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 communication is responsive to Applicant’s submission filed on September 10, 2026. No claims have been amended. Claims 1-20, 25 and 39 have been canceled. Claims 21-24, 26-38, and 40-41 are pending and examined in the instant office action. The rejections are as stated below.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2013685.9, filed on 09/01/2020.
Response to Arguments
Applicant's arguments filed 09/10/2026 have been fully considered but they are not persuasive. Applicant argues that the combination of Panzica, Konrardy, and Wheeler fails to teach or suggest all limitations of independent claims 21, 35 and 40. Applicant’s arguments are addressed below.
The Cited Art Fails to Teach or Suggest “Combining” Data Layers into a Data Tile Prior to Transmission.
Applicant Argument: Panzica teaches away from combining the restriction information with the underlying map data.
Applicant argues that Panzica teaches away from the claimed combination because Panzica states that certain changes are transient and “are not desirable to be included in the map” (Panzica ¶54). Applicant further relies on Panzica ¶103, which describes transmitting document images representing constraint information to selected autonomous vehicles and subsequently merging the document images with corresponding existing autonomy maps at the vehicles. Applicant contends that modifying Panzica to form the claimed data tile prior to transmission would render Panzica unsatisfactory for its intended purpose and fundamentally alter its principle of operation.
Response: The Examiner respectfully disagrees. The proposed combination does not require replacing Panzica’s underlying autonomy map, permanently incorporating transient operating conditions into the underlying autonomy map, or generating a new autonomy map release containing such transient conditions. Rather, Panzica continues to provide dynamically configurable restriction information for use with an existing autonomy map. Panzica ¶103 itself confirms that document images representing constraint information may be transmitted to selected autonomous vehicles for subsequent integration with the corresponding existing autonomy maps. Further, the claims recite combining together the amount of map data and the driving automation restriction layer with the one or more driving restriction attributes to form a data tile. The claims do not require that the restriction information be permanently incorporated into the underlying map data or cease to exist as separately manageable restriction information. Consistent with this distinction, Panzica discloses configuring constraint layers associated with autonomy maps, including activating and deactivating such constraint layers (See Panzica ¶62). A reference teaches away where it criticizes discredits, or otherwise discourages the claimed solution; the mere disclosure of a preference or alternative does not, by itself, constitute a teaching away. See MPEP 2141.02(VI) and 2145(X)(D). Here, Panzica’s statement in ¶54 that certain transient changes are “not desirable to be included in the map” concerns incorporation of such transient information into the underlying autonomy map. Panzica does not discourage maintaining such restriction information separately for use with the map. Accordingly, the cited disclosure does not establish that Panzica teaches away from the proposed combination. As set forth in the prior Office Action, Wheeler is relied upon for the additional teaching concerning the organization of map information together with associated driving restriction information in tiled map data. Thus, the proposed combination does not require eliminating Panzica’s layer based constraint management functionality or permanently incorporating transient information into Panzica’s underlying autonomy map. Accordingly, Panzica’s statement in ¶54 does not establish that the proposed modification would render Panzica unsatisfactory for its intended purpose or fundamentally change its principle of operation.
Applicant Argument: Wheeler separately maintains and transmits the OMap and LMap and therefore does not teach the claimed data tile.
Applicant argues that Wheeler fails to teach the claimed “combining together” limitation because Wheeler maintains its occupancy map (OMap) and Landmark Map (LMap) separately and transmits the respective information in separate tile wire formats.
Response: The Examiner respectfully disagrees with Applicant’s characterization of the disclosure relied upon in the rejection. As set forth in the prior Office Action, the rejection does not rely upon combining Wheeler’s OMap and LMap into a single data tile. Rather, Wheeler is relied upon for teaching map information together with associated driving restriction information in tiled form. In particular, Wheeler teaches that lane elements of the HD map include geometric information together with driving restrictions, including legal direction, speed limits, lane type restrictions, termination restrictions, and other road feature information – See Wheeler ¶68. Wheeler further teaches that the HD-map data is discretized into map tiles. – See Wheeler ¶71. Wheeler additionally teaches that Landmark Map data is organized in LMap tiles and that the information in the Landmark Map includes semantics, rules, and geometry. See Wheeler ¶¶91-92. See Wheeler ¶58; FIGS. 5 and 9. Accordingly, Applicant’s observation that Wheeler separately maintains and transmits OMap and LMap formats does not negate the disclosure relied upon in the rejection. The rejection does not equate the claimed amount of map data solely with Wheeler’s OMap. Rather, Wheeler’s Landmark Map itself includes spatial and geometric map information together with semantic rules and driving restrictions and is organized into LMap tiles. Applicant’s reliance on Wheeler ¶80 likewise does not alter the relied upon teaching. Although ¶80 describes separate tile wire formats for the OMap and LMap, that separate transmission does not negate the disclosure that the Landmark Map (LMap) carries semantics, rules, and geometry and is organized in LMap tiles. Wheeler further teaches providing tiled map information to vehicles via a delivery channel. Specifically, Wheeler teaches that the online HD map system interacts with a plurality of vehicles and, upon receiving a request through the HD map system interface, transmits one or more compressed map tiles to the requesting vehicle through a network such as the Internet. See Wheeler ¶¶29-32, 36, and 76-77; FIGS. 1 and 10. For clarity, Wheeler is not relied upon to independently disclose the entirety of Panzica’s restriction layer architecture. Panzica provides the restriction layer framework, while Wheeler provides the additional teaching concerning the organization and delivery of map information together with the associated driving restriction information in tiled map data.
