DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to amendment filed on 24 November 2025. Claims 1, 6, 12 and 19 have been amended. Claims 1-20 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory 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 rejected on the ground of nonstatutory double patenting over claims 1-20 of U.S. Patent No. 11,551,264 since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
The subject matter claimed in the instant application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
18/796,589
11,551,264
A computer-implemented method associated with intelligent advertisement-based routing comprising:
comparing processed data to at least one database of advertisements geographically located in proximity to a destination, wherein the proposed data is generated at least based on the destination;
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route;
receiving location data associated with at least one ride-share vehicle and synchronized business data associated with at least one advertisement location along the first advertisement-based route; and
based on the location data, generating a second advertisement-based route.
A computer-implemented method for generating advertisement-based routes comprising:
receiving a destination;
receiving a desired time of arrival; receiving user profile data associated with a user;
processing the destination, desired time of arrival, and user profile data utilizing at least one machine-learning algorithm;
comparing the processed data to at least one database of physical advertisements geographically located in proximity to the destination;
based on the comparison of the processed data to the at least one database of physical advertisements geographically located in proximity to the destination, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route;
receiving location data associated with at least one ride-share vehicle transporting the user and synchronized business data associated with at least one physical advertisement location along the first advertisement-based route;
based on the location data associated with the at least one ride-share vehicle transporting the user and the synchronized business data associated with the at least one physical advertisement location along the first advertisement-based route, generating a second advertisement-based route; and dynamically re-routing the at least one ride-share vehicle to the second advertisement-based route.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitation carried are either inherently implied or would have been obvious to one or ordinary skill in the art. 18/796,589 recites “comparing processed data to at least one database of advertisements geographically located in proximity to a destination, wherein the proposed data is generated at least based on the destination”. One of ordinary skill in the art would have contemplated that the processed data must have been received from destination , a desired time of arrival, user profile data assocted with a user and processed by machine learing algorithm. 18/796,589 recites “ advertisement” which is merely a different way of wording to the patent’s “physical advertisements” as a language do not carry patentable weights. 18/796,589 does not have “transporting the user” and “generating a second advertismetn based on the route” limitation, the wordings are different, the limitation carried are either inherently implied or would have been obvious to tone of ordinary skill in the art. 18/796,589’s another major difference in language by the lacking of the step of “
dynamically re-routing the at least one ride-share vehicle to the second advertisement-based route” . One ordinary skill would contemplate that the re-routing would be occur upon generating of the secondary advertisements, though the wordings are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art, or otherwise described in language that do not carry patentable weight. Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Claims 1-20 rejected on the ground of nonstatutory double patenting over claims 1-20 of U.S. Patent No. 12,279,839 since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
18/796,589
12,079,839
A computer-implemented method associated with intelligent advertisement-based routing comprising:
comparing processed data to at least one database of advertisements geographically located in proximity to a destination, wherein the proposed data is generated at least based on the destination;
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route;
receiving location data associated with at least one ride-share vehicle and synchronized business data associated with at least one advertisement location along the first advertisement-based route; and
based on the location data, generating a second advertisement-based route.
A computer-implemented method associated with intelligent advertisement-based routing comprising:
processing a destination, a desired time of arrival data, and user profile data associated with a user to form processed data; comparing the processed data to at least one database of advertisements geographically located in proximity to the destination;
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route;
receiving location data associated with at least one ride-share vehicle transporting the user and synchronized business data associated with at least one advertisement location along the first advertisement-based route;
based on the location data, generating a second advertisement-based route; and
dynamically re-routing the at least one ride-share vehicle to the second advertisement-based route.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitation carried are either inherently implied or would have been obvious to one or ordinary skill in the art. 18/796,589 recites “comparing processed data to at least one database of advertisements geographically located in proximity to a destination, wherein the proposed data is generated at least based on the destination”. One of ordinary skill in the art would have contemplated that the processed data must have been received from destination , a desired time of arrival, user profile data assocted with a user and processed by machine learing algorithm. 18/796,589 does not have “transporting the user” the wordings are different, the limitation carried are either inherently implied or would have been obvious to tone of ordinary skill in the art. 18/796,589’s another major difference in language by the lacking of the step of “ dynamically re-routing the at least one ride-share vehicle to the second advertisement-based route” . One ordinary skill would contemplate that the re-routing would be occur upon generating of the secondary advertisements,
though the wordings are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art, or otherwise described in language that do not carry patentable weight. Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claims 1-11 are a metho, and claims 12-20 are a system and . Thus, each
independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101.
