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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 14, 2025 is considered by
the examiner.
Claim Objections
Claim 26 is objected to because of the following informalities: In line 4 of claim 26, the word “thresold” is misspelled and needs to be corrected to “threshold”.
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 11 and 18-19 are rejected under 35 U.S.C. 101 because the claim invention is directed
toward an abstract idea with significantly more.
Regarding claim 11,
101 Analysis – Step 1
Claim 11 is directed toward a method for automatic hand detection on a steering wheel of a vehicle which consists of the steps of determining a parameter for evaluating a safety relevance of a situation and carrying out the hand detection on the basis of either a machine learning or a model-based algorithm on the basis of the parameter (a process). Therefore, claim 11 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to
determine whether they recite subject matter that falls within one of the follow groups of abstract
ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental
processes.
Independent claim 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 automatic improved hand detection on a steering wheel of a vehicle, comprising:
determining a parameter for evaluating a safety relevance of a situation;
and carrying out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining”, and “carrying out”, in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the 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, therefore since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, 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 integration of the abstract idea into a practical application, there are no additional limitations that amount to significantly more.
Dependent claims 12-17 and 27-30 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim 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.
Claim 12 uses the limitation of “carried out on the basis of the model-based algorithm if the parameter exceeds a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 13 uses the limitation of “carried out on the basis of the machine learning algorithm if the parameter is below a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 14 uses the limitations of “obtaining an item of surroundings information of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 15 uses the limitations of “obtaining an item of status information relating to a status of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 16 uses the limitations of “determining an item of interior information of the vehicle” and “using the item of interior information for the hand detection”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 17 uses the limitation of “carried out on the basis of the item of interior information and the machine learning algorithm if the parameter exceeds a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 27 uses the limitation of “carried out on the basis of the machine learning algorithm if the parameter is below a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 28 uses the limitations of “obtaining an item of surroundings information of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 29 uses the limitations of “obtaining an item of surroundings information of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 30 uses the limitations of “obtaining an item of status information relating to a status of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Regarding claim 18,
101 Analysis – Step 1
Claim 18 is directed toward a non-transitory storage medium comprising instructions executed on a computer, processor, or programmable hardware component which provides the processes of determining a parameter for evaluating a safety relevance of a situation and carrying out the hand detection on the basis of either a machine learning or a model-based algorithm on the basis of the parameter (a machine). Therefore, claim 18 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to
determine whether they recite subject matter that falls within one of the follow groups of abstract
ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental
processes.
Independent claim 18 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 18 recites:
A non-transitory storage medium comprising instructions, that when executed on a computer, a processor, or a programmable hardware component provide:
determining a parameter for evaluating a safety relevance of a situation;
and carrying out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining”, and “carrying out”, in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the 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, a computer, processor, or programmable hardware is provided for the purpose of implementing the abstract ideas. However, these features are mere computers and do not give practical application to the determining and carrying out steps.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 18 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, there are no additional limitations that amount to significantly more.
Regarding claim 19,
101 Analysis – Step 1
Claim 19 is directed toward a device which improves the recognition of hand detection on a steering wheel of a vehicle which comprises interfaces for communication and a data processing circuit which controls the interfaces to determine a parameter for evaluating a safety relevance of a situation and carry out the hand detection on the basis of either a machine learning or a model-based algorithm on the basis of the parameter (a machine). Therefore, claim 19 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to
determine whether they recite subject matter that falls within one of the follow groups of abstract
ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental
processes.
Independent claim 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 device for improving recognition of hand detection on a steering wheel of a vehicle, comprising:
one or more interfaces for communication;
and a data processing circuit which is configured to control the one or more interfaces and to:
determine a parameter for evaluating a safety relevance of a situation;
and carry out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining”, and “carrying out”, in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the 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, interfaces and a data processing circuit are provided for the purpose of implementing the abstract ideas. However, these features are mere computers and do not give practical application to the determining and carrying out steps.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, 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 integration of the abstract idea into a practical application, there are no additional limitations that amount to significantly more.
