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
Claims 12-13, 15-17, and 24-37 are pending in this application.
Claims 1-11, 14 and 18-23 are cancelled.
Claims 12-13, 15-17, and 24-37 are amended.
Claims 12-13, 15-17, and 24-37 are presented for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendments
Applicant’s amendments, filed on 5 December, 2025, with respect to the objection of claim 12 has been fully considered, the objection has been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 12-13, 16, 25, 27, 29, and 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Censi et al. (US Publication 2019/0079527 A1) in view of Matus et al. (US Publication 2019/0005812 A1).
Regarding claim 12, Censi teaches a method of determining a personalised route using machine learning to approximate missing user-input data from known data, the method comprising: defining a cost parameter comprising a function of feature values (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline or from all the strategic guidelines of a priority group), wherein the cost parameter comprises user-input feature values indicating subjective data personal to the user (Censi: Para. 128, 203; interface may allow a user to generate strategic guidelines; strategic factors (e.g., distances, speeds, accelerations, decelerations, orientations, times, temperatures, seasons, chemical concentrations, zones, heights, and weights, to name a few) associated with the conditions and proper actions), and wherein the cost parameter is associated with a cost value comprising an evaluation of the cost parameter over a particular link (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline); accessing a first database to obtain known data for a first subset of links and known data for a second subset of links, wherein the first subset of links and the second subset of links are non-overlapping (Censi: Para. 9, 113; geographic information system databases; maps describing properties; determining values of factors, identifying missing factors using a linking process); ……… is unknown for the second subset of the links (Censi: Para. 9; identifying missing factors); calculating one or more cost values for the first subset of links (Censi: Para. 184; cost evaluation to compute a cost of a trajectory); using machine learning to identify a relationship in the first subset between the user-input feature value corresponding to the user-input feature value comprising the indication of ………. which is unknown in the second subset and the known data for the first subset of links (Censi: Para. 9, 47; identifying one or more factors associated with a logical expression, determining values of factors, identifying missing factors); using the estimated user-input feature values and the known data for the second subset of links to calculate one or more cost values for the second subset of links (Censi: Para. 9, 47; values of the one or more factors may be determined by a machine learning algorithm; one or more missing factors may be identified by a linking process); and determining, based on the cost values for the second subset of links, a personalised route by selecting, from a plurality of evaluated routes (Censi: Para. 145; quantitatively evaluate candidate trajectories that it has under consideration and then select an optimal trajectory) between a start point and an end point on a map (Censi: Para. 181; navigate from a start position and a goal position) defined by a plurality of links for representing roads and a plurality of nodes for representing junctions between roads, a route having the lowest cost parameter between the start point and the end point, wherein the plurality of links meet at respective nodes and comprise a plurality of features having associated feature values (Censi: Para. 196-197; components of strategic guidelines (e.g., conditions) guiding the decision of the trajectories and motion actions of the autonomous system; cost evaluation of a trajectory may be performed on parts of the trajectory).
Censi doesn’t explicitly teach accessing a user-input feature value database to obtain user-input feature values for the first subset of links comprises an indication of pollution, and wherein the at least one user-input feature value comprising the indication of pollution ……. pollution.
