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 .
DETAILED ACTION
1. This is a first non-final Office Action on the merits for application 18978832. Claims 1-20 are pending examination.
Double Patenting
2. 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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1-12 of U.S. Patent No. 12,203,763. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed to the same subject matter, perform similar method steps and a person of ordinary skill in the art would not be free to practice one of the claimed inventions without infringing upon the other inventions.
Application number: 18978832
1. A method comprising: determining by a computing system (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device; based on reference data that correlates past route information with the determined content and geographic location, predicting by the computing system a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content; and based on the prediction of the future route, configuring by the computing system the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route.
Patent number: 12,203,7631. A method comprising:
determining by a computing system (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device;
based on reference data that correlates past route information with the determined content and geographic location, predicting by the computing system a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content; and
based on the prediction of the future route, configuring by the computing system the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route,
wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises using a machine-learning model that is trained to correlate (i) input data defining content and location to (ii) output data defining route probability, wherein using the machine-learning model comprises processing through the machine-learning model the determined content and geographic location information to cause the machine-learning model to output, based on the determined content and the geographic location, a prediction of the future route of the device,
the method further comprising training, by the computing system, the machine-learning model with information that associates content information with route information.
8. A computing system comprising: one or more processors; and a memory in communication with the one or more processors, wherein the memory stores instruction code that, when executed by the one or more processors, causes the computing system to perform operations including: determining (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device, based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content, and based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route.
6. A computing system comprising:
one or more processors; and
a memory in communication with the one or more processors, wherein the memory stores instruction code that, when executed by the one or more processors, causes the computing system to perform operations including:
determining (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device,
based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content, and
based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route,
wherein predicting the future route of the device based on reference to the data that correlates past route information with the determined content and geographic location comprises using a machine-learning model that is trained to correlate (i) input data defining content and location to (ii) output data defining route probability, wherein using the machine-learning model comprises processing through the machine-learning model the determined content and geographic location information to cause the machine-learning model to output, based on the determined content and the geographic location, a prediction of the future route of the device, and
wherein the operations additionally include training the machine-learning model with information that associates content information with route information.
15. A non-transitory computer-readable medium having stored thereon instruction code that, when executed by one or more processors of a computing system, causes the computing system to perform operations comprising: determining (i) content of a broadcast that is being presented by device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device, based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content, and based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route.
11. A non-transitory computer-readable medium having stored thereon instruction code that, when executed by one or more processors of a computing system, causes the computing system to perform operations comprising:
determining (i) content of a broadcast that is being presented by device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device,
based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device, wherein the reference data comprises information about panelist consumption of the determined content, and
based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about the one or more determined points along the predicted future route,
wherein predicting the future route of the device based on reference to the data that correlates past route information with the determined content and geographic location comprises using a machine-learning model that is trained to correlate (i) input data defining content and location to (ii) output data defining route probability, wherein using the machine-learning model comprises processing through the machine-learning model the determined content and geographic location information to cause the machine-learning model to output, based on the determined content and the geographic location, a prediction of the future route of the device, and
wherein the operations additionally comprise training the machine-learning model with information that associates content information with route information.
As to the independent claims:
Instant claim 2 is fully disclosed in claim 2 of the copending Patent number 12,203,763.
Instant claim 3 is fully disclosed in claim 3 of the copending Patent number 12,203,763 .
Instant claim 4 is fully disclosed in claim 4 of the copending Patent number 12,203,763 .
Instant claim 5 is fully disclosed in claim 5 of the copending Patent number 12,203,763 .
Limitations presented in claim 6 is an obvious variation of additional limitations presented in Patent number 12,203,763 claim 1.
Limitations presented in claim 8 is an obvious variation of additional limitations presented in Patent number 12,203,763 claim 1.
Instant claim 9 is fully disclosed in claim 7 of the copending Patent number 12,203,763.
Instant claim 10 is fully disclosed in claim 8 of the copending Patent number 12,203,763 .