Dependent claims 21-26 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim 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.
Claim 21 uses the limitation of “carried out on the basis of the model-based algorithm if the parameter exceeds a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 22 uses the limitation of “carried out on the basis of the machine learning algorithm if the parameter is below a threshold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 23 uses the limitation of “obtaining an item of surroundings information of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 24 uses the limitation of “obtaining an item of status information relating to a status of the vehicle” and “determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 25 uses the limitation of “determining an item of interior information of the vehicle” and “using the item of interior information for the hand detection”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 26 uses the limitation of “carried out on the basis of the item of interior information and the machine learning algorithm if the parameter exceeds a thresold value”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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 11-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Barth, et al. (U.S. Patent No. 11535280).
Regarding claim 11, Barth, et al. teaches: A method for automatic improved hand detection on a steering wheel of a vehicle, comprising: determining a parameter for evaluating a safety relevance of a situation; (Step (100), Fig. 1, Col. 20, lines 58-65: "FIG. 1 illustrates a method for determining an estimate of the capability of a vehicle driver to take over control of the vehicle. [...] The method comprises a step (100) in which one or more estimation parameters are determined, as indicated by reference numeral (102) in FIG. 1 [safety parameter for situation]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].")
and carrying out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter (Step (102), Fig. 1, Col. 20, line 65 to Col. 1, lines 1-3: "Each of the estimation parameters (102) represents an influencing factor for the capability of the vehicle driver to take over control of the vehicle. In step (104), an estimate (106) of the capability of the vehicle driver to take over control of the vehicle is determined on the basis of the estimation parameters (102) [hand detection carried based on parameter linked to machine learning algorithm]." ; Col. 22, lines 32-41: "It is understood that the estimation parameters can at least partially be determined on the basis of image data of the sensors (11), (11'), wherein for example the distances of the respective parts of the body relative to their desired positions and/or states is determined from the image data by known techniques of image processing [hand techniques for parameter]. As one example, machine learning models may be trained and then used for detecting the body parts and extracting the desired information on this basis, which can also be carried out using trained machine learning models [machine learning algorithm]." ; Col. 21, lines 23-32: "Due to the mounting position of the sensor (11') the corresponding image data provided by the sensor (11') captures the steering wheel (14) and also a region around the steering wheel (14). This allows detecting hands of the driver (13) when they are not grasping the steering wheel (14). The hands are indicated schematically by reference numeral (17) in FIG. 2. A time required for the hands (17) to move to the steering wheel (14) can be determined on the basis of the minimum distance d between the hands (17) and the steering wheel (14) [hand detection procedure]" ; Col. 10, lines 34-38: "Other information, for example the drowsiness of the driver can be extracted from one or more images of the driver, wherein the drowsiness and/or gestures can be estimated, e.g., using a machine learning-algorithm [machine learning-algorithm application].").
Regarding claim 12, Barth, et al. teaches: The method of claim 11, wherein the hand detection is carried out on the basis of the model-based algorithm if the parameter exceeds a threshold value (Col. 13, lines 14-24: "As another aspect, many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 27, lines 7-12: "Alternatively, a weighted or unweighted average of the different results or a subset of thereof with a high rating (above a threshold) can be determined and used to determine the position and orientation of the model (50) matching with the steering wheel (14) [hand detection parameter exceeds threshold value].").
Regarding claim 13, Barth, et al. teaches: The method of claim 11, wherein the hand detection is carried out on the basis of the machine learning algorithm if the parameter is below a threshold value (Col. 13, lines 14-24: "…many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 6, lines 39-42: "Another example is to use the time period required to reach the reference object and/or position. If the time period is below a threshold the driver can be expected to have partial or full control of the vehicle [hand detection parameter is below a threshold value].").
