Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to Application No. 18/457609, filed on 29-AUG-2023. Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected as follows.
Status of Application
This final office action is in response to Applicant’s amendment received by the Office on 19-AUG-2025. Claims 1-20 have been presented in the application, of which, 2, 3, 5, 9, 10, 11, 12, 16, 17, 19 are original. Claims 1, 4, 6, 7, 8, 13, 14, 15, 18, and 20 are amended. Accordingly, pending claims 1-20 are addressed herein. The amendments overcome the 101 rejection.
Response to Arguments
Applicant’s arguments, filed 05-AUG-2025, with respect to the rejections of independent claims 1, 8, and 15 under 102 have been fully considered but are not persuasive.
Regarding the 102 and 103 rejections, applicant argues that the prior art does not disclose the new amendment limitations, including the generating use cases that define interaction sequences between external agents like human users and vehicle subsystems; and defining system boundaries, preconditions, triggers for external agents. The examiner disagrees. These limitations are described with a high level of generality and are not further defined within the claims. The broadest reasonable interpretation of interaction sequences includes simulating how the vehicle would move around the agent. The broadest reasonable interpretation of system boundaries and preconditions includes any constraints, and triggers or inputs includes inputs that indicate the presence of the agent; all these limitations are taught by the agents being within the travel network and updating their presence based on sensor data. A more detailed rejection can be seen below in the 102 and 103 rejections.
Claim Rejections - 35 USC § 112
Claim 5 recites the limitations “the elements” and " the types". There is insufficient antecedent basis for these limitations in the claims. The element and type is expressed as singular in prior claims but as plural in this claim.
Claim 7 recites the limitations “the different elements” and "the testing procedure". There is insufficient antecedent basis for this limitation in the claims.
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.
Claims 1-4, 8-11, and 15-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Falk et al. (US 20220185315 A1).
Regarding claim 1, Falk et al. teaches: A method for autonomous driving using an autonomous driving system (ADS), the method comprising: receiving vehicle requirements about an autonomous vehicle (Figure 8; element 810); creating, using the vehicle requirements, a multi-domain ontology data structure (Paragraph [49], "The operations computing system can provide travel way network data indicative of at least one of the one or more operational domains to an autonomous vehicle of the fleet of autonomous vehicles"), wherein the multi-domain ontology data structure (Paragraph [32], "More particularly, the operations computing system (e.g., a backend operational domain service) of the service entity can generate one or more operational domains for a travel way network based, at least in part, on map data representative of the travel way network and operational domain parameters") includes information about tasks (Paragraph [36]), objects (Paragraph [52], "The real-time travel way data can be indicative of temporary travel way (e.g., road, etc.) conditions associated with the one or more operational travel way segments. As an example, the temporary travel way conditions can be indicative of one or more travel way defects (e.g., potholes, etc.)"), missions (Paragraph [158], "operation type"), events (Paragraph [52], "travel way maintenance events (e.g., construction, etc.)"), and responses of the autonomous vehicle in a plurality of operational design domains (Paragraph [33], "vehicle maneuvers"); generating use cases for the autonomous vehicle (Paragraph [56], "The proxy vehicle computing system can be configured to simulate the operations of an autonomous vehicle…") based on the multi-domain ontology data structure (Paragraph [56], "… based, at least in part, on simulated data indicative of an operating environment.") wherein the use cases are distinct interaction sequences (Paragraph [162], "In such a case, the current routable travel way network 444 can be indicative of a subset of the one or more operational travel way segments”) between external agents enabled by a function of the autonomous vehicle, wherein the external agents are human users (Paragraph [168], "generating an updated current routable travel way network based, at least in part, on updated real-time travel way data 472…The sensor data, for example, can include … crowds (e.g., indicative of parades, etc.)”) and vehicle subsystems that interact with the ADS (Paragraph [165], “the proxy vehicle computing system can include a remote service (e.g., running on the operations computing system 320) configured to represent the operations of a vehicle computing system onboard an autonomous vehicle”), and wherein the use cases define system boundaries including the external agents triggering the use cases and external agents affected by the use cases (Paragraph [168], “…The operations computing system 320 (e.g., current routable network supervision system 480) can obtain data indicative of the sensor data (e.g., the sensor data and/or one or more determinations made based, at least in part, on the sensor data) and update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data”), preconditions that the external agents need to satisfy (Paragraph [168], “within the travel way network”), and triggers or inputs the external agents provide (Paragraph [168], “one or more determinations made based, at least in part, on the sensor data”); describing a behavior of the autonomous vehicle under various operational design domains (element 436) through use cases (element 444); and operating, using the ADS, the autonomous vehicle based on the described behavior under the use cases (Paragraph [165], “The current routable travel way network 444 can be signed by the proxy vehicle cryptographic signature (e.g., via one or more cryptographic signing techniques) to validate the current routable travel way network before deployment”)
Regarding claim 2, Falk et al. teaches: The method of claim 1, wherein the multi-domain ontology data structure includes a description of each of the plurality of operational design domains in terms of a plurality of ontological concepts (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment"), and plurality of ontological concepts are expressed as constrained parameters (Paragraph [45], "The domain characteristics can identify the operational domain parameters, one or more manual travel way segment inclusions to the operational domain, and/or the plurality of segment attributes for each of the one or more operational travel way segments").
