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 .
Status of Claims
This Office Action is in response to the Applicant’s Response filed on June 26th, 2025. Claims 1-20 are presently pending and are presented for examination.
Response to Amendment
In response to Applicant’s response dated June 26th, 2025, Examiner withdraws the previous 112(f) claim interpretations; withdraws the 35 U.S.C. 112(b) claim rejections; and maintains the 35 U.S.C. 103 prior art rejections.
Response to Arguments
Applicant argues that Brautigam does not disclose determining at least a first current ADS—related operational condition comprising one or both states of vehicle surroundings and states of the vehicle. Examiner respectfully disagrees, the ADS-related operating condition is a broad parameter and Examiner is equating this operating condition to determining whether the autonomous mode is currently active as determined at [0050]. The mode of the vehicle is equal to the state of the vehicle. Additionally, when using the statement of “one or both” Examiner may use the broader term of or; therefore, only requiring one of the following conditions to be required. Therefore, Examiner maintains that the prior art discloses determining a first-current Ads-related operational condition.
Additionally, applicant argues that the prior art does not teach the start or end time point one or both of applicable to and valid for the triggering event. Examiner respectfully disagrees as Brautigam clearly states that the time window is selected based on when the triggering event occurs, a time window inherently includes a start and an end time point. Since the time window is selected specifically based on the triggering event the values are applicable to said event. Therefore, Examiner maintains the corresponding rejection.
All of the remaining arguments are essentially the same as those addressed above and/or below and are unpersuasive for at least the same reasons.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 6, 7, 10, 15, 16, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as anticipated by US-20200210336 (hereinafter, “Brautigam”).
Regarding claim 1 Brautigam discloses a method performed by a buffer segment length adjusting system for dynamically adjusting an event segment length of data stored in an event recording buffer of an Automated Driving System, ADS, of a vehicle (see at least [0001]; “The present disclosure relates to a method for situation-dependent storage of data of a system and a recording system for storing data of a system in the event of the occurrence of a predetermined trigger event,” and [0013]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured and/or transferred to the read-only memory in said time window or time range. This means that the time window is adapted or parameterized dynamically depending on a respective trigger event”), the method comprising:
obtaining sensor data of one or more sensors onboard the vehicle (see at least [0021]; “acquiring data of the system by reading sensors and/or controllers at time t0”);
identifying, upon one or both of the sensor data rendering fulfilment and a state of a software of the ADS rendering fulfilment of event recording triggering criteria, conditions of a triggering event underlying the fulfilment (see at least [0050]; “At a time t1, a trigger event occurs,” and [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” based upon the operational state of the vehicle a triggering event is identified);
determining at least a first current ADS-related operational condition comprising one or both of states of vehicle surroundings and states of the vehicle (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation,” determining that the vehicle is originating in the autonomous mode corresponds to an ADS-related operational condition comprising the state of the vehicle); and
setting a respective start time point and end time point of the event recording buffer one or both of applicable to and valid for the triggering event based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” the time range, which would inherently include a start and an end point is selected based on the triggering event, the triggering event can include a change in operation state, therefore the time range is based on a current operational state of the vehicle as well as the triggering event).
Regarding claim 6 Brautigam discloses all of the limitations of claim 1. Additionally, Brautigam discloses wherein the setting a respective start time point and end time point of the event recording buffer comprises deriving one or both of the start time point and the end time point from predefined start and end time candidates pre-associated with differing triggering event conditions and ADS-related operational conditions (see at least [0030]; “In some embodiments, it is provided that the predetermined trigger event is predetermined by a list of predetermined trigger events, It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 7 Brautigam discloses all of the limitations of claim 1. Additionally, Brautigam discloses wherein the setting a respective start time point and end time point of the event recording buffer comprises assessing at least a portion of the obtained sensor data for identifying one or more events underlying the triggering event, a time range of the one or more events forming basis for the one or both of the start and the end time points (see at least [0030]; “the predetermined trigger event is predetermined by a list of predetermined trigger events. It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” sensors are used to determine a triggering event, which corresponds to a predetermined time range, and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 10 Brautigam discloses a buffer segment length adjusting system for dynamically adjusting an event segment length of data stored in an event recording buffer of an Automated Driving System, ADS, of a vehicle, the buffer segment length adjusting system (see at least [0001]; “The present disclosure relates to a method for situation-dependent storage of data of a system and a recording system for storing data of a system in the event of the occurrence of a predetermined trigger event,” and [0014]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured and/or transferred to the read-only memory in said time window or time range. This means that the time window is adapted or parameterized dynamically depending on a respective trigger event”) comprising one or more processors configured to:
obtain sensor data of one or more sensors onboard the vehicle (see at least [0021]; “acquiring data of the system by reading sensors and/or controllers at time t0”);
identify, upon one or both of the sensor data rendering fulfilment and a state of a software of the ADS rendering fulfilment of event recording triggering criteria, conditions of a triggering event underlying the fulfilment (see at least [0050]; “At a time t1, a trigger event occurs,” and [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” based upon the operational state of the vehicle a triggering event is identified”);
determine at least a first current ADS-related operational condition comprising one or both of states of vehicle surroundings and states of the vehicle (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation,” in order to detect a change in the vehicle mode, it inherently determines at least a first current operation condition); and
set a respective start time point and end time point of the event recording buffer one or both applicable to and valid for the triggering event based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” the time range, which would inherently include a start and an end point is selected based on the triggering event, the triggering event can include a change in operation state, therefore the time range is based on a current operational state of the vehicle as well as the triggering event).