Regarding claim 14, Barth, et al. teaches: The method of claim 11, further comprising: obtaining an item of surroundings information of the vehicle; (Col. 22, lines 8-12: "As another estimation parameter the time period (t3)-(t2) required by the driver (13) to bring his head to an orientation towards a relevant traffic event in the surrounding of the vehicle (112) and to understand the situation (114) is determined [surroundings information from vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Regarding claim 15, Barth, et al. teaches: The method of claim 11, further comprising: obtaining an item of status information relating to a status of the vehicle; (Col. 10, lines 55-57: "It is understood that a given estimation parameter can for example represent both an operational status of the vehicle and distraction information for the driver [status of vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Regarding claim 16, Barth, et al. teaches: The method of claim 11, further comprising: determining an item of interior information of the vehicle; (Col. 13, lines 14-17: "As another aspect, many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [determining interior information of vehicle]")
and using the item of interior information for the hand detection (Col. 13, lines 20-24: "…an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [interior information used for hand detection]").
Regarding claim 17, Barth, et al. teaches: The method of claim 16, wherein the hand detection is carried out on the basis of the item of interior information and the machine learning algorithm if the parameter exceeds a threshold value (Col. 13, lines 14-24: "As another aspect, many different estimation parameters can be determined from, e.g., one image of the passenger cabin [interior information] by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 27, lines 7-12: "Alternatively, a weighted or unweighted average of the different results or a subset of thereof with a high rating (above a threshold) can be determined and used to determine the position and orientation of the model (50) matching with the steering wheel (14) [hand detection parameter exceeds threshold value].").
Regarding claim 18, Barth, et al. teaches: A non-transitory storage medium comprising instructions, that when executed on a computer, a processor, or a programmable hardware component provide: (Col. 19, lines 43-47: "According to yet another aspect a non-transitory computer readable medium is provided. The medium comprises instructions, which when executed by said processing unit, cause the processing unit to carry out the method according to one of the embodiments disclosed herein [non-transitory storage medium with instructions executed by processor].")
determining a parameter for evaluating a safety relevance of a situation; (Col. 23, lines 10-14: "It is understood that the processing unit (16) is configured to determine the time instances (t5) and/or (t6) relative to the time instance (t1), e.g., in the form of differences or time periods (t5-t1), (t6-t1), (t6-t1), thereby providing the estimate of the driver's capability to take over control of the vehicle [methodology of determining value of safety parameter]." ; Col. 23, lines 25-29: "The estimate can be determined in a very short amount of time, in particular in real time, so that the estimate is available effectively at the first-time instance (t1). The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].")
and carrying out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter (Col. 22, lines 32-41: "…the estimation parameters can at least partially be determined on the basis of image data of the sensors (11), (11'), wherein for example the distances of the respective parts of the body relative to their desired positions and/or states is determined from the image data by known techniques of image processing [hand techniques for parameter]. As one example, machine learning models may be trained and then used for detecting the body parts and extracting the desired information on this basis, which can also be carried out using trained machine learning models [machine learning algorithm]." ; Col. 21, lines 23-32: "Due to the mounting position of the sensor (11') the corresponding image data provided by the sensor (11') captures the steering wheel (14) and also a region around the steering wheel (14). This allows detecting hands of the driver (13) when they are not grasping the steering wheel (14). The hands are indicated schematically by reference numeral (17) in FIG. 2. A time required for the hands (17) to move to the steering wheel (14) can be determined on the basis of the minimum distance d between the hands (17) and the steering wheel (14) [hand detection procedure]" ; Col. 10, lines 34-38: "…the drowsiness of the driver can be extracted from one or more images of the driver, wherein the drowsiness and/or gestures can be estimated, e.g., using a machine learning-algorithm [machine learning-algorithm application].").