However Matus, in the same field of endeavor, teaches accessing a user-input feature value database to obtain user-input feature values for the first subset of links comprises an indication of pollution, and wherein the at least one user-input feature value comprising the indication of pollution (Matus: Para. 32, 46; roadway database; collecting environmental data; air quality e.g., pollution levels) ……. pollution (Matus: Para. 32; air quality e.g., pollution levels).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 13, Censi teaches a method of determining a personalised route using machine learning to approximate missing user-input data from known data, the method comprising: defining a cost parameter comprising a function of feature values (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline or from all the strategic guidelines of a priority group), wherein the cost parameter comprises user-input feature values indicating subjective data personal to the user (Censi: Para. 128, 203; interface may allow a user to generate strategic guidelines; strategic factors (e.g., distances, speeds, accelerations, decelerations, orientations, times, temperatures, seasons, chemical concentrations, zones, heights, and weights, to name a few) associated with the conditions and proper actions), and wherein the cost parameter is associated with a cost value comprising an evaluation of the cost parameter over a particular link (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline); accessing a first database to obtain known data for a first subset of links and known data for a second subset of links, wherein the first subset of links and the second subset of links are non-overlapping (Censi: Para. 9, 113; geographic information system databases; maps describing properties; determining values of factors, identifying missing factors using a linking process) ………. is unknown for the second subset of links (Censi: Para. 9; identifying missing factors); calculating one or more cost values for the first subset of links (Censi: Para. 184; cost evaluation to compute a cost of a trajectory), using machine learning to identify a relationship in the first subset between the feature value corresponding to the at least one user-input feature value which is unknown in the second subset and the known data for the first subset of links (Censi: Para. 9, 47; identifying one or more factors associated with a logical expression, determining values of factors, identifying missing factors); applying the identified relationship in the second subset of links to obtain estimated user-input feature values for the second subset of links using the known data for the second subset of links (Censi: Para. 9, 47; identifying missing factors using a linking process); using the estimated user-input feature values and the known data for the second subset of links to calculate one or more cost values for the second subset of links (Censi: Para. 9, 47; values of the one or more factors may be determined by a machine learning algorithm; one or more missing factors may be identified by a linking process); and determining, based on the cost values for the second subset of links, a personalised route by selecting, from a plurality of evaluated routes (Censi: Para. 145; quantitatively evaluate candidate trajectories that it has under consideration and then select an optimal trajectory) between a start point and an end point on a map (Censi: Para. 181; navigate from a start position and a goal position) defined by a plurality of links for representing roads and a plurality of nodes representing junctions between roads, a route having the lowest cost parameter between the starting point and the end point, wherein the plurality of links meet at respective nodes and comprise a plurality of features having associated feature values (Censi: Para. 196-197; components of strategic guidelines (e.g., conditions) guiding the decision of the trajectories and motion actions of the autonomous system; cost evaluation of a trajectory may be performed on parts of the trajectory).
Censi doesn’t explicitly teach accessing a user-input feature value database to obtain user-input feature values for the first subset of links, wherein at least one user-input feature value.
However Matus, in the same field of endeavor, teaches accessing a user-input feature value database to obtain user-input feature values for the first subset of links, wherein at least one user-input feature value (Matus: Para. 32, 46; roadway database; collecting environmental data; air quality e.g., pollution levels).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 16, Censi doesn’t explicitly teach wherein at least a portion of the known data for the second subset of links comprises publicly accessible data.
However Matus, in the same field of endeavor, teaches wherein at least a portion of the known data for the second subset of links comprises publicly accessible data (Matus: Para. 32; weather forecast describing thunderstorms proximal the driver's location).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 25, Censi doesn’t explicitly teach wherein at least a portion of the known data for the second subset of links comprises publicly accessible data.
However Matus, in the same field of endeavor, teaches wherein at least a portion of the known data for the second subset of links comprises publicly accessible data (Matus: Para. 32; weather forecast describing thunderstorms proximal the driver's location).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 27, Censi teaches a method of determining a route using machine learning to approximate missing user-input data from known data, the method comprising: defining a cost parameter comprising a function of feature values (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline or from all the strategic guidelines of a priority group), wherein the cost parameter comprises user-input feature values indicating data provided by the user (Censi: Para. 128, 203; interface may allow a user to generate strategic guidelines; strategic factors (e.g., distances, speeds, accelerations, decelerations, orientations, times, temperatures, seasons, chemical concentrations, zones, heights, and weights, to name a few) associated with the conditions and proper actions), and wherein the cost parameter is associated with a cost value comprising an evaluation of the cost parameter over a particular link (Censi: Para. 185; a cost function may be a numerical score characterizing the degree of deviation of a motion action from a strategic guideline); accessing a first database to obtain known data for a first subset of links and known data for a second subset of links, wherein the first subset of links and the second subset of links are non-overlapping (Censi: Para. 9, 113; geographic information system databases; maps describing properties; determining values of factors, identifying missing factors using a linking process); ……….. , and wherein the at least one user-input feature value comprising the indication of ………. is unknown for the second subset of the links (Censi: Para. 9; identifying missing factors); calculating one or more cost values for the first subset of links (Censi: Para. 184; cost evaluation to compute a cost of a trajectory); using machine learning to identify a relationship in the first subset between the user-input feature value corresponding to the user-input feature value comprising an indication of pollutions which is unknown in the second subsets and the known data for the first subset of links (Censi: Para. 9, 47; identifying one or more factors associated with a logical expression, determining values of factors, identifying missing factors); applying the identified relationship in the second subset of links to obtain estimated user-input feature values for the second subset of links using the known data for the second subset of links (Censi: Para. 9, 47; identifying missing factors using a linking process); using the estimated user-input feature values and the known data for the second subset of links to calculate one or more cost values for the second subset of links (Censi: Para. 9, 47; values of the one or more factors may be determined by a machine learning algorithm; one or more missing factors may be identified by a linking process); and determining, based on the cost values for the second subset of links, a route by selecting, from a plurality of evaluated routes (Censi: Para. 145; quantitatively evaluate candidate trajectories that it has under consideration and then select an optimal trajectory) between a start point and an end point on a map (Censi: Para. 181; navigate from a start position and a goal position) defined by a plurality of links for representing roads and a plurality of nodes for representing junctions between roads, a route having the lowest cost parameter between the start point and the end point, wherein the plurality of links meet at respective nodes and comprise a plurality of features having associated feature values (Censi: Para. 196-197; components of strategic guidelines (e.g., conditions) guiding the decision of the trajectories and motion actions of the autonomous system; cost evaluation of a trajectory may be performed on parts of the trajectory).
