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 .
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show brief description related to “B1-B3” and “A1-A5”. as described in the specification. Text description is required. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 14 of U.S. Patent No. 12233746. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 1 and 16 of present application are mere broader version of claims 1 and 14 of patent ‘746, and claims 1 and 14 of patent ‘746 encompass whole subject matter recited in claims 1 and 16 of the present application. Examiner recommends filing of e-terminal disclaimer.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an [AltContent: connector]abstract idea without significantly more.
[AltContent: connector]101 Analysis – Step 1
[AltContent: connector]Claim 1 is directed to a method for predicting a capability of a battery to be able to start a vehicle engine of a vehicle. Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
1. A method for predicting a capability of a battery to be able to start a vehicle engine of a vehicle, the method comprising:
generating engine-start data characteristic of the battery with a sensor coupled to the battery;
calculating, using an evaluation device, a predicted start capability of the battery through application of the generated engine-start data and historical engine start data to an engine-start prediction algorithm housed in the evaluation device; and
providing a result of the calculation, with the result being an increased accuracy in the prediction of the capability of the battery to be able to start the vehicle engine of a specific vehicle make, a specific vehicle model and/or a specific vehicle variant based at one or more temperatures of the vehicle engine.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “calculating…providing…” in the context of this claim encompasses a person viewing a given data, and using the data to formulate a simple judgement (e.g. calculating predicted start capability with given data (e.g. generated engine-start data, historical engine start data, vehicle make/model/variant, temperature), then providing opinion (e.g., providing result of calculation). Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
1. A method for predicting a capability of a battery to be able to start a vehicle engine of a vehicle, the method comprising:
generating engine-start data characteristic of the battery with a sensor coupled to the battery;
calculating, using an evaluation device, a predicted start capability of the battery through application of the generated engine-start data and historical engine start data to an engine-start prediction algorithm housed in the evaluation device; and
providing a result of the calculation, with the result being an increased accuracy in the prediction of the capability of the battery to be able to start the vehicle engine of a specific vehicle make, a specific vehicle model and/or a specific vehicle variant based at one or more temperatures of the vehicle engine.
For the following reason, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “generating…” the examiner submits that this limitation is insignificant extra-solution activities. In particular, the generating step is recited at a high level of generality (i.e. as a general means of gathering vehicle data) and merely performs its intended function, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Also, the mere presence of the “evaluation device” and “engine-start prediction algorithm” and “sensor” do not integrate the judicial exception into a practical application because claims merely use computer as a tool to perform an abstract idea. Also, these additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “generating…engine-start prediction algorithm” amount to nothing more than performing generic computer functions on a generic computer. And as discussed above, the additional limitations of “generating…” the examiner submits that these limitations are insignificant extra-solution activities.
Lastly, “sensor” and “evaluation device” are recited at a high level of generality merely reciting generic computer component used in its conventional capacity, and thus are not sufficient to amount to significantly more than the judicial exception.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well- understood, routine, conventional activity in the field. The additional limitations of “generating…” are well-understood, routine, and conventional activities because the background of the specification indicate that mere generation of engine-start data characteristic is a well‐understood, routine, and conventional function (paragraphs 7-8). Also as noted above, “evaluation device” and “engine-start prediction algorithm” are also well‐understood, routine, and conventional functions since they are claimed in a merely generic manner.
Dependent claim 2 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely defining what historical engine start data is) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 2 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claim 3 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim further narrow the abstract idea (e.g. calculating comprises comparison…predicted start capability is a capability…) and also are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“machine learning…”, “providing the result…”). “Providing the result” is mere insignificant extra-solution activity of data outputting. Therefore, dependent claim 3 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 4 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely further defining what engine-start data is) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 4 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claim 5 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely further defining what engine-start data is) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 5 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claim 6 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely further defining what engine-start data is) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 6 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claim 7 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“generated…”). Therefore, dependent claim 7 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 8 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“generated…”). Therefore, dependent claim 8 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 9 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely further defining capability of the battery to be able to start the vehicle) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 9 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claim 10 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“inputting data into algorithm from machine learning model”).