Regarding claim 19, Barth, et al. teaches: A device for improving recognition of hand detection on a steering wheel of a vehicle, comprising: one or more interfaces for communication; (Col. 23, lines 22-24: "The processing unit (16) has a communication interface for providing the estimate to other processing systems of the vehicle [interface for communication].")
and a data processing circuit which is configured to control the one or more interfaces and to: (Col. 21, lines 4-11: "The system comprises a data processing unit (16) [data processing circuit], which is connected to sensors (11), (11'). The sensors (11), (11') can be configured as cameras for capturing image data of a portion of the passenger cabin of the vehicle [controlling interface for gathering data about hand position].")
determine a parameter for evaluating a safety relevance of a situation; (Col. 23, lines 10-14: "It is understood that the processing unit (16) is configured to determine the time instances (t5) and/or (t6) relative to the time instance (t1), e.g., in the form of differences or time periods (t5-t1), (t6-t1), (t6-t1), thereby providing the estimate of the driver's capability to take over control of the vehicle [methodology of determining value of safety parameter]." ; Col. 23, lines 25-29: "The estimate can be determined in a very short amount of time, in particular in real time, so that the estimate is available effectively at the first-time instance (t1). The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].")
and carry out the hand detection on the basis of at least one of a machine learning algorithm and a model-based algorithm on the basis of the parameter (Col. 22, lines 32-41: "…the estimation parameters can at least partially be determined on the basis of image data of the sensors (11), (11'), wherein for example the distances of the respective parts of the body relative to their desired positions and/or states is determined from the image data by known techniques of image processing [hand techniques for parameter]. As one example, machine learning models may be trained and then used for detecting the body parts and extracting the desired information on this basis, which can also be carried out using trained machine learning models [machine learning algorithm]." ; Col. 21, lines 23-32: "Due to the mounting position of the sensor (11') the corresponding image data provided by the sensor (11') captures the steering wheel (14) and also a region around the steering wheel (14). This allows detecting hands of the driver (13) when they are not grasping the steering wheel (14). The hands are indicated schematically by reference numeral (17) in FIG. 2. A time required for the hands (17) to move to the steering wheel (14) can be determined on the basis of the minimum distance d between the hands (17) and the steering wheel (14) [hand detection procedure]" ; Col. 10, lines 34-38: "…the drowsiness of the driver can be extracted from one or more images of the driver, wherein the drowsiness and/or gestures can be estimated, e.g., using a machine learning-algorithm [machine learning-algorithm application].").
Regarding claim 20, Barth, et al. teaches: A vehicle comprising the device of claim 19 (Col. 21, lines 10-12: "The sensor (11) has an orientation towards the driver seat (15), wherein a vehicle driver (13) is sitting on the driver seat (15) [driver sitting within vehicle]").
Regarding claim 21, Barth, et al. teaches: The device of claim 19, wherein the hand detection is carried out on the basis of the model-based algorithm if the parameter exceeds a threshold value (Col. 13, lines 14-24: "…many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 27, lines 7-12: "Alternatively, a weighted or unweighted average of the different results or a subset of thereof with a high rating (above a threshold) can be determined and used to determine the position and orientation of the model (50) matching with the steering wheel (14) [hand detection parameter exceeds threshold value].").
Regarding claim 22, Barth, et al. teaches: The device of claim 19, wherein the hand detection is carried out on the basis of the machine learning algorithm if the parameter is below a threshold value (Col. 13, lines 14-24: "…many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 6, lines 39-42: "Another example is to use the time period required to reach the reference object and/or position. If the time period is below a threshold the driver can be expected to have partial or full control of the vehicle [hand detection parameter is below a threshold value].").
Regarding claim 23, Barth, et al. teaches: The device of claim 19, further comprising: obtaining an item of surroundings information of the vehicle; (Col. 22, lines 8-12: "As another estimation parameter the time period (t3)-(t2) required by the driver (13) to bring his head to an orientation towards a relevant traffic event in the surrounding of the vehicle (112) and to understand the situation (114) is determined [surroundings information from vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].")