Instant claim 11 is fully disclosed in claim 9 of the copending Patent number 12,203,763 .
Instant claim 12 is fully disclosed in claim 10 of the copending Patent number 12,203,763 .
Limitations presented in claim 13 is an obvious variation of additional limitations presented in Patent number 12,203,763 claim 6.
Limitations presented in claim 14 is an obvious variation of additional limitations presented in Patent number 12,203,763 claim 6.
Instant claim 17 is fully disclosed in claim 12 of the copending Patent number 12,203,763 .
Limitations presented in claim 20 is an obvious variation of additional limitations presented in Patent number 12,203,763 claim 11.
It would have been obvious to one having ordinary skill in the art to make the changes above in order to cover slightly broader limitations. Furthermore, the claimed elements perform the same function as before.
Claim Rejections - 35 USC § 101
3. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 is/are drawn to method (i.e., a process), claim(s) 8 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 15 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 1, 8, and 15 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. Specifically, claim(s) 1, 8, and 15 recite(s) determining by a (i) content of a broadcast that is being presented and (i) geographic location, wherein determining the content of the broadcast that is being presented comprises receiving fingerprint data representing the content of the broadcast that is being presented, and matching the received fingerprint data with fingerprint data in a content matching record, wherein comprises location, and wherein determining the geographic location comprises receiving location data from the location; based on reference data that correlates past route information with the determined content and geographic location, predicting a future route, wherein the reference data comprises information about panelist consumption of the determined content; and based on the prediction of the future route, configuring to insert, into the content being presented, information about one or more determined points along the predicted future route, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
determining by a (i) content of a broadcast that is being presented and (i) geographic location, wherein determining the content of the broadcast that is being presented comprises receiving fingerprint data representing the content of the broadcast that is being presented, and matching the received fingerprint data with fingerprint data in a content matching record, wherein comprises location, and wherein determining the geographic location comprises receiving location data from the location; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
based on reference data that correlates past route information with the determined content and geographic location, predicting a future route, wherein the reference data comprises information about panelist consumption of the determined content; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
based on the prediction of the future route, configuring to insert, into the content being presented, information about one or more determined points along the predicted future route; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as system, non transitory computer readable medium, computing system, processor, memory, device, vehicle, circuitry merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the system, non transitory computer readable medium, computing system, processor, memory, device, vehicle, circuitry perform(s) the steps or functions of determining by a (i) content of a broadcast that is being presented and (i) geographic location, wherein determining the content of the broadcast that is being presented comprises receiving fingerprint data representing the content of the broadcast that is being presented, and matching the received fingerprint data with fingerprint data in a content matching record, wherein comprises location, and wherein determining the geographic location comprises receiving location data from the location; based on reference data that correlates past route information with the determined content and geographic location, predicting a future route, wherein the reference data comprises information about panelist consumption of the determined content; and based on the prediction of the future route, configuring to insert, into the content being presented, information about one or more determined points along the predicted future route. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, non transitory computer readable medium, computing system, processor, memory, device, vehicle, circuitry to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. As discussed above, taking the claim elements separately, the system, non transitory computer readable medium, computing system, processor, memory, device, vehicle, circuitry perform(s) the steps or functions of determining by a (i) content of a broadcast that is being presented and (i) geographic location, wherein determining the content of the broadcast that is being presented comprises receiving fingerprint data representing the content of the broadcast that is being presented, and matching the received fingerprint data with fingerprint data in a content matching record, wherein comprises location, and wherein determining the geographic location comprises receiving location data from the location; based on reference data that correlates past route information with the determined content and geographic location, predicting a future route, wherein the reference data comprises information about panelist consumption of the determined content; and based on the prediction of the future route, configuring to insert, into the content being presented, information about one or more determined points along the predicted future route. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2-7, 9-14, and 16-20 further describe the abstract idea of present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. Claim(s) 2-7, 9-14, and 16-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, non-transitory computer readable medium, device, machine-learning model to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. As discussed above, taking the claim elements separately, the system, non-transitory computer readable medium, device, machine-learning model, perform(s) the steps or functions of wherein predicting the future route based on the reference data that correlates past route information with the determined content and geographic location comprises (i) selecting a set of data records based on each data record in the selected set indicating panelist consumption of the determined content and (ii) searching the selected set of data records for a route matching the determined geographic location; wherein configuring to present the information about the one or more determined points along the predicted future route comprises communicating the information to enable to present the information; determining, based on updated geographic location, is not following the predicted future route; and responsive to the determining is not following the predicted future route, causing to refrain from presenting the information about the one or more determined points along the predicted future route; wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay; wherein predicting the future route based on the reference data that correlates past route information with the determined content and geographic location comprises; training, information that associates content information with route information. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of present content of a broadcast to a device located inside the vehicle based on correlation of past route information and geographic location and prediction of future routes. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
4. 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.
