Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 12/24/2025 have been fully considered but they are not persuasive.
Applicant argues the claims are subject matter eligible and should not be rejected under 35 USC § 101. Applicant argues the claims cannot be directed to a mental process because the claimed features cannot be practically performed in the human mind, as the recited machine learning in large amounts cannot be performed in the human mind, let alone practically performed. Here, Applicant argues, the claims are not directed to any of the abstract idea categories and the grouping of the limitations of the claims have not been appropriately expanded. Applicant continues, even were the claims to recite an abstract concept, they are integrated into practical application, because as disclosed in the specification, the claims are directed to optimizing dynamic control of a chassis based on anticipation of the coefficient of friction of the vehicle on the road over which the vehicle travels, and therefore the claims are directed towards a practical application and the rejection must be withdrawn, as well as what is significantly more including technical improvements and more than well-understood, routine, or conventional. Therefore, Applicant concludes the § 101 rejection should be withdrawn.
However, the claims at no point recite the amount of data is large, let alone do they define what large may mean in comparison to smaller quantities, nor can the claims be reasonably considered limited to only large quantities of data. Instead, the quantity of data is only described as being within a database, which could include one data point, two datapoints, or any other number of data points. As the data is not limited to a large quantity, the claims cannot reasonably preclude a human from interpreting the data practically within their mind for this reason. The previously identified “estimating…” and “learning…” are both fully capable of being performed within the human mind, as simple estimation from data and learning from data have both been done by humans thousands of times every day for thousands of years. Further, even where the learning identified as impractically performed within the human mind, a machine learning model learning data from a database is a well-known feature, that is well established within the art and would not integrate the estimation into practical application which has been identified as both a mathematical concept and a mental process/step. At no point have the limitations been improperly grouped, but instead the limitations have been carefully and properly analyzed to determine which limitations present judicial exceptions and which limitations present additional subject matter, as presented previously and below.
While the claim certainly does recite “using machine learning”, this language merely applies the abstract concept of “learning” information and is recited at a high level of generality such that it amounts to mere instructions to apply the exception using generic computer components. See MPEP 2106.05. Mere instructions to apply an exception using a generic computer component do not integrate the exception into practical application or amount to significantly more. See USPTO Subject Matter Eligibility Example 47 Claim 2, which demonstrates that the mere recitation of neural network or other machine learning unit does not necessarily render the claim incapable of being practically performed in the human mind merely be being recited when the neural network or machine learning model is merely used as a tool and significant details of the model are not provided.
Further, despite the Applicant’s assertions, independent claim 19 is not directed towards optimizing dynamic control of a chassis based on anticipation of the coefficient of friction, where this the case, the claim would recite at least some features of control, but it simply does not. Instead, the claim is quite clearly and quite explicitly directed to “training an algorithm”, which is to say math and judgments based upon the math, not control. Claims 29 and 36 actually recite language related to these Applicant argued features, and therefore clearly are integrated into practical application. As such, those claims are found to be subject matter eligible, but claim 19-28 and 30-35 are not. Further, as the Applicant has merely provide conclusory statements that this provides technical improvement, but has not provided any explanation of what improvement is to be realized from claim 19, at least beyond that it is adjacent to later subject matter eligible claims, it is difficult to distinguish what advantage or technical improvement is actually present. As such, the broadest reasonable interpretation must be taken that the technical improvement in fact lies with the other claims 29 and 36, not with claims 19-28 and 30-35.
As such, this argument is unpersuasive.
In regards to the prior art rejection of claim 19, Applicant argues Engel (DE 102013222634) does not teach each and every aspect of the claim. Applicant argues Engel is silent with respect to the recited training and learning aspect of road slip coefficient estimation algorithm and at best discusses only inference and reinforcement phases of its algorithm and does not provide details on training or learning. Applicant continues, Engel fails to teach or suggest that a learning phase is carried out from the recited database. Further, Applicant argues Engel merely predicts an overall slip rate for a given vehicle traveling along a certain section of road, not an extrinsic component of the slip rate, and instead each measured coefficient is necessarily dependent on the vehicle’s weight and no step of extracting the extrinsic component is recited. Therefore, Applicant concludes that Engel does not disclose or suggest the claimed features, and the claim is not anticipated.