The Cited Art Fails to Teach Restrictions Enforced Based on a “Specific Property”
Applicant Argument: Konrardy is directed to predetermined geographic road segments rather than road elements having a specific property.
Applicant argues that Konrardy merely evaluates predetermined geographic road segments using historical operating information and does not disclose road elements possessing a specific physical property, such as gradient, curvature, or width.
Response: The Examiner respectfully disagrees. As set forth in the prior Office Action, Konrardy teaches obtaining and processing data to determine suitability of locations, including road segments, for various degrees of autonomous or semi-autonomous vehicle operation. Konrardy further teaches that road segments may be selected for analysis based upon characteristics of the road segments, including, for example, highway road segments and urban area road segments. See Konrardy, col. 37, lines 13-20; col. 38, lines 60-65; and col. 39, lines 1-10. Thus, the rejection relies upon Konrardy’s road segments as the recited road elements and upon the disclosed characteristics of those road segments as the recited “specific property.” The claims do not limit the recited “specific property” to a physical or geometric property such as gradient, curvature, or width. Therefore, Applicant’s interpretation appears to be narrower than the claim language. Konrardy is also not limited to identifying individual road segments merely by geographic location. Konrardy expressly teaches selecting road segments based upon characteristics of the road segments and grouping road segments based upon such characteristics. Accordingly, Konrardy teaches road elements having an identified road segment characteristic, as relied upon in the rejection.
Applicant Argument: Konrardy’s risk scores do not constitute driving automation restriction attributes for road elements having the specific property.
Applicant further argues that Konrardy merely calculates statistical risk scores for geographic road segments and does not teach determining driving automation restriction attributes associated with map data for road elements having the claimed specific property.
Response: The Examiner respectfully disagrees. Konrardy teaches determining autonomous operation risk or suitability information for road segments. In particular, Konrardy teaches that a resulting score may represent a maximum recommended or permitted level of autonomous operation feature use for the road segment, including limitations on autonomous operation. Konrardy further teaches that such scores may indicate recommended or allowed autonomous operation feature usage or usage levels for road segments or areas. See Konrardy, col. 39, lines 15-65; col. 40, lines 1-18. Konrardy additionally teaches associating such information with electronic map data, including graphical map tiles, overlay tiles, or data entries in a map database. See Konrardy, FIG. 5, blocks 506-518; col. 40, lines 46-47. Accordingly, Konrardy does more than merely assign statistical risk values to arbitrary geographic locations. Konrardy teaches selecting road segments based upon characteristics of those road segments, evaluating the selected road segments for autonomous operation suitability, determining recommended or permitted autonomous operation information for the selected road segments, and associating that information with electronic map data. For clarity, the Examiner does not rely upon factors such as time of day, weather, traffic conditions, vehicle type, or driver behavior as the claimed “specific property.” Those factors are information Konrardy may consider when determining autonomous operation suitability or risk. The relied upon “specific property” is the disclosed characteristic of the road segment used in selecting the road segment for analysis. The claims recite a restriction indicated as applicable to road elements that have a specific property and restriction attributes determined for road elements that have the specific property. The claims do not limit the specific property to a particular physical or geometric characteristic or otherwise specify how the restriction must be derived from that property. Konrardy teaches selecting road segments based upon road segment characteristics, such as highway road segments or urban area road segments, and determining recommended or permitted autonomous levels for the selected road segments. Thus, the resulting autonomous operation information is associated with and applicable to road elements having the identified road segment characteristic. Stated differently, Konrardy’s road segments correspond to the claimed road elements; the disclosed road segment characteristics correspond to the claimed specific property; the recommended or permitted autonomous operation levels correspond to the relied upon driving automation restriction information; and the electronic map tiles, overlay tiles, or map database entries provide the associated map data.
Applicant Argument: Wheeler’s restrictions are geographically or object bound rather than based on the claimed “specific property”
Applicant further argues that Wheeler’s restrictions are associated with particular lane elements, road signs, or geographic locations rather than with an underlying physical property of a road element.
Response: Applicant’s argument is not persuasive because Wheeler was not relied upon for the “specific property” limitation. As set forth in the prior Office Action, Konrardy was relied upon for the road element characteristic and associated autonomous operation restriction teachings. Wheeler was relied upon for the separate limitations concerning the organization and delivery of map information together with associated driving restriction information in tiled map data. Accordingly, whether Wheeler independently teaches the claimed ”specific property” does not address the respective teachings relied upon in the combination.
Additional Applicant argument: The cited combination lacks a sufficient reason to combine.
Applicant further argues that there is no reason that would have prompted one of ordinary skill in the art to combine the cited teachings in the manner claimed.
