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 05/12/2026 have been fully considered.
In regards to the subject matter eligibility rejection of independent claim 19, Applicant argues just because a claim may involve an exception does not mean it should be rejected under §101. Applicant argues the claimed features cannot be practically performed in the human mind, at least the training of an algorithm for automatically estimating, the learning, and the deducing, where the learning is applicable to controlling of a chassis of a vehicle, and these are not simple judgments, and instead are complicated tasks as discussed in the specification. Applicant argues the limitations of the claim are not directed to any of the categories of judicial exception. Further, Applicant argues the claim is directed to a practical application, as disclosed in the specification, the claim achieves the optimizing dynamic control of the vehicle’s chassis by the recited features including the learning. Therefore, Applicant concludes the §101 rejection should be withdrawn.
However, estimating in the context of the claim may be either or both of a mathematical operation or a mental operation as it is precisely either performing a mathematical calculation or judging the a value, which is a simple judgment. Then, learning, in the context of the claim, but for the recitation of “using machine learning”, is a further mental process in which one of ordinary skill looks at a database and judging associated values, which is another simple judgment. Performing a deduction, then, comprises that same person logically deducing a value from data, which is another simple judgment. Each of these are practically performed in the human mind. Therefore, at least one judicial exception is recited. The machine learning is recited as merely a tool used to perform the abstract concept, and therefore amounts to merely applying the abstract concept using a generically recited computer component. Further, the operations are only generically linked to the technical field of training an algorithm, but the steps do not appear to be particularly recited in any detail, but rather instead, learning is broadly subsequently recited. The recited learning being applicable to control amounts to applying the exception, but falls short of actually performing control of any sort, instead merely amount to mere instructions to apply the exception. Similarly, the data collection and prior known information both amount to mere insignificant extra-solution activity of data collection. The claim falls short of integrating into practical application because it amounts to merely generally stating future applicability of control, but falls short of actually reciting control. Were the claim to actually recite controlling the chassis or vehicle based upon the learned information, it would likely be integrated into practical application. The claim falls short of providing the disclosed optimization because the claim does not include more than mere instructions to apply the exception, see MPEP 2106.05(a), which would be required to for the claim to reflect improvements to the technical field, such as optimizing chassis control.
As such, this argument is unpersuasive. The Examiner has provided suggested language that would likely overcome the subject matter eligibility rejection.
In regards to the prior art rejection of independent claim 36, Applicant argues Engel (DE 102013222634) and Rander (US 20170166215) fail to disclose or suggest the claimed features, at least being silent to the prediction limitation, and the cited paragraphs of Rander instead suggest alternative approaches, not the required determination dependent on both a component extrinsic to the vehicle of the coefficient of friction and a component intrinsic to the vehicle of the coefficient of friction specific to the vehicle, and no link or connection between the cited paragraph is ever disclosed. Therefore, Applicant concludes the cited references do not render obvious the features of the claim and the rejection should be withdrawn.
However, Rander teaches a determined grip value is correlated to a determined coefficient of friction which is affected by weather factors such as precipitation, snow, and road type, and in particular embodiments this further includes 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. Despite the Applicant’s assertions, these do not appear to be unrelated alternative embodiments, but rather additional operations that may be performed along with other embodiments. The disclosure of the reference does not appear to support the Applicant’s assertion of no link being present between the paragraphs, but instead discloses that the teachings of paragraph [0090] are performed in addition to and along with the disclosure of paragraph [0024]. Instead, the disclosure of Rander will be read for what is explicitly and implicitly recites, that in some embodiments further teachings are performed, which one of ordinary skill would have recognized, as combined into Engel, teaches that the received coefficient of friction is converted into a predicted coefficient of friction form determined grip value and the external coefficient of friction in combination with internal characteristics of the vehicle, which is precisely what is required by the claim.
As such, this argument is unpersuasive.
Claim Objections
Claims 29, 37-39 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. No single prior art reference has been found to anticipate the limitations of these claims, nor any combination of prior art references to render these claims obvious, when viewed in the context of the remaining limitations to which the claim depends. Likewise, these claims are all subject matter eligible.
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, 20, and 22-28 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 learning being applicable to controlling of a chassis of the vehicle based on the component extrinsic to the vehicle of the coefficient of friction,
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 known prior to the collection of the state data by 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 learning being applicable to controlling of a chassis of the vehicle based on the component extrinsic to the vehicle of the coefficient of friction,
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 [mental process/step], the coefficient of friction being known prior to the collection of the state data by 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. Further, the recited “wherein the component…” in the context of the claim encompasses that same person performing a further simple judgment of deduction. 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)] the learning being applicable to controlling of a chassis of the vehicle based on the component extrinsic to the vehicle of the coefficient of friction [mere instructions to apply exception],
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 known prior to the collection of the state data by 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 data…” and “the coefficient of friction being known…”, the Examiner submits that these limitation are recited at a high level of generality and amount 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). Further, the recited “the learning being…” amount to mere instructions to apply the exception recited generally, that falls short of providing actual control of the vehicle.
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. Similarly, the learning being applicable to control related operations, amounts to mere application, but falls short of actually operating control. 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.
In short, independent claim 19 merely provides instructions to relate the disclosure to control, but falls short of actually performing control, which likely make the claim patent eligible. The Examiner suggests amending claim 19 to recite something along the lines of “configuring parameter of the chassis of the vehicle based on the leaning” at the end of the claim.
Dependent claims 20, 22-28, 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 § 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.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Engel (DE 102013222634) in view of Rander (US 20170166215).
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, the prediction of the value of the coefficient of friction associated with the road segment being dependent on both the retrieved value of the component extrinsic to the vehicle of the coefficient of friction and on a component intrinsic to the vehicle of the coefficient of friction, the component intrinsic to the vehicle of the coefficient of friction being specific to the vehicle, 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 from the determined grip value and external coefficient of friction in combination with internal characteristics of the vehicle which are intrinsic components 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 that, as acknowledged by Rander, this allows for improved automation and safety of the vehicle ([0003], [0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Armeni et al. (US 20190188467) teaches a coefficient of friction depends upon conditions including the wear of the tires and other factors
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHIAS S WEISFELD whose telephone number is (571)272-7258. The examiner can normally be reached Monday-Thursday 7:00 AM - 4:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya Burgess can be reached at Ramya.Burgess@USPTO.GOV. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
Prosecution Insights