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 .
The current application is CON. of application No. 17/057077, now Pat. No. 12,188782, relates PCT/US19/67562 filed on Dec. 19, 2019.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1-15 of U.S. Patent No. 12,188782. Although the conflicting claims are not identical, they are not patentably distinct from each other because CLAIM 1, e.g., is generic to all that is recited in claims 1 & 4, e.g., of US Patent No.12,188782. In other words, claims 1 & 4 of US Patent No. 12,188782 fully encompasses the subject matter of the current application’s CLAIM 1 and therefore anticipated.
With regard to claims 1, 10 & 16, are anticipated in the Pat. No. 18,188782 claims 4, 9, & 13 accordingly.
With regard to claims 2, 11 & 17 are anticipated in the Pat. No. 18,188782 claim 5.
With regard to claims 3-4, 12-13 & 18-19 are anticipated in the Pat. No. 18,188782 claim 4.
With regard to claims 5-6 are anticipated in the Pat. No. 18,188782 claim 3.
With regard to claims 7-8 & 14-15 are anticipated in the Pat. No. 18,188782 claims 2 & 6.
With regard to claims 9 & 20 are anticipated in the Pat. No. 18,188782 claim 7.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 10 & 16 as shown below:
Claim 1. A method for selectively generating map tiles, the method comprising:
receiving, at one or more processors from a user device, a request for map data for a particular geographic region;
determining, by the one or more processors, a type of map tile for presenting the map data for the particular geographic region;
for at least a portion of the particular geographic region, determining, by the one or more processors, that there is no map tile corresponding to the determined type of map tile;
obtaining, by the one or more processors, a historic map tile corresponding to the portion of the particular geographic region;
applying, by the one or more processors, data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile; and
transmitting, by the one or more processors, the new map tile to the user device for display.
Claim 10. A server device for selectively generating map tiles, the server device comprising: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors and storing instructions thereon that, when executed by the one or more processors, cause the server device to:
receive, from a user device, a request for map data for a particular geographic region;
determine a type of map tile for presenting the map data for the particular geographic region;
for at least a portion of the particular geographic region, determine that there is no map tile corresponding to the determined type of map tile;
obtain a historic map tile corresponding to the portion of the particular geographic region;
apply data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile; and
transmit the new map tile to the user device for display.
Claim 16. A non-transitory computer-readable medium storing instructions for selectively generating map tiles that, when executed by one or more processors in a computing device, cause the one or more processors to:
receive, from a user device, a request for map data for a particular geographic region;
determine a type of map tile for presenting the map data for the particular geographic region;
for at least a portion of the particular geographic region, determine that there is no map tile corresponding to the determined type of map tile;
obtain a historic map tile corresponding to the portion of the particular geographic region;
apply data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile; and
transmit the new map tile to the user device for display.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes.
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of “for at least a portion of the particular geographic region, determine that there is no map tile corresponding to the determined type of map tile” & “apply data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile”. These limitations, as drafted, are simple processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “a generative machine learning model”. That is, other than reciting the “generative machine learning model” nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the “a generative machine learning model” language, the claim encompasses a person looking at data collected and forming a simple map. The mere nominal recitation of by the machine learning model does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing bolded limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of “receiving, at one or more processor from a user device, a request for map data …determining, by the one or more processor, a type of map tile …; determining, by the one or more processor, that there is no map tile ….; applying, by the one or more processor, data …” amount to mere data gathering, which is a form of insignificant extra-solution activity. The “transmitting, by the one or more processors, the new map tile to the user device for display” step is also recited at a high level of generality (i.e. as a general means of displaying the weather evaluation result from the evaluating step), and amounts to mere post solution for displaying, which is a form of insignificant extra-solution activity. The “one or more processor”, “user device” and “a generative machine learning model” merely describe how to generally “apply” the otherwise mental judgements using well-known device, generic software and generic a computer. The server and “non-transitory computer-readable medium” (claims 10 and claim 16) are recited at a high level of generality generic element and software in a generic computer.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving, determining, obtaining, applying, steps and the transmitting data step for displaying on a user device were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The claims do not provide any indication that system is anything other than a conventional computer for providing general map information. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017).
Thus, the claims are ineligible.
Dependent Claims
Dependent claims(s) 2-9, 11-15 & 17-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-9, 11-15 & 17-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 10 & 16.
Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all reqobviousness 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) 1-4, 9-13 & 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piemonte (20130328862) in view of Young (20210049901).