Response: The Examiner respectfully disagrees. The rationale for the proposed combination was set forth in the prior Office Action and remains applicable. As previously stated, the proposed combination would provide the most updated road conditions for safe navigation. Panzica teaches location dependent constraint information for autonomous vehicle operation and constraint information associated with events occurring at particular geographic locations. See Panzica ¶¶75 and 121. Konrardy teaches determining recommended or permitted levels of autonomous operation feature use for road segments. See Konrardy, col. 39, lines 15-65; col. 40, lines 1-18. Wheeler teaches organizing map information together with associated semantic rules and driving restrictions in tiled map information and providing such tiled information to vehicles. See Wheeler ¶¶58, 68, 71, 76-77, and 90-92. Thus, using Wheeler’s tiled organization and delivery with the Panzica/Konrardy system would have predictably provided the vehicle with relevant map information together with the associated autonomous operation restriction information for the location, consistent with the previously stated purpose of providing the most updated road conditions for safe navigation. Applicant’s arguments do not show that the proposed combination would eliminate Panzica’s constraint management functionality, render the cited references unsatisfactory for their intended purposes, or require a change in their principle of operation.
Dependent claims: With respect to the dependent claims, Applicant has not presented separate substantive arguments directed to the additional limitations thereof. To the extent Applicant relies upon the alleged deficiencies of independent claims 21, 35 and 40 as establishing patentability of the dependent claims, those arguments are not persuasive for the reasons discussed above. Accordingly, the rejections of the dependent claims are maintained for the reasons previously set forth in the prior Office Action.
Conclusion: Accordingly, Applicant’s arguments do not overcome the teachings of Panzica in view of Konrardy and Wheeler as applied in the prior Office Action. Therefore, the rejection of claims 21-24, 26-38, and 40-41 under 35 U.S.C. 103 over Panzica in view of Konrardy and Wheeler is 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.
Claim(s) 21-24, 26-38 and 40-41 are rejected under 35 U.S.C. 103 as being unpatentable over Panzica et al., US 20200233415 A1, in view of Konrardy et al., US 10386192B1, in view of Mark Wheeler, US 20180189323A1, hereinafter referred to as Panzica, Konrardy and Wheeler, respectively.
Regarding claim 21, Panzica discloses a method for providing data to one or more vehicles for controlling respective automated driving systems of the one or more vehicles (Constraint data can be received, for example, from one or more remote computing devices configured to control operation of a fleet of autonomous vehicles – See at least ¶34), the method comprising:
obtaining an indication of a vehicle fleet (Graphical user interface can include one or more selectable interface elements for identifying particular application targets for a given constraint file. For instance, user selection of interface element can be used to apply selected constraint files to one or more particular vehicles as identified in drop down menu. User selection of interface element can be used to apply selected constraint files to one or more particular fleets of vehicles, also as identified using drop down menu – See at least ¶116 and FIG.10);
obtaining, from a restriction repository storing restriction data that indicates one or more location-dependent driving automation restrictions for automated driving systems, restriction data that corresponds to a portion of a road network and that is applicable to the indicated vehicle fleet (The computing devices also can retrieve or otherwise obtain constraint data that provides information descriptive of one or more geographic areas and/or geographic identifiers within a map for which associated navigational constraints are defined. In some examples, constraint data (i.e. restriction data) can identify geographic areas within map data that should be included and/or excluded from permissible areas (or preferred and/or not preferred) for navigation by autonomous vehicle – See at least ¶75. The computing system can further include a database storing (i.e. restriction repository) autonomy map documents and audit logs – See at least ¶88),
generating, or performing an update process for, a driving automation restriction layer for an amount of map data that corresponds to the portion of the road network based on the obtained restriction data (One or more remote computing devices generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles, the map constraint interface enabling the fleet operator to configure a set of constraint layers (i.e. restriction layer) of one or more autonomy maps utilized by the AVs for navigation, where each constraint layer in the set of constraint layers comprises a toggle feature that enables the fleet operator to activate and deactivate the constraint layer. A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62. Map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105), and
controlling the automated driving system of at least one of the one or more vehicles based on the amount of map data and the driving automation restriction layer (The method can further include receiving one or more constraint files descriptive of additional navigational constraints for one or more geographic areas and/or geographic identifiers. In some implementations, the one or more constraint files received at can be received from one or more remote computing devices that are remote from the autonomous vehicle and that are configured to control operation of a fleet of autonomous vehicles. The one or more constraint files can be generated by the one or more remote computing devices, for example, in response to identification of an event at some geographic location that will impact navigation at such location (e.g., a street fair, sporting event, traffic accident, parade, etc.) at a present and/or future time – See at least ¶121 and FIG. 11).
Panzica fails to explicitly disclose wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property.