However, the claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: Independent claims (1, 12 and 19) recite comparing processed data to at least one of advertisements geographically located in proximity to a destination, wherein the proposed data is generated at least based on the destination; based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route; receiving location data associated with at least one ride-share vehicle ; and based on the location data, generating a second advertisement-based route. These limitations fall within “Certain Method of Organizing Human Activity” for commercial or legal interactions (including: advertising, marketing or sales activities or behavior; business relations). The claims as drafted, are a process that under the broadest reasonable interpretation cover the user selecting longer route to the destination with more advertisement exposure to decrease the overall cost of the ride.
Claims 2-11, 13-18 and 20 merely providing additional abstract concept and narrow the abstract ideas of claims 1, 12 and 19. Further claims 1-20 are recited at such a high level that the claimed steps amount to no mor than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content and authorize compensation. Thus, the claims are directed to an abstract idea.
Step 2A-Prong 2: The only additional elements in independent claims is some form of
computerized system, which is also recited in independent claims 1, 12, and 19 . These
computerized systems are recited at a high-level of generality (i.e., as a generic processor, machine learing engine) performing a generic computer function of processing data and a generic memory and database storing data) such that it amounts no more adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Independent claims also recite additional limitation of “synchronized business data associated with at least one advertisement location along the first advertisement-based route” is recited at high level of generality” offers no more that providing advertisement location as result from agreement between the user within the ride-share and any third party and merely adds insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the claimed computer systems amount to no more than “apply” a selection of content on the systems.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hollinger et al (US Pub., 2020/0143409 A1) in view of Rudchenko et al (US 2019/0034038 A1) and futher view of Modi et al (US Pub., 2020/0044757 A1)
With respect to claim 1, Hollinger teaches a computer-implemented method associated with intelligent advertisement-based routing comprising:
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route(paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and
based on the location data, generating a second advertisement-based route(paragraph [0037], discloses advertisement generator generates the second offers in response to receiving
a ride share request from the mobile device of the user. For example, the advertisement generator accesses user data associated with the user from the user profile database 142 and accesses business data associated with one or business along the route from business database
and generates first offers based on the user data and business data, and selected offers, and paragraph [0038], discloses the second offers are transmitted to a second mobile device of a second user… ).
Hollinger teaches the above elements including comparing processed data to at least one database of advertisements geographically located in proximity to a destination(paragraph [0052], discloses the advertisement generator compares the user data corresponding to the user or th the business within the business database to identify which business provide product and/or service that match at least one parameter of the user data.., the advertisement generate may compare parameters of the rid share request to the business within the business database to identify which business provide product and/or service that match one or more of the ride share parameters ) , wherein the processed data is generated at least based on the destination, (paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and receiving location data associated with at least one ride-share vehicle (paragraphs [0016]-[0017], [0020]-[0022]discloses receiving pick up location, destination, arrival times and rout information of users and from the ride share incentive system) and advertisement generator generates one or more offers and communicates with the offer to the mobile device (paragraph [0031]). Hollinger failed to explicitly teach the processed data is compared , by a machine learning engine, and wherein the processed data includes at least one lexical feature or at least one contextual feature and received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Rudchenko teaches compared , by a machine learning engine(paragraph [0007], discloses analyzing eye-gaze input , the method includes receiving eye-gaze input on an electronic device, determining at least one gaze location associated with the eye-gaze input, and applying at least one machine-learning algorithm to the at least one gaze location [processed data], and paragraph [0032], discloses apply a machine learing algorithm .., to determine a predicted response assocted with the at least one gaze location) , and wherein the processed data includes at least one lexical feature or at least one contextual feature(paragraph [0032], discloses the database that comprise lexical feature data and contextual feature data ..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko in order to prove direct, efficient identification of a content.
Rudchenko failed to teach received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Modi teaches synchronized business data associated with at least one advertisement location along the first advertisement-based route (Fig. 4, 408-412, discloses identifying reference audio content that has at least a threshold extent of similarity with the capture audio content, identify the geographic location assocted with identified reference audio content and based at least on the identified geographic location associated with the identified reference audio content, outputting, via the user interface of the vehicle-based media system, prompt to navigate to the identified geographic location, paragraph [0046], discloses vehicle-based media system can cause the navigation system to display a navigational route beginning at the vehicle's current location and ending at the location associated with the identified advertisement and paragraph [0047], discloses the vehicle-based media system can use the geographic location corresponding to a particular advertisement as a first geographic location (e.g., a nearby location of the restaurant ) and can determine a second geographic location associated with the vehicle-based media system (e.g., the current geographic location of the vehicle in which the vehicle-based media system is located)). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for generates one or more offers and communicates with the offer to the mobile device of Hollinger and using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko with audio ad and navigation-related action synchronization feature of Modi in order to retrieve geographical location data for advertisements that are associated with a location within a threshold proximity distance (e.g., within a five mile radius) of the current location of the vehicle destination location of the vehicle or some location on a route between the current and destined location (see, Modi, paragraph [0049]).