Regarding claim 24, Barth, et al. teaches: The device of claim 19, further comprising: obtaining an item of status information relating to a status of the vehicle; (Col. 10, lines 55-57: "It is understood that a given estimation parameter can for example represent both an operational status of the vehicle and distraction information for the driver [status of vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Regarding claim 25, Barth, et al. teaches: The device of claim 19, further comprising: determining an item of interior information of the vehicle; (Col. 13, lines 14-17: "As another aspect, many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [determining interior information of vehicle])
and using the item of interior information for the hand detection (Col. 13, lines 20-24: "…an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [interior information used for hand detection]")
Regarding claim 26, Barth, et al. teaches: The device of claim 19, wherein the hand detection is carried out on the basis of the item of interior information and the machine learning algorithm if the parameter exceeds a thresold value (Col. 13, lines 14-24: "…many different estimation parameters can be determined from, e.g., one image of the passenger cabin [interior information] by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 27, lines 7-12: "Alternatively, a weighted or unweighted average of the different results or a subset of thereof with a high rating (above a threshold) can be determined and used to determine the position and orientation of the model (50) matching with the steering wheel (14) [hand detection parameter exceeds threshold value].").
Regarding claim 27, Barth, et al. teaches: The method of claim 12, wherein the hand detection is carried out on the basis of the machine learning algorithm if the parameter is below a threshold value (Col. 13, lines 14-24: "…many different estimation parameters can be determined from, e.g., one image of the passenger cabin by using known or novel image-processing algorithms [...] For example, an estimation parameter can be formed by classification data about the spatial relationship between the hands and the steering element (e.g., relative position or distance), wherein a plurality of position categories can be defined for the spatial relationship [algorithm and hand detection procedure]" ; Col. 6, lines 39-42: "Another example is to use the time period required to reach the reference object and/or position. If the time period is below a threshold the driver can be expected to have partial or full control of the vehicle [hand detection parameter is below a threshold value].").
Regarding claim 28, Barth, et al. teaches: The method of claim 12, further comprising: obtaining an item of surroundings information of the vehicle; (Col. 22, lines 8-12: "As another estimation parameter the time period (t3)-(t2) required by the driver (13) to bring his head to an orientation towards a relevant traffic event in the surrounding of the vehicle (112) and to understand the situation (114) is determined [surroundings information from vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Regarding claim 29, Barth, et al. teaches: The method of claim 13, further comprising: obtaining an item of surroundings information of the vehicle; (Col. 22, lines 8-12: "As another estimation parameter the time period (t3)-(t2) required by the driver (13) to bring his head to an orientation towards a relevant traffic event in the surrounding of the vehicle (112) and to understand the situation (114) is determined [surroundings information from vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of surroundings information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Regarding claim 30, Barth, et al. teaches: The method of claim 12, further comprising: obtaining an item of status information relating to a status of the vehicle; (Col. 10, lines 55-57: "It is understood that a given estimation parameter can for example represent both an operational status of the vehicle and distraction information for the driver [status of vehicle].")
and determining the parameter for evaluating the safety relevance on the basis of the obtained item of status information (Col. 23, lines 27-29: "The estimate can also be regarded as a prediction when the driver (13) will be in control of the vehicle [evaluation of safety parameter]." ; Col. 23, lines 30-34: "An automated driving function of the vehicle can be modified, in particular activated or deactivated on the basis of the estimate. In this way, the ability of the driver (13) to perform control actions is taken into account and safe operation of the vehicle is ensured [parameter used for safety relevance].").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barth, et al. (U.S. Patent No. 11878708) teaches systems and techniques for monitoring an occupant in a vehicle interior based on detected body points.
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/TORRENCE S MARUNDA II/ Examiner, Art Unit 3663
/ANGELA Y ORTIZ/ Supervisory Patent Examiner, Art Unit 3663
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