The mere presence of the machine-learning model does not integrate the judicial exception into a practical application. The use of generic computer components or algorithms to implement an abstract idea, without more, does not impose any meaningful limits on the judicial exception. The claim does not recite any specific improvement to the functioning of the machine-learning model itself, to computer technology, or to another technical field. Rather, the model is used as a generic tool for performing processing to generate output to the claimed “engine-start data” inputted.
Therefore, dependent claim 10 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 11 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“inputting data into algorithm and generate output”). These are insignificant extra solution activities.
Therefore, dependent claim 11 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 12 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim merely narrow the abstract idea, but does not change the nature of the claim from the abstract idea to a practical application. Narrowing the abstract idea (e.g. merely further defining what engine-start data is) with field of use and/or to a specific data type does not render the claim patent eligible. Thus, dependent claim 12 is also ineligible because it still recites or depends upon the same abstract idea without adding significantly more.
Dependent claims 13-15 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“inputting data into algorithm and generate output”). These are insignificant extra solution activities.
Therefore, dependent claims 13-15 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Claim 16 is similar to claim 1, therefore, it is rejected under similar rationale.
Claim 17 is similar to claim 2, therefore, it is rejected under similar rationale.
Dependent claim 18 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim further narrow the abstract idea (e.g. calculating has an increased accuracy…capability of the battery to be able to start…). Therefore, dependent claim 18 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 19 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (“machine learning model”).
The mere presence of the machine-learning model does not integrate the judicial exception into a practical application. The use of generic computer components or algorithms to implement an abstract idea, without more, does not impose any meaningful limits on the judicial exception. The claim does not recite any specific improvement to the functioning of the machine-learning model itself, to computer technology, or to another technical field. Rather, the model is used as a generic tool for performing processing to generate output to the claimed “engine-start data” inputted.
Therefore, dependent claim 19 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claim 20 does not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of this claim are directed toward additional aspects of the judicial exception (e.g. additional insignificant extra-solution activities) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (output interface”). “Outputting a result” is mere insignificant extra-solution activity of data outputting. Therefore, dependent claim 20 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1 – 20 are ineligible under 35 U.S.C. §101.
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.
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.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20100026306 A1) in view of Mackel et al. (US 20040239332 A1)
In regards to claim 1, Zhang teaches, A method for predicting a capability of a battery to be able to start a vehicle engine of a vehicle, the method comprising: (See paragraph 2, determining that the state of charge (SOC) of a vehicle battery may be too low to start the vehicle at the next vehicle start-up … The back-office module 24 runs an algorithm in a predetermined schedule to predict vehicle no-start conditions for each individual vehicle)
generating engine-start data characteristic of the battery with a sensor coupled to the battery; (See paragraph 19, The on-board module 18 receives various information from vehicle sensors 20…paragraph 20, the on-board module 18 continuously monitors… battery current, battery voltage, battery temperature… provided by the vehicle sensors 20…paragraph 40, the algorithm collects vehicle data from sensors and other ECUs on the vehicle 12 that are related to the operation of the battery 14.)
calculating, using an evaluation device, a predicted start capability of the battery through application of the generated engine-start data and historical engine start data to an engine-start prediction algorithm housed in the evaluation device; and (See paragraph 23, The back-office module 24 archives the uploaded vehicle engineering data in a database for individual vehicles. The back-office module 24 runs an algorithm in a predetermined schedule to predict vehicle no-start conditions for each individual vehicle…paragraph 27, the algorithm determines whether the open circuit voltage (OVC) of the battery 14 is less than a first predetermined voltage threshold Cal_11, such as 12.3 volts. The algorithm also determines if the average open circuit voltage over the past three months of vehicle use has been less than a second predetermined voltage threshold…paragraph 32, the vehicle battery parameters at a future time of cranking the vehicle 12 are determined as a function of history, and the vehicle startability is determined based on the predicted vehicle battery parameters)
providing a result of the calculation, with the result being an increased accuracy in the prediction of the capability of the battery to be able to start the vehicle engine of a specific vehicle make, a specific vehicle model and/or a specific vehicle variant…(See paragraph 41, The back-office module 24 then runs a back-office algorithm to predict no-start situations… The algorithm then determines whether an imminent no-start is present at decision diamond 88, and if not, returns to the box 82 to archive uploaded vehicle data. If an imminent no-start is detected at the decision diamond 88, then the back-office module 24 will notify the vehicle operator/owner based on the urgency of the no-start situation at box 90…paragraph 25, The back-office module algorithm can be much more sophisticated than the on-board module algorithm…paragraph 37, the values used in the algorithms are for illustration purposes and can be different for different vehicle types and makes.)