With respect to claim 2, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 1, furthermore, Hollinger teaches the method further comprising: recording at least one user interaction with at least one advertisement associated with the second advertisement-based route(paragraph [0025], discloses historical data of the user may include previous orders made the business, business ordered from, previously accepted offers, previously rejected offers, etc.) ; and
saving the at least one user interaction in a database for future processing(paragraph [0025], discloses the user profile databases may include user data corresponding the one or more users, user database may include historical data of a user).
With respect to claim 3, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 2, furthermore, Hollinger teaches the method, wherein the at least one user interaction is measured according to at least one of: an engagement time, a click, an eye-gaze duration, and a subsequent purchase(paragraph [0025], discloses the user profile databases may include user data corresponding the one or more users, user database may include historical data of a user).
With respect to claim 4, Hollinger i in view of Rudchenko and further view of Modi teaches elements of claim 1, furthermore, Hollinger teaches the method further comprising presenting at least one advertisement associated with the second advertisement-based route, the method further comprises determining at least one advertisement display time(paragraph [0022], discloses the offer may include an incentive for one or more users to purchase a produce and/or service from a business, offer included an incentive for one or more user to purchase a product and/or service form a business .., an incentive for a user to stop at location corresponding route…)
wherein the advertisement display time is dynamically calculated based on at least one of:
a current location of a user, the at least one advertisement location, and an advertisement data transmission rate(paragraph [0052], discloses advertisement generator may compare parameter of the ride share request to the businesses within the business database 144 to identify which businesses provide products and/or services that match one or more of the ride share parameters. For example, the ride share parameters may indicate that the time of day corresponds to breakfast, and the advertisement generator 148 may identify which businesses offer breakfast).
With respect to claim 5, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 1, furthermore, Hollinger teaches the method further comprising receiving destination input data, wherein the destination input data comprises at least one of: an address, a GPS location, a description, an hours of operation, and a customer rating (paragraph [0048], discloses global positioning system (GPS)).
With respect to claim 6, Hollinger i in view of Rudchenko and further view of Modi teaches elements of claim 1, furthermore, Hollinger teaches the method wherein the proposed data is generated at least based on a user profile, and wherein the user profile data comprises at least one of: social media profile data, at least one user preference, text message data, email data, contacts data, GPS location data, and historical advertisement interaction data(paragraph [0021], discloses user profile database, paragraph [0025], discloses user database may include historical data and paragraph [0048], discloses GPS location).
With respect to claim 7, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 5, except extracting features from the destination input data.
However, Rudchenko teaches extracting features from the destination input data(Fig. 3, 304, discloses feature extraction engine and paragraph [0044], discloses feature extraction engine may be configured to analyze the various token in conjunction with both lexical feature and contextual feature ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature extraction engine of Rudchenko in order to analyze the various tokens in conjunction with both lexical figure and contextual feature (see, Rudchenko , paragraph [0044]).
With respect to claim 8, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 7, extract wherein the extracted features comprise at least one of: contextual features and lexical features.
However, Rudchenko teaches contextual features and lexical features data(paragraph (paragraph [0032], discloses the database that comprise lexical feature data and contextual feature data ..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko in order to prove direct, efficient identification of a content.
With respect to claim 9, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 8, extract classifying at least one domain associated with the contextual features or lexical features of the destination input data.
However, Rudchenko teaches classifying at least one domain associated with the contextual features or lexical features of the destination input data(paragraph [0045], discloses domain classification engine 306 may consider the lexical and contextual feature from feature extraction engine in determine a domain for classing the intended words or action ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature extraction engine of Rudchenko in order to analyze the various tokens in conjunction with both lexical figure and contextual feature (see, Rudchenko , paragraph [0044]).
With respect to claim 10, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 9, furthermore, Hollinger teaches the method further comprising determining at least one user intent based on classification of the destination input data(paragraph [0048], discloses pick up location and destination).