However, Konrardy teaches:
wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property (The method may be used to obtain and process data from multiple sources to determine suitability of locations such as road segments for various degrees of autonomous or semi-autonomous vehicle operation. For example, operating data from a plurality of autonomous vehicles may be used to determine whether each of a plurality of road segments may be safely traversed by vehicles using particular autonomous operation features or technologies – See at least column 37, lines 13-20. As another example, a subset of the received road segments may be selected for analysis, either by a user or automatically. A user may select a group of road segments to analyze or may select characteristics of road segments to generate a group (e.g., by selecting road segments within a geographic area, highway road segments, urban area road segments, etc.). Alternatively, a group of road segments may be automatically identified for analysis upon the occurrence of an event, such as a request from a vehicle for data near the vehicle's current position or along a route – See at least column 38, lined 60-65 and column 39, lines 1-10);
determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property (Generating the electronic map may include generating graphical map tiles, overlay tiles in a map database, or data entries in a map database to store the electronic map data for further use in generating a visible map or for autonomous vehicle navigation. The generated electronic map (or portions thereof) may be displayed or presented to a user to aid in vehicle operation or route selection. The one or more risk levels may include summary levels associated with groupings of combinations of risk factors, such as fully autonomous operation or semi-autonomous operation in which the driver actively steers the vehicle. In some embodiments, a risk level may be determined for each autonomous operation feature or category of autonomous operation features (which risk level may ignore or assume a default effect of interactions between autonomous operation features) – See at least FIG. 5, blocks 506-518; col. 39, lines 15-65; col. 40, lines 1-18 and 46-47).
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 invention of Panzica and include the feature of wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; wherein said generating or updating the driving automation restriction layer comprises determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property, as taught by Konrardy, to perform an appropriate route search for a vehicle by searching for a route based on restriction information.
The combination of Panzica and Konrardy fail to disclose combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel.
However, Wheeler teaches:
combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile (The landmark map includes lane spatial and geometric information together with semantic information comprising driving restrictions, including direction, speed, lane type, crossing restrictions, connectivity, vehicle restrictions, termination restrictions, and road features relevant to autonomous driving; the HD-map data is discretized into map tiles, and an Lmap tiles contains lane-element semantics, rules, and geometry – See at least ¶58, 68, 71, 90-92; FIGs. 5 and 9):
providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel (The online HD map system interacts with a plurality of vehicles and sends required HD map data to individual vehicles; upon receiving a request through the HD map system interface, the online system responds by transmitting one or more compressed map tiles to the requesting vehicle through a network such as the internet – See at least ¶29-32, 36 and 76-77, Figs. 1 and 10).
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 combination of Panzica and Konrardy and include the feature of combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel, as taught by Wheeler, to provide the most updated road conditions for safe navigation.
Regarding claim 22, Panzica, as modified, discloses wherein the driving automation restriction layer comprises one or more driving automation restriction attributes, each driving automation restriction attribute associated with a respective segment of the portion of the road network and for controlling operation of automated driving systems for that segment (In certain implementations, the map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105).
Regarding claim 23, Panzica, as modified, discloses wherein the obtained restriction data comprises at least one driving automation restriction indicated as applicable to a corresponding geographical feature of a map, and wherein said generating, or performing an update process for, the driving automation restriction layer comprises specifying the at least one driving automation restriction as one or more driving automation restriction attributes associated with map data for a road element of the road network corresponding to geographical feature (In accordance with embodiments described herein, the unified document model can comprise a series of autonomy map documents readily editable by the fleet operator in the manner described herein. Thus, the fleet operator can access any autonomy map document and, for example, toggle (activate or deactivate) existing map constraint layers, create new map constraint layers, and provide annotations for any route portion of the autonomy grid map. For example, if a construction project is scheduled for a certain route segment, the fleet operator can access the corresponding autonomy map document via the map constraint interface and append an annotation to the document indicating the construction zone, (geographical feature) and/or activate a particular navigational constraint to either forbid the AV from entering the construction zone, or cause the AV to be routed around it – See at least ¶102).
Regarding claim 24, Panzica, as modified, discloses wherein the obtained restriction data comprises at least one driving automation restriction indicated as applicable to a geographic region, and wherein said generating, or performing an update process for, the driving automation restriction layer comprises specifying the at least one driving automation restriction as one or more driving automation restriction attributes associated with map data for one or more road elements of the road network located within that geographic region (Obtaining a constraint set having a default state (e.g., permit or forbid) and being descriptive of zero or more geographic identifiers (e.g., polygons) and associated application types. In a specific implementation, method can include obtaining constraint data descriptive of one or more polygons having an associated application type. Each polygon can correspond, for example, to an inclusion polygon identifying an area for inclusion in a permissible area for navigation by an autonomous vehicle and/or an exclusion polygon identifying an area for exclusion from a permissible area for navigation by an autonomous vehicle – See at least ¶135).
Regarding claim 26, Panzica, as modified, discloses receiving a request for map data from the vehicle, the request indicating the portion of the road network (In addition to the sensor data, the vehicle computing system can retrieve, access, or otherwise obtain map data that provides other detailed information about the surrounding environment of the autonomous vehicle. The map data can provide information regarding the identity and location of different travel ways (e.g., roads, road segments, lanes, lane segments, parking lanes, turning lanes, bicycle lanes, or other portions of a particular travel way) – See at least ¶37. The vehicle computing device(s) can obtain map data and/or constraint data via interaction with the remote computing device(s) that are communicatively coupled over the network – See at least ¶85).