With regard to claim 1, Piemonte discloses a method for selectively generating map tiles, the method comprising:
receiving, at one or more processors from a user device, a request for map data for a particular geographic region (a device includes a mapping application 110 sends a request for a 3D map view 150 (a virtual camera 100 sends a field of view 140 to the mapping application, see; [0024]+);
determining, by the one or more processors, a type of map tile for presenting the map data for the particular geographic region (the mapping application identifies a set of map tiles 130 necessary to render the 3D map 105, see [0024]+);
for at least a portion of the particular geographic region, determining, by the one or more processors, that there is no map tile corresponding to the determined type of map tile (a map provider 120 cannot retrieve the requested map tiles, see [0026]+);
obtaining, by the one or more processors, a historic map tile corresponding to the portion of the particular geographic region (the tile processor 1050 sends a set of the available map tiles to the geospatial tile generator 1085 which uses the data contained in the set of available tiles to generate geospatial map tiles for the unavailable map tiles, see [0067]-[0068]+);
applying, by the one or more processors, data indicating the determined type of map tile and the historic map tile to generator and builder (such as a mesh builder 1015) to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile (a mesh builder 1015 uses several different functions to build the mesh which responsible for the buildings, shadow, textures for the land cover regions around the roads to provide virtual map tiles, see [0075]+);
transmitting, by the one or more processors, the new map tile to the user device for display (send the map tiles and the new map to the requestor, see [0066]+ & [0088]-[0089]+) .
Piemonte fails to teach applying data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile.
Young discloses a system for vehicle map data update (see the abstract). The system comprises platforms (e.g., OEM platform, mapping platform, and etc.) which including a neural network (machine learning) for receiving input features sets, stored map data to generate new map, see [0091]-[0095]+.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Piemonte by including a neural network (machine learning) for creating a new map tile based on the stored map data and the input features sets as taught by Young for improving the map accuracy.
With regard to claim 2, Young teaches that the method of claim 1, further comprising: training, by the one or more processors, the generative machine learning model using (i) a plurality of previously generated map tiles, and (ii) indications of a corresponding type of each of the plurality of previously generated map tiles (Content providers provides content and data which are trained by the machine learning, see [0094]-[0096]+).
With regard to claim 3, Young teaches that the method of claim 2, wherein the generative machine learning model includes i) a generator that generates the new map tile based on the determined type of map tile and the historic map tile, and ii) a discriminator that compares the new map tile to the previously generated map tiles to determine whether characteristics of the new map tile are consistent with characteristics of the previously generated map tiles (see [0066]+).
With regard to claim 4, Young teaches that the method of claim 3, wherein the new map tile is transmitted to the user device for display in response to the discriminator determining that characteristics of the new map tile are consistent with characteristics of the previously generated map tiles (display object identifier, display/object type, latitude, longitude, heading, altitude, and etc., see [0052]+).
With regard to claim 9, Piemonte teaches that the method of claim 1, wherein determining a type of map tile includes: determining, by the one or more processors, that the type of map tile is at least one of: (i) a standard map tile, (ii) a terrain map tile, (iii) a satellite map tile, (iv) a hybrid map tile, (v) a seasonal map tile, (vi) a time of day map tile, or (vii) a map tile reflecting a particular type of weather condition (a map service provides various standard, satellite map tiles, see [0073] & [0114]+).
With regard to claims 10 & 16, Piemonte discloses a server device for selectively generating map tiles, the server device comprising: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors and storing instructions thereon that, when executed by the one or more processors (a processing pipeline 1000 performed by the mapping application in order to render a map for display at the client device, wherein the tile processor 1050 as a mapping service server, see Fig. 10, [0066]+) , cause the server device to:
receiving, at one or more processors from a user device, a request for map data for a particular geographic region (a device includes a mapping application 110 sends a request for a 3D map view 150 (a virtual camera 100 sends a field of view 140 to the mapping application, see; [0024]+);
determining, by the one or more processors, a type of map tile for presenting the map data for the particular geographic region (the mapping application identifies a set of map tiles 130 necessary to render the 3D map 105, see [0024]+);
for at least a portion of the particular geographic region, determining, by the one or more processors, that there is no map tile corresponding to the determined type of map tile (a map provider 120 cannot retrieve the requested map tiles, see [0026]+);
obtaining, by the one or more processors, a historic map tile corresponding to the portion of the particular geographic region (the tile processor 1050 sends a set of the available map tiles to the geospatial tile generator 1085 which uses the data contained in the set of available tiles to generate geospatial map tiles for the unavailable map tiles, see [0067]-[0068]+);
applying, by the one or more processors, data indicating the determined type of map tile and the historic map tile to generator and builder (such as a mesh builder 1015) to generate a new map tile corresponding to the determined type of map tile based on the historic map tile and the data indicating the determined type of map tile (a mesh builder 1015 uses several different functions to build the mesh which responsible for the buildings, shadow, textures for the land cover regions around the roads to provide virtual map tiles, see [0075]+);
transmitting, by the one or more processors, the new map tile to the user device for display (send the map tiles and the new map to the requestor, see [0066]+ & [0088]-[0089]+) .
