DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Final Office Action on the merits. Claims 11-20 are currently pending and are addressed below.
Response to Amendment
Claims 14 was objected to due to minor informalities. Applicant amended the claim accordingly; therefore, the objection is withdrawn.
Claims 11-20 were rejected under 35 U.S.C. 112 as being indefinite. Applicant amended the claims accordingly; therefore, the rejection is withdrawn.
Response to Arguments
Applicant’s arguments on page 5 of the response, with respect to the rejection(s) of claim(s) 11-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues the “claims recite patent-eligible subject matter for at least the reason that the claims require a specific driving assistance system, the recited improved operation of which cannot be characterized as a mental process. Even so, the claims reflect a specific technological improvement to the field of assisted vehicle guidance. Thus, the claims reflect both a practical application, in addition to amounting to something significantly more than a mental process”. Examiner respectfully disagrees. The claims are still directed to abstract ideas without significantly more. The limitations “identifying a current environmental scenario from the plurality of environmental scenario via the sensor data”, “determining, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources”, “amalgamating the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario”, and “predicting the driving path from the amalgamated data” are all recited at a high level of generality such that they can be performed by any generic computer. Furthermore, there is no technological improvement to the field of assisted vehicle guidance because the limitations of the “driving assistance system”, the “interface that captures sensor data…”, the “memory that stores a database…”, and the “processor configured to predict a driving path…” are also recited at a high level of generality such that they merely integrate the otherwise abstract ideas described above into a technological environment using generic computer components.
Applicant’s arguments on pages 5-6 of the response, with respect to the rejection(s) of claim(s) 11-20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Münning.
Claim Objections
Claims 11 and 19 objected to because of the following informalities:
Claim 11 recites “…plurality of environmental scenario…” in lines 10-11, in which the underlined portion appears to be a typographical error.
Claim 19 recites “a current environmental scenario from the plurality of environmental scenarios”, which appears to be grammatically incorrect due to a markup error.
NOTE: In view of claim 11, which recites analogous limitations, the limitation “a current environmental scenario from the plurality of environmental scenario via the sensor data” in claim 19 will be interpreted as “identifying a current environmental scenario from the plurality of environmental scenario via the sensor data”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the vehicle" in line 4. There is insufficient antecedent basis for this limitation in the claim.
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 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Independent Claim 11:
Step 1: Claim 11 is directed to a method for predicting a driving path of a motor vehicle (i.e., a process). Therefore, claim 11 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 11 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 11 recites:
A method for operating a driving assistance system, wherein the driving assistance system comprises: an interface that captures sensor data from one or more sensors of the vehicle, a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness, and a processor configured to predict a driving path using data from the plurality of data sources, wherein the processor is further configured to carry out the method comprising:
identifying a current environmental scenario from the plurality of environmental scenario via the sensor data;
determining, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources;
amalgamating the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario; and
predicting the driving path from the amalgamated data.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. For example, the limitation “predict a driving path using data from the plurality of data sources” in the context of this claim encompasses mentally predicting a driving path using data from a plurality of data sources. The limitation “identifying a current environmental scenario from the plurality of environmental scenario via the sensor data” in the context of this claim encompasses mentally identifying or recognizing a current environmental scenario using sensor data. The limitation “determining, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources” encompasses mentally determining or recognizing a degree of trustworthiness of each of the data sources for the current environmental scenario using a database. The limitation “amalgamating the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario” encompasses mentally amalgamating data in a weighted manner using associated degrees of trustworthiness of a corresponding data source for the current scenario. The limitation “predicting the driving path from the amalgamated data” encompasses mentally predicting a driving path from the amalgamated data.
Step 2A Prong 2: 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”):
A method for operating a driving assistance system, wherein the driving assistance system comprises: an interface that captures sensor data from one or more sensors of the vehicle, a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness, and a processor configured to predict a driving path using data from the plurality of data sources, wherein the processor is further configured to carry out the method comprising:
identifying a current environmental scenario from the plurality of environmental scenario via the sensor data;
determining, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources;
amalgamating the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario; and
predicting the driving path from the amalgamated data.
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.
The additional limitations of the “driving assistance system”, “an interface that captures sensor data from one or more sensors of the vehicle”, “a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness”, and “a processor” merely integrate the abstract ideas into a generic or general purpose vehicle control environment using generic computer components.
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(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.