Regarding claim 27, Panzica, as modified, discloses identifying the vehicle fleet based on the request (In some examples, existing constraint data can be provided at and obtained by one or more computing devices located on-board an autonomous vehicle. In some examples, the constraint data can be received from one or more remote computing devices configured to control operation of a fleet of autonomous vehicles. Constraint data can be the same for some or all vehicles in a fleet, or it can be customized per vehicle depending on factors such as the operation location, operating mode, etc. of each autonomous vehicle – See at least ¶39).
Regarding claim 28, Panzica, as modified, discloses receiving a restriction input to provide new restriction data for the restriction repository or an update to restriction data stored in the restriction repository for a location-dependent driving automation restriction (One or more remote computing devices generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles (AVs), the map constraint interface enabling the fleet operator to configure a set of constraint layers of one or more autonomy maps utilized by the AVs for navigation, where each constraint layer in the set of constraint layers comprises a toggle feature that enables the fleet operator to activate and deactivate the constraint layer. A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62).
Regarding claim 29, Panzica, as modified, discloses wherein the restriction input is provided by an entity associated with the vehicle fleet, wherein the restriction input is for restriction data for a location-dependent driving automation restriction specific to vehicles in the vehicle fleet (In some implementations, the autonomous vehicle can be associated with an entity (e.g., a service provider) that provides one or more vehicle service(s) to a plurality of users via a fleet of vehicles that includes, for example, the autonomous vehicle – See at least ¶67).
Regarding claim 30, Panzica, as modified, discloses wherein the restriction input is for restriction data for a location-dependent driving automation restriction applicable to all vehicles (A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62).
Regarding claim 31, Panzica, as modified, discloses providing a user interface for specifying the restriction input, wherein the user interface uses map data stored in a map repository for visualization of at least a part of the road network and for enabling a user to identify one or more geographic features for the restriction input (The selected autonomy map document can be displayed once selected, and the fleet operator can interact with various features of the map constraint interface to create a shape and configure an input constraint for the shape (e.g., utilizing category and effect manifest). In the example shown, the fleet operator can configure shapes encompassing respective route segments of roads, and select particular map constraints to be applied to each route segment encompassed by a particular shape – See at least ¶97).
Regarding claim 32, Panzica, as modified, discloses wherein the restriction repository stores, for each of a plurality of vehicle fleets, respective restriction data applicable to that vehicle fleet (In some implementations, one or more polygons or other constraint data can be saved as one or more separate constraint files for selectively uploading and applying to one or more autonomous vehicles, fleets of vehicles, and/or geographic districts – See at least ¶110. One or more selectable interface elements can be provided to upload new constraint files. One or more selectable interface elements can be provided to apply selected constraint files to particular vehicles(s), fleet(s) and/or district(s) – See at least ¶113).
Regarding claim 33, Panzica, as modified, discloses receiving a restriction input to provide new restriction data for the restriction repository or an update to restriction data stored in the restriction repository for a location-dependent driving automation restriction (One or more remote computing devices generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles (AVs), the map constraint interface enabling the fleet operator to configure a set of constraint layers of one or more autonomy maps utilized by the AVs for navigation, where each constraint layer in the set of constraint layers comprises a toggle feature that enables the fleet operator to activate and deactivate the constraint layer. A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62).
Regarding claim 34, Panzica discloses a method for a map data client of a vehicle to provide data for controlling an automated driving system of the vehicle (Map data descriptive of the identity and location of different travel ways within the surrounding environment of the autonomous vehicle can be accessed and evaluated relative to the constraint data in order to determine a travel route for navigating the autonomous vehicle. Motion of the autonomous vehicle then can be controlled based at least in part on the determined travel route – See at least ¶34), the method comprising:
the map data client obtaining, from a system arranged to carry out the method of claim 21, an amount of map data that corresponds to a portion of a road network and that has a driving automation restriction layer (More particularly, an autonomous vehicle (e.g., a ground-based vehicle, air-based vehicle, other vehicle type) can include a vehicle computing system that implements a variety of systems including the navigation constraint system described herein – See at least ¶36);
the map data client identifying, based on the driving automation restriction layer and a specified geographic feature, one or more driving automation restriction attributes corresponding to the specified geographic feature (Map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105),
the one or more driving automation restriction attributes for use to control operation of the automated driving system (The method can further include receiving one or more constraint files descriptive of additional navigational constraints for one or more geographic areas and/or geographic identifiers. In some implementations, the one or more constraint files received at can be received from one or more remote computing devices that are remote from the autonomous vehicle and that are configured to control operation of a fleet of autonomous vehicles. The one or more constraint files can be generated by the one or more remote computing devices, for example, in response to identification of an event at some geographic location that will impact navigation at such location (e.g., a street fair, sporting event, traffic accident, parade, etc.) at a present and/or future time – See at least ¶121 and FIG. 11); and
providing the one or more driving automation restriction attributes to the driving automation system (Once a set of autonomy map documents is finalized, the fleet operator can select a finalization or deploy feature on the map constraint interface. The computer system can respond to deployment selection by flattening or compressing the map constraint layers of each document into a document image and compile the document images representing each autonomy map document into a document container. The document container may then be transmitted over-the-air to the selected AVs. The selected AVs then can merge the document images of each autonomy map document into the corresponding existing autonomy map – See at least ¶103. In certain implementations, the AV can communicate a constraint management state to the computer system, indicating the operational constraints currently applied by the AV control system. For example, when the AV starts up, the AV can transmit an initial communication of the constraint management state of the AV to the computer system, which can determine whether the AV has applied the current autonomy map updates – See at least ¶108).