Regarding claim 3, Falk et al. teaches: The method of claim 2, further comprising generating a plurality of autonomous driving requirements using the multi-domain ontology data structure (Paragraph [133], " the operational protocol(s) 405A can include a requirement to constrain an operational domain to travel way segments within a particular range of road grades"), wherein the plurality of autonomous driving requirements describes at least one action that the autonomous vehicle is expected to perform based on an external environment conditions and vehicle states of the autonomous vehicle (Paragraph [133], "By way of example, an autonomous vehicle's operational capabilities can identify a reduction in performance when traversing travel way segments with road grades below negative ten percent and/or above positive ten percent")
Regarding claim 4, Falk et al. teaches: The method of claim 3, wherein the vehicle requirements includes a description of each of the plurality of operational design domains (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment "), a plurality of vehicle states of the autonomous vehicle (Paragraph [39], "performance threshold "), vehicle responses and vehicle actions (Paragraph [33], "vehicle maneuvers"), and Object and Event Detection and Response (OEDR) data (Paragraph [87])
Regarding claim 8, Falk et al. teaches: An automated driving system (ADS), comprising: a plurality of sensors (element 135); a controller including a processor (Paragraph [180]), wherein the controller is in communication with the plurality of sensors (Figure 1; Paragraph [77]) and is programmed to: receive vehicle data about an autonomous vehicle (Figure 8; element 810); create, using the vehicle data, a multi-domain ontology data structure (Paragraph [49], "The operations computing system can provide travel way network data indicative of at least one of the one or more operational domains to an autonomous vehicle of the fleet of autonomous vehicles"), wherein the multi-domain ontology data structure (Paragraph [32], "More particularly, the operations computing system (e.g., a backend operational domain service) of the service entity can generate one or more operational domains for a travel way network based, at least in part, on map data representative of the travel way network and operational domain parameters") includes information about tasks (Paragraph [36]), objects (Paragraph [52], "The real-time travel way data can be indicative of temporary travel way (e.g., road, etc.) conditions associated with the one or more operational travel way segments. As an example, the temporary travel way conditions can be indicative of one or more travel way defects (e.g., potholes, etc.)"), missions (Paragraph [158], "operation type"), events (Paragraph [52], "travel way maintenance events (e.g., construction, etc.)"), and responses of the autonomous vehicle in a plurality of operational design domains (Paragraph [33], "vehicle maneuvers"); generate use cases for the autonomous (Paragraph [56], "The proxy vehicle computing system can be configured to simulate the operations of an autonomous vehicle…") based on the multi-domain ontology data structure (Paragraph [56], "… based, at least in part, on simulated data indicative of an operating environment."), wherein the use cases are distinct interaction sequences (Paragraph [162], "In such a case, the current routable travel way network 444 can be indicative of a subset of the one or more operational travel way segments”) between external agents enabled by a function of the autonomous vehicle, wherein the external agents are human users (Paragraph [168], "generating an updated current routable travel way network based, at least in part, on updated real-time travel way data 472…The sensor data, for example, can include … crowds (e.g., indicative of parades, etc.)”) and vehicle subsystems that interact with the ADS (Paragraph [165], “the proxy vehicle computing system can include a remote service (e.g., running on the operations computing system 320) configured to represent the operations of a vehicle computing system onboard an autonomous vehicle”), and wherein the use cases define system boundaries including the external agents triggering the use cases and external agents affected by the use cases (Paragraph [168], “…The operations computing system 320 (e.g., current routable network supervision system 480) can obtain data indicative of the sensor data (e.g., the sensor data and/or one or more determinations made based, at least in part, on the sensor data) and update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data”), preconditions that the external agents need to satisfy (Paragraph [168], “within the travel way network”), and triggers or inputs the external agents provide (Paragraph [168], “one or more determinations made based, at least in part, on the sensor data”); and describe a behavior of the autonomous vehicle under various operational design domains (element 436) through use cases generated using the multi-domain ontology data structure. (element 444).
