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 amendments filed on October 31, 2025. Claims 1-10 and 12-16 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 27, 2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Response to Amendments
In response to applicant’s amendments, filed April 28, 2025, the Examiner maintains the previous 35 U.S.C. 103 rejections.
Response to Arguments
Applicant's arguments filed October 31, 2025, have been fully considered but they are not persuasive.
Regarding Applicant’s arguments pertaining to the instant application being conducted in real-time (see page 8 of instant arguments), the Examiner is unpersuaded. The instant application does not require that the performance of the invention occur in real-time. The application of Simoudis still reads on the claims, as written. The arguments that the process must be conducted in real-time are considered irrelevant and moot. As such, the Examiner is unpersuaded and maintains the corresponding rejections.
Regarding Applicant’s arguments pertaining to the core of the invention being a “proactive, predictive arbitration system …” (see page 8-9 of instant arguments), the Examiner is unpersuaded. The claims do not require active, real-time, or proactive determinations for collecting, analyzing, and disseminating data. The claims only require data collection for vehicle data at some point between a current and future time periods. As such, these arguments are considered irrelevant, and the Examiner is unpersuaded and maintains the corresponding rejections.
Further, regarding Applicant’s arguments of the key features of the instant application, and with regards to the application of Simoudis and Bin-Nun (see page 9 of instant arguments), the Examiner is unpersuaded. Simoudis teaches that the system determines when, how much, and what type of data to transmit, and further teaches that the data can be acquired and orchestrated in real-time (see at least Paragraphs [0063], [0077]-[0078], [0084], of Simoudis). Regarding the limitation of a “predicted scenario”, Simoudis teaches that models may be created to determine a specific scenario for which to generate and retrieve specific vehicle data in order to generate the predictive model, such as a scenario for when a vehicle is making a right-hand turn to merge from a city street onto a freeway during a cloudy morning (see at least Paragraphs [0106]-[0107], [0154] of Simoudis). In other words, Simoudis teaches a method for determining a predictive model by making predictions against real data (i.e. a multi-faceted scene or scenario). Bin-Nun further teaches that the model for creating an operational design domain for parameters for improving autonomous features, which can be based on time of day, weather, type of road, etc. (see at least Paragraphs [0046], [0049], [0070] of Bin-Nun). As such, both Simoudis and Bin-Nun, teach that the system uses real world data to predict future behaviors. As such, the Examiner is unpersuaded and maintains the corresponding rejections.
Regarding Applicant’s arguments pertaining to the priority scheme (see Page 9-10 of instant arguments), the Examiner is unpersuaded. Bin-Nun teaches a method for evaluating performance of an autonomous vehicle, where the developers can prioritize the development of AV features based on similarity and validity of operational design domain parameters, where each parameter can be compared against prior validated data sets and then be used to refine and tune the development of certain features when compared to each other (see at least Paragraphs [0049], [0062] of Bin-Nun). In other words, Simoudis, in view of Bin-Nun, teaches the features of a priority scheme for development of AV features based on the score of each feature relative to the other features. As such, the Examiner is unpersuaded and maintains the corresponding rejections.
Regarding Applicant’s arguments pertaining to the priority scheme being an active input as well as the “prioritization” as an outcome of an offline analysis (see Page 11 of instant arguments), the Examiner is unpersuaded. The claims do not require active, real-time, or proactive determinations for collecting, analyzing, and disseminating data, or prioritizing development, nor do the claims require when the analysis is performed (e.g., at the vehicle, remotely, etc.). The claims only require that the priority scheme be based on the development priority for each feature. As such, these arguments are considered irrelevant, and the Examiner is unpersuaded and maintains the corresponding rejections.
In response to applicant's arguments (see page 11 of instant arguments), that the Examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Simoudis teaches a method for managing the collection, transmission, and analysis of vehicle data based on transmission priorities for autonomous vehicles, and Bin-Nun further teaches that data relating to autonomous vehicle features are collected and compared to historical and real data to determine when to prioritize the development of a collective feature. Simoudis and Bin-Nun are in similar fields of endeavor, and both relate to the collection of vehicle data. One of ordinary skill would combine the teachings without hindsight in order to prioritize the development of autonomous vehicle features.