However, Engel teaches collecting external information from other road users transmitted to the own vehicle, which forms a corresponding database of relevant external information associated with particular locations, which is analyzed by a neural network which uses machine learning to determine the coefficient of friction. A camera on the vehicle is used to image the area ahead of the vehicle to determine the external conditions including topography, road type, ambient temperature, weather, and the like, through a learning algorithm. Both these retrieved external information measured by the own vehicle and retrieved from other vehicles are components of the coefficient of friction extrinsic to the vehicle that are learned as they are directly used to calculate the coefficient of friction and are unrelated to the vehicle itself. More particularly, values of the external information is determined including at least temperature, humidity, wind, rain, and the like. The claim does not recite extracting a component, but instead only broadly recites that learning from a database is performed, which under its broadest reasonable interpretation is taught by Engel. As Engel operates a learning algorithm and neural network, both of which learn through collecting the data and performing plausibility checks as outlined, the learning algorithm and neural network are trained at least to the same requirements as recited by the claim. Further, inference and reinforcement phases, which the Applicant readily admits are present within Engel, are both phases of training and learning and performing such phases still anticipates generically recited training and learning in the context of the art, meeting the required features of the claim.
As such, this argument is unpersuasive.
Applicant argues the remaining references do not remedy the deficiencies of the rejection of claim 19.
This argument is unpersuasive as no other reference is required to remedy any challenged limitation of the claim.
Applicant argues independent claim 36 recites similar features to independent claim 19 and therefore the rejection should be withdrawn for the same reasons.
This argument is unpersuasive for the same reasons as given above.
Applicant argues the dependent claims are allowable.
This argument is unpersuasive as each independent and dependent claim has been fully rejected and for the reasons as given above.
Claim Objections
Claim 33 objected to because of the following informalities: recites “potential of the chassis the vehicle is new” which should read “the potential of the chassis of the vehicle is new” (emphasis added). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 19-28 and 30-35 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 19 will be treated as a representative claim and reads:
A method for training an algorithm for automatically estimating a component extrinsic to a vehicle of a coefficient of friction of a road segment that matches, to state data relating to the road segment provided by way of input, an output value of the component extrinsic to the vehicle of the coefficient of friction of the road segment, the method comprising:
learning, using machine learning, from a database of data on the state of road segments associated with values of components extrinsic to the vehicle of the coefficient of friction of the road segments, the data on the state of the road segments being collected by one or more vehicles equipped with a camera oriented toward a front of the vehicle.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of training an algorithm. 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, 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. See MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for training an algorithm for automatically estimating a component extrinsic to a vehicle of a coefficient of friction of a road segment that matches, to state data relating to the road segment provided by way of input, an output value of the component extrinsic to the vehicle of the coefficient of friction of the road segment [mental process/step and mathematical concept], the method comprising:
learning, using machine learning, from a database of data on the state of road segments associated with values of components extrinsic to the vehicle of the coefficient of friction of the road segments [mental process/step], the data on the state of the road segments being collected by one or more vehicles equipped with a camera oriented toward a front of the vehicle.
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, “estimating…” in the context of the claim, encompasses a person looking at collected data and approximating a numerical value which is a simple judgment. Similarly, “learning” in the context of the data encompasses that same person performing another simple judgment of relative importance of viewed data. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. See MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A method for training an algorithm for automatically [generic linking to a technical field] estimating a component extrinsic to a vehicle of a coefficient of friction of a road segment that matches, to state data relating to the road segment provided by way of input, an output value of the component extrinsic to the vehicle of the coefficient of friction of the road segment, the method comprising:
learning, using machine learning [applying the abstract idea using generic computer component], from a database of data on the state of road segments associated with values of components extrinsic to the vehicle of the coefficient of friction of the road segments, the data on the state of the road segments being collected by one or more vehicles equipped with a camera oriented toward a front of the vehicle [insignificant extra-solution activity (data gathering)].