With respect to claim 11, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 1, furthermore, Hollinger teaches the method further comprising analyzing historical advertisement data associated with the second advertisement-based route, wherein the historical advertisement data comprises at least one of: a past user interaction history with the at least one advertisement location and an overall statistical interaction summary of the at least one advertisement based on multiple users(paragraph [0025], discloses the user profile databases may include user data corresponding the one or more users, user database may include historical data of a user).
With respect to claim 12, Hollinger teaches a system comprising:
at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor(paragraph [0006], discloses the computer-readable program code is executable by one or more computer processor to generate first offers based on the first user data, and paragraph [0023], discloses the ride share incentivization system may include one or more processor and memory .., computer usable program code that may be executed by one or more processor… ), performs a method comprising:
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route(paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and
based on the location data, generating a second advertisement-based route(paragraph [0037], discloses advertisement generator generates the second offers in response to receiving
a ride share request from the mobile device of the user. For example, the advertisement generator accesses user data associated with the user from the user profile database 142 and accesses business data associated with one or business along the route from business database
and generates first offers based on the user data and business data, and selected offers, and paragraph [0038], discloses the second offers are transmitted to a second mobile device of a second user… ).
Hollinger teaches the above elements including comparing processed data to at least one database of advertisements geographically located in proximity to a destination(paragraph [0052], discloses the advertisement generator compares the user data corresponding to the user or th the business within the business database to identify which business provide product and/or service that match at least one parameter of the user data.., the advertisement generate may compare parameters of the rid share request to the business within the business database to identify which business provide product and/or service that match one or more of the ride share parameters ) , wherein the processed data is generated at least based on the destination, (paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and receiving location data associated with at least one ride-share vehicle (paragraphs [0016]-[0017], [0020]-[0022]discloses receiving pick up location, destination, arrival times and rout information of users and from the ride share incentive system) and advertisement generator generates one or more offers and communicates with the offer to the mobile device (paragraph [0031]). Hollinger failed to explicitly teach the processed data is compared , by a machine learning engine, and wherein the processed data includes at least one lexical feature or at least one contextual feature and received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Rudchenko teaches compared , by a machine learning engine(paragraph [0007], discloses analyzing eye-gaze input , the method includes receiving eye-gaze input on an electronic device, determining at least one gaze location associated with the eye-gaze input, and applying at least one machine-learning algorithm to the at least one gaze location [processed data], and paragraph [0032], discloses apply a machine learing algorithm .., to determine a predicted response assocted with the at least one gaze location) , and wherein the processed data includes at least one lexical feature or at least one contextual feature(paragraph [0032], discloses the database that comprise lexical feature data and contextual feature data ..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko in order to prove direct, efficient identification of a content.
Rudchenko failed to teach received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Modi teaches synchronized business data associated with at least one advertisement location along the first advertisement-based route (Fig. 4, 408-412, discloses identifying reference audio content that has at least a threshold extent of similarity with the capture audio content, identify the geographic location assocted with identified reference audio content and based at least on the identified geographic location associated with the identified reference audio content, outputting, via the user interface of the vehicle-based media system, prompt to navigate to the identified geographic location, paragraph [0046], discloses vehicle-based media system can cause the navigation system to display a navigational route beginning at the vehicle's current location and ending at the location associated with the identified advertisement and paragraph [0047], discloses the vehicle-based media system can use the geographic location corresponding to a particular advertisement as a first geographic location (e.g., a nearby location of the restaurant ) and can determine a second geographic location associated with the vehicle-based media system (e.g., the current geographic location of the vehicle in which the vehicle-based media system is located)). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for generates one or more offers and communicates with the offer to the mobile device of Hollinger and using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko with audio ad and navigation-related action synchronization feature of Modi in order to retrieve geographical location data for advertisements that are associated with a location within a threshold proximity distance (e.g., within a five mile radius) of the current location of the vehicle destination location of the vehicle or some location on a route between the current and destined location (see, Modi, paragraph [0049]).
With respect to claim 13, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 12, furthermore, Hollinger teaches the method further comprising receiving input data, wherein the input data comprises at least one of: an image, a video, a social media post, a destination address, a destination description, destination geocoordinates, a user preference, and a user profile(Fig. 6, 610 discloses receive request for ride, and paragraph [0020], discloses ride share request includes at least a pickup location and destination, request includes arrival time).