Censi doesn’t explicitly teach accessing a user-input feature value database to obtain user-input feature values for the first subset of links, wherein at least one user-input feature value of the user-input feature values for the first subset of links comprises an indication of pollution …….. pollution.
However Matus, in the same field of endeavor, teaches accessing a user-input feature value database to obtain user-input feature values for the first subset of links, wherein at least one user-input feature value of the user-input feature values for the first subset of links comprises an indication of pollution (Matus: Para. 32, 46; roadway database; collecting environmental data; air quality e.g., pollution levels) …….. pollution (Matus: Para. 32; air quality e.g., pollution levels).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 29, Censi doesn’t explicitly teach wherein at least a portion of the known data for the second subset of links comprises publicly accessible data.
However Matus, in the same field of endeavor, teaches wherein at least a portion of the known data for the second subset of links comprises publicly accessible data (Matus: Para. 32; weather forecast describing thunderstorms proximal the driver's location).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 33, Censi doesn’t explicitly teach wherein the pollution corresponds to air quality.
However Matus, in the same field of endeavor, teaches wherein the pollution corresponds to air quality (Matus: Para. 32; environmental data; air quality (e.g., pollution levels, etc.)).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Regarding claim 34, Censi doesn’t explicitly teach wherein at least one user-input feature value of the user-input feature values for the first subset of links comprises an indication of pollution.
However Matus, in the same field of endeavor, teaches wherein at least one user-input feature value of the user-input feature values for the first subset of links comprises an indication of pollution (Matus: Para. 32; environmental data; air quality (e.g., pollution levels, etc.)).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) with a reasonable expectation of success because the use of non-generic sensor data and supplemental data improves the correlation between data and traffic-related characteristics leading to an increased understanding of variables affecting user behavior while driving (Matus: Para. 17).
Claims 15 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Censi et al. (US Publication 2019/0079527 A1) in view of Matus et al. (US Publication 2019/0005812 A1) and in further view of Cerecke et al. (US Publication 2015/0285651 A1).
Regarding claim 15, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises user feedback.
However Cerecke, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises user feedback (Cerecke: Para. 159; the weights can be adjusted or at least based in part, on user input; user interface can receive user input (e.g., textual input selections from a drop-down menu, checked boxes, etc.) that can provide operator override of the routing selection methods; a user can provide input that causes the routing module to adjust the weights of the factors such that a route is selected that minimizes or reduces estimated transit time (e.g., the time cost component)).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the input of user feedback (Cerecke: Para. 159) with a reasonable expectation of success because user input to change the weight on the user’s preferred features will personalize the suggested path (Cerecke: Para. 10, 159).
Regarding claim 24, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises user feedback.
However Cerecke, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises user feedback (Cerecke: Para. 135, 159; the weights can be adjusted or at least based in part, on user input; user interface can receive user input (e.g., textual input selections from a drop-down menu, checked boxes, etc.) that can provide operator override of the routing selection methods; a user can provide input that causes the routing module to adjust the weights of the factors such that a route is selected that minimizes or reduces estimated transit time (e.g., the time cost component)).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the input of user feedback (Cerecke: Para. 159) with a reasonable expectation of success because user input to change the weight on the user’s preferred features will personalize the suggested path (Cerecke: Para. 10, 159).
Claims 17, 26, 28, 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Censi et al. (US Publication 2019/0079527 A1) in view of Matus et al. (US Publication 2019/0005812 A1) and in further view of Park et al. (Foreign Reference KR20100064248A).
Regarding claim 17, Censi and Matus don’t explicitly teach wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices.