Piemonte fails to teach applying data indicating the determined type of map tile and the historic map tile to a generative machine learning model to generate a new map tile.
Young discloses a system for vehicle map data update (see the abstract). The system comprises platforms (e.g., OEM platform, mapping platform, and etc.) which including a neural network (machine learning) for receiving input features sets, stored map data to generate new map, see [0091]-[0095]+.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Piemonte by including a neural network (machine learning) for creating a new map tile based on the stored map data and the input features sets as taught by Young for improving the map accuracy.
With regard to claims 11 & 17, Young teaches that the server device of claim 10, wherein the instructions further cause the server device to: train the generative machine learning model using (i) a plurality of previously generated map tiles, and (ii) indications of a corresponding type of each of the plurality of previously generated map tiles (Content providers provides content and data which are trained by the machine learning, see [0094]-[0096]+).
With regard to claims 12 & 18, Young teaches that the server device of claim 11, wherein the generative machine learning model includes i) a generator that generates the new map tile based on the determined type of map tile and the historic map tile, and ii) a discriminator that compares the new map tile to the previously generated map tiles to determine whether characteristics of the new map tile are consistent with characteristics of the previously generated map tiles (see [0066]+).
With regard to claims 13 & 19, Young teaches that the server device of claim 12, wherein the new map tile is transmitted to the user device for display in response to the discriminator determining that characteristics of the new map tile are consistent with characteristics of the previously generated map tiles (display object identifier, display/object type, latitude, longitude, heading, altitude, and etc., see [0052]+).
Claim(s) 5-8, 14-15 & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piemonte and Young as applied to claims 1 & 10 above, and further in view of Stout (20130035853).
With regard to claims 5, 14 & 20, Piemonte and young disclose the claimed subject matter but fail to teach obtaining, by the one or more processors, a set of user contextual data; and determining, by the one or more processors, the type of map tile from a set of map tile types for the set of user contextual data.
Stout discloses a system for generating map for the vehicle (see the abstract). The system includes a context analyzer for obtaining the user contexture and determining the map tile, see [0042]+.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Piemonte by including a neural network (machine learning) for creating a new map tile based on the stored map data and the input features sets as taught by Young, and further including obtaining user contextual data for determining the type of map tile as taught by Stout. The combination of Piemonte, Young and Stout is an adapted system for creating the map tile for more efficiency.
With regard to claims 6 & 15, Stout teaches that the method of claim 5, wherein determining the type of map tile for the set of user contextual data includes: determining, by the one or more processors, a confidence score for each map tile type in the set of map tile types based on the set of user contextual data; and selecting, by the one or more processors, the map tile type having the highest confidence score (see [0038]+).
With regard to claim 7, Young teaches that the method of claim 5, wherein determining the type of map tile for the set of user contextual data includes: training, by the one or more processors, a machine learning model using (i) a plurality of map tile types previously displayed on user devices, and for each of the plurality of map tile types, (ii) user contextual data for the map tile type, and (iii) an indication of whether a user requested a different map tile type in response to displaying the map tile type; and applying, by the one or more processors, the machine learning model to the set of map tile types and the set of user contextual data to select the type of map tile from the set of map tile types (see [0066]-[0070]+).
With regard to claim 8, Stout teaches that the method of claim 5, wherein the set of user contextual data includes at least one of: (i) current user activity data indicative of a user travelling, the user planning a trip, or the user using a mapping application; (ii) a current date; (iii) a current time; (iv) a weather forecast; (v) a set of location metadata associated with the particular geographic region; (vi) connectivity data indicative of a current status of a communication network in which the user device communicates; or (vii) battery life data indicative of a current battery status of the user device (the context analyzer determines a current view of the map to be displayed, and current searches, and etc. ; specifies a geographic region of interest to the user on the map, see [0044]-[0045]+) .
Conclusion
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/NGA X NGUYEN/Primary Examiner, Art Unit 3662
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