With respect to claim 14, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 12, furthermore, Hollinger teaches the method further comprising receiving real- time business information, wherein the real-time business information comprises at least one of: a closure notification, an opening notification, a surrounding activity indicator, a quality of external signage ranking, an hourly business indicator, and a day-of-week business indicator(paragraph [0051], discloses the advertisement generator identifies which business proved one or more product and/or services that correspond to one or mor parameters of user data and/or business data).
With respect to claim 15, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 12, furthermore, Hollinger teaches the method wherein the first advertisement- based route is one of: a direct route, a minor detour, an average detour, a major detour, and a free route(paragraph [0050], discloses the generation of a first route from ride share request).
With respect to claim 16, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 12, furthermore, Hollinger teaches the method further comprising receiving, on a user device, at least one electronic advertisement associated with the first advertisement-based route, wherein the user device is in proximity to the at least one advertisement location (paragraph [0053], discloses the advertisement generator may generate a list of businesses that were identified to provide products and/or services that correspond to at least one parameter of the user data and/or the business data. The advertisement generator may prioritize businesses that are located within closer proximity to the route)
With respect to claim 17, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 16, furthermore, Hollinger teaches the method wherein the user device comprises at least one of: a mobile phone, a tablet, a laptop, and a vehicular device(Fig. 1, 134 discloses mobile device and paragraph [0004], discloses the plurlity of mobile devices).
With respect to claim 18, Hollinger in view of Rudchenko and further view of Modi teaches elements of claim 16, furthermore, Hollinger teaches the method wherein the at least one electronic advertisement comprises at least one of: a mobile advertisement, a static image advertisement, a video advertisement, an audio advertisement, an augmented-reality advertisement, and an in- vehicle vending machine advertisement (paragraph [0031], disclose the advertismetn generator generates one or mor offer and communicates the offers to the mobile advice and the user ).
With respect to claim 19, Hollinger teaches a vehicular computer comprising:
memory; and at least one processor; and coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor(paragraph [0006], discloses the computer-readable program code is executable by one or more computer processor to generate first offers based on the first user data, and paragraph [0023], discloses the ride share incentivization system may include one or more processor and memory .., computer usable program code that may be executed by one or more processor… ), performs a method comprising:
based on the comparison of the processed data to the at least one database of advertisements, generating a first advertisement-based route and an estimated time of arrival associated with the first advertisement-based route(paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and
based on the location data, generating a second advertisement-based route(paragraph [0037], discloses advertisement generator generates the second offers in response to receiving
a ride share request from the mobile device of the user. For example, the advertisement generator accesses user data associated with the user from the user profile database 142 and accesses business data associated with one or business along the route from business database
and generates first offers based on the user data and business data, and selected offers, and paragraph [0038], discloses the second offers are transmitted to a second mobile device of a second user… ).
Hollinger teaches the above elements including comparing processed data to at least one database of advertisements geographically located in proximity to a destination(paragraph [0052], discloses the advertisement generator compares the user data corresponding to the user or th the business within the business database to identify which business provide product and/or service that match at least one parameter of the user data.., the advertisement generate may compare parameters of the rid share request to the business within the business database to identify which business provide product and/or service that match one or more of the ride share parameters ) , wherein the processed data is generated at least based on the destination, (paragraph [0053]-[0054], discloses the advertisement generator may prioritize business that are located within closer proximity to the route, have shorter wait times and/or are partnered with the ride sharing .., offers are generated .., advertismetn generates offers that correspond to the identified business ); and receiving location data associated with at least one ride-share vehicle (paragraphs [0016]-[0017], [0020]-[0022]discloses receiving pick up location, destination, arrival times and rout information of users and from the ride share incentive system) and advertisement generator generates one or more offers and communicates with the offer to the mobile device (paragraph [0031]). Hollinger failed to explicitly teach the processed data is compared , by a machine learning engine, and wherein the processed data includes at least one lexical feature or at least one contextual feature and received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Rudchenko teaches compared , by a machine learning engine(paragraph [0007], discloses analyzing eye-gaze input , the method includes receiving eye-gaze input on an electronic device, determining at least one gaze location associated with the eye-gaze input, and applying at least one machine-learning algorithm to the at least one gaze location [processed data], and paragraph [0032], discloses apply a machine learing algorithm .., to determine a predicted response assocted with the at least one gaze location) , and wherein the processed data includes at least one lexical feature or at least one contextual feature(paragraph [0032], discloses the database that comprise lexical feature data and contextual feature data ..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a ride share incentivization using a user profile database for generating offer of Hollinger using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko in order to prove direct, efficient identification of a content.