For the following reason(s), 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 “a method…”, the Examiner submits, that this limitation merely generically links the limitations of the claim to the field of training an algorithm to automatically perform operations, which does not add significantly more. Regarding “the collected data…”, the Examiner submits that this limitation is recited at a high level of generality and amounts to no more than mere extra-solution data gathering. Further, the “using machine learning”, the Examiner submits, is further recited at a high level of generality such that it merely applies the abstract idea using a generically recited computer component with generically recited operation, such that it amounts to mere instructions to apply the abstract concept using generic machine learning. Here, the claim merely invokes generic machine learning computers as a tool to performing the abstract concept, which cannot integrate into practical application, see MPEP 2016.05(f).
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. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/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 Revised Guidance, 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 applying to a technical field of training an algorithm, merely generically links the limitations of the claim to the field of training an algorithm to automatically perform operations, which does not add significantly more. Further, the data collection is recited at a high level of generality, such that it is an insignificant extra-solution activity. Still further, the using machine learning merely generally invokes using generic computer components to apply the abstract concepts, which is mere instructions to apply the exception, which does not amount to significantly more. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 20-28, 30-35 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, each either providing additional mathematical calculations, additional data gathering, or the like. Therefore, dependent claims 20-28, 30-35 are not patent eligible under the same rationale as provided for in the rejection of independent claim 19.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 19-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Engel (DE 102013222634).
In regards to claim 19, Engel teaches a method for training an algorithm for automatically estimating a component extrinsic to a vehicle of a coefficient of friction of a road segment that matches, to state data relating to the road segment provided by way of input, an output value of the component extrinsic to the vehicle of the coefficient of friction of the road segment, the method comprising: (Fig 2, 3, Page 3, Page 4, in steps S2-S5, a coefficient of friction of particular road area ahead of the vehicle is determined, where the coefficient of friction is determined based on the environmental conditions including topography, road type, ambient temperature, and weather. These operations are performed by a learning algorithm that is trained.)
learning, using machine learning, from a database of data on the state of road segments associated with values of components extrinsic to the vehicle of the coefficient of friction of the road segments, the data on the state of the road segments being collected by one or more vehicles equipped with a camera oriented toward a front of the vehicle. (Page 2, external information from other road users may be transmitted to the own vehicle, which when collected forms a corresponding database of the relevant external information associated with particular locations and is then analyzed by neural network which uses machine learning. Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions. These are used to determine the coefficient of friction of the imaged segment by operating a learning algorithm.)
In regards to claim 20, Engel teaches the method as claimed in claim 19, wherein the data on the state of road segments comprise images of the road segment captured by a camera placed at the front of the vehicle and information on weather and temperature at a time of capture of the image. (Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions and temperature and weather may be determined corresponding to the analyzed road segments.)
In regards to claim 21, Engel teaches the method as claimed in claim 19, wherein the component extrinsic to the vehicle of the coefficient of friction associated with the data on the state of the road segments is deduced from the coefficient of friction associated with the road segments, the coefficient of friction being measured by the vehicle capturing the state data or being known prior to the collection of the state data by the vehicle. (Pages 1, 2, 4, the current coefficient of friction the vehicle is currently experiencing is determined in parallel with the coefficient of road friction measurements ahead and used as a plausibility check and to improve future predictions, where the coefficient of friction is determined based on sensor information of the own vehicle which captures state information. This determines an external component of friction by analysis of a current coefficient of friction.)
In regards to claim 22, Engel teaches a method for determining the component extrinsic to the vehicle of the coefficient of friction of a road segment located in front of a motor vehicle moving toward said road segment, the method comprising:
acquiring at least one state datum relating to the road segment; (Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions and temperature and weather may be determined corresponding to the analyzed road segments. The camera image, temperature, and weather may be or may include state data and each serve as an individual state datum.) and
determining a value of the component extrinsic to the vehicle of the coefficient of friction of said road segment by an algorithm for automatically estimating the component extrinsic to the vehicle of the coefficient of friction of a road segment trained according to the method as claimed in claim 19. (Page 3, Page 4, in steps S2-S5, a coefficient of friction of particular road area ahead of the vehicle is determined, where the coefficient of friction is determined based on the environmental conditions including topography, road type, ambient temperature, and weather. These operations are performed by a learning algorithm that is trained.)