The remaining arguments are essentially the same as those addresses above and/or below and are unpersuasive for essentially the same reasons. Therefore, the corresponding rejections are maintained.
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.
Claims 1-2, 7-9, and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2020/0364953 A1, to Simoudis (hereinafter referred to as Simoudis; previously of record), in view of U.S. Patent Publication No. 2019/0163185 A1, to Bin-Nun (hereinafter referred to as Bin-Nun; previously of record).
As per Claim 1, Simoudis discloses the features of a method for prioritized activation of sensor hardware of a vehicle for development, evaluation, and/or testing of automated driving system (ADS) features (e.g. Paragraphs [0053], [0129], [0321]; Claim 1; where a data orchestrator for managing autonomous vehicle data stores vehicle data and determines when to transmit vehicle data to a remote entity, using models to develop predictive models for vehicle performance and capabilities), the method comprising:
obtaining data indicative of a set of platform constraints of the vehicle (e.g. Paragraphs [0081], [0084], [0088] [0101]; where the vehicle may register in the cloud data and may be identified by the capabilities of each sensor (i.e. platform constraints));
obtaining data indicative of a set of requirements for each of a plurality of ADS features being developed, evaluated, and/or tested (e.g. Paragraphs [0077]-[0078], [0084], [0103]; where the vehicle may register may register in the cloud data lake and may be identified by its vehicle ID, the various data-applications it uses, the sensors it uses, the capabilities of each sensors; and where a transmission flag may be set by the local/vehicle application may indicate whether requested data is available for transmission (i.e. data indicative of a set of requirements); and where the system may process data from the vehicle to the cloud applications in order to apply the data for use cases such as vehicle design, test, and manufacturing, to include improvement of autonomous features and capabilities for a vehicle or a fleet of vehicles, and the data may be communicated to third parties, such as to the original equipment manufacturer (OEM) for learning; and where the system can determine certain types of vehicles or certain types of data to look for based on a request sent by the system manager, to provide data management and predictive models applied to an entire lifecycle of a vehicle or type of vehicles to include the vehicle’s mobility, and automated features in order to improve overall performance);
obtaining data indicative of a priority scheme for the plurality of the ADS features (e.g. Paragraphs [0065]-[0066]; where the data orchestrator may determine which of the data or which portion is to be transmitted to which data centers and/or entities and when to transmit this data. For example, some of the autonomous vehicle data (e.g., a first portion of data or package of data) may need to be communicated immediately or when the autonomous vehicle is in motion, whereas other data (e.g., a second portion of data or package of data) may be communicated with autonomous vehicle is stationary (i.e. a priority scheme for autonomous vehicle characteristics to be transmitted)), ‘…’
obtaining data indicative of a predicted traffic scene or traffic scenario, wherein the predicted traffic scene or traffic scenario defines one or more conditions that the vehicle is expected to be exposed to at a future moment in time (e.g. Paragraphs [0069], [0144], [0147], [0176]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions); and where the data may include data acquired from the vehicle, such as fleet data operating environment data, transportation data, etc. and may include spatio-temporal point measurements of an environment (i.e. the vehicle is gathering environmental data), predicts data values over time, and that the vehicle data may be augmented or combined with traffic congestion and weather data to determine vehicle arrival times (i.e. the system predicts a future condition, such as an arrival time) based on existing environmental factors);
generating, based on the platform constraints, the set of requirements, the priority scheme and the predicted traffic scene or traffic scenario, an arbitration signal indicative of a sensor hardware activation and a resource allocation of the platform of the vehicle for at least one of the plurality of the ADS features (e.g. Paragraphs [0103], [0115], [0150], [0179]; where a data transmission scheme is generated to determine the type of data requested to be transmitted to a remote entity for processing; and where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle (i.e. a signal is generated to look for specific sensor data and type of data collected); and where the data transmission scheme is generated to determine the type, amount, and time that the data should be collected, and that data captured may be aligned with respect to time, where the user may specify a time window for which data is to be aligned); and
activating, during at least a portion of the future time period, the sensor hardware for data collection in accordance with the generated arbitration signal (e.g. Paragraphs [0103]-[0104], [0107], [0150], [0152]; where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle and push the request to a queue in the vehicle to gather the requested data; and the data orchestrator associated with the vehicle may transmit the requested vehicle data back to the requesting application (i.e. a signal is sent to activate or look at specific sensors for data collection); and where the user or system owner may specify that data collected from which sensors or sources are to be aligned and may specify the time window during which the data is to be collected).