Regarding claim 9, Falk et al. teaches: The automated driving system of claim 8, wherein the multi-domain ontology data structure includes a descriptions of each of the plurality of operational design domains in terms of a plurality of ontological concepts (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment"), and plurality of ontological concepts are expressed as constrained parameters (Paragraph [45], "The domain characteristics can identify the operational domain parameters, one or more manual travel way segment inclusions to the operational domain, and/or the plurality of segment attributes for each of the one or more operational travel way segments").
Regarding claim 10, Falk et al. teaches: The automated driving system of claim 9, wherein the automated driving system requirements describe at least one action that the autonomous vehicle is expected to perform based on an external environment conditions and vehicle states of the autonomous vehicle (Paragraph [133], "By way of example, an autonomous vehicle's operational capabilities can identify a reduction in performance when traversing travel way segments with road grades below negative ten percent and/or above positive ten percent")
Regarding claim 11, Falk et al. teaches: The automated driving system of claim 10, wherein the vehicle data includes a description of each of the plurality of operational design domains (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment "), a plurality of vehicle states of the autonomous vehicle (Paragraph [39], "performance threshold "), vehicle responses and vehicle actions (Paragraph [33], "vehicle maneuvers"), and Object and Event Detection and Response (OEDR) data (Paragraph [87])
Regarding claim 15, Falk et al. teaches: A tangible, non-transitory, machine-readable medium, comprising machine-readable instructions, that when executed by a processor, cause the processor to (Paragraph [5]): receive vehicle requirements about an autonomous vehicle using an autonomous driving system (ADS) (Figure 8; element 810); create, using the vehicle requirements, a multi-domain ontology data structure (Paragraph [49], "The operations computing system can provide travel way network data indicative of at least one of the one or more operational domains to an autonomous vehicle of the fleet of autonomous vehicles"), wherein the multi-domain ontology data structure (Paragraph [32], "More particularly, the operations computing system (e.g., a backend operational domain service) of the service entity can generate one or more operational domains for a travel way network based, at least in part, on map data representative of the travel way network and operational domain parameters") includes information about tasks (Paragraph [36]), objects (Paragraph [52], "The real-time travel way data can be indicative of temporary travel way (e.g., road, etc.) conditions associated with the one or more operational travel way segments. As an example, the temporary travel way conditions can be indicative of one or more travel way defects (e.g., potholes, etc.)"), missions (Paragraph [158], "operation type"), events (Paragraph [52], "travel way maintenance events (e.g., construction, etc.)"), and responses of the autonomous vehicle in a plurality of operational design domains (Paragraph [33], "vehicle maneuvers"); generate use cases for the autonomous vehicle (Paragraph [56], "The proxy vehicle computing system can be configured to simulate the operations of an autonomous vehicle…") based on the multi-domain ontology data structure (Paragraph [56], "… based, at least in part, on simulated data indicative of an operating environment."); and describe a behavior of the autonomous vehicle in accordance with the use cases (element 444) generated using the multi-domain ontology data structure (element 436), wherein the use cases are distinct interaction sequences (Paragraph [162], "In such a case, the current routable travel way network 444 can be indicative of a subset of the one or more operational travel way segments”) between external agents enabled by a function of the autonomous vehicle, wherein the external agents are human users (Paragraph [168], "generating an updated current routable travel way network based, at