Regarding claim 15 Brautigam discloses all of the limitations of claim 10. Additionally, Brautigam discloses wherein the one or more processors is configured to derive the one of the start time point and the end time point from predefined start and end time candidates pre-associated with differing triggering event conditions and ADS-related operational conditions (see at least [0030]; “In some embodiments, it is provided that the predetermined trigger event is predetermined by a list of predetermined trigger events, It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 16 Brautigam discloses all of the limitations of claim 10. Additionally, Brautigam discloses wherein the one or more processors is configured to assess at least a portion of the obtained sensor data for identifying one or more events underlying the triggering event, a time range of one or both of the one or more events forming basis for the start and the end time points (see at least [0030]; “the predetermined trigger event is predetermined by a list of predetermined trigger events. It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” sensors are used to determine a triggering event, which corresponds to a predetermined time range, and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 19 Brautigam discloses all of the limitations of claim 10. Additionally, Brautigam discloses wherein the buffer segment length adjusting system is comprised in a vehicle (see at least [0002]; “Systems, such as vehicles, may be equipped with data recorders”).
Regarding claim 20 Brautigam discloses a computer storage medium storing a computer program configured to cause a computer or a processor to perform a method for dynamically adjusting an event segment length of data stored in an event recording buffer of an Automated Driving System, ADS, of a vehicle (see at least [0001]; “The present disclosure relates to a method for situation-dependent storage of data of a system and a recording system for storing data of a system in the event of the occurrence of a predetermined trigger event,” and [0014]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured and/or transferred to the read-only memory in said time window or time range. This means that the time window is adapted or parameterized dynamically depending on a respective trigger event”), the method comprising:
obtaining sensor data of one or more sensors onboard the vehicle (see at least [0021]; “acquiring data of the system by reading sensors and/or controllers at time t0”);
identifying, upon one or both of the sensor data rendering fulfilment and a state of a software of the ADS rendering fulfilment of event recording triggering criteria, conditions of a triggering event underlying the fulfilment (see at least [0050]; “At a time t1, a trigger event occurs,” and [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” based upon the operational state of the vehicle a triggering event is identified”);
determining at least a first current ADS-related operational condition comprising one or both of states of vehicle surroundings and states of the vehicle (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation,” in order to detect a change in the vehicle mode, it inherently determines at least a first current operation condition); and
setting a respective start time point and end time point of the event recording buffer one or both of applicable to and valid for the triggering event based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0053]; “when a trigger event is detected at time t1, such as the changes in the operating state of the vehicle from the autonomous operation to the manual operation, a time window is selected in which data is transmitted to the volatile memory and finally to the read-only memory, depending on the trigger event,” the time range, which would inherently include a start and an end point is selected based on the triggering event, the triggering event can include a change in operation state, therefore the time range is based on a current operational state of the vehicle as well as the triggering event).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-4, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Brautigam in view of US-12087102 (hereinafter, “Bates”).
Regarding claim 2 Brautigam discloses all of the limitations of claim 1. Additionally, Brautigam discloses wherein the setting a respective start time point and end time point of the event recording buffer comprises one or both of:
setting the start time point based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0013]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured…a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which the data collection ends, to the respective trigger events”)…and
setting the end time point based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0013]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured…a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which the data collection ends, to the respective trigger events”).