Simoudis fails to disclose every feature of wherein the priority scheme comprises a predefined development priority of each ADS feature relative to the other ADS features of the plurality of ADS features.
However, Bin-Nun teaches a method for evaluating performance of an autonomous vehicle, where the developers can prioritize the development of AV features based on similarity and validity of operational design domain parameters (e.g. Paragraph [0049]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for managing vehicle data in the system of Simoudis, with the feature of having a development priority in the system of Bin-Nun, in order to inform further development goals (see at least Paragraph [0049] of Bin-Nun).
As per Claim 2, Simoudis, in view of Bin-Nun, teaches the features of Claim 1, and Simoudis further discloses the features of wherein obtaining data indicative of the predicted traffic scene or traffic scenario comprises:
obtaining route data indicative of a geographical position of the vehicle at the future point in time (e.g. Paragraphs [0144], [0147]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions at an arrival or future location); and where the data collected may include geolocation data and time series data such as spatio-temporal point measurements of an environment);
obtaining scene data indicative of at least one of a weather forecast, a time of day, one or more traffic conditions, and one or more environmental conditions at the geographical position at the future moment in time (e.g. Paragraph [0147]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions)); and
predicting the traffic scene or traffic scenario in the surrounding environment of the vehicle that the vehicle is expected to be exposed to at the future moment in time based on the obtained route data and scene data (e.g. Paragraph [0147]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions)).
As per Claim 7, Simoudis, in view of Bin-Nun, teaches the features of Claim 1, and Simoudis further discloses the features of further comprising:
storing, during a time period, a first set of sensor data generated by the activated sensor hardware (e.g. Paragraphs [0064], [0097], [0115]; where raw data is aggregated across a time duration, and sent to remote entity as a package for storage in a local repository; and where data in the in-vehicle database may be preserved in storage for a predetermined period of time after it is offloaded to the edge/fog database);
based on the resource allocation indicated by the arbitration signal, performing at least one of: transmitting at least a portion of the first set of sensor data to a remote entity for offline processing (e.g. Paragraph [0065]; where the data orchestrator may determine which of the data or which portion is to be transmitted to which data centers and/or entities and when to transmit this data. For example, some of the autonomous vehicle data (e.g., a first portion of data or package of data) may need to be communicated immediately or when the autonomous vehicle is in motion, whereas other data (e.g., a second portion of data or package of data) may be communicated with autonomous vehicle is stationary (i.e. a priority scheme for autonomous vehicle characteristics to be transmitted));
evaluating, in accordance with the resource allocation, an output of at least one ADS feature using at least a portion of first set of sensor data as input (e.g. Paragraphs [0077], [0132]; where the cloud applications may further process or analyze data transmitted from the autonomous vehicle); and
updating, in accordance with the resource allocation, at least one ADS feature using at least one portion of the first set of sensor data as input (e.g. Paragraphs [0133], [0136]; where the analysis results produced may determine whether the dataset needs to be corrected, and if correction is necessary, correction is performed ; and where an updated predicted model is stored and downloaded to one or more vehicles).