least in part, on updated real-time travel way data 472…The sensor data, for example, can include … crowds (e.g., indicative of parades, etc.)”) and vehicle subsystems that interact with the ADS (Paragraph [165], “the proxy vehicle computing system can include a remote service (e.g., running on the operations computing system 320) configured to represent the operations of a vehicle computing system onboard an autonomous vehicle”), and wherein the use cases define system boundaries including the external agents triggering the use cases and external agents affected by the use cases (Paragraph [168], “…The operations computing system 320 (e.g., current routable network supervision system 480) can obtain data indicative of the sensor data (e.g., the sensor data and/or one or more determinations made based, at least in part, on the sensor data) and update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data”), preconditions that the external agents need to satisfy (Paragraph [168], “within the travel way network”), and triggers or inputs the external agents provide (Paragraph [168], “one or more determinations made based, at least in part, on the sensor data”); and describe a behavior of the autonomous vehicle in accordance with the use cases (element 444) generated using the multi-domain ontology data structure (element 436); and operate, using the ADS, the autonomous vehicle based on the described behavior under the use cases (Paragraph [165], “The current routable travel way network 444 can be signed by the proxy vehicle cryptographic signature (e.g., via one or more cryptographic signing techniques) to validate the current routable travel way network before deployment”).
Regarding claim 16, Falk et al. teaches: The tangible, non-transitory, machine-readable medium of claim 15, wherein the multi-domain ontology data structure includes a description of each of the plurality of operational design domains in terms of a plurality of ontological concepts (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment"), and the plurality of ontological concepts are expressed as constrained parameters (Paragraph [45], "The domain characteristics can identify the operational domain parameters, one or more manual travel way segment inclusions to the operational domain, and/or the plurality of segment attributes for each of the one or more operational travel way segments").
Regarding claim 17, Falk et al. teaches: The tangible, non-transitory, machine-readable medium of claim 16, wherein the tangible, non-transitory, machine-readable medium further comprising machine-readable instructions, that when executed by the processor, causes the processor to generate autonomous driving requirements using the multi-domain ontology data structure (Paragraph [133], " the operational protocol(s) 405A can include a requirement to constrain an operational domain to travel way segments within a particular range of road grades"), and the autonomous driving requirements describes at least one action that the autonomous vehicle is expected to perform based on an external environment conditions and vehicle states of the autonomous vehicle (Paragraph [133], "By way of example, an autonomous vehicle's operational capabilities can identify a reduction in performance when traversing travel way segments with road grades below negative ten percent and/or above positive ten percent")
Regarding claim 18, Falk et al. teaches: The tangible, non-transitory, machine-readable medium of claim 17, wherein the vehicle requirements include a description of each of the plurality of operational design domains (Paragraph [33], "For example, the map data can include a plurality of high fidelity tags, each tag describing an attribute or characteristic of a travel way segment "), a plurality of vehicle states of the autonomous vehicle (Paragraph [39], "performance threshold "), vehicle responses and vehicle actions (Paragraph [33], "vehicle maneuvers"), and Object and Event Detection and Response (OEDR) data (Paragraph [87])
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.
Claim 5-7, 12-14, and 19-20 is rejected under 35 U.S.C. 103 as being unpatentable over Falk et al. (US 20220185315 A1) in view of Lee et al. (US 20240157971 A1).