A. Claim(s) 1-3, 6-10, 13-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al., (U.S. Patent Application Publication No. 20210095986) in view of Brenner et al., (U.S. Patent Application Publication No. 20150074526).
As to Claim 1, Brown teaches a method comprising:based on reference data that correlates past route information with the determined content and geographic location, predicting by the computing system a future route of the device (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences), wherein the reference data comprises information about panelist consumption of the determined content; and (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416),based on the prediction of the future route, configuring by the computing system the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown does not teach determining by a computing system (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record;
wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device.
However Brenner teaches determining by a computing system (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, (0020: In some example embodiments, the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things.), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on), wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record; (0020: the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on), wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device; (0027: In some example embodiments, the information selection module 220 is configured and/or programmed to select an information segment representative of information associated with the playback device 130 to present during the identified break. Examples of information segments representative of information associated with the playback device 130 include information segments based on information captured or received by a messaging application of the playback device 130 (e.g., a mail client or app of amobike device), information captured by a location determination component of the playback device 130 (e.g., a GPS device within a car area network), and/or other information stored, contained, received and/or captured by other components of the playback device 130 that are not associated with playing content. See also, [0056] for circuitry),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown to include wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device of Brenner. Motivation to do so comes from the knowledge well known in the art that wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 2, Brown and Brenner teach the method of claim 1.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises (i) selecting a set of data records based on each data record in the selected set indicating panelist consumption of the determined content and (ii) searching the selected set of data records for a route matching the determined geographic location; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 3, Brown and Brenner teach the method of claim 1.
Brown further teaches wherein configuring the device to present the information about the one or more determined points along the predicted future route comprises communicating the information to the device to enable the device to present the information; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 6, Brown and Brenner teach the method of claim 1.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises using a machine-learning model; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
As to Claim 7, Brown and Brenner teach the method of claim 6.
Brown further teaches further comprising training, by the computing system, the machine-learning model with information that associates content information with route information; (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
As to Claim 8, Brown teaches a computing system comprising: one or more processors; and a memory in communication with the one or more processors (0086 and Fig. 5: processors), wherein the memory (0051: memory) stores instruction code that, when executed by the one or more processors, causes the computing system to perform operations including:based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences), wherein the reference data comprises information about panelist consumption of the determined content, and (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416),based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown does not teach determining (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record;
wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device.
However Brenner teaches determining (i) content of a broadcast that is being presented by a device in a vehicle and (i) geographic location of the device, (0020: In some example embodiments, the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things.), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on),wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record; (0020: the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on),wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device; (0027: In some example embodiments, the information selection module 220 is configured and/or programmed to select an information segment representative of information associated with the playback device 130 to present during the identified break. Examples of information segments representative of information associated with the playback device 130 include information segments based on information captured or received by a messaging application of the playback device 130 (e.g., a mail client or app of amobike device), information captured by a location determination component of the playback device 130 (e.g., a GPS device within a car area network), and/or other information stored, contained, received and/or captured by other components of the playback device 130 that are not associated with playing content. See also, [0056] for circuitry).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown to include wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device of Brenner. Motivation to do so comes from the knowledge well known in the art that wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 9, Brown and Brenner teach the computing system of claim 8.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises (i) selecting a set of data records based on each data record in the selected set indicating panelist consumption of the determined content and (ii) searching the selected set of data records for a route matching the determined geographic location; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 10, Brown and Brenner teach the computing system of claim 8.