As per Claim 8, Simoudis, in view of Bin-Nun, teaches the features of Claim 7, and Simoudis further discloses the features of further comprising:
storing, during a time period, a second set of sensor data generated by platform-native sensors of the vehicle (e.g. Paragraphs [0064], [0097], [0115]; where raw data is aggregated across a time duration, and sent to remote entity as a package for storage in a local repository; and where data in the in-vehicle database may be preserved in storage for a predetermined period of time after it is offloaded to the edge/fog database);
based on the resource allocation indicated by the arbitration signal, performing at least one of: transmitting at least a portion of the second set of sensor data to a remote entity for offline processing (e.g. Paragraph [0065]; where the data orchestrator may determine which of the data or which portion is to be transmitted to which data centers and/or remote entities and when to transmit this data. For example, some of the autonomous vehicle data (e.g., a first portion of data or package of data) may need to be communicated immediately or when the autonomous vehicle is in motion, whereas other data (e.g., a second portion of data or package of data) may be communicated with autonomous vehicle is stationary (i.e. a priority scheme for autonomous vehicle characteristics to be transmitted));
evaluating, in accordance with the resource allocation, an output of at least one ADS feature using at least a portion of second set of sensor data as input (e.g. Paragraphs [0077], [0132]; where the cloud applications may further process or analyze data transmitted from the autonomous vehicle); and
updating, in accordance with the resource allocation, at least one ADS feature using at least one portion of the second set of sensor data as input (e.g. Paragraphs [0133], [0136]; where the analysis results produced may determine whether the dataset needs to be corrected, and if correction is necessary, correction is performed ; and where an updated predicted model is stored and downloaded to one or more vehicles).
As per Claim 9, Simoudis, in view of Bin-Nun, teaches the features of Claim 1, and Simoudis further discloses the features of wherein the set of platform constraints include at least one of: available power, available computational power, available data storage capacity and available bandwidth for data transmission (e.g. Paragraphs [0063], [0083]-[0084], [0108]; where the fog computing may imply distribution of the communication, computation, and storage resources and services on or in proximity to devices and systems in the control of end-users or end-nodes; and where a node (507) may store metadata about the model parameters, which can include information about the computational resources required to execute a model; and where the vehicle may register may register in the cloud data lake and may be identified by its vehicle ID, the various data-applications it uses, the sensors it uses, the capabilities of each sensors; and where a transmission flag may be set by the local/vehicle application may indicate whether requested data is available for transmission (i.e. data indicative of available power, bandwidth, computation, and storage capacity is determined).
As per Claim 12, Simoudis discloses the features of a non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of an in-vehicle processing system, the one or more programs comprising instructions for performing the method for prioritized activation of sensor hardware of a vehicle for development, evaluation, and/or testing of automated driving system (ADS) features (e.g. Paragraphs [0171], [0321]; Claim 1; where machine-executable code can be stored on an electronic, non-transitory storage unit, such as memory, for providing instructions to a processor for execution; and where a data orchestrator for managing vehicle data stores vehicle data and determines when to transmit vehicle data to a remote entity), the method comprising:
obtaining data indicative of a set of platform constraints of the vehicle (e.g. Paragraph [0081], [0084], [0101]; where the predictive models knowledge base (500) may include rules and predictive models that may specify a data transmission scheme (time on transmission such as a delay time or frequency); and where the vehicle may register in the cloud data and may be identified by the capabilities of each sensor);
obtaining data indicative of a set of requirements for each of a plurality of the ADS features being developed, evaluated (.g. Paragraphs [0077]-[0078], [0084], [0103]; where the vehicle may register may register in the cloud data lake and may be identified by its vehicle ID, the various data-applications it uses, the sensors it uses, the capabilities of each sensors; and where a transmission flag may be set by the local/vehicle application may indicate whether requested data is available for transmission (i.e. data indicative of a set of requirements); and where the system may process data from the vehicle to the cloud applications in order to apply the data for use cases such as vehicle design, test, and manufacturing, to include improvement of autonomous features and capabilities for a vehicle or a fleet of vehicles, and the data may be communicated to third parties, such as to the original equipment manufacturer (OEM) for learning; and where the system can determine certain types of vehicles or certain types of data to look for based on a request sent by the system manager, to provide data management and predictive models applied to an entire lifecycle of a vehicle or type of vehicles to include the vehicle’s mobility, and automated features in order to improve overall performance);