Brautigam does not disclose setting the start time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold…
setting the end time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold.
Bates, in the same field of endeavor, teaches setting the start time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold (see at least [Col. 21, lines 48-61]; “such collision data may include, other than an indication that a collision has been detected, a time of onset of the detected collision and/or a time of offset of the detected collision (e.g., a time of collision initiation and/or cessation). The vehicle computing system may represent such times as timestamps. The collision data may also include one or more confidence values associated with one or more onset and/or offset times (e.g., confidence values associated with the channels on which the onset/offset times are based),” and [Col. 6, lines 10-22]; “the post-processing component 350 may also, or instead, utilize confidence values in the collision determination. For example, the post-processing component 350 may include in a count of positive collision determinations those determinations that are associated with channels (e.g., audio data sensor channels) having a confidence value that is equal to or greater than a threshold confidence value while not including in the count of positive collision determinations those determinations that are associated with channels having a confidence value less than the threshold confidence value,” the onset time corresponds to Applicant’s start time point)…
setting the end time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold (see at least [Col. 21, lines 48-61] “such collision data may include, other than an indication that a collision has been detected, a time of onset of the detected collision and/or a time of offset of the detected collision (e.g., a time of collision initiation and/or cessation). The vehicle computing system may represent such times as timestamps. The collision data may also include one or more confidence values associated with one or more onset and/or offset times (e.g., confidence values associated with the channels on which the onset/offset times are based),” and [Col. 6, lines 10-22]; “he post-processing component 350 may also, or instead, utilize confidence values in the collision determination. For example, the post-processing component 350 may include in a count of positive collision determinations those determinations that are associated with channels (e.g., audio data sensor channels) having a confidence value that is equal to or greater than a threshold confidence value while not including in the count of positive collision determinations those determinations that are associated with channels having a confidence value less than the threshold confidence value,” the offset time corresponds to Applicant’s end time point).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the confidence value of Bates. One of ordinary skill in the art would have been motivated to make this modification for the benefit of more accurately detecting and locating low impact and/or low energy collisions and therefore facilitate safer navigation through the environment (see at least [Col. 3, lines 1-30]).
Regarding claim 3 Brautigam in view of Bates renders obvious all of the limitations of claim 2. Additionally, Brautigam discloses wherein the setting a respective start time point and end time point of the event recording buffer comprises deriving one or both of the start time point and the end time point from predefined start and end time candidates pre-associated with differing triggering event conditions and ADS-related operational conditions (see at least [0030]; “In some embodiments, it is provided that the predetermined trigger event is predetermined by a list of predetermined trigger events, It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 4 Brautigam in view of Bates renders obvious all of the limitations of claim 2. Additionally, Brautigam discloses wherein the setting a respective start time point and end time point of the event recording buffer comprises assessing at least a portion of the obtained sensor data for identifying one or more events underlying the triggering event, a time range of the one or more events forming basis for the one or both of the start and the end time points (see at least [0030]; “the predetermined trigger event is predetermined by a list of predetermined trigger events. It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” sensors are used to determine a triggering event, which corresponds to a predetermined time range, and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 11 Brautigam discloses all of the limitations of claim 10. Additionally, Brautigam discloses wherein the one or more processors is configured to one or both:
set the start time point based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0013]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured…a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which the data collection ends, to the respective trigger events”)…and
set the end time point based on the triggering event conditions and the at least first current ADS-related operational condition (see at least [0013]; “it is provided that the time window or time range is selected according to a respective trigger event, i.e., to a trigger, wherein data, in particular operating data of a system, are to be captured…a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which the data collection ends, to the respective trigger events”).
Brautigam does not disclose set the start time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold…
set the end time point… provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold.