Brown further teaches wherein configuring the device to present the information about the one or more determined points along the predicted future route comprises communicating the information to the device to enable the device to present the information; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 13, Brown and Brenner teach the computing system of claim 8.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises using a machine-learning model; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
As to Claim 14, Brown and Brenner teach the computing system of claim 13.
Brown further teaches wherein the operations additionally include training the machine-learning model with information that associates content information with route information; (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
As to Claim 15, Brown teaches a non-transitory computer-readable medium having stored thereon instruction code that, when executed by one or more processors of a computing system, causes the computing system to perform operations comprising:based on reference data that correlates past route information with the determined content and geographic location, predicting a future route of the device (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences), wherein the reference data comprises information about panelist consumption of the determined content, and (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416),based on the prediction of the future route, configuring the device to insert, into the content being presented by the device, information about one or more determined points along the predicted future route; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown does not teach determining (i) content of a broadcast that is being presented by device in a vehicle and (i) geographic location of the device, wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device.
However Brenner teaches determining (i) content of a broadcast that is being presented by device in a vehicle and (i) geographic location of the device, (0020: In some example embodiments, the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things.), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on),wherein determining the content of the broadcast that is being presented by the device in the vehicle comprises receiving fingerprint data representing the content of the broadcast that is being presented by the device in the vehicle, and matching the received fingerprint data with fingerprint data in a content matching record; (0020: the content provider 110 and/or the playback device 130 may include one or more fingerprint generators 112 or fingerprint generators 137 configured to generate identifiers for content being transmitted or broadcast by the content provider 110 or 135 and/or received or accessed by the playback device 130. For example, the fingerprint generators 112 or 137 may include a reference fingerprint generator (e.g., a component that calculates a hash value from a portion of content) that is configured to generate reference fingerprints or other identifiers of received content, among other things), and (0032: In operation 310, the information insertion engine 150 identifies a break in content playing via the playback device 130. For example, the content break module 210 may identify a break in content by comparing a fingerprint of the playing content to a group of reference fingerprints (e.g., embedded on the playback device 130 and/or stored in the cloud) to identify the content and any breaks playing via the playback device 130, may identify the break in content based on metadata associated with the playing content, may identify the break in content based on audio and/or video characteristics of the playing content, and so on), wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device, (0027: In some example embodiments, the information selection module 220 is configured and/or programmed to select an information segment representative of information associated with the playback device 130 to present during the identified break. Examples of information segments representative of information associated with the playback device 130 include information segments based on information captured or received by a messaging application of the playback device 130 (e.g., a mail client or app of amobike device), information captured by a location determination component of the playback device 130 (e.g., a GPS device within a car area network), and/or other information stored, contained, received and/or captured by other components of the playback device 130 that are not associated with playing content. See also, [0056] for circuitry)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown to include wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device of Brenner. Motivation to do so comes from the knowledge well known in the art that wherein the device comprises location circuitry, and wherein determining the geographic location of the device comprises receiving location data from the location circuitry of the device would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 16, Brown and Brenner teach the non-transitory computer-readable medium of claim 15.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises (i) selecting a set of data records based on each data record in the selected set indicating panelist consumption of the determined content and (ii) searching the selected set of data records for a route matching the determined geographic location; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 17, Brown and Brenner teach the non-transitory computer-readable medium of claim 15.