obtaining data indicative of a priority scheme for the plurality of the ADS features (e.g. Paragraph [0065]; where the data orchestrator may determine which of the data or which portion is to be transmitted to which data centers and/or entities and when to transmit this data. For example, some of the autonomous vehicle data (e.g., a first portion of data or package of data) may need to be communicated immediately or when the autonomous vehicle is in motion, whereas other data (e.g., a second portion of data or package of data) may be communicated with autonomous vehicle is stationary (i.e. a priority scheme for autonomous vehicle characteristics to be transmitted)); ‘…’
obtaining data indicative of a predicted traffic scene or traffic scenario, wherein the predicted traffic scene or traffic scenario defines one or more conditions that the vehicle is expected to be exposed to at a future moment in time (e.g. Paragraphs [0069], [0144], [0147], [0176]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions); and where the data may include data acquired from the vehicle, such as fleet data operating environment data, transportation data, etc. and may include spatio-temporal point measurements of an environment (i.e. the vehicle is gathering environmental data), predicts data values over time, and that the vehicle data may be augmented or combined with traffic congestion and weather data to determine vehicle arrival times (i.e. the system predicts a future condition, such as an arrival time) based on existing environmental factors);
generating, based on the platform constraints, the set of requirements, the priority scheme and the predicted traffic scene or traffic scenario, an arbitration signal indicative of a sensor hardware activation and a resource allocation of the platform of the vehicle for at least one of the plurality of the ADS features (e.g. Paragraphs [0103], [0115], [0150], [0179]; where a data transmission scheme is generated to determine the type of data requested to be transmitted to a remote entity for processing; and where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle (i.e. a signal is generated to look for specific sensor data and type of data collected); and where the data transmission scheme is generated to determine the type, amount, and time that the data should be collected, and that data captured may be aligned with respect to time, where the user may specify a time window for which data is to be aligned); and
activating, during at least a portion of the future time period, the sensor hardware for data collection in accordance with the generated arbitration signal (e.g. Paragraphs [0103]-[0104], [0107], [0150], [0152]; where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle and push the request to a queue in the vehicle to gather the requested data; and the data orchestrator associated with the vehicle may transmit the requested vehicle data back to the requesting application (i.e. a signal is sent to activate or look at specific sensors for data collection); and where the user or system owner may specify that data collected from which sensors or sources are to be aligned and may specify the time window during which the data is to be collected).
Simoudis fails to disclose every feature of wherein the priority scheme comprises a predefined development priority of each ADS feature relative to the other ADS features of the plurality of ADS features.
However, Bin-Nun teaches a method for evaluating performance of an autonomous vehicle, where the developers can prioritize the development of AV features based on similarity and validity of operational design domain parameters (e.g. Paragraph [0049]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for managing vehicle data in the system of Simoudis, with the feature of having a development priority in the system of Bin-Nun, in order to inform further development goals (see at least Paragraph [0049] of Bin-Nun).
As per Claim 13, Simoudis discloses the features of a system for prioritized activation of sensor hardware of a vehicle for development, evaluation, and/or testing of automated driving system (ADS) features (e.g. Paragraph [0321]; Claim 1; where a data orchestrator for managing vehicle data stores vehicle data and determines when to transmit vehicle data to a remote entity), the method comprising:
one or more processors (e.g. Paragraph [0087]; where the system may be implemented using one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, configured to receive data and instructions and transmit the data to a storage device) configured to:
obtain data indicative of a set of platform constraints of the vehicle (e.g. Paragraphs [0081], [0084], [0088] [0101]; where the predictive models knowledge base (500) may include rules and predictive models that may specify a data transmission scheme (time of transmission such as a delay time or frequency); and where the vehicle may register in the cloud data and may be identified by the capabilities of each sensor (i.e. platform constraints));
obtain data indicative of a set of requirements for each of a plurality of ADS features being developed, evaluated, and/or tested (e.g. Paragraphs [0077]-[0078], [0084], [0103]; where the vehicle may register may register in the cloud data lake and may be identified by its vehicle ID, the various data-applications it uses, the sensors it uses, the capabilities of each sensors; and where a transmission flag may be set by the local/vehicle application may indicate whether requested data is available for transmission (i.e. data indicative of a set of requirements); and where the system may process data from the vehicle to the cloud applications in order to apply the data for use cases such as vehicle design, test, and manufacturing, to include improvement of autonomous features and capabilities for a vehicle or a fleet of vehicles, and the data may be communicated to third parties, such as to the original equipment manufacturer (OEM) for learning; and where the system can determine certain types of vehicles or certain types of data to look for based on a request sent by the system manager, to provide data management and predictive models applied to an entire lifecycle of a vehicle or type of vehicles to include the vehicle’s mobility, and automated features in order to improve overall performance);
