pre-set value representing a combined impact of said at least two impact factors on the energy consumption of the vehicle.”
Baglino Paragraph [0021] discloses pre-storing at least road topology data. Paragraph [0021] discloses including road parameters that are measured in discrete values such as length. Additionally, a multitude of the disclosed potential parameters are measured in discrete values such as average speed.
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 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.
Claims 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baglino in view of Zeng (US 2020/0294323).
With regards to Claim 6, Baglino discloses all of the limitations of Claim 5 but does not disclose the following limitations that Zeng does disclose:
“the prediction for the energy consumption of the vehicle for the route as a whole or said least one road segment thereof is further based on applying a machine-learning-based classifier to the consumption data and or the updated one or more values of the set of energy consumption impact parameters”
Zeng Paragraph [0030] discloses using a machine learning model to predict energy consumption.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the machine-learning mechanism disclosed by Zeng. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by utilizing a self-training model to iterate on solutions.
With regards to Claim 20, Baglino in view of Zeng discloses all of the limitations of Claim 6 and Baglino further discloses following limitations:
“determining whether and if so, to what extent the consumption data has been impaired by traffic; and selectively using only such consumption data or components thereof as a basis for the updating of one or more of the values of the set of energy consumption impact parameters and/or as a basis for determining the prediction for the energy consumption of the vehicle for the route, for which consumption data or components thereof, respectively, no such impairment has been determined.”
Baglino Paragraph [021] discloses including traffic data into account when making energy predictions.
Claims 9 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Baglino in view of Meyer (US 2016/0061616).
With regards to Claim 9, Baglino discloses all of the limitations of Claim 1 but does not disclose the following limitations that Meyer does disclose:
“further comprising updating the energy consumption model by adjusting it based on one or more of:[[-]] a comparison of the energy consumption predicted for the route by means of the energy consumption model with a corresponding actually measured energy consumption acquired for the same vehicle or one or more comparable other vehicles along the route; and [[-]] training data referring to so far uncovered geographical regions or unknown route conditions”
Meyer Paragraph [0056] discloses using historical performances to update a predictive model of energy consumption along a route.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the historical elements disclosed by Meyer. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by relying on historical datapoints.
With regards to Claim 22, Baglino discloses all of the limitations of Claim 2 but does not disclose the following limitations that Meyer does disclose:
“further comprising updating the energy consumption model by adjusting it based on one or more of: a comparison of the energy consumption predicted for the route by means of the energy consumption model with a corresponding actually measured energy consumption acquired for the same vehicle or one or more comparable other vehicles along the route; and training data referring to so far uncovered geographical regions or unknown route conditions.”
Meyer Paragraph [0056] discloses using historical performances to update a predictive model of energy consumption along a route.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the historical elements disclosed by Meyer. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by relying on historical datapoints.
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Baglino in view of Matsumura (US 2019/0113354).
With regards to Claim 10, Baglino discloses all of the limitations of Claim 1 but does not disclose the following limitations that Matsumura does disclose:
“A routing method for determining for a vehicle an optimal route between a starting point and a destination point, the routing method comprising: predicting, according to the method of claim 1 any one of the preceding claims, a respective energy consumption of the vehicle for each of a set of different possible routes between the starting point and the destination point; and selecting or proposing an optimal route among the set of routes according to a defined optimization criterion as a function of the predicted respective energy consumptions of the vehicle for the different routes in the set of routes.”
Matsumura Paragraph [0105] discloses determining a route based on predicted energy usage.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the optimized routing disclosed by Matsumura. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing for the optimization of routing processes.
With regards to Claim 11, Baglino discloses all of the limitations of Claim 10 but does not disclose the following limitations that Matsumura does disclose:
“wherein the optimization criterion is defined such that the route being selected as the optimal route from the set of routes is optimal in that it has the lowest predicted energy consumption”
Matsumura Paragraph [0105] discloses determining a route based on predicted energy usage.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the optimized routing disclosed by Matsumura. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing for the optimization of routing processes.
With regards to Claim 12, Baglino discloses all of the limitations of Claim 10 but does not disclose the following limitations that Matsumura does disclose:
“predicting (522) for the vehicle and each of the routes in the set of routes a respective travel time between the starting point and the destination point along the respective route; wherein the optimization criterion is defined such that the route being selected or proposed as the optimal route from the set of routes is optimal in that it has the lowest predicted travel time weighted by a factor reflecting the predicted energy consumption of the same route.”
Matsumura Paragraph [0105] discloses determining a route based on predicted energy usage.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the optimized routing disclosed by Matsumura. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing for the optimization of routing processes.
With regards to Claim 13, Baglino discloses all of the limitations of Claim 10 but does not disclose the following limitations that Matsumura does disclose:
“wherein predicting a respective energy consumption of the vehicle for each of a set of different possible routes between the starting point and the destination point comprises predicting a respective energy consumption of the vehicle for each of said possible routes as a function of different energy consumption-related settings of the vehicle; and selecting or proposing an optimal route among the set of routes according to a defined optimization criterion comprises selecting or proposing, respectively, the optimal route as a function of both the predicted respective energy consumptions of the vehicle for the different routes in the set of routes and the different settings of the vehicle.”
Matsumura Paragraph [0105] discloses determining a route based on predicted energy usage.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Baglino with the optimized routing disclosed by Matsumura. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by allowing for the optimization of routing processes.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Godfrey Maciorowski, whose telephone number is (571) 272-4652. The examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm EST.
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If attempts to reach examiner by telephone are unsuccessful the examiner’s supervisor, Vivek Koppikar can be reached on (571) 272-5109. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/GODFREY ALEKSANDER MACIOROWSKI/Examiner, Art Unit 3667
/JOAN T GOODBODY/Examiner, Art Unit 3667