Step 2B: Regarding Step 2B of the 2019 PEG, representative independent claim 11 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 the integration of the abstract idea into a practical application, the additional limitations of the “driving assistance system”, “an interface that captures sensor data from one or more sensors of the vehicle”, “a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness”, and “a processor” are recited at a high level of generality such that they merely apply the abstract ideas into a generic or general purpose vehicle control environment using generic computer components.
Therefore, claim 11 is ineligible under 35 U.S.C §101.
Regarding Independent Claim 19:
Step 1: Claim 19 is directed to a method for predicting a driving path of a motor vehicle (i.e., a process). Therefore, claim 19 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 19 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 19 recites:
A driving assistance system for a motor vehicle, comprising:
an interface that captures sensor data from one or more sensors of the motor vehicle;
a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness; and
a processor that predicts a driving path using data from the plurality of data sources, wherein the processor is configured to:
a current environmental scenario from the plurality of environmental scenarios;
determine, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources,
amalgamate the data from the data sources in a weighted manner according to the associated degrees of trustworthiness, and
predict the driving path from the amalgamated data.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. For example, the limitation “predicts a driving path using data from the plurality of data sources” in the context of this claim encompasses mentally predicting a driving path using data from a plurality of data sources. The limitation “identifying a current environmental scenario from the plurality of environmental scenarios” in the context of this claim encompasses mentally identifying or recognizing a current environmental scenario using sensor data. The limitation “determine, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources” encompasses mentally determining or recognizing a degree of trustworthiness of each of the data sources for the current environmental scenario using a database. The limitation “amalgamate the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario” encompasses mentally amalgamating data in a weighted manner using associated degrees of trustworthiness of a corresponding data source for the current scenario. The limitation “predict the driving path from the amalgamated data” encompasses mentally predicting a driving path from the amalgamated data.
Step 2A Prong 2: 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”):
A driving assistance system for a motor vehicle, comprising:
an interface that captures sensor data from one or more sensors of the motor vehicle;
a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness; and
a processor that predicts a driving path using data from the plurality of data sources, wherein the processor is configured to:
a current environmental scenario from the plurality of environmental scenarios;
determine, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources,
amalgamate the data from the data sources in a weighted manner according to the associated degrees of trustworthiness, and
predict the driving path from the amalgamated data.
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.
The additional limitations of the “motor vehicle”, “an interface that captures sensor data from one or more sensors of the motor vehicle”, “a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness”, and “a processor” merely integrate the abstract ideas into a generic or general purpose vehicle control environment using generic computer components.
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(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.
Step 2B: Regarding Step 2B of the 2019 PEG, representative independent claim 19 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 the integration of the abstract idea into a practical application, the additional limitations “motor vehicle”, “an interface that captures sensor data from one or more sensors of the motor vehicle”, “a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness”, and “a processor” are recited at a high level of generality such that they merely apply the abstract ideas to a generic or general purpose vehicle control environment using generic computer components.
Therefore, claim 19 is ineligible under 35 U.S.C §101.
Dependent Claims
Dependent claims 12-18 and 20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception. Dependent claim 12 further describes the data and/or data sources, dependent claim 13 is further directed to the abstract idea of inferring degrees of trustworthiness, dependent claim 14 is further directed to recording environmental data (i.e., data gathering) and the abstract idea of ascertaining the degrees of trustworthiness dynamically, dependent claim 15 is further directed to the abstract ideas of ascertaining a distance and predicting the driving path, dependent claim 16 is further directed to the abstract idea of amalgamating data sources and/or data, dependent claim 17 is further directed to the abstract idea of ascertaining uncertainty and weighing data, dependent claim 18 is further directed to the abstract idea of selecting objects in the respective environment of the motor vehicle, and dependent claim 20 is further directed to data gathering and additional elements that merely integrate the abstract idea into a generic vehicle control environment. Therefore, dependent claims 12-18 and 20 are not patent eligible under the same rationale as provided for in the rejection of claims 11 and 19.
Therefore, claim(s) 11-20 is/are ineligible under 35 U.S.C. §101.
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)(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.
Claim(s) 11-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Münning of US 20220332316 A1, filed 07/28/2020, hereinafter “Münning”.