In regards to claim 23, Engel teaches the method as claimed in claim 22, wherein the at least one state datum relating to the road segment comprises one or more images of the road segment captured by a camera placed at the front of the vehicle. (Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions. The camera image may be or may include state data and serves as an individual state datum.)
In regards to claim 24, Engel teaches the method as claimed in claim 22, wherein the at least one state datum of the road segment comprises one or more pieces of weather and temperature information. (Page 3, temperature and weather may be determined corresponding to the analyzed road segments. The temperature and weather may be or may include state data and each serve as an individual state datum.)
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.
Claims 25-32 and 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Engel in view of Rander (US 20170166215).
In regards to claim 25, Engel teaches the method as claimed in claim 22.
Engel does not teach: further comprising, following the determining the value of the component extrinsic to the vehicle of the coefficient of friction, generating and updating a map of vehicle-road grip quality.
However, Rander teaches grip values may be received from a population of vehicles, normalized to account for vehicle specific factors, and aggregated into a map by averaging the received grip values ([0063], [0087], [0089]). The grip values are the numeric quality of grip of each road segment. This includes generating the map initially when the first data is retrieved and updating the map for each subsequent retrieved data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, by incorporating the teachings of Rander, such that a road grip map is constructed of averaged grip values of the road segments traversed by vehicles and reflecting extrinsic components of the coefficient of friction and updated with the collection of new information.
The motivation to do so is that, as acknowledged by Rander, this allows for improved automation and safety of the vehicle ([0003], [0004]).
In regards to claim 26, Engel, as modified by Rander, teaches the method as claimed in claim 25.
Engel also teaches temperature and weather may be determined corresponding to the analyzed road segments (Page 3), which may also be acquired from a preceding road user traveling ahead of the own vehicle (Page 4). This information must be stored and matched with the relevant particular locations.
Rander teaches grip values may be received from a population of vehicles, normalized to account for vehicle specific factors, and aggregated into a map by averaging the received grip values ([0063], [0087], [0089]). The grip values are the numeric quality of grip of each road segment. This includes generating the map initially when the first data is retrieved and updating the map for each subsequent retrieved data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander, such that a road grip map is constructed specifically of averaged grip values of matching temperature and weather information of the road segments traversed by vehicles and reflecting extrinsic components of the coefficient of friction and updated with the collection of new information.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 27, Rander teaches a server that receives and transmits grip values to construct a road grip map from a population of different vehicles, storing the road grip map within a server shared by the population of vehicles, which is composed of averaged grip values ([0060], [0063], [0087], [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander, such that a server shared between and with a population of vehicles stores the road grip map which is composed of averaged grip values associated with weather and temperature data, as in Engel.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 28, Rander teaches determining a probability retrieved and assigned to each grip value, which is obtained from a variety of sources including calculation instructions which is then stored in map information ([0030], [0063]). These calculation instructions include using algorithms, including statistical algorithms to determine the probability, which is a confidence for each grip value stored in map information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander, such that the probability of each grip value is calculated, including by performing statistical algorithms, and stored with the grip map data.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 29, Engel teaches a method for optimizing dynamic control of a chassis of a vehicle based on a component extrinsic to the vehicle of the coefficient of friction obtained using the method as claimed in claim 22, the method for optimizing dynamic control comprising:
retrieving a value of the component extrinsic to the vehicle of the coefficient of friction associated with a road segment in front of the vehicle; (Page 3, road coefficient for forecast road segment ahead of the vehicle is determined, which requires retrieval by actuators and processing units.)
Engel also teaches preconditioning and controlling the vehicle including the vehicle’s chassis based on the predicted upcoming road friction (Page 4).