obtain data indicative of a priority scheme for the plurality of the ADS features (e.g. Paragraphs [0065]-[0066]; where the data orchestrator may determine which of the data or which portion is to be transmitted to which data centers and/or entities and when to transmit this data. For example, some of the autonomous vehicle data (e.g., a first portion of data or package of data) may need to be communicated immediately or when the autonomous vehicle is in motion, whereas other data (e.g., a second portion of data or package of data) may be communicated with autonomous vehicle is stationary (i.e. a priority scheme for autonomous vehicle characteristics to be transmitted)), ‘…’
obtain data indicative of a predicted scene or scenario in the surrounding environment of the vehicle that the vehicle is expected to be exposed to at a future moment in time (e.g. Paragraphs [0069], [0144], [0147], [0176]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions); and where the data may include data acquired from the vehicle, such as fleet data operating environment data, transportation data, etc. and may include spatio-temporal point measurements of an environment (i.e. the vehicle is gathering environmental data), predicts data values over time, and that the vehicle data may be augmented or combined with traffic congestion and weather data to determine vehicle arrival times (i.e. the system predicts a future condition, such as an arrival time) based on existing environmental factors);
obtain data indicative of a predicted traffic scene or traffic scenario, wherein the predicted traffic scene or traffic scenario defines one or more conditions that the vehicle is expected to be exposed to at a future moment in time (e.g. Paragraphs [0069], [0144], [0147], [0176]; where data augmentation can be used to combine traffic congestion data with weather data to predict travel time and arrival times during a specific time of day (i.e. predicted or future conditions); and where the data may include data acquired from the vehicle, such as fleet data operating environment data, transportation data, etc. and may include spatio-temporal point measurements of an environment (i.e. the vehicle is gathering environmental data), predicts data values over time, and that the vehicle data may be augmented or combined with traffic congestion and weather data to determine vehicle arrival times (i.e. the system predicts a future condition, such as an arrival time) based on existing environmental factors);
generate, based on the platform constraints, the set of requirements, the priority scheme and the predicted traffic scene or traffic scenario, an arbitration signal indicative of a sensor hardware activation and a resource allocation of the platform of the vehicle for at least one of the plurality of the ADS features (e.g. Paragraphs [0103], [0115], [0150], [0179]; where a data transmission scheme is generated to determine the type of data requested to be transmitted to a remote entity for processing; and where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle (i.e. a signal is generated to look for specific sensor data and type of data collected); and where the data transmission scheme is generated to determine the type, amount, and time that the data should be collected, and that data captured may be aligned with respect to time, where the user may specify a time window for which data is to be aligned); and
activate, during at least a portion of the future time period, the sensor hardware for data collection in accordance with the generated arbitration signal (e.g. Paragraphs [0103]-[0104], [0107], [0150], [0152]; where a requesting application may send to the OEM system associated with a target vehicle, a request indicating the type of data and frequency of such data as needed from the target vehicle, and the OEM may pass the request to the data orchestrator of the respective target vehicle and push the request to a queue in the vehicle to gather the requested data; and the data orchestrator associated with the vehicle may transmit the requested vehicle data back to the requesting application (i.e. a signal is sent to activate or look at specific sensors for data collection); and where the user or system owner may specify that data collected from which sensors or sources are to be aligned and may specify the time window during which the data is to be collected).
Simoudis fails to disclose every feature of wherein the priority scheme comprises a predefined development priority of each ADS feature relative to the other ADS features of the plurality of ADS features.
However, Bin-Nun teaches a method for evaluating performance of an autonomous vehicle, where the developers can prioritize the development of AV features based on similarity and validity of operational design domain parameters (e.g. Paragraph [0049]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for managing vehicle data in the system of Simoudis, with the feature of having a development priority in the system of Bin-Nun, in order to inform further development goals (see at least Paragraph [0049] of Bin-Nun).