Regarding claim 11, Münning teaches:
A method for operating a driving assistance system, wherein the driving assistance system comprises: an interface that captures sensor data from one or more sensors of the vehicle, (See at least [0066-0067]: “Turning to FIG. 1, the drawing shows a vehicle 10, which is driving along a road 12 (or also path) in the driving direction F. The vehicle 10 comprises a system 1 according to some aspects of the present disclosure. The system 1 comprises a control device 14, which is designed as a single control unit in the shown example. The control unit 14 is wirelessly connected to a vehicle-external server, and more precisely, to a cloud server 16, by way of a mobile communication link. The system 1 may also include a detection device 15, which is a camera sensor in the shown example. The camera sensor is arranged in the vehicle 10 so as to detect a road section situated ahead, and to output image data of the road according to a detection frequency. This image data can be evaluated in the camera sensor itself, or by the control device 14, and more particularly such that predetermined environment properties are ascertained therein.”)
a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness, and (See at least [0064]: “Generally speaking, the control device can thus be configured to read out, request and/or receive the stored environment data, in particular by or from a vehicle-external memory device. For checking the plausibility of the stored environment data, a comparison of any of the type described above can be carried out, for example, and/or a quality of the stored environment data, and preferably also a minimum quality to be met, can be ascertained by the control device” & [0073-0074]: “In some examples, the shown solution presupposes that several other vehicles have already traveled along a comparable route R in the past along the road 12, and in the process detected the same environment properties LF, RF. It is further presupposed that these vehicles transmitted the corresponding environment data to the vehicle-external memory device 16. There, this environment data can be stored, for example, in a location-dependent manner, as was described above in general terms. It is also possible to store the actually covered driving routes R of the particular vehicles. If the currently considered vehicle 10 is driving along the road 12, the currently detected environment data can, in each case, be ascertained by the detection device 16 or control device 14. Likewise, however, it is possible for the stored environment data of those vehicles to be ascertained from the memory device 16 which previously traveled the road 12 in this region (that is, in the same route section). Relevant environment data can be ascertained, for example, based on the covered routes R and/or a vehicle position, which should agree as much as possible with the position and route R of the vehicle 10 currently traveling the road 12” & [0082]: “For this purpose, preferably first a minimum quality M to be met is ascertained, which can be carried out at any arbitrary point in time and, for example, also prior to step S2b. The minimum quality M makes it possible, in the manner described above in general terms, to establish, as a function of different circumstances, what quality G can realistically be expected for the current driving scenario and/or the general operating situation.” See also [0040] regarding the stored environment data being swarm data and [0059-0060] regarding historical quality of stored environment data for specific regions and driving routes.)
a processor configured to predict a driving path using data from the plurality of data sources, wherein the processor is further configured to carry out the method comprising: (See at least [0038-0039]: “Carrying out the lateral control based on the environment data that has been checked for plausibility (or, in the following case, based on the environment data detected by the vehicle) can comprise that a permissible movement corridor is defined based on the environment data, and/or at least one boundary in the lateral direction that is not to be crossed. For example, roadway markings or roadway boundaries can be ascertained based on the environment data, and these can define a maximum permissible position in the lateral direction for the vehicle. Within the scope of the lateral control, it can accordingly be monitored whether the vehicle is at risk of crossing this boundary, and it is then possible, for example, to counter-steer in a driver-autonomous manner” & [0070]: “Generally speaking, the control device 14 may include at least one processor device, and in particular a computer processor. This processor device can execute program instructions which, for example, are stored in a memory device (likewise not shown) of the control device 14. The execution of these program instructions can prompt the control device to carry out or prompt all the functions and/or steps and/or measures described herein.”)
identifying a current environmental scenario from the plurality of environmental scenario via the sensor data; (See at least [0054]: “Location information for the path section situated ahead can be ascertained from a currently detected position of the vehicle and known detection ranges of the vehicle sensors. It is possible, in general, within the scope of the present invention, for example, to determine a considered roadway section that is situated ahead, for example as to whether the detected environment data is detected within a range of 0 to 20 m, 0 to 40 m, or 0 to 60 m ahead of the vehicle (in the driving direction). Environment data having identical location information can then be ascertained from the stored environment data, and this data can be compared to the detected environment data (situated ahead) along the driving route situated ahead, and in particular within a defined path section situated, for example, 0 to 20 m, or 0 to 40 m ahead” & [0074]: “If the currently considered vehicle 10 is driving along the road 12, the currently detected environment data can, in each case, be ascertained by the detection device 16 or control device 14. Likewise, however, it is possible for the stored environment data of those vehicles to be ascertained from the memory device 16 which previously traveled the road 12 in this region (that is, in the same route section). Relevant environment data can be ascertained, for example, based on the covered routes R and/or a vehicle position, which should agree as much as possible with the position and route R of the vehicle 10 currently traveling the road 12.”)