Engel does not teach:
determining a prediction of the value of the coefficient of friction associated with said road segment based on the retrieved component extrinsic to the vehicle of the coefficient of friction; and
configuring parameters of the chassis of the vehicle depending on the prediction.
However, Rander teaches a determined grip value has a correlation to a determined coefficient of friction, which may be affected by other factors such as precipitation, snow, and type of road ([0024]) and the measured data of the upcoming road may be converted to a coefficient of friction or range of coefficient of friction based on internal characteristics of the vehicle ([0091]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, by incorporating the teachings of Rander such that the received extrinsic coefficient of friction is converted into a coefficient of friction for the vehicle traveling through and the vehicle is controlled based upon the converted coefficient of friction.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 30, Engel, as modified by Rander, teaches the method for optimizing dynamic control as claimed in claim 29, wherein the value of the component extrinsic to the vehicle of the coefficient of friction retrieved in the retrieving is the value calculated in the determining the value of the component extrinsic to the vehicle of the coefficient of friction, the vehicle the chassis of which is modified being the same as the one that performed the determining the value of the component. (Page 4, vehicle is preconditioned and controlled, including the vehicle’s chassis based on the predicted upcoming road friction.)
In regards to claim 31, Engel, as modified by Rander, teaches the method for optimizing dynamic control as claimed in claim 29.
Engel also teaches temperature and weather may be determined corresponding to the analyzed road segments (Page 3), which may also be acquired from a preceding road user traveling ahead of the own vehicle (Page 4). This information must be stored and matched with the relevant particular locations.
Rander teaches a server that receives and transmits grip values to construct a road grip map from a population of different vehicles, storing the road grip map within a server shared by the population of vehicles, which is composed of averaged grip values, which are then retrieved by vehicles ([0060], [0063], [0087], [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander, such that the vehicle retrieves extrinsic components of friction from a road grip map composed of grip values which are numerical grip qualities based on location, weather, and temperature.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 32, Rander teaches a determined grip value has a correlation to a determined coefficient of friction, which may be affected by other factors such as precipitation, snow, and type of road ([0024]) and the measured data of the upcoming road may be converted to a coefficient of friction or range of coefficient of friction based on internal characteristics of the vehicle ([0091]). These internal characteristics are an intrinsic component of friction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander, such that the coefficient of friction is predicted from the determined grip value and external friction in combination with internal characteristics of the vehicle which are intrinsic components of friction.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
In regards to claim 34, Engel, as modified by Rander, teaches the method for optimizing dynamic control as claimed in claim 29, wherein the prediction of the value of the coefficient of friction associated with the road segment is determined on board the vehicle. (Page 4, operations are performed by computing device on board the vehicle including the prediction as modified.)
In regards to claim 35, Engel, as modified by Rander, teaches the method for optimizing dynamic control as claimed in claim 29, wherein the prediction of the value of the coefficient of friction associated with the road segment is determined in real time. (Page 1, Page 3, road coefficient must be determined in real time conventionally and estimated early and continuously, which is in real time, before the vehicle arrives at the particular road segment.)
In regards to claim 36, Engel teaches a motor vehicle comprising: (Fig 1, 4.)
circuitry configured to optimize dynamic control of a chassis of the vehicle based on a component extrinsic to the vehicle of a coefficient of friction of a road segment located in front of the vehicle moving toward the road segment, the component extrinsic to the vehicle of a coefficient of friction of the road segment being obtained by (Page 3, Page 4, computing device performs operations, which is circuitry, including determining a coefficient of friction of particular road area ahead of the vehicle, where the coefficient of friction is determined based on the environmental conditions including topography, road type, ambient temperature, and weather. These operations are performed by a learning algorithm that is trained.)