Regarding claim 35, Panzica discloses a system comprising one or more processors arranged to carry out a method for providing data to one or more vehicles for controlling respective automated driving systems of the one or more vehicles (Constraint data can be received, for example, from one or more remote computing devices configured to control operation of a fleet of autonomous vehicles – See at least ¶34), the method comprising:
obtaining an indication of a vehicle fleet (Graphical user interface can include one or more selectable interface elements for identifying particular application targets for a given constraint file. For instance, user selection of interface element can be used to apply selected constraint files to one or more particular vehicles as identified in drop down menu. User selection of interface element can be used to apply selected constraint files to one or more particular fleets of vehicles, also as identified using drop down menu – See at least ¶116 and FIG.10);
obtaining, from a restriction repository storing restriction data that indicates one or more location-dependent driving automation restrictions for automated driving systems, restriction data that corresponds to a portion of a road network and that is applicable to the indicated vehicle fleet, wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network (The computing devices also can retrieve or otherwise obtain constraint data that provides information descriptive of one or more geographic areas and/or geographic identifiers within a map for which associated navigational constraints are defined. In some examples, constraint data (i.e. restriction data) can identify geographic areas within map data that should be included and/or excluded from permissible areas (or preferred and/or not preferred) for navigation by autonomous vehicle – See at least ¶75. The computing system can further include a database storing (i.e. restriction repository) autonomy map documents and audit logs – See at least ¶88),
generating, or performing an update process for, a driving automation restriction layer for an amount of map data that corresponds to the portion of the road network based on the obtained restriction data, wherein said generating or updating the driving automation restriction layer comprises determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network (One or more remote computing devices generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles, the map constraint interface enabling the fleet operator to configure a set of constraint layers (i.e. restriction layer) of one or more autonomy maps utilized by the AVs for navigation, where each constraint layer in the set of constraint layers comprises a toggle feature that enables the fleet operator to activate and deactivate the constraint layer. A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62. Map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105), and
controlling the automated driving system of at least one of the one or more vehicles based on the amount of map data and the driving automation restriction layer (The method can further include receiving one or more constraint files descriptive of additional navigational constraints for one or more geographic areas and/or geographic identifiers. In some implementations, the one or more constraint files received at can be received from one or more remote computing devices that are remote from the autonomous vehicle and that are configured to control operation of a fleet of autonomous vehicles. The one or more constraint files can be generated by the one or more remote computing devices, for example, in response to identification of an event at some geographic location that will impact navigation at such location (e.g., a street fair, sporting event, traffic accident, parade, etc.) at a present and/or future time – See at least ¶121 and FIG. 11).
Panzica fails to explicitly disclose wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property.
However, Konrardy teaches:
wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property (The method may be used to obtain and process data from multiple sources to determine suitability of locations such as road segments for various degrees of autonomous or semi-autonomous vehicle operation. For example, operating data from a plurality of autonomous vehicles may be used to determine whether each of a plurality of road segments may be safely traversed by vehicles using particular autonomous operation features or technologies – See at least column 37, lines 13-20. As another example, a subset of the received road segments may be selected for analysis, either by a user or automatically. A user may select a group of road segments to analyze or may select characteristics of road segments to generate a group (e.g., by selecting road segments within a geographic area, highway road segments, urban area road segments, etc.). Alternatively, a group of road segments may be automatically identified for analysis upon the occurrence of an event, such as a request from a vehicle for data near the vehicle's current position or along a route – See at least column 38, lined 60-65 and column 39, lines 1-10 );
determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property (Generating the electronic map may include generating graphical map tiles, overlay tiles in a map database, or data entries in a map database to store the electronic map data for further use in generating a visible map or for autonomous vehicle navigation. The generated electronic map (or portions thereof) may be displayed or presented to a user to aid in vehicle operation or route selection. The one or more risk levels may include summary levels associated with groupings of combinations of risk factors, such as fully autonomous operation or semi-autonomous operation in which the driver actively steers the vehicle. In some embodiments, a risk level may be determined for each autonomous operation feature or category of autonomous operation features (which risk level may ignore or assume a default effect of interactions between autonomous operation features) – See at least FIG. 5, blocks 506-518; col. 39, lines 15-65; col. 40, lines 1-18 and 46-47).
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 invention of Panzica and include the feature of wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; wherein said generating or updating the driving automation restriction layer comprises determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property, as taught by Konrardy, to perform an appropriate route search for a vehicle by searching for a route based on restriction information.
The combination of Panzica and Konrardy fail to disclose combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel.