Bates, in the same field of endeavor, teaches set the start time point…provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold (see at least [Col. 21, lines 48-61]; “such collision data may include, other than an indication that a collision has been detected, a time of onset of the detected collision and/or a time of offset of the detected collision (e.g., a time of collision initiation and/or cessation). The vehicle computing system may represent such times as timestamps. The collision data may also include one or more confidence values associated with one or more onset and/or offset times (e.g., confidence values associated with the channels on which the onset/offset times are based),” and [Col. 6, lines 10-22]; “he post-processing component 350 may also, or instead, utilize confidence values in the collision determination. For example, the post-processing component 350 may include in a count of positive collision determinations those determinations that are associated with channels (e.g., audio data sensor channels) having a confidence value that is equal to or greater than a threshold confidence value while not including in the count of positive collision determinations those determinations that are associated with channels having a confidence value less than the threshold confidence value,” the onset time corresponds to Applicant’s start time point)…
set the end time point…provided an estimated confidence level corresponding to the start time point exceeds a start time point confidence threshold (see at least [Col. 21, lines 48-61uch collision data may include, other than an indication that a collision has been detected, a time of onset of the detected collision and/or a time of offset of the detected collision (e.g., a time of collision initiation and/or cessation). The vehicle computing system may represent such times as timestamps. The collision data may also include one or more confidence values associated with one or more onset and/or offset times (e.g., confidence values associated with the channels on which the onset/offset times are based),” and [Col. 6, lines 10-22]; “he post-processing component 350 may also, or instead, utilize confidence values in the collision determination. For example, the post-processing component 350 may include in a count of positive collision determinations those determinations that are associated with channels (e.g., audio data sensor channels) having a confidence value that is equal to or greater than a threshold confidence value while not including in the count of positive collision determinations those determinations that are associated with channels having a confidence value less than the threshold confidence value,” the offset time corresponds to Applicant’s end time point).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the confidence value of Bates. One of ordinary skill in the art would have been motivated to make this modification for the benefit of more accurately detecting and locating low impact and/or low energy collisions and therefore facilitate safer navigation through the environment (see at least [Col. 3, lines 1-30]).
Regarding claim 12 Brautigam in view of Bates renders obvious all of the limitations of claim 11. Additionally, Brautigam discloses wherein the one or more processors is configured to derive the one of the start time point and the end time point from predefined start and end time candidates pre-associated with differing triggering event conditions and ADS-related operational conditions (see at least [0030]; “In some embodiments, it is provided that the predetermined trigger event is predetermined by a list of predetermined trigger events, It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Regarding claim 13 Brautigam in view of Bates renders obvious all of the limitations of claim 11. Additionally, Brautigam discloses wherein the one or more processors is configured to assess at least a portion of the obtained sensor data for identifying one or more events underlying the triggering event, a time range of one or both of the one or more events forming basis for the start and the end time points (see at least [0030]; “the predetermined trigger event is predetermined by a list of predetermined trigger events. It is provided that at least one value of a sensor of the system and/or a state of a component of the system is assigned by the list to each trigger event of the list,” sensors are used to determine a triggering event, which corresponds to a predetermined time range, and [0013]; “a list may be provided that assigns a time window, i.e., a first time at which data acquisition starts and a second time at which data collection ends,” the triggering event may be related an ADS condition as stated at [0053]).
Claim(s) 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Brautigam and Bates, as applied to claims 2 and 11, in view of US-11386325 (hereinafter, “Srinivasan”).
Regarding claim 5 Brautigam in view of Bates renders obvious all of the limitations of claim 2. Brautigam does not disclose wherein the setting a respective start time point and end time point of the event recording buffer comprises feeding the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model.
Srinivasan, in the same field of endeavor, teaches wherein the setting a respective start time point and end time point of the event recording buffer comprises feeding the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model trained (see at least [Col. 8, lines 37-58]; “The machine learning model implemented by the vehicle device may be an ensemble machine learning model, which generally refers to pooling of inferences from multiple machine learning models to identify the occurrence or non-occurrence of an event. Further, the machine learning model implemented by the vehicle device may be a modular machine learning model. For example, the modular machine learning model may include a modifiable series of layers that can be segmented. The segmentation of the modular machine learning model can enable the independent tuning and retraining of defined thin layers of the modular machine learning model. Further, the machine learning model may be a multi-modal machine learning model. For example, the multi-modal machine learning model may receive sensor data from a plurality of sources (e.g., a camera, an accelerometer, a location detection system, etc.). Further, the machine learning model may be a stateful machine learning model. For example, the stateful machine learning model may store start and end times for events in order to make inferences about the start and end times of particular events,” and [Col. 9, lines 23-48]; “A fifth layer of the example ensemble machine learning model may include a stateful machine learning model to predict a start time and/or end time of the distraction of the user,” and [Col. 19, lines 10-22]; When implemented as a multi-modal machine learning model, the machine learning model can obtain input data (e.g., sensor data) from multiple data sources associated with the vehicle device. For example, the machine learning model can obtain input data associated with a camera sensor, an accelerometer, a location detection sensor (e.g., a GPS sensor), and/or any other sensor. The machine learning model can obtain the input data from the multiple data sources and utilize the input data to generate an output for the multi-modal machine learning model.).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam as modified by Bates with the machine learning prediction model of Srinivasan. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing the accuracy of the detection of particular events [Col. 2, lines 1-9]).