determining, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources; (See at least [0077-0083]: “In a step S2, the stored environment data is read out or requested by the control device 14 as a function of a current location of the vehicle 10. Thereafter, at least one, or also arbitrary combinations of the plausibility measures described hereafter can be carried out. Initially, it can optionally be ascertained whether a route R of those vehicles which generated the corresponding stored environment data agrees with the route R situated ahead of the currently considered vehicle 10. For this purpose, information about route R can be stored during the detection of the stored environment data as an integral part of the stored environment data and can be ascertained by the control device 14. A current route R can, for example, be ascertained based on known image evaluation algorithms, in particular as a center line between the left and right roadway boundaries LF, RF. The comparison between a route R situated ahead according to stored environment data (or swarm data) and a detected route R situated ahead is carried out in FIG. 2 in step S2a. When sufficient agreement is established, the environment data can be assessed as being plausible…For example, mathematical comparison methods, such as a method of the individual squared errors or a general correlation determination, can be applied to ascertain an agreement (or a deviation) between the stored and the detected environment data, and preferably for each individual environment property. As a result, a quality G of the stored environment data can be ascertained, the quality increasing with decreasing deviation from and/or increasing agreement with the actually detected environment data or environment properties. In step S3, this quality G can then be used to carry out the actual plausibility check… In step S4, it is then established whether, in step S3, a comparison between the ascertained quality and of the minimum quality M to be met or, in step S2a, the comparison of the route R, has yielded that the stored environment data is plausible or not.”)
amalgamating the data from the data sources in a weighted manner according to the associated degrees of trustworthiness of the corresponding data source for the current scenario; and (See at least [0055-0060]: “The present disclosure can furthermore provide that a minimum quality is ascertained, which the stored environment data must meet to be considered to be plausible or assessed as being plausible. The minimum quality can be ascertained as a function of at least one of the following features or variables…A region within which the vehicle is situated. This can be a geographical region, which is defined, for example, based on geographical properties and/or map data. However, this can also be a radius of a predetermined number of kilometers. It can be ascertained, for this region, to what extent stored environment data is available and/or what the quality of the environment data in this region. has been (historically)…A historical quality of the stored environment data. In the process, previously ascertained qualities of the environment data can be considered, regardless of a connection with certain regions and, for example, with respect to the currently considered vehicle and/or driving route” & [0062]: “In some examples, depending on the number of considered variables, the minimum quality can be a multi-dimensional characteristic map or can be ascertained based thereon, wherein the dimension is determined, for example, by the number of variables. The variables can be weighted or weightable. The weighting can be flexibility adaptable. In this way, a minimum quality can be ascertained, for example based on a linear combination of possibly weighted individual values, wherein an individual value is, in each case, ascertained as and/or based on an individual variable. The individual value can be a scale value, which is obtained as a function of current values of a corresponding variable (for example, an evaluation scale of the historical quality from 0 (unusable) to 10 (very good)).”)
predicting the driving path from the amalgamated data. (See at least [0044]: “In some examples, the environment data relates to different environment properties, and a dedicated plausibility check is carried out for each environment property. The lateral control of the vehicle can consequently be carried out based on the environment properties that have been checked for plausibility. In contrast, the environment properties that were not checked for plausibility can be detected by the vehicle, so that mixed forms of stored environment properties and detected environment properties can be used for laterally controlling the vehicle, depending on the result of the plausibility check. This increases the safety and the accuracy, since it is checked separately for each property whether the property actually reflects the current state” & [0065]: “For carrying out the lateral control of the vehicle, the control device can provide known control functions, and in particular use the environment data for establishing lateral positions not to be crossed and/or for defining a permissible (virtual) movement corridor. The control device can furthermore compare current lateral positions of the vehicle to a corresponding movement corridor or a general movement boundary and, when there is a risk that this corridor is departed from or this boundary is crossed, can initiate driver-autonomous counter-measures, and in particular driver-autonomous steering interventions.” See also [0083] regarding performing lateral control exclusively based on the stored environment data if it is assessed as plausible.)
Regarding claim 12, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein the data and/or data sources comprise predetermined map data, a road model for estimating a road contour situated ahead, an estimated road contour situated ahead, a detection of a roadway-edge, cluster data specifying earlier vehicle movements, live trajectories of other road-users moving within the current environmental scenario, a maneuver hypothesis of an assistance system of the motor vehicle, steering data pertaining to the motor vehicle, a yaw-rate of the motor vehicle and/or a driving-path prediction of a device for machine learning. (See at least [0045-0048]: “In general, it can be provided that an environment property that is detected by way of the environment data, or that is ascertained therefrom, is one of the following: a left roadway marking or roadway boundary, from the view of the vehicle (that is, in the driving direction); a right roadway marking or roadway boundary, from the view of the vehicle; and/or at least one outer roadway edge.”)