Brown further teaches wherein configuring the device to present the information about the one or more determined points along the predicted future route comprises communicating the information to the device to enable the device to present the information; (0046: Travel paths training data 209 stores a plurality of paths taken by different users, times during which such paths were taken, and/or geofences traversed by each of the paths. These travel paths training data 209 are used by the travel notification system 124 to train the machine learning techniques used to predict a geofence that will be traversed by a user in the future based on the user's current location and current time. As an example, the travel notification system 124 may generate a first path for a first user in response to determining that the first user has left a first location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a second location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the first path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate a second path for the first user in response to determining that the first user has left a third location at which the user was previously for a threshold period of time (e.g., more than 2 hours) and has reached a fourth location where the user remained for more than another threshold period of time (e.g., more than 1 hour). The travel notification system 124 may store a time period during which the second path was traversed by the first user in the travel paths training data 209. The travel notification system 124 may generate such paths for multiple users of a messaging client application 104.), and (0076: At a future time, the current path information module 411 may determine the current time and the current location for a given user. The current path information module 411 may provide the current time and the current location to the machine learning technique module 412. The machine learning technique module 412 applies a trained machine learning technique or model to the current time and the current location of the given user. The machine learning technique module 412 predicts a path that will be traversed by the user and a list of geofences that will be traversed by the user on the path in the future. The machine learning technique module 412 may identify the times in the future when the given user will likely traverse each of the geofences in the list. The machine learning technique module 412 provides the list of geofences to the geofence selection module 416).
As to Claim 20, Brown and Brenner teach the non-transitory computer-readable medium of claim 15.
Brown further teaches wherein predicting the future route of the device based on the reference data that correlates past route information with the determined content and geographic location comprises using a machine-learning model; (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
B. Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al., (U.S. Patent Application Publication No. 20210095986) in view of Brenner et al., (U.S. Patent Application Publication No. 20150074526) in view of Takemura, (U.S. Patent Application Publication No. 20220005076).
As to Claim 4, Brown and Brenner teach the method of claim 1.
Brown further teaches further comprising: determining by the computing system, based on updated geographic location of the device, that the device is not following the predicted future route; and (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown and Brenner do not teach responsive to the determining that the device is not following the predicted future route, causing by computing system the device to refrain from presenting the information about the one or more determined points along the predicted future route.
However Takemura teaches responsive to the determining that the device is not following the predicted future route, causing by computing system the device to refrain from presenting the information about the one or more determined points along the predicted future route; (0067: In some embodiments, providing the selected subset of the one or more proposals may involve confirming that the moving user is in a target route segment (or about to enter the route segment). In some embodiments, confirmation that the moving user is in the target route segment may be based on sensor data tracking the location of the moving user. For example, it is possible that the route along which the moving user moved has changed from the predicted route, and the moving user never enters the route segment or does not enter the route segment in a predicted time period. Thus, in some embodiments, before each, all, or some of the selected proposals are presented to the moving user, it may be confirmed that the moving user is in the target route segment.” [0040]. See, “In some embodiments, method 260 proceeds to decision block 282. At decision block 282, it will be determined if the driver is in the predicted route segment. In some embodiments, method 260 proceeds to operation 284. At operation 284, if the driver is not in the route segment, the slot manager will be notified. In some embodiments, method 260 proceeds to operation 286. At operation 286, if the driver is in the predicted route segment, the advertisement will be provided to the driver.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include determining by the computing system, based on updated geographic location of the device, that the device is not following the predicted future route; and responsive to the determining that the device is not following the predicted future route, causing by computing system the device to refrain from presenting the information about the one or more determined points along the predicted future route of Takemura. Motivation to do so comes from the knowledge well known in the art that determining by the computing system, based on updated geographic location of the device, that the device is not following the predicted future route; and responsive to the determining that the device is not following the predicted future route, causing by computing system the device to refrain from presenting the information about the one or more determined points along the predicted future route would provide a more accurate content that would match the location of the device and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
As to Claim 11, Brown and Brenner teach the computing system of claim 8.