As per Claim 14, Simoudis, in view of Bin-Nun, teaches the features of Claim 13, and Simoudis further discloses the features of a vehicle comprising: one or more sensors configured to monitor a surrounding environment of the vehicle; and the system (e.g. Claims 1, 2; where data repository is located on a vehicle for storage and transmission of vehicle-related data).
As per Claim 15, and similarly for Claim 16, Simoudis, in view of Bin-Nun, teaches the features of Claims 1 and 13, respectively, and Simoudis further discloses the features of wherein the plurality of ADS features is a plurality of perception functions of the ADS (e.g. Paragraph [0072]; where the autonomous vehicle stack may comprise perception, data fusion, localization, behavior, control and safety domains, and where the perception can include sensors/detectors, cameras, lidar, radar, GPS, etc.).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Simoudis, in view of Bin-Nun, Claim 1 above, and further in view of U.S. Patent Publication No. 2019/0113920 A1, to Englard, et al (hereinafter referred to as Englard; previously of record), and further in view of U.S. Patent Publication No. 2018/0284755 A1, to Cella, et al (hereinafter referred to as Cella; previously of record).
As per Claim 3, Simoudis, in view of Bin-Nun, teaches the features of Claim 1, but the combination of Simoudis, in view of Bin-Nun, teaches fails to disclose every feature of further comprising: evaluating the traffic predicted scene or traffic scenario in order to determine a score indicative of a potential development gain of using at least a portion of output data generated by the activated sensor hardware at the predicted traffic scene or traffic scenario as input for each of the plurality of the ADS features; wherein generating the arbitration signal further comprises: generating, based on the platform constraints and the set of requirements, the arbitration signal indicative of the sensor hardware activation and a resource allocation of the platform of the vehicle for at least one of the plurality of the ADS features in accordance with the determined score and the priority scheme.
However, Englard, in the same field of endeavor, teaches the features of evaluating the predicted traffic scene or traffic scenario in order to determine a score indicative of a potential development gain of using at least a portion of output data generated by the activated sensor hardware at the predicted traffic scene or traffic scenario as input for each of the plurality of the ADS features.
Englard teaches a method for implementing a self-driving control architecture for a vehicle, where the decision arbiter (108) assigns a score to each maneuver that is output by the SDCA, and the SDCA’s may be used a labels, weights, or scores for a supervised training process (e.g. Paragraphs [0068], [0076], [0085]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun, with the feature of determining a score for development of ADS features in the system of Englard, in order to improve the performance and safety of the autonomous vehicle (see at least Paragraph [0042] of Englard).
Cella, in the same field of endeavor, further teaches the features of wherein generating the arbitration signal further comprises: generating, based on the platform constraints and the set of requirements, the arbitration signal indicative of the sensor hardware activation and a resource allocation of the platform of the vehicle for at least one of the plurality of the ADS features in accordance with the determined score and the priority scheme.
Cella teaches a method for collecting large amounts of data for a vehicle, where a combination of inputs (including selection of what sensors or input sources to turn “on” or “off”) may be performed under the control of machine-based intelligence (e.g. Paragraphs [0212], [0327], [0479], [0508]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun and Englard, with the feature of generating an arbitration signal in the system of Cella, in order to optimize operational parameters for data gathering and analysis (see at least Paragraph [0319] of Cella).
As per Claim 4, Simoudis, in view of Bin-Nun, Englard, and Cella, teaches the features of Claim 3, and Englard further teaches the features of wherein evaluating the predicted traffic scene or traffic scenario is performed using a heuristic algorithm.
Englard teaches a method for implementing a self-driving control architecture for a vehicle, where the self-driving control architecture (SDCA) selector (144) may select from among the candidate decisions (106) using heuristic techniques; and where the motion planner (240) may utilize any suitable types of rules, algorithms, heuristic models, machine learning models or other suitable techniques to make driving decisions based on the perception signals, prediction signals, and mapping and navigation signals (e.g. Paragraphs [0073], [0096]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun, with the feature of using a heuristics algorithm in the system of Englard, in order to select the decisions appropriate to the corresponding conditions and situations (see at least Paragraph [0081] of Englard).