Zhang does not specifically teach, providing a result of the calculation …based at one or more temperatures of the vehicle engine.
Mackel further teaches, providing a result of the calculation …based at one or more temperatures of the vehicle engine. (See paragraph 9, it is possible to determine the ability of the battery to start at a different temperature, and thus make predictions, on the basis of a currently sensed temperature of the battery and a currently sensed temperature of the starter circuit, in particular of the engine…paragraph 10, the ability of the battery to start can be predicted by taking into account the temperature profile over time. This ensures that the ability to start at a later time can be assessed when the temperature is dropping, in particular when the temperature of the starter circuit is dropping, for example at the temperature of the engine at which the starting power rises overproportionately…paragraph 26, the temperatures T.sub.battery and T.sub.engine are sensed separately for the battery 4 and the starter circuit and used to determine an ability to start at a different temperature T2.)
Therefore, it would have been obvious by one of ordinary skilled in the art before the time the claimed invention was effectively filed to modify the start capability prediction method of Zhang to further comprise engine temperature based start prediction method taught by Mackel because improvement in predicting start capability is further achieved by incorporating engine temperature based prediction, which ensures that the ability to start at a later time can be assessed when the temperature is dropping, in particular when the temperature of the starter circuit is dropping, for example at the temperature of the engine at which the starting power rises overproportionately (Mackel, paragraph 10).
In regards to claim 2, Zhang-Mackel teaches the method of claim 1, wherein the historical engine start data includes data for different vehicle makes and vehicle models (See Zhang paragraph 37, the values used in the algorithms are for illustration purposes and can be different for different vehicle types and makes… paragraph 27, the algorithm determines whether the open circuit voltage (OVC) of the battery 14 is less than a first predetermined voltage threshold Cal_11, such as 12.3 volts. The algorithm also determines if the average open circuit voltage over the past three months of vehicle use has been less than a second predetermined voltage threshold…paragraph 32, the vehicle battery parameters at a future time of cranking the vehicle 12 are determined as a function of history, and the vehicle startability is determined based on the predicted vehicle battery parameters…paragraph 23, The back-office module 24 archives the uploaded vehicle engineering data in a database for individual vehicles.)
In regards to claim 3, Zhang-Mackel teaches the method of claim 1, wherein the calculating comprises comparison of the generated engine start data to the historical engine start data pursuant to a principle of machine learning; the predicted start capability is a capability of the battery to be able to cold-start or warm-start the vehicle engine of the vehicle taken into account in the calculation; and the providing the result is on a display. (See Zhang paragraphs 27 (comparison), paragraph 35, Both of these embodiments can employ various models, such as decision trees, Bayesian networks, neural networks, regression, support vector machines, and their combinations, etc. to perform the analysis…paragraph 26-31, whether the vehicle was in a “warm environment”…see Mackel paragraphs 9-10, determine the ability of the battery to start at a different temperature, and thus make predictions, on the basis of a currently sensed temperature of the battery and a currently sensed temperature of the starter circuit, in particular of the engine. Lastly see Zhang paragraph 39, After the back-office module 24 determines the notification content and the notification urgency, the driver notification can be initiated by various devices, such as a vehicle computer system, by personal operators or by a combination of both. A driver notification can be conducted through various preset communication channels including, but not limited to, a vehicle imbedded phone, cell phone, station phone, etc., e-mail and short text messages.)