Regarding claim 13, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein the degrees of trustworthiness are inferred at least partially from a predetermined map in which a location-specific degree of trustworthiness has been specified for at least one data source. (See at least [0073]: “In some examples, the shown solution presupposes that several other vehicles have already traveled along a comparable route R in the past along the road 12, and in the process detected the same environment properties LF, RF. It is further presupposed that these vehicles transmitted the corresponding environment data to the vehicle-external memory device 16. There, this environment data can be stored, for example, in a location-dependent manner, as was described above in general terms. It is also possible to store the actually covered driving routes R of the particular vehicles” & [0059-0060]: “A region within which the vehicle is situated. This can be a geographical region, which is defined, for example, based on geographical properties and/or map data. However, this can also be a radius of a predetermined number of kilometers. It can be ascertained, for this region, to what extent stored environment data is available and/or what the quality of the environment data in this region. has been (historically). If the region is characterized by a small amount of stored environment data and/or a low quality of this environment data, the requirements with regard to the minimum quality to be met can be accordingly low since, in general, a high detection uncertainty is to be assumed. On the other hand, the requirements with regard to the minimum quality can be increased when a plurality of stored environment data exists and/or a high quality exists in this region. A historical quality of the stored environment data. In the process, previously ascertained qualities of the environment data can be considered, regardless of a connection with certain regions and, for example, with respect to the currently considered vehicle and/or driving route. If these qualities are comparatively high, the requirements with regard to the minimum quality can be increased, and otherwise they can be reduced.”)
Regarding claim 14, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein environmental data that characterize the current environmental scenario are recorded via environmental sensors of the motor vehicle during the operation of the motor vehicle, and (See at least [0019]: “The environment data can, for example, relate to data regarding a roadway environment and, in particular, to a roadway section situated ahead. The data can indicate and/or denote environment properties described hereafter. The detection advantageously takes place in a driver-autonomous manner, for example by means of detection sensors. For example, a camera, an ultrasonic sensor, a LIDAR sensor or the like can be used as detection sensors.”)
on the basis of said environmental data the degrees of trustworthiness are ascertained dynamically, at least partially. (See at least [0075]: “Thereafter, the stored environment data is compared to the currently detected environment data. Specifically, each of the considered environment properties LF, RF is individually compared to the corresponding stored environment properties. If it is established in the process that the stored environment data, and more precisely, environment properties, do not correspond to those currently detected, which can be established by the control device 14 by carrying out corresponding comparison and evaluation steps, the stored environment data can be assessed as not being plausible. Instead, the currently detected environment data, and preferably, exclusively the detected environment data, can then be used for the lateral control. If, in contrast, plausibility is established, the stored environment data and preferably, exclusively the stored environment data, can be used for the lateral control.”)
Regarding claim 15, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein a distance, starting from a current position of the motor vehicle is ascertained as a function of the environmental scenario and is used as data for predicting the driving path. (See at least [0028]: “When referring to environment data in the plural, this does not necessarily mean that a multitude of environment properties have to be detected. In some examples, it is also possible to only detect one environment property which, however, is continuously detected as a function of the location and/or along a path situated ahead. Since this location-dependent environment property is thus updated multiple times when the path is traveled, a corresponding multitude of individual environment data is also obtained while the path is being traveled (for example, data about an environment property present at each considered location or path section situated ahead).”)