As per Claim 5, Simoudis, in view of Bin-Nun, Englard, and Cella, teaches the features of Claim 3, and Englard further teaches the features of wherein evaluating the predicted traffic scene or traffic scenario is performed using a clustering algorithm.
Englard teaches a method for implementing a self-driving control architecture for a vehicle, where the segmentation module (210) may use predetermined rules or algorithms to define objects, and may use clustering techniques of points that meet a certain criteria (e.g. Paragraph [0089]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun, with the feature of using a heuristics algorithm in the system of Englard, in order to improve the performance and safety of the autonomous vehicle (see at least Paragraph [0042] of Englard).
As per Claim 6, Simoudis, in view of Bin-Nun, Englard, and Cella, teaches the features of Claim 5, and Englard further teaches the features of wherein evaluating the predicted traffic scene or traffic scenario comprises: processing, using the clustering algorithm, the predicted traffic scene or traffic scenario in order to place the predicted traffic scene or traffic scenario in a clustering space, wherein the clustering space is indicative of sub-clusters formed based on a set of predefined evaluation conditions foreach ADS feature of the plurality of the ADS features; and determining the score indicative of the potential development gain based on a position of the placed predicted scene or scenario in the clustering space relative to each sub-cluster.
Englard, teaches the features of processing, using the clustering algorithm, the predicted traffic scene or traffic scenario in order to place the predicted traffic scene or traffic scenario in a clustering space, wherein the clustering space is indicative of sub-clusters formed based on a set of predefined evaluation conditions foreach ADS feature of the plurality of the ADS features. Englard teaches a method for implementing a self-driving control architecture for a vehicle, where the segmentation module (210) may use predetermined rules or algorithms to define objects, and may use clustering techniques of points that meet a certain criteria (e.g., having no more than a certain maximum distance between all points in the cluster, etc.); and where the tracking module (214) may associate existing identifiers with specific objects where appropriate (e.g., for lidar data, by associating the same identifier with different clusters of points, at different locations, in successive cloud point frames) (e.g. Paragraphs [0089], [0091]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun, with the feature of using a heuristics algorithm in the system of Englard, in order to improve the performance and safety of the autonomous vehicle (see at least Paragraph [0042] of Englard).
Englard, further teaches the features of determining the score indicative of the potential development gain based on a position of the placed predicted scene or scenario in the clustering space relative to each sub-cluster. Englard teaches a method for implementing a self-driving control architecture for a vehicle, where the decision arbiter (108) assigns a score to each maneuver that is output by the SDCA, and the SDCA’s may be used a labels, weights, or scores for a supervised training process (e.g. Paragraphs [0068], [0076], [0085]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for managing vehicle data in the system of Simoudis with the feature of using a heuristics algorithm in the system of Englard, in order to improve the performance and safety of the autonomous vehicle (see at least Paragraph [0042] of Englard).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Simoudis, in view of Bin-Nun, as applied to Claim 1 above, and further in view of U.S. Patent Publication No. 2018/0284755 A1, to Cella, et al (hereinafter referred to as Cella; previously of record).
As per Claim 10, Simoudis, in view of Bin-Nun, teaches the features of Claim 1, but the combination of Simoudis, in view of Bin-Nun, fails to disclose every feature of wherein the set of requirements for each of the plurality of the ADS features comprises an estimated power consumption, an estimated computational resource need, an estimated data storage need and an estimated bandwidth need.
However, Cella in the same field of endeavor, teaches a method for collecting large amounts of data for a vehicle, where the transmission conditions include network performance parameter such as estimated limitations of the network; where the sensor data storage implementation circuit utilizes a currently existing data storage profile sensor implementation circuit, which may be based on initial estimates of system performance, including storage capacity, bandwidth; and where fuel consumption is estimated to provide input to train and improve consumption (e.g. Paragraphs [0319] [1513], [1621], [1945]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for managing vehicle data in the system of Simoudis, in view of Bin-Nun, with the feature of determining an estimated computational, power, or resource capability in the system of Cella, in order to optimize operational parameters