` In regards to claim 4, Zhang-Mackel teaches the method of claim 1, wherein the engine-start data which are characteristic of the battery comprise one or more of an engine-start voltage and/or an engine-start voltage profile of the battery. (See Zhang paragraph 20, the on-board module 18 continuously monitors…battery voltage…generator voltage…paragraphs 27 and 33, open circuit voltage and past voltage behavior)
In regards to claim 5, Zhang-Mackel teaches the method of claim 4, wherein the engine-start data which are characteristic of the battery comprise a temperature of the battery when one or more of the engine-start voltage is generated and/or the one or more of the engine-start voltage profile is generated; and the engine-start data comprise state of charge data of the battery when one or more of the engine-start voltage is generated and/or one or more of the engine-start voltage profile is generated. (See Zhang paragraph 20, battery temperature. Also see paragraphs 21, 27, 33, SOH/OCV method)
In regards to claim 6, Zhang-Mackel teaches the method of claim 1, wherein the engine-start data comprises one or more minimum value of a voltage of the battery during an engine start, an engine-start time which is dependent in particular on a state of charge of the battery, and/or a number of engine starts already carried out by the battery. (See Mackel paragraph 22, The evaluation unit 16 also determines a maximum value I.sub.max for the current I and a minimum value U.sub.min for the voltage U by reference…)
In regards to claim 7, Zhang-Mackel teaches the method of claim 1, wherein the engine-start data are generated by the sensor arranged in particular in the vehicle to be started by the battery. (See Zhang paragraphs 19-20, on-board sensors 20 in the vehicle)
In regards to claim 8, Zhang-Mackel teaches the method of claim 1, wherein the engine-start data are generated and/or provided by a vehicle diagnostic system of the vehicle to be started by the battery for the calculation; and/or the engine-start data are generated and/or provided by the sensor which is preferably galvanically connected directly to an electrical connection of the battery. (See Zhang paragraph 20, This information may also come from other on-board systems or electronic control units (ECUs). The data can be unprocessed raw measurements, such as voltage, or on-board pre-processed information, such as trouble codes…paragraph 40, the algorithm collects vehicle data from sensors and other ECUs on the vehicle 12 that are related to the operation of the battery 14…Mackel paragraph 20, The battery control device 10 comprises a current sensor 12 for sensing and determining current values I and a voltage sensor 14 for sensing and determining voltage values U of the battery 4.)
In regards to claim 10, Zhang-Mackel teaches the method of claim 1, wherein the engine-start data are input into the engine-start prediction algorithm based on a principle of machine learning in order to calculate the generated engine-start data. (See Zhang paragraph 35, Both of these embodiments can employ various models, such as decision trees, Bayesian networks, neural networks, regression, support vector machines, and their combinations, etc. to perform the analysis.)
In regards to claim 11, Zhang-Mackel teaches the method of claim 1, wherein in order to calculate the generated engine-start data, the engine-start data are input into an engine-start data prediction algorithm, in which the engine-start data are divided according to classifications into different categories, which differ in characteristics patterns. (See Zhang paragraph 26, The process starts at decision diamond 32 where the algorithm determines whether the vehicle is a high content vehicle… the various thresholds that are used to determine the battery status will depend on whether the vehicle is a high content vehicle, whether the vehicle is being driven in a warm environment or not, and possibly other factors…paragraph 35, Both of these embodiments can employ various models, such as decision trees,)
In regards to claim 12, Zhang-Mackel teaches the method of claim 11, wherein the engine-start data are divided into different categories depending on a vehicle make, a vehicle model and/or a vehicle variant of the vehicle to be started by the battery. (See Zhang paragraph 37, the values used in the algorithms …can be different for different vehicle types and makes.)
In regards to claim 13, Zhang-Mackel teaches the method of claim 1, further comprising performing a learning phase of an engine-start prediction algorithm, with learning data are input into the engine-start prediction algorithm in the learning phase, and the engine-start prediction algorithm identifies patterns and/or regularities in the input learning data, which patterns and/or regularities are applied when calculating the generated engine-start data. (See Zhang paragraph 35, discloses models such as “neural networks…” (which includes training phase)…paragraph 32, the vehicle battery parameters at a future time of cranking the vehicle 12 are determined as a function of history, and the vehicle startability is determined based on the predicted vehicle battery parameters…paragraph 23, The back-office module 24 archives the uploaded vehicle engineering data in a database for individual vehicles.)
In regards to claim 14, Zhang-Mackel teaches the method of claim 13, wherein the learning data comprise characteristic engine-start data for a large number of batteries, in particular those that have aged differently, from a large number of different vehicle makes, vehicle models and/or vehicle variants. (See Zhang paragraph 36, the state of health at the box 104 can be determined based on battery defects at box 112 and battery age at box 114… Battery age can be determined based on mileage at box 120, battery temperature at box 122 and days in service at box 124…paragraph 23, archives the uploaded vehicle engineering data in a database for individual vehicles. Also see paragraph 37, different vehicle types and makes.)