Regarding claim 16, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein only those data sources and/or data whose degree of trustworthiness corresponds to at least a predetermined minimum degree of trustworthiness are amalgamated. (See at least [0055-0062]: “The present disclosure can furthermore provide that a minimum quality is ascertained, which the stored environment data must meet to be considered to be plausible or assessed as being plausible. The minimum quality can be ascertained as a function of at least one of the following features or variables…A region within which the vehicle is situated. This can be a geographical region, which is defined, for example, based on geographical properties and/or map data. However, this can also be a radius of a predetermined number of kilometers. It can be ascertained, for this region, to what extent stored environment data is available and/or what the quality of the environment data in this region. has been (historically). If the region is characterized by a small amount of stored environment data and/or a low quality of this environment data, the requirements with regard to the minimum quality to be met can be accordingly low since, in general, a high detection uncertainty is to be assumed. On the other hand, the requirements with regard to the minimum quality can be increased when a plurality of stored environment data exists and/or a high quality exists in this region. A historical quality of the stored environment data. In the process, previously ascertained qualities of the environment data can be considered, regardless of a connection with certain regions and, for example, with respect to the currently considered vehicle and/or driving route. If these qualities are comparatively high, the requirements with regard to the minimum quality can be increased, and otherwise they can be reduced…In some examples, depending on the number of considered variables, the minimum quality can be a multi-dimensional characteristic map or can be ascertained based thereon, wherein the dimension is determined, for example, by the number of variables. The variables can be weighted or weightable. The weighting can be flexibility adaptable. In this way, a minimum quality can be ascertained, for example based on a linear combination of possibly weighted individual values, wherein an individual value is, in each case, ascertained as and/or based on an individual variable. The individual value can be a scale value, which is obtained as a function of current values of a corresponding variable (for example, an evaluation scale of the historical quality from 0 (unusable) to 10 (very good)).”)
Regarding claim 17, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein for at least some of the data, an uncertainty is ascertained, and the data are weighted in accordance with their uncertainties, so that a greater uncertainty results in a lower weighting. (See at least [0059-0062]: “A region within which the vehicle is situated. This can be a geographical region, which is defined, for example, based on geographical properties and/or map data. However, this can also be a radius of a predetermined number of kilometers. It can be ascertained, for this region, to what extent stored environment data is available and/or what the quality of the environment data in this region. has been (historically). If the region is characterized by a small amount of stored environment data and/or a low quality of this environment data, the requirements with regard to the minimum quality to be met can be accordingly low since, in general, a high detection uncertainty is to be assumed. On the other hand, the requirements with regard to the minimum quality can be increased when a plurality of stored environment data exists and/or a high quality exists in this region…In some examples, depending on the number of considered variables, the minimum quality can be a multi-dimensional characteristic map or can be ascertained based thereon, wherein the dimension is determined, for example, by the number of variables. The variables can be weighted or weightable. The weighting can be flexibility adaptable. In this way, a minimum quality can be ascertained, for example based on a linear combination of possibly weighted individual values, wherein an individual value is, in each case, ascertained as and/or based on an individual variable. The individual value can be a scale value, which is obtained as a function of current values of a corresponding variable (for example, an evaluation scale of the historical quality from 0 (unusable) to 10 (very good)).”)
Regarding claim 18, Münning discloses all the limitations of claim 11 as discussed above.
Münning additionally discloses:
wherein objects in the current environment of the motor vehicle that are relevant for guidance of the motor vehicle are selected based on the predicted driving path. (See at least [0038]: “In some examples, a vehicle can generally be laterally controlled in a driver-autonomous manner. In particular, the aforementioned control can take place for this purpose within a permissible movement corridor, including potential driver-autonomous steering interventions. Known control algorithms can be employed so as to ascertain an impermissible lateral movement of the vehicle based on current movement variables and, if necessary, to initiate (driver-autonomous) counter-measures based thereon. Carrying out the lateral control based on the environment data that has been checked for plausibility (or, in the following case, based on the environment data detected by the vehicle) can comprise that a permissible movement corridor is defined based on the environment data, and/or at least one boundary in the lateral direction that is not to be crossed. For example, roadway markings or roadway boundaries can be ascertained based on the environment data, and these can define a maximum permissible position in the lateral direction for the vehicle.”)
Regarding claim 19, Münning discloses:
A driving assistance system for a motor vehicle, comprising: (See at least [0066]: “Turning to FIG. 1, the drawing shows a vehicle 10, which is driving along a road 12 (or also path) in the driving direction F. The vehicle 10 comprises a system 1 according to some aspects of the present disclosure. The system 1 comprises a control device 14, which is designed as a single control unit in the shown example. The control unit 14 is wirelessly connected to a vehicle-external server, and more precisely, to a cloud server 16, by way of a mobile communication link.”)
an interface that captures sensor data from one or more sensors of the motor vehicle; (See at least [0067]: “The system 1 may also include a detection device 15, which is a camera sensor in the shown example. The camera sensor is arranged in the vehicle 10 so as to detect a road section situated ahead, and to output image data of the road according to a detection frequency. This image data can be evaluated in the camera sensor itself, or by the control device 14, and more particularly such that predetermined environment properties are ascertained therein.”)