Regarding claim 5, Falk et al. teaches: The method of claim 4, wherein the multi-domain ontology data structure includes a plurality of concepts identifiers (Paragraph [33], "The map data can include a plurality of segment attributes for each travel way segment of the plurality of travel way segments "), the plurality of concepts identifiers includes an element and a type (Paragraph [33], "The plurality of segment attributes for a respective travel way segment can be indicative of one or more speed limits, road grades, lane widths, traffic signs, turn radiuses, vehicle maneuvers, and/or any other characteristic of a travel way segment"), and parameters and constraints relating to the elements (Paragraph [131], "The operational domain parameter(s) 402 can be indicative of a range of acceptable values for the plurality of segment attributes for each travel way segment of the plurality of travel way segments")
While Falk et al. teaches the limitations as stated above, it does expressly disclose:
the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types
However, Lee et al. teaches: the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types (Tables 1, 2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the travel network and operational domain data generation of Falk et al., to include the classification of the design domains into layers of attributes and categories, as taught by Lee et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: a travel network with operational domain data classified into layers of attributes and categories.
Regarding claim 6, Falk et al. teaches: The method of claim 5, wherein the use cases are generated by extracting every external interface to the ADS from the multi-domain ontology data structure (Paragraph [29], “Each environment can be included in a travel way network (e.g., road network) associated with map data (e.g., a preconstructed and dynamically updated road map information, flight map information including one or more defined ski lanes, water map information including waterways, etc.)”), enumerating possible combinations of input interfaces (Paragraph [55], “update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data.”), output interfaces (element 444) and vehicle states along with their constraints (Paragraph [29-30], " operational capabilities of the autonomous vehicle"), associating one or more tasks with each of the enumerated possible combinations (Paragraph [36]), filtering out any inconsistent combinations using constraint solving, and refining driver inputs to generate the use cases (Paragraph [29], "For example, an autonomous vehicle can be approved for operating within an environment due to legal allowances (e.g., traffic regulations allowing autonomous driving, noise allowance allowing for aerial vehicles, etc.), operational capabilities of the autonomous vehicle, and/or any other factor that can affect the safe operation of the autonomous vehicle within a particular environment.").
Regarding claim 7, Falk et al. teaches: The method of claim 6, further comprising generating test cases for the autonomous vehicle based on the multi-domain ontology data structure, wherein the test cases are instantiations of specific values to the different elements in the multi-domain ontology data structure where output or response elements are not considered (Paragraph [38, 22], “verification data (e.g., a cryptographic validation signature) for the operational domain by comparing the operational travel way segments (and/or attributes thereof) to the approval criteria”), and wherein a test input to the testing procedure is an amount of coverage given by parameter ranges of the individual elements and the combination of elements (Paragraph [35-36;22], “approval criteria (e.g., criteria outlining requirements for autonomous vehicle navigation based, at least in part, on autonomous capabilities and other policy considerations of the service entity)”), and the test cases are generated using a test data development (TDD) step and an expected output generation (EOG) step (Paragraph [44], “In some implementations, the operational domain can be verified during a two-step procedure.), wherein the TDD step includes interactive enumerating the test inputs and measuring the amount of coverage (Paragraph [44], “During the first step, the operational domain can be verified with respect to any manual inclusions to the operational domain.”; Paragraph [46], “In some implementations, the verification data can be generated based, at least in part, on a domain risk”), and the EOG step uses an additional test input of the of the individual elements and extracts a response or output element corresponding to test data from the TDD step to generate the test cases (Paragraph [44], “During the second step, the operational domain can be verified with respect to each operational travel way segment of the operational domain.”; Paragraph [47])
Regarding claim 12, Falk et al. teaches: The automated driving system of claim 11, wherein the multi-domain ontology data structure includes a plurality of concepts identifiers (Paragraph [33], "The map data can include a plurality of segment attributes for each travel way segment of the plurality of travel way segments "), the plurality of concepts identifiers includes an element and a type (Paragraph [33], "The plurality of segment attributes for a respective travel way segment can be indicative of one or more speed limits, road grades, lane widths, traffic signs, turn radiuses, vehicle maneuvers, and/or any other characteristic of a travel way segment"), and parameters and constraints relating to the elements (Paragraph [131], "The operational domain parameter(s) 402 can be indicative of a range of acceptable values for the plurality of segment attributes for each travel way segment of the plurality of travel way segments")
While Falk et al. teaches the limitations as stated above, it does expressly disclose:
the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types
However, Lee et al. teaches: the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types (Tables 1, 2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the travel network and operational domain data generation of Falk et al., to include the classification of the design domains into layers of attributes and categories, as taught by Lee et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: a travel network with operational domain data classified into layers of attributes and categories.