Regarding claim 14 Brautigam in view of Bates renders obvious all of the limitations of claim 11. Brautigam does not disclose wherein the one or more processors is configured to feed the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model.
Srinivasan, in the same field of endeavor, teaches wherein the one or more processors is configured to feed the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model (see at least [Col. 8, lines 37-58]; “The machine learning model implemented by the vehicle device may be an ensemble machine learning model, which generally refers to pooling of inferences from multiple machine learning models to identify the occurrence or non-occurrence of an event. Further, the machine learning model implemented by the vehicle device may be a modular machine learning model. For example, the modular machine learning model may include a modifiable series of layers that can be segmented. The segmentation of the modular machine learning model can enable the independent tuning and retraining of defined thin layers of the modular machine learning model. Further, the machine learning model may be a multi-modal machine learning model. For example, the multi-modal machine learning model may receive sensor data from a plurality of sources (e.g., a camera, an accelerometer, a location detection system, etc.). Further, the machine learning model may be a stateful machine learning model. For example, the stateful machine learning model may store start and end times for events in order to make inferences about the start and end times of particular events,” and [Col. 9, lines 23-48]; “A fifth layer of the example ensemble machine learning model may include a stateful machine learning model to predict a start time and/or end time of the distraction of the user,” and [Col. 19, lines 10-22]; When implemented as a multi-modal machine learning model, the machine learning model can obtain input data (e.g., sensor data) from multiple data sources associated with the vehicle device. For example, the machine learning model can obtain input data associated with a camera sensor, an accelerometer, a location detection sensor (e.g., a GPS sensor), and/or any other sensor. The machine learning model can obtain the input data from the multiple data sources and utilize the input data to generate an output for the multi-modal machine learning model.).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam as modified by Bates with the machine learning prediction model of Srinivasan. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing the accuracy of the detection of particular events [Col. 2, lines 1-9]).
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Brautigam, as applied to claims 1 and 10, in view of US-11386325 (hereinafter, “Srinivasan”).
Regarding claim 8 Brautigam discloses all of the limitations of claim 1. Brautigam does not disclose wherein the setting a respective start time point and end time point of the event recording buffer comprises feeding the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model.
Srinivasan, in the same field of endeavor, teaches wherein the setting a respective start time point and end time point of the event recording buffer comprises feeding the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model (see at least [Col. 8, lines 37-58]; “The machine learning model implemented by the vehicle device may be an ensemble machine learning model, which generally refers to pooling of inferences from multiple machine learning models to identify the occurrence or non-occurrence of an event. Further, the machine learning model implemented by the vehicle device may be a modular machine learning model. For example, the modular machine learning model may include a modifiable series of layers that can be segmented. The segmentation of the modular machine learning model can enable the independent tuning and retraining of defined thin layers of the modular machine learning model. Further, the machine learning model may be a multi-modal machine learning model. For example, the multi-modal machine learning model may receive sensor data from a plurality of sources (e.g., a camera, an accelerometer, a location detection system, etc.). Further, the machine learning model may be a stateful machine learning model. For example, the stateful machine learning model may store start and end times for events in order to make inferences about the start and end times of particular events,” and [Col. 9, lines 23-48]; “A fifth layer of the example ensemble machine learning model may include a stateful machine learning model to predict a start time and/or end time of the distraction of the user,” and [Col. 19, lines 10-22]; When implemented as a multi-modal machine learning model, the machine learning model can obtain input data (e.g., sensor data) from multiple data sources associated with the vehicle device. For example, the machine learning model can obtain input data associated with a camera sensor, an accelerometer, a location detection sensor (e.g., a GPS sensor), and/or any other sensor. The machine learning model can obtain the input data from the multiple data sources and utilize the input data to generate an output for the multi-modal machine learning model.).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the machine learning prediction model of Srinivasan. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing the accuracy of the detection of particular events [Col. 2, lines 1-9]).