In regards to claim 15, Zhang-Mackel teaches the method of claim 13, wherein the generated engine-start data of the battery whose engine-start performance is to be predicted are used as learning data during a learning phase of the battery. (See Zhang, paragraph 23, The back-office module 24 archives the uploaded vehicle engineering data in a database for individual vehicles…paragraph 32, startability “determined as a function of history”, the vehicle’s own archived history feeds its own prediction)
In regards to claim 16, Zhang teaches, A system for predicting a capability of a battery to be able to start a vehicle engine of a vehicle, the system comprising: (See paragraph 2, determining that the state of charge (SOC) of a vehicle battery may be too low to start the vehicle at the next vehicle start-up … The back-office module 24 runs an algorithm in a predetermined schedule to predict vehicle no-start conditions for each individual vehicle)
an input interface being an access for an input of generated engine-start data which are characteristic of the battery, with the input interface having one or more sensors electrically coupled to the battery; (See paragraph 19, The on-board module 18 receives various information from vehicle sensors 20…paragraph 20, the on-board module 18 continuously monitors… battery current, battery voltage, battery temperature… provided by the vehicle sensors 20…paragraph 40, the algorithm collects vehicle data from sensors and other ECUs on the vehicle 12 that are related to the operation of the battery 14…paragraph 24, the back-office module algorithm receives as inputs…the vehicle engineering data uploaded from the specific vehicle))
an evaluation device having an engine-start prediction algorithm for a calculation of a predicted start capability of the battery applying the generated engine-start data and a learned engine-start data, (See paragraph 23, The back-office module 24 archives the uploaded vehicle engineering data in a database for individual vehicles. The back-office module 24 runs an algorithm in a predetermined schedule to predict vehicle no-start conditions for each individual vehicle…paragraph 27, the algorithm determines whether the open circuit voltage (OVC) of the battery 14 is less than a first predetermined voltage threshold Cal_11, such as 12.3 volts. The algorithm also determines if the average open circuit voltage over the past three months of vehicle use has been less than a second predetermined voltage threshold…paragraph 32, the vehicle battery parameters at a future time of cranking the vehicle 12 are determined as a function of history, and the vehicle startability is determined based on the predicted vehicle battery parameters)
with the evaluation device having a consideration of: a vehicle make, a vehicle model and/or a vehicle variant of the vehicle to be started by the battery; and (See paragraph 41, The back-office module 24 then runs a back-office algorithm to predict no-start situations… The algorithm then determines whether an imminent no-start is present at decision diamond 88, and if not, returns to the box 82 to archive uploaded vehicle data. If an imminent no-start is detected at the decision diamond 88, then the back-office module 24 will notify the vehicle operator/owner based on the urgency of the no-start situation at box 90…paragraph 25, The back-office module algorithm can be much more sophisticated than the on-board module algorithm…paragraph 37, the values used in the algorithms are for illustration purposes and can be different for different vehicle types and makes.)
Zhang does not specifically teach, a temperature of the vehicle engine to be started by the battery.
Mackel further teaches, a temperature of the vehicle engine to be started by the battery. (See paragraph 9, it is possible to determine the ability of the battery to start at a different temperature, and thus make predictions, on the basis of a currently sensed temperature of the battery and a currently sensed temperature of the starter circuit, in particular of the engine…paragraph 10, the ability of the battery to start can be predicted by taking into account the temperature profile over time. This ensures that the ability to start at a later time can be assessed when the temperature is dropping, in particular when the temperature of the starter circuit is dropping, for example at the temperature of the engine at which the starting power rises overproportionately…paragraph 26, the temperatures T.sub.battery and T.sub.engine are sensed separately for the battery 4 and the starter circuit and used to determine an ability to start at a different temperature T2.)
Therefore, it would have been obvious by one of ordinary skilled in the art before the time the claimed invention was effectively filed to modify the start capability prediction system of Zhang to further comprise engine temperature based start prediction system taught by Mackel because improvement in predicting start capability is further achieved by incorporating engine temperature based prediction, which ensures that the ability to start at a later time can be assessed when the temperature is dropping, in particular when the temperature of the starter circuit is dropping, for example at the temperature of the engine at which the starting power rises overproportionately (Mackel, paragraph 10).