Brown further teaches wherein the operations additionally include: determining, based on updated geographic location of the device, that the device is not following the predicted future route; and (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown and Brenner do not teach responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route.
However Takemura teaches responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route; (0067: In some embodiments, providing the selected subset of the one or more proposals may involve confirming that the moving user is in a target route segment (or about to enter the route segment). In some embodiments, confirmation that the moving user is in the target route segment may be based on sensor data tracking the location of the moving user. For example, it is possible that the route along which the moving user moved has changed from the predicted route, and the moving user never enters the route segment or does not enter the route segment in a predicted time period. Thus, in some embodiments, before each, all, or some of the selected proposals are presented to the moving user, it may be confirmed that the moving user is in the target route segment.” [0040]. See, “In some embodiments, method 260 proceeds to decision block 282. At decision block 282, it will be determined if the driver is in the predicted route segment. In some embodiments, method 260 proceeds to operation 284. At operation 284, if the driver is not in the route segment, the slot manager will be notified. In some embodiments, method 260 proceeds to operation 286. At operation 286, if the driver is in the predicted route segment, the advertisement will be provided to the driver.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route of Takemura. Motivation to do so comes from the knowledge well known in the art that responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route would provide a more accurate content that would match the location of the device and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
As to Claim 18, Brown and Brenner teach the method of claim 1.
Brown further teaches wherein the operations additionally comprise: determining, based on updated geographic location of the device, that the device is not following the predicted future route; and (0026: The travel notification system 124 serves advertisements to one or more users based on their future destinations, locations, navigation paths, and/or modes of transportation. Specifically, the travel notification system 124 tracks and stores locations of each user of the messaging client application 104 and the times at which the users were at the various locations. In some cases, the travel notification system 124 requests express authorization from each of the users to track their current locations. The travel notification system 124 computes and/or forms paths between the locations based on how long the users spend at specific locations. For example, if a user was home for several hours (e.g., a first location) and then started traveling before reaching work and staying at work for several hours (e.g., a second location), the travel notification system 124 determines that the locations traversed between when the user was home and when the user reached work form a single navigation path. The travel notification system 124 identifies geofences traversed by each of the locations and/or paths that the users travel), and (0027: the machine learning technique can predict which set of geofences will be traversed by the user when the user is in a particular location and/or is along one of the previously traversed navigation paths in the future. The travel notification system 124 selects an advertisement to serve to the users based on the points of interest that are within the predicted set of geofences).
Brown and Brenner do not teach responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route.
However Takemura teaches responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route; (0067: In some embodiments, providing the selected subset of the one or more proposals may involve confirming that the moving user is in a target route segment (or about to enter the route segment). In some embodiments, confirmation that the moving user is in the target route segment may be based on sensor data tracking the location of the moving user. For example, it is possible that the route along which the moving user moved has changed from the predicted route, and the moving user never enters the route segment or does not enter the route segment in a predicted time period. Thus, in some embodiments, before each, all, or some of the selected proposals are presented to the moving user, it may be confirmed that the moving user is in the target route segment.” [0040]. See, “In some embodiments, method 260 proceeds to decision block 282. At decision block 282, it will be determined if the driver is in the predicted route segment. In some embodiments, method 260 proceeds to operation 284. At operation 284, if the driver is not in the route segment, the slot manager will be notified. In some embodiments, method 260 proceeds to operation 286. At operation 286, if the driver is in the predicted route segment, the advertisement will be provided to the driver.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route of Takemura. Motivation to do so comes from the knowledge well known in the art that responsive to the determining that the device is not following the predicted future route, causing the device to refrain from presenting the information about the one or more determined points along the predicted future route would provide a more accurate content that would match the location of the device and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
C. Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al., (U.S. Patent Application Publication No. 20210095986) in view of Brenner et al., (U.S. Patent Application Publication No. 20150074526) in view of MacDonald et al., (U.S. Patent Application Publication No. 20080032721).