a memory that stores a database associating each of a plurality of data sources in each of a plurality of environmental scenarios with a degree of trustworthiness; and (See at least [0064]: “Generally speaking, the control device can thus be configured to read out, request and/or receive the stored environment data, in particular by or from a vehicle-external memory device. For checking the plausibility of the stored environment data, a comparison of any of the type described above can be carried out, for example, and/or a quality of the stored environment data, and preferably also a minimum quality to be met, can be ascertained by the control device” & [0073-0074]: “In some examples, the shown solution presupposes that several other vehicles have already traveled along a comparable route R in the past along the road 12, and in the process detected the same environment properties LF, RF. It is further presupposed that these vehicles transmitted the corresponding environment data to the vehicle-external memory device 16. There, this environment data can be stored, for example, in a location-dependent manner, as was described above in general terms. It is also possible to store the actually covered driving routes R of the particular vehicles. If the currently considered vehicle 10 is driving along the road 12, the currently detected environment data can, in each case, be ascertained by the detection device 16 or control device 14. Likewise, however, it is possible for the stored environment data of those vehicles to be ascertained from the memory device 16 which previously traveled the road 12 in this region (that is, in the same route section). Relevant environment data can be ascertained, for example, based on the covered routes R and/or a vehicle position, which should agree as much as possible with the position and route R of the vehicle 10 currently traveling the road 12” & [0082]: “For this purpose, preferably first a minimum quality M to be met is ascertained, which can be carried out at any arbitrary point in time and, for example, also prior to step S2b. The minimum quality M makes it possible, in the manner described above in general terms, to establish, as a function of different circumstances, what quality G can realistically be expected for the current driving scenario and/or the general operating situation.” See also [0040] regarding the stored environment data being swarm data and [0059-0060] regarding historical quality of stored environment data for specific regions and driving routes.)
a processor that predicts a driving path using data from the plurality of data sources, wherein the processor is configured to: (See at least [0038-0039]: “Carrying out the lateral control based on the environment data that has been checked for plausibility (or, in the following case, based on the environment data detected by the vehicle) can comprise that a permissible movement corridor is defined based on the environment data, and/or at least one boundary in the lateral direction that is not to be crossed. For example, roadway markings or roadway boundaries can be ascertained based on the environment data, and these can define a maximum permissible position in the lateral direction for the vehicle. Within the scope of the lateral control, it can accordingly be monitored whether the vehicle is at risk of crossing this boundary, and it is then possible, for example, to counter-steer in a driver-autonomous manner” & [0070]: “Generally speaking, the control device 14 may include at least one processor device, and in particular a computer processor. This processor device can execute program instructions which, for example, are stored in a memory device (likewise not shown) of the control device 14. The execution of these program instructions can prompt the control device to carry out or prompt all the functions and/or steps and/or measures described herein.”)
a current environmental scenario from the plurality of environmental scenarios, (See at least [0054]: “Location information for the path section situated ahead can be ascertained from a currently detected position of the vehicle and known detection ranges of the vehicle sensors. It is possible, in general, within the scope of the present invention, for example, to determine a considered roadway section that is situated ahead, for example as to whether the detected environment data is detected within a range of 0 to 20 m, 0 to 40 m, or 0 to 60 m ahead of the vehicle (in the driving direction). Environment data having identical location information can then be ascertained from the stored environment data, and this data can be compared to the detected environment data (situated ahead) along the driving route situated ahead, and in particular within a defined path section situated, for example, 0 to 20 m, or 0 to 40 m ahead” & [0074]: “If the currently considered vehicle 10 is driving along the road 12, the currently detected environment data can, in each case, be ascertained by the detection device 16 or control device 14. Likewise, however, it is possible for the stored environment data of those vehicles to be ascertained from the memory device 16 which previously traveled the road 12 in this region (that is, in the same route section). Relevant environment data can be ascertained, for example, based on the covered routes R and/or a vehicle position, which should agree as much as possible with the position and route R of the vehicle 10 currently traveling the road 12.”)