However, Wheeler teaches:
combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile (The landmark map includes lane spatial and geometric information together with semantic information comprising driving restrictions, including direction, speed, lane type, crossing restrictions, connectivity, vehicle restrictions, termination restrictions, and road features relevant to autonomous driving; the HD-map data is discretized into map tiles, and an Lmap tiles contains lane-element semantics, rules, and geometry – See at least ¶58, 68, 71, 90-92; FIGs. 5 and 9):
providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel (The online HD map system interacts with a plurality of vehicles and sends required HD map data to individual vehicles; upon receiving a request through the HD map system interface, the online system responds by transmitting one or more compressed map tiles to the requesting vehicle through a network such as the internet – See at least ¶29-32, 36 and 76-77, Figs. 1 and 10).
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 combination of Panzica and Konrardy and include the feature of combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel, as taught by Wheeler, to provide the most updated road conditions for safe navigation.
Regarding claim 36, Panzica, as modified, discloses wherein the driving automation restriction layer comprises one or more driving automation restriction attributes, each driving automation restriction attribute associated with a respective segment of the portion of the road network and for controlling operation of automated driving systems for that segment (In certain implementations, the map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105).
Regarding claim 37, Panzica, as modified, discloses wherein the obtained restriction data comprises at least one driving automation restriction indicated as applicable to a corresponding geographical feature of a map, and wherein said generating, or performing an update process for, the driving automation restriction layer comprises specifying the at least one driving automation restriction as one or more driving automation restriction attributes associated with map data for a road element of the road network corresponding to geographical feature (In accordance with embodiments described herein, the unified document model can comprise a series of autonomy map documents readily editable by the fleet operator in the manner described herein. Thus, the fleet operator can access any autonomy map document and, for example, toggle (activate or deactivate) existing map constraint layers, create new map constraint layers, and provide annotations for any route portion of the autonomy grid map. For example, if a construction project is scheduled for a certain route segment, the fleet operator can access the corresponding autonomy map document via the map constraint interface and append an annotation to the document indicating the construction zone, (geographical feature) and/or activate a particular navigational constraint to either forbid the AV from entering the construction zone, or cause the AV to be routed around it – See at least ¶102).
Regarding claim 38, Panzica, as modified, discloses wherein the obtained restriction data comprises at least one driving automation restriction indicated as applicable to a geographic region, and wherein said generating, or performing an update process for, the driving automation restriction layer comprises specifying the at least one driving automation restriction as one or more driving automation restriction attributes associated with map data for one or more road elements of the road network located within that geographic region (Obtaining a constraint set having a default state (e.g., permit or forbid) and being descriptive of zero or more geographic identifiers (e.g., polygons) and associated application types. In a specific implementation, method can include obtaining constraint data descriptive of one or more polygons having an associated application type. Each polygon can correspond, for example, to an inclusion polygon identifying an area for inclusion in a permissible area for navigation by an autonomous vehicle and/or an exclusion polygon identifying an area for exclusion from a permissible area for navigation by an autonomous vehicle – See at least ¶135).
Regarding claim 40, Panzica discloses a non-transitory computer readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for providing data to one or more vehicles for controlling respective automated driving systems of the one or more vehicles, the method comprising (The memory can include one or more non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory can store data and instructions which are executed by the processor to cause the vehicle computing device to perform operations – See at least ¶84), the method comprising:
obtaining an indication of a vehicle fleet (Graphical user interface can include one or more selectable interface elements for identifying particular application targets for a given constraint file. For instance, user selection of interface element can be used to apply selected constraint files to one or more particular vehicles as identified in drop down menu. User selection of interface element can be used to apply selected constraint files to one or more particular fleets of vehicles, also as identified using drop down menu – See at least ¶116 and FIG.10);
obtaining, from a restriction repository storing restriction data that indicates one or more location-dependent driving automation restrictions for automated driving systems, restriction data that corresponds to a portion of a road network and that is applicable to the indicated vehicle fleet, wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network (The computing devices also can retrieve or otherwise obtain constraint data that provides information descriptive of one or more geographic areas and/or geographic identifiers within a map for which associated navigational constraints are defined. In some examples, constraint data (i.e. restriction data) can identify geographic areas within map data that should be included and/or excluded from permissible areas (or preferred and/or not preferred) for navigation by autonomous vehicle – See at least ¶75. The computing system can further include a database storing (i.e. restriction repository) autonomy map documents and audit logs – See at least ¶88),
generating, or performing an update process for, a driving automation restriction layer for an amount of map data that corresponds to the portion of the road network based on the obtained restriction data, wherein said generating or updating the driving automation restriction layer comprises determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network (One or more remote computing devices generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles, the map constraint interface enabling the fleet operator to configure a set of constraint layers (i.e. restriction layer) of one or more autonomy maps utilized by the AVs for navigation, where each constraint layer in the set of constraint layers comprises a toggle feature that enables the fleet operator to activate and deactivate the constraint layer. A set of inputs configuring the set of constraint layers of the one or more autonomy maps is received via the map constraint interface and a set of map constraints corresponding to the configured set of constraint layers is compiled into a document container that is output via a communication interface over one or more networks to a subset of the AVs to enable the subset of AVs to integrate the set of map constraints with the one or more autonomy maps – See at least ¶62. Map constraint interface may also provide a policy mask feature that enables the fleet operator to override an entire existing policy utilized by the AVs. For example, an AV may operate in accordance with an existing policy using an autonomy map for a current route segment. The existing policy can comprise a set of labels and or rules (e.g., initial map constraints) that the AV must abide by when traversing the current route segment – See at least ¶105), and
controlling the automated driving system of at least one of the one or more vehicles based on the amount of map data and the driving automation restriction layer (The method can further include receiving one or more constraint files descriptive of additional navigational constraints for one or more geographic areas and/or geographic identifiers. In some implementations, the one or more constraint files received at can be received from one or more remote computing devices that are remote from the autonomous vehicle and that are configured to control operation of a fleet of autonomous vehicles. The one or more constraint files can be generated by the one or more remote computing devices, for example, in response to identification of an event at some geographic location that will impact navigation at such location (e.g., a street fair, sporting event, traffic accident, parade, etc.) at a present and/or future time – See at least ¶121 and FIG. 11).