Rudchenko failed to teach received location and generated offer is synchronized business data associated with at least one advertisement location along the first advertisement-based route of Hollinger with vehicle-based route.
However, Modi teaches synchronized business data associated with at least one advertisement location along the first advertisement-based route (Fig. 4, 408-412, discloses identifying reference audio content that has at least a threshold extent of similarity with the capture audio content, identify the geographic location assocted with identified reference audio content and based at least on the identified geographic location associated with the identified reference audio content, outputting, via the user interface of the vehicle-based media system, prompt to navigate to the identified geographic location, paragraph [0046], discloses vehicle-based media system can cause the navigation system to display a navigational route beginning at the vehicle's current location and ending at the location associated with the identified advertisement and paragraph [0047], discloses the vehicle-based media system can use the geographic location corresponding to a particular advertisement as a first geographic location (e.g., a nearby location of the restaurant ) and can determine a second geographic location associated with the vehicle-based media system (e.g., the current geographic location of the vehicle in which the vehicle-based media system is located)). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for generates one or more offers and communicates with the offer to the mobile device of Hollinger and using a feature machine learing engine and a database that comprise lexical feature data and contextual feature data of Rudchenko with audio ad and navigation-related action synchronization feature of Modi in order to retrieve geographical location data for advertisements that are associated with a location within a threshold proximity distance (e.g., within a five mile radius) of the current location of the vehicle destination location of the vehicle or some location on a route between the current and destined location (see, Modi, paragraph [0049]).
With respect to claim 20, Hollinger in view of Rudchenko and further view of Modi and further view of Mundinger teaches elements of claim 19 , furthermore, Hollinger teaches the vehicular computer further comprising a route guidance module, wherein the route guidance module is configured to guide an autonomous vehicle according to the first advertisement-based route(paragraph [0017], discloses destination and arrival time is proved and route information .., ).
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-20 filed 24 November 2025 have been fully considered but they are not persuasive. Applicants’ arguments claim 1, as amended, now recites “comparing, by a machine learning engine , processed data to at least one database of advertisement geographically located in proximity to a destination, wherein the processed data is generated at least based on the destination,, wherein the processed data includes at least one lexical feature or at least one contextual feature is not persuasive. The claims directed to an abstract idea because the core of the claims routing based on advertismetn which is a method of "method of organizing human activity" or a "fundamental business practice" (advertising/marketing) performed on a computer, rather than a technical improvement. Further, the user of a “machine learning engine” to compare data and generate routes, without detailing how the engine is improved, is treated as applying convention technology, which does not confer patent eligibility. Simply receiving location data and updating a route event using machine learning is conserved a conventional, well-understood function of general-purpose computer, which is “lack of significantly more”.
In order to overcome such a rejection, the claim must demonstrate that the invention improves the functioning of the computer itself, or solves a technical problem in a non-conventional way. The 35 U.S.C 101 rejections with respect to claims 1-20 is maintained.
Applicant's arguments of 35 U.S.C 103 rejections with respect to claims 1-20 filed 24 November 2025 have been fully considered have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior arts
Hollinger et al (US Pub., 2020/01443409 A1) discloses method and apparatus for incentivizing ride share users, where offers are presented to the users and based on selected offers, corresponding fares may be reduced. A ride share incentivization may include user profile database, a business database, a route generator, and an advertisement generator.
Rudchenko et al (US 2019/0034038 A1) discloses systems and methods related to intelligent typing and responses using eye-gaze technology are disclosed herein. In
some example aspects, a dwell-free typing system is provided to a user typing with eye-gaze. A prediction processor may intelligently determine the desired word or action of the user. In some aspects, the prediction processor may contain elements of a natural language processor
Modi et al (US Pub., 2020/0044757 A1) discloses in one aspect, an example method to be performed by a vehicle-based media system includes (a) receiving audio content; (b) causing one or more speakers to output the received audio content; ( c) using a microphone of the vehicle-based media system to capture the output audio content; (d) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; (e) identifying a geographic location associated with the identified reference audio content;
Mundinger et al (US Pub., 2010/0280748 A1) discloses a method for optimized route planning for a user, including: (a) determining a departure point and a destination point for multimodal travel; (b) based on said departure point and destination point, computing and proposing criteria for restricting the number of candidate routes to consider, ( c) proposing an updated list of candidate routes between said departure point and said destination point, said updated list being either: i) automatically displayed after a delay, and/or: ii) based on user selection of said criteria.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621