As to Claim 5, Brown and Brenner teach the method of claim 1.
Brown and Brenner do not teach wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay.
However MacDonald teaches wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay; (0020: deliver real-time weather map information for overlay display on navigation screens; deliver real-time parking availability information to be displayed on a navigation system or other displays; deliver e-mail information to be viewed on a vehicle screen or other display; deliver content to an infotainment system for educational and/or entertainment-based viewing; deliver content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay of MacDonald. Motivation to do so comes from the knowledge well known in the art that wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay would provide information that would overlay the content which would force the user to see and click on the content and would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
As to Claim 5, Brown and Brenner teach the computing system of claim 8.
Brown and Brenner do not teach wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay.
However MacDonald teaches wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay; (0020: deliver real-time weather map information for overlay display on navigation screens; deliver real-time parking availability information to be displayed on a navigation system or other displays; deliver e-mail information to be viewed on a vehicle screen or other display; deliver content to an infotainment system for educational and/or entertainment-based viewing; deliver content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay of MacDonald. Motivation to do so comes from the knowledge well known in the art that wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay would provide information that would overlay the content which would force the user to see and click on the content and would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
As to Claim 5, Brown and Brenner teach the non-transitory computer-readable medium of claim 15.
Brown and Brenner do not teach wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay.
However MacDonald teaches wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay; (0020: deliver real-time weather map information for overlay display on navigation screens; deliver real-time parking availability information to be displayed on a navigation system or other displays; deliver e-mail information to be viewed on a vehicle screen or other display; deliver content to an infotainment system for educational and/or entertainment-based viewing; deliver content).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brown and Brenner to include wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay of MacDonald. Motivation to do so comes from the knowledge well known in the art that wherein presenting the information about the one or more determined points along the predicted future route comprises presenting the information as a content overlay would provide information that would overlay the content which would force the user to see and click on the content and would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable and accurate.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Short-term travel behavior prediction with GPS, land use, and point of interest data” describes “In everyday travel, U.S. commuters will each spend 38 h a year stuck in traffic and waste over $800 in fuel (TTI, 2015). Yet, despite this statistic, the regular commute of drivers is often predictable, leading many federal projects to aim at alleviating congestion through traveler information and intelligent transportation systems (e.g., INFLO, Queue WARN, CACC, EnableATIS, ATIS2.0). Short-term destination prediction is a developing field of research that can improve these approaches through real-traveler information, such as route, traffic incidence, and congestion levels. The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle's destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. To study these concepts, a database of GPS driving traces (260 participants for 70 days) was collected. To model the user's trip purpose in the prediction algorithm, a new data source was explored: point of interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data and travel patterns, trip purpose was calculated with machine learning methods. To take advantage of this data source, a new prediction model structure was developed that uses trip purpose when it is available and that falls back on traditional spatial temporal Markov models when it is not. For the first time, there is an understanding of “why” a trip is taken (not just “where” and “when”), allowing the use of “why” in the prediction model. This paper explores the baseline model followed by the inclusion of trip purpose.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#US 20100332315 A1 teaches similar invention which describes A mobile device may present advertisements to users. However, advertisements may be ineffective or dangerous if presented when the attention of the user is unavailable (e.g., while operating a vehicle at a busy intersection.) It may also be desirable to select a sequence of advertisements that interrelate, or that relate the route of the user to an advertised product or service. Therefore, potential routes may be identified (e.g., based on user history or nearby locations of interest), and for potential routes, advertisement opportunities may be identified where the user may have an at least partial attention availability (e.g., traffic signals and fuel stops.) Advertisements may be selected for presentation at the advertisement opportunities of respective potential routes. Additionally, advertisement opportunities may be offered to advertisers in an auction model, and advertisers may specify conditions of advertisements (e.g., competitive placement exclusive of competitors' advertisements, or combinatorial placement of several advertisements.
Conclusion
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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