Panzica fails to explicitly disclose wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property.
However, Konrardy teaches:
wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property (The method may be used to obtain and process data from multiple sources to determine suitability of locations such as road segments for various degrees of autonomous or semi-autonomous vehicle operation. For example, operating data from a plurality of autonomous vehicles may be used to determine whether each of a plurality of road segments may be safely traversed by vehicles using particular autonomous operation features or technologies – See at least column 37, lines 13-20. As another example, a subset of the received road segments may be selected for analysis, either by a user or automatically. A user may select a group of road segments to analyze or may select characteristics of road segments to generate a group (e.g., by selecting road segments within a geographic area, highway road segments, urban area road segments, etc.). Alternatively, a group of road segments may be automatically identified for analysis upon the occurrence of an event, such as a request from a vehicle for data near the vehicle's current position or along a route – See at least column 38, lined 60-65 and column 39, lines 1-10 );
determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property (Generating the electronic map may include generating graphical map tiles, overlay tiles in a map database, or data entries in a map database to store the electronic map data for further use in generating a visible map or for autonomous vehicle navigation. The generated electronic map (or portions thereof) may be displayed or presented to a user to aid in vehicle operation or route selection. The one or more risk levels may include summary levels associated with groupings of combinations of risk factors, such as fully autonomous operation or semi-autonomous operation in which the driver actively steers the vehicle. In some embodiments, a risk level may be determined for each autonomous operation feature or category of autonomous operation features (which risk level may ignore or assume a default effect of interactions between autonomous operation features) – See at least FIG. 5, blocks 506-518; col. 39, lines 15-65; col. 40, lines 1-18 and 46-47).
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 invention of Panzica and include the feature of wherein the one or more location-dependent driving automation restrictions comprise at least one driving automation restriction indicated as applicable to road elements of the road network that have a specific property; wherein said generating or updating the driving automation restriction layer comprises determining one or more driving automation restriction attributes associated with map data for one or more of the road elements of the road network that have the specific property, as taught by Konrardy, to perform an appropriate route search for a vehicle by searching for a route based on restriction information.
The combination of Panzica and Konrardy fail to disclose combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel.
However, Wheeler teaches:
combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile (The landmark map includes lane spatial and geometric information together with semantic information comprising driving restrictions, including direction, speed, lane type, crossing restrictions, connectivity, vehicle restrictions, termination restrictions, and road features relevant to autonomous driving; the HD-map data is discretized into map tiles, and an Lmap tiles contains lane-element semantics, rules, and geometry – See at least ¶58, 68, 71, 90-92; FIGs. 5 and 9):
providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel (The online HD map system interacts with a plurality of vehicles and sends required HD map data to individual vehicles; upon receiving a request through the HD map system interface, the online system responds by transmitting one or more compressed map tiles to the requesting vehicle through a network such as the internet – See at least ¶29-32, 36 and 76-77, Figs. 1 and 10).
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 combination of Panzica and Konrardy and include the feature of combining together the amount of map data and the driving automation restriction layer with the one or more driving automation restriction attributes to form a data tile: providing the amount of map data, with the driving automation restriction layer, the data tile to each of one or more vehicles in the vehicle fleet via a delivery channel, as taught by Wheeler, to provide the most updated road conditions for safe navigation.
Regarding claim 41, Panzica fails to disclose wherein the specific property comprises one or more of: a road gradient, a road curvature, or a road width.
However, Konrardy teaches wherein the specific property comprises one or more of: a road gradient, a road curvature, or a road width (The sensor data may further include information regarding road conditions (e.g., slope) – See at least col 33, Lines 35-45).
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 invention of Panzica and include the feature of wherein the specific property comprises one or more of: a road gradient, a road curvature, or a road width, as taught by Konrardy, to perform an appropriate route search for a vehicle by searching for a route based on restriction information.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chia-Yang Chen (US 20100292918 A1) discloses a vehicle navigation system comprises a location detector to acquire a current location of a vehicle, a calculation unit to calculate a navigation route from a departure location to a destination, a database including map data and a plurality of restrictions associated with restricted sites on the map data, the calculation unit calculates a navigation route from the departure location to the destination based on the map data and the plurality of restrictions.
THIS ACTION IS MADE FINAL. 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 MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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RESPECTFULLY SUBMITTED
/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