acquiring at least one state datum relating to the road segment, (Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions and temperature and weather may be determined corresponding to the analyzed road segments. The camera image, temperature, and weather may be or may include state data and each serve as an individual state datum.) and
determining a value of the component extrinsic to the vehicle of the coefficient of friction of the road segment by an algorithm for automatically estimating the component extrinsic to the vehicle of the coefficient of friction of the road segment trained by (Page 2, external information from other road users may be transmitted to the own vehicle, which when collected forms a corresponding database of the relevant external information associated with particular locations and is then analyzed by neural network which uses machine learning. Page 3, Page 4, camera on vehicle may image area ahead of vehicle to determine external conditions and a coefficient of friction of particular road area ahead of the vehicle is determined, where the coefficient of friction is determined based on the environmental conditions including topography, road type, ambient temperature, and weather. These operations are performed by a learning algorithm that is trained including determining values of components extrinsic to the vehicle of the coefficient of friction of a road segment.)
learning, using machine learning, from a database of data on a state of road segments associated with values of components extrinsic to the vehicle of the coefficient of friction of these the road segments, the data on the state of the road segments being collected by one or more vehicles equipped with a camera oriented toward a front of the vehicle, (Page 2, external information from other road users may be transmitted to the own vehicle, which when collected forms a corresponding database of the relevant external information associated with particular locations and is then analyzed by neural network which uses machine learning. Page 3, camera on vehicle may image area ahead of vehicle to determine external conditions. These are used to determine the coefficient of friction of the imaged segment by operating a learning algorithm.)
the circuitry being configured to (Page 3, computing device performs operations.)
retrieve a value of the component extrinsic to the vehicle of the coefficient of friction associated with the road segment in front of the vehicle, (Page 3, road coefficient for forecast road segment ahead of the vehicle is determined, which requires retrieval by actuators and processing units.)
Engel does not teach:
determine a prediction of the value of the coefficient of friction associated with the road segment based on the retrieved component extrinsic to the vehicle of the coefficient of friction, and
configure parameters of the chassis of the vehicle depending on the prediction.
Rander teaches a determined grip value has a correlation to a determined coefficient of friction, which may be affected by other factors such as precipitation, snow, and type of road ([0024]) and the measured data of the upcoming road may be converted to a coefficient of friction or range of coefficient of friction based on internal characteristics of the vehicle ([0091]). This predicts a value of the coefficient of friction associated with the road segment based on retrieved extrinsic parameters.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle system of Engel, by incorporating the teachings of Rander such that the received extrinsic coefficient of friction is converted into a predicted coefficient of friction for the vehicle traveling through and the vehicle is controlled based upon the converted coefficient of friction.
The motivation to do so is the same as acknowledged by Rander in regards to claim 25.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Engel in view of Rander, in further view of Armeni et al. (US 20190188467).
In regards to claim 33, Engel, as modified by Rander, teaches the method for optimizing dynamic control as claimed in claim 32.
Rander teaches converting sensor information into coefficient of friction information by accounting for factors such as such as tire dimension, tire weight, vehicle weight, tread type, material type, and the like ([0091]) which are factors representative of a potential of the chassis of the new vehicle.
Engel, as modified by Rander, does not teach: wherein the component intrinsic to the vehicle of the coefficient of friction is dependent on a wear factor of the chassis of the vehicle,
However, Armeni teaches that a coefficient of friction depends upon conditions including the wear of the tires and other factors ([0094]). Notably the Applicant’s specification describes chassis wear as including tire wear, as explained on page 8 of the disclosure as originally filed.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Engel, as already modified by Rander, by further incorporating the teachings of Rander and incorporating the teachings of Armeni, such that internal characteristics of the vehicle are used to transform to a coefficient of friction, where the internal characteristics include tire dimension, tire weight, vehicle weight, tread type, material type, and the like, which are factors that represent potential of the chassis of the vehicle including the vehicle being new, and include wear of the tire.
The motivation to determine the friction coefficient based on internal characteristics is the same as acknowledged by Rander in regards to claim 25. The motivation to determine friction coefficient based on tire wear is that, as acknowledged by Armeni, this allows for better determination of driving conditions for safety ([0092]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hagenlocher (US 20190118821) teaches determining the coefficient of friction of an area around the own vehicle with different conditions.
Singh (US 20150284006) teaches determining tire state as a factor of friction for a vehicle.
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.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661