However Park, in the same field of endeavor, teaches wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Regarding claim 26, Censi and Matus don’t explicitly teach wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices.
However Park, in the same field of endeavor, teaches wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Regarding claim 28, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises noise level.
However Park, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises noise level (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Regarding claim 30, Censi and Matus don’t explicitly teach wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices.
However Park, in the same field of endeavor, teaches wherein the user-input feature values for the first subset of links comprise one or more recordings of noise captured via one or more user devices (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Regarding claim 31, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprise noise level.
However Park, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprise noise level (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Regarding claim 32, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises noise level.
However Park, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises noise level (Park: Pg. 2 Lines 43-45, Pg. 6 Lines 11-16; microphone; recording mode; store the road state; traffic noise information).
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using the stored noise and smoke location information (Park: Pg. 6 Lines 11-16) with a reasonable expectation of success because the route search section information includes options of soot avoidance and noise areas as taught by Park (Park: Pg. 7 Line 58 - Pg. 8 Line 2).
Claims 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Censi et al. (US Publication 2019/0079527 A1) in view of Matus et al. (US Publication 2019/0005812 A1) and in further view of Nascimento et al. (US Publication 2018/0122237 A1).
Regarding claim 35, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
However Nascimento, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
Nascimento teaches globally optimizing vehicle actions. The examples include route or vehicle actions that would reduce the congestion, less traveling distance, less time spent in traffic jams, less fuel consumption, and pollution emission. The system gives a scenario where a first vehicle is on the open road and a second vehicle is in congested bumper-bumper traffic (Nascimento: Para. 158, 169). A vehicle route optimized to travel on an open road or with less time in traffic jams with pollution emission would be a highly aesthetic vehicle route choice. The prior art does contain factors that optimized would create a more aesthetic vehicle route choice.
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using pollution emission taught in Nascimento (Nascimento: Para. 158) with a reasonable expectation of success because globally optimizing the route drive by the vehicle based on the level of reduced congestion, less traveling distance, less time spend traffic jams, less fuel consumption or pollution emission as taught by Nascimento (Nascimento: Para. 158).
Regarding claim 36, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
However Nascimento, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
Nascimento teaches globally optimizing vehicle actions. The examples include route or vehicle actions that would reduce the congestion, less traveling distance, less time spent in traffic jams, less fuel consumption, and pollution emission. The system gives a scenario where a first vehicle is on the open road and a second vehicle is in congested bumper-bumper traffic (Nascimento: Para. 158, 169). A vehicle route optimized to travel on an open road or with less time in traffic jams with pollution emission would be a highly aesthetic vehicle route choice. The prior art does contain factors that optimized would create a more aesthetic vehicle route choice.
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using pollution emission taught in Nascimento (Nascimento: Para. 158) with a reasonable expectation of success because globally optimizing the route drive by the vehicle based on the level of reduced congestion, less traveling distance, less time spend traffic jams, less fuel consumption or pollution emission as taught by Nascimento (Nascimento: Para. 158).
Regarding claim 37, Censi and Matus don’t explicitly teach wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
However Nascimento, in the same field of endeavor, teaches wherein at least one second user-input feature value of the user-input feature values for the first subset of links comprises a measurement of aesthetics.
Nascimento teaches globally optimizing vehicle actions. The examples include route or vehicle actions that would reduce the congestion, less traveling distance, less time spent in traffic jams, less fuel consumption, and pollution emission. The system gives a scenario where a first vehicle is on the open road and a second vehicle is in congested bumper-bumper traffic (Nascimento: Para. 158, 169). A vehicle route optimized to travel on an open road or with less time in traffic jams with pollution emission would be a highly aesthetic vehicle route choice. The prior art does contain factors that optimized would create a more aesthetic vehicle route choice.
It would have been obvious to one of ordinary skill before the effective filing date to have modify the trajectory cost function (Censi: Para. 185) with the information on pollution (Matus: Para. 32) and the route optimization using pollution emission taught in Nascimento (Nascimento: Para. 158) with a reasonable expectation of success because globally optimizing the route drive by the vehicle based on the level of reduced congestion, less traveling distance, less time spend traffic jams, less fuel consumption or pollution emission as taught by Nascimento (Nascimento: Para. 158).
Response to Arguments
Applicant’s arguments, filed on 5 December 2025, with respect to claims 12-13, 15-17, and 24-37 have been considered but are moot because the arguments do not apply to the references being used in the current rejection.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571)272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663