Claim 17 is similar in scope to claim 2, therefore, it is rejected under similar rationale as set forth above.
In regards to claim 18, Zhang-Mackel teaches the system of claim 16, wherein the calculation has an increased accuracy in the predicted start capability taking into account the vehicle make, the vehicle model and/or the vehicle variant of a vehicle to be started by the battery, and the temperature; and the capability of the battery to be able to start the vehicle engine is a capability of the battery to be able to cold-start or warm-start the vehicle engine of the vehicle make taken into account the calculation, the vehicle model taken into account in the calculation and/or the vehicle variant taken into account in the calculation. (See Zhang paragraphs 27 (comparison), paragraph 37, the values used in the algorithms are for illustration purposes and can be different for different vehicle types and makes …paragraph 26-31, whether the vehicle was in a “warm environment”…see Mackel paragraphs 9-10, determine the ability of the battery to start at a different temperature, and thus make predictions, on the basis of a currently sensed temperature of the battery and a currently sensed temperature of the starter circuit, in particular of the engine…paragraph 25, Due to the age of the vehicle 2, in particular due to abrasion in cylinders and due to aging of the engine oil, the reference resistance value R.sub.S of the starter circuit changes as a function of the time t. …The threshold value S for the reference resistance value R.sub.S is predefined as a function of the type of vehicle, type of battery or age. For example, the threshold value S varies between 1% to 20% of the reference resistance value R.sub.S of the starter circuit, in particular of the engine.)
In regards to claim 19, Zhang-Mackel teaches the system of claim 16, wherein the engine-start prediction algorithm is based on a principle of machine learning. (See Zhang paragraphs 27 (comparison), paragraph 35, Both of these embodiments can employ various models, such as decision trees, Bayesian networks, neural networks, regression, support vector machines, and their combinations, etc. to perform the analysis)
In regards to claim 20, Zhang-Mackel teaches the system of claim 16, wherein the system comprises an output interface for outputting a result of the calculation carried out by the evaluation device, with the output interface being a display. (See Zhang paragraph 8, warn a vehicle driver about a possible vehicle no-start condition…paragraph 39, After the back-office module 24 determines the notification content and the notification urgency, the driver notification can be initiated by various devices, such as a vehicle computer system, by personal operators or by a combination of both. A driver notification can be conducted through various preset communication channels including, but not limited to, a vehicle imbedded phone, cell phone, station phone, etc., e-mail and short text messages.)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20100026306 A1) in view of Mackel et al. (US 20040239332 A1), and further in view of Karner; Don et al. (US 20190033396 A1)
In regards to claim 9, Zhang-Mackel teaches the method of claim 1.
Zhang-Mackel does not specifically teach, wherein the capability of the battery to be able to start the vehicle engine of the specific vehicle make, the specific vehicle model and/or the specific vehicle variant (See rejection of claim 1) are indicative of a number of vehicle engine start processes which can still be carried out successfully by way of the battery.
Karner further teaches, wherein the capability of the battery to be able to start the vehicle engine of the specific vehicle make, the specific vehicle model and/or the specific vehicle variant are indicative of a number of vehicle engine start processes which can still be carried out successfully by way of the battery. (See paragraph 130, predicting a number of cranks remaining (Remaining Life) of the internal combustion engine for the battery based on analysis of trends in Crank Temperature and Crank Voltage over a plurality of crank events over time... predicting Remaining Life further comprises counting the number of crank events recorded in a single cell or a plurality of cells of a crank matrix.)
Therefore, it would have been obvious by one of ordinary skilled in the art before the time the claimed invention was effectively filed to modify the start capability prediction method of Zhang-Mackel to further comprise predicting number of cranks remaining method taught by Karner because vehicle maintenance can be improved through tracking number of cranks remaining. Furthermore, more advantages are provided in paragraph 124 of Karner, which reduces service and replacement costs performed under warranty because the Crank voltage and crank temperature data along with other battery-specific and application-specific data provides a more detailed history of the battery.
Conclusion
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/JUSTIN S LEE/Primary Examiner, Art Unit 3668
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