determine, via the database and for the current environmental scenario, the degree of trustworthiness of each of the data sources, (See at least [0077-0083]: “In a step S2, the stored environment data is read out or requested by the control device 14 as a function of a current location of the vehicle 10. Thereafter, at least one, or also arbitrary combinations of the plausibility measures described hereafter can be carried out. Initially, it can optionally be ascertained whether a route R of those vehicles which generated the corresponding stored environment data agrees with the route R situated ahead of the currently considered vehicle 10. For this purpose, information about route R can be stored during the detection of the stored environment data as an integral part of the stored environment data and can be ascertained by the control device 14. A current route R can, for example, be ascertained based on known image evaluation algorithms, in particular as a center line between the left and right roadway boundaries LF, RF. The comparison between a route R situated ahead according to stored environment data (or swarm data) and a detected route R situated ahead is carried out in FIG. 2 in step S2a. When sufficient agreement is established, the environment data can be assessed as being plausible…For example, mathematical comparison methods, such as a method of the individual squared errors or a general correlation determination, can be applied to ascertain an agreement (or a deviation) between the stored and the detected environment data, and preferably for each individual environment property. As a result, a quality G of the stored environment data can be ascertained, the quality increasing with decreasing deviation from and/or increasing agreement with the actually detected environment data or environment properties. In step S3, this quality G can then be used to carry out the actual plausibility check… In step S4, it is then established whether, in step S3, a comparison between the ascertained quality and of the minimum quality M to be met or, in step S2a, the comparison of the route R, has yielded that the stored environment data is plausible or not.”)
amalgamate the data from the data sources in a weighted manner according to the associated degrees of trustworthiness, and (See at least [0055-0060]: “The present disclosure can furthermore provide that a minimum quality is ascertained, which the stored environment data must meet to be considered to be plausible or assessed as being plausible. The minimum quality can be ascertained as a function of at least one of the following features or variables…A region within which the vehicle is situated. This can be a geographical region, which is defined, for example, based on geographical properties and/or map data. However, this can also be a radius of a predetermined number of kilometers. It can be ascertained, for this region, to what extent stored environment data is available and/or what the quality of the environment data in this region. has been (historically)…A historical quality of the stored environment data. In the process, previously ascertained qualities of the environment data can be considered, regardless of a connection with certain regions and, for example, with respect to the currently considered vehicle and/or driving route” & [0062]: “In some examples, depending on the number of considered variables, the minimum quality can be a multi-dimensional characteristic map or can be ascertained based thereon, wherein the dimension is determined, for example, by the number of variables. The variables can be weighted or weightable. The weighting can be flexibility adaptable. In this way, a minimum quality can be ascertained, for example based on a linear combination of possibly weighted individual values, wherein an individual value is, in each case, ascertained as and/or based on an individual variable. The individual value can be a scale value, which is obtained as a function of current values of a corresponding variable (for example, an evaluation scale of the historical quality from 0 (unusable) to 10 (very good)).”)
predict the driving path from the amalgamated data. (See at least [0044]: “In some examples, the environment data relates to different environment properties, and a dedicated plausibility check is carried out for each environment property. The lateral control of the vehicle can consequently be carried out based on the environment properties that have been checked for plausibility. In contrast, the environment properties that were not checked for plausibility can be detected by the vehicle, so that mixed forms of stored environment properties and detected environment properties can be used for laterally controlling the vehicle, depending on the result of the plausibility check. This increases the safety and the accuracy, since it is checked separately for each property whether the property actually reflects the current state” & [0065]: “For carrying out the lateral control of the vehicle, the control device can provide known control functions, and in particular use the environment data for establishing lateral positions not to be crossed and/or for defining a permissible (virtual) movement corridor. The control device can furthermore compare current lateral positions of the vehicle to a corresponding movement corridor or a general movement boundary and, when there is a risk that this corridor is departed from or this boundary is crossed, can initiate driver-autonomous counter-measures, and in particular driver-autonomous steering interventions.” See also [0083] regarding performing lateral control exclusively based on the stored environment data if it is assessed as plausible.)
Regarding claim 20, Münning discloses all the limitations of claim 19 as discussed above.
Münning additionally discloses:
A motor vehicle, comprising: environmental sensorics for recording environmental data that characterize an environmental scenario of the motor vehicle; and the assistance system according to claim 19. (See at least [0021]: “Environment data can be output data of sensors for environment detection or be ascertained based on such output data (or output signals). For example, the environment data can be data about predetermined environment properties, wherein these environment properties are ascertained from image data (as output data) of a camera sensor. This ascertainment can, in general, be carried out by a control device or control unit of the vehicle, as the storage of the detected environment data can be. In principle, any method steps or method measures described herein can be carried out by a control device of the vehicle, as will be described in more detail hereafter.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIKKI MARIE M MOLINA whose telephone number is (571)272-5180. The examiner can normally be reached M-F, 9am-6pm PT.
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/NIKKI MARIE M MOLINA/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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