Regarding claim 17 Brautigam discloses all of the limitations of claim 10. Brautigam does not disclose wherein the one or more processors is configured to feed the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model.
Srinivasan, in the same field of endeavor, teaches wherein the one or more processors is configured to feed the triggering event conditions and the at least first current ADS-related operational condition as input to a machine learning model (see at least [Col. 8, lines 37-58]; “The machine learning model implemented by the vehicle device may be an ensemble machine learning model, which generally refers to pooling of inferences from multiple machine learning models to identify the occurrence or non-occurrence of an event. Further, the machine learning model implemented by the vehicle device may be a modular machine learning model. For example, the modular machine learning model may include a modifiable series of layers that can be segmented. The segmentation of the modular machine learning model can enable the independent tuning and retraining of defined thin layers of the modular machine learning model. Further, the machine learning model may be a multi-modal machine learning model. For example, the multi-modal machine learning model may receive sensor data from a plurality of sources (e.g., a camera, an accelerometer, a location detection system, etc.). Further, the machine learning model may be a stateful machine learning model. For example, the stateful machine learning model may store start and end times for events in order to make inferences about the start and end times of particular events,” and [Col. 9, lines 23-48]; “A fifth layer of the example ensemble machine learning model may include a stateful machine learning model to predict a start time and/or end time of the distraction of the user,” and [Col. 19, lines 10-22]; When implemented as a multi-modal machine learning model, the machine learning model can obtain input data (e.g., sensor data) from multiple data sources associated with the vehicle device. For example, the machine learning model can obtain input data associated with a camera sensor, an accelerometer, a location detection sensor (e.g., a GPS sensor), and/or any other sensor. The machine learning model can obtain the input data from the multiple data sources and utilize the input data to generate an output for the multi-modal machine learning model.).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the machine learning prediction model of Srinivasan. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing the accuracy of the detection of particular events [Col. 2, lines 1-9]).
Claim(s) 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Brautigam, as applied to claims 1 and 10 above, in view of US-20200250901 (hereinafter, “Golov”).
Regarding claim 9 Brautigam discloses all of the limitations of claim 1. Brautigam does not disclose further comprising: determining at least a first current buffer-related constraint, wherein the setting a respective start time point and end time point of the event recording buffer comprises setting the one or both of the start time point and the end time point additionally based on the at least first current buffer-related constraint.
Golov, in the same field of endeavor, teaches further comprising: determining at least a first current buffer-related constraint, wherein the setting a respective start time point and end time point of the event recording buffer comprises setting the one or both of the start time point and the end time point additionally based on the at least first current buffer-related constraint (Examiner Note: applicant defines the buffer-related constraint as the following [Page 11, lines 17-30]; “the buffer-related constraints may be represented by any feasible one or more constraints applicable in view of the event recording buffer 3”)(see at least [0031]; “the accident data cyclic buffer is configured to buffer sensor data for a period of time (e.g., 30 seconds immediately before the accident signal,” the 30 second buffer corresponds to Applicant’s buffer-related constraint, which is used to adjust the start time of data collection”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the recording buffer of Golov. One of ordinary skill in the art would have been motivated to make this modification for the benefit of reviewing sensor data to assist in potentially determining the cause of an accident [0003).
Regarding claim 18 Brautigam discloses all of the limitations of claim 10. Brautigam does not disclose one or more processors configured to: determine at least a first current buffer-related constraint, wherein the one or more processors is configured to set the one or both of the start time point and the end time point additionally based on the at least first current buffer-related constraint.
Golov, in the same field of endeavor, teaches one or more processors configured to: determine at least a first current buffer-related constraint, wherein the one or more processors is configured to set the one or both of the start time point and the end time point additionally based on the at least first current buffer-related constraint (Examiner Note: applicant defines the buffer-related constraint as the following [Page 11, lines 17-30]; “the buffer-related constraints may be represented by any feasible one or more constraints applicable in view of the event recording buffer 3”)(see at least [0031]; “the accident data cyclic buffer is configured to buffer sensor data for a period of time (e.g., 30 seconds immediately before the accident signal,” the 30 second buffer corresponds to Applicant’s buffer-related constraint, which is used to adjust the start time of data collection”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the data storage system of Brautigam with the recording buffer of Golov. One of ordinary skill in the art would have been motivated to make this modification for the benefit of reviewing