Regarding claim 13, Falk et al. teaches: The automated driving system of claim 12, wherein the use cases are generated by extracting every external interface to the ADS from the multi-domain ontology data structure (Paragraph [29], “Each environment can be included in a travel way network (e.g., road network) associated with map data (e.g., a preconstructed and dynamically updated road map information, flight map information including one or more defined ski lanes, water map information including waterways, etc.)”), enumerating possible combinations of input interfaces (Paragraph [55], “update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data.”), output interfaces (element 444) and vehicle states along with their constraints (Paragraph [29-30], " operational capabilities of the autonomous vehicle"), associating one or more tasks with each of the enumerated possible combinations (Paragraph [36]), filtering out any inconsistent combinations using constraint solving, and refining driver inputs to generate the use cases (Paragraph [29], "For example, an autonomous vehicle can be approved for operating within an environment due to legal allowances (e.g., traffic regulations allowing autonomous driving, noise allowance allowing for aerial vehicles, etc.), operational capabilities of the autonomous vehicle, and/or any other factor that can affect the safe operation of the autonomous vehicle within a particular environment.").
Regarding claim 14, Falk et al. teaches: The automated driving system of claim 13, wherein the controller is programmed to capture a subset of vehicle data linked to the multi-domain ontology data structure and monitored to help ensure a vehicle is not operating outside an operational design domain (Paragraph [62-63]).
Regarding claim 19, Falk et al. teaches: The tangible, non-transitory, machine-readable medium of claim 18, wherein the multi-domain ontology data structure includes a plurality of concepts identifiers (Paragraph [33], "The map data can include a plurality of segment attributes for each travel way segment of the plurality of travel way segments "), the plurality of concepts identifiers includes an element and a type (Paragraph [33], "The plurality of segment attributes for a respective travel way segment can be indicative of one or more speed limits, road grades, lane widths, traffic signs, turn radiuses, vehicle maneuvers, and/or any other characteristic of a travel way segment"), and parameters and constraints relating to the elements (Paragraph [131], "The operational domain parameter(s) 402 can be indicative of a range of acceptable values for the plurality of segment attributes for each travel way segment of the plurality of travel way segments")
While Falk et al. teaches the limitations as stated above, it does expressly disclose:
the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types
However, Lee et al. teaches: the multi-domain ontology data structure further includes a hierarchical relationship connecting the elements to the types (Tables 1, 2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the travel network and operational domain data generation of Falk et al., to include the classification of the design domains into layers of attributes and categories, as taught by Lee et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: a travel network with operational domain data classified into layers of attributes and categories.
Regarding claim 20, Falk et al. teaches: The tangible, non-transitory, machine-readable medium of claim 19, wherein the use cases are generated by extracting every external interface to the ADS from the multi-domain ontology data structure (Paragraph [29], “Each environment can be included in a travel way network (e.g., road network) associated with map data (e.g., a preconstructed and dynamically updated road map information, flight map information including one or more defined ski lanes, water map information including waterways, etc.)”), enumerating possible combinations of input interfaces (Paragraph [55], “update one or more statuses of the plurality of travel way segments within the travel way network based, at least in part, on the data.”), output interfaces (element 444) and vehicle states along with their constraints (Paragraph [29-30], " operational capabilities of the autonomous vehicle"), associating one or more tasks with each of the enumerated possible combinations (Paragraph [36]), filtering out any inconsistent combinations using constraint solving, and refining driver inputs to generate the use cases (Paragraph [29], "For example, an autonomous vehicle can be approved for operating within an environment due to legal allowances (e.g., traffic regulations allowing autonomous driving, noise allowance allowing for aerial vehicles, etc.), operational capabilities of the autonomous vehicle, and/or any other factor that can affect the safe operation of the autonomous vehicle within a particular environment.").
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSE TRAMANH TRAN whose telephone number is (703)756-5879. The examiner can normally be reached M-F 8:30am-5pm ET.
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/A.T.T./Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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