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 .
Claims in Consideration
Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 4/23/2025 have been fully considered but they are not persuasive. To overcome the deficiencies of the combination of Dean and Assouad, the examiner has added Cahan.
Claim Rejection Notes
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.
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.
Claim(s) 1-3, 5-6, 8-9-18, and 20, are rejected under 35 U.S.C. 103 as being unpatentable over Dean et al. (US 20200019175 A1, published: 1/16/2020), in view of Assouad (US 20210244365 A1, published: 8/12/2021).
Claim 1. (Currently Amended): Dean teaches a computer-implemented method for navigation (a navigation route [Dean, 0074]), the method comprising:
receiving an indication of at least one risk factor associated with at least one geographical area (an on-demand transport management system (or “transport system”) can receive requests for transportation from requesting users via a designated rider application executing on the users' computing devices (610). The transport system can receive a transport request and identify a number of proximate available vehicles relative to the user. The transport system may then select an available vehicle to service the transport request based on a number of criteria, including risk factors [Dean, 0072]); and
determining a risk level for the user along at least one candidate navigation route comprising the at least one geographical area (a navigation route cost system determines a set of risk factor weights to apply to each of the various cost layers in the annotated maps (620) [Dean, 0074]), wherein the risk level is determined based on: the at least one risk factor; and at least one characteristic associated with the user (these cost layers include travel time and risk factors such as an intervention risk factor (i.e., a risk that a safety driver for the AV has to take control), including events that indicate the vehicle is incapable of autonomous operation along a given path segment; a bad experience risk factor, including events that indicate hard stops, jerking motions, and other events that impact a passenger experience; and a harmful event risk factor, including collisions involving the vehicle [Dean, 0012]; Examiner's Note: hard stops (change in velocity), jerking motions (change in acceleration), are both physical characteristics that effect occupants/users of a car).
Dean does not teach receiving an indication of at least one risk factor associated with at least one geographical area, wherein the at least one risk factor includes a risk factor that affects a health status of a user within the at least one geographical area.
However, Assouad teaches receiving an indication of at least one risk factor associated with at least one geographical area, wherein the at least one risk factor includes a risk factor that affects a health status of a user within the at least one geographical area (the geographical tracking of asymptomatic users is automatically implemented and retroactively evaluated by a digital processor upon any of said asymptomatic users later triggering a said health risk indicator so to track potential retroactive geographical infection transmission from said asymptomatic users [Assouad, 0034]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the determination of risk factors for calculating navigation routes invention of Dean to include the indication of geographical health risk to a user features of Assouad.
One would have been motivated to make this modification to make user's aware that certain geographic areas may be hazardous to their health. Such keeps users informed of dangerous areas.
The combination of Dean and Assouad, does not teach wherein, for each candidate navigation route, the risk level indicates risk to the health status of the user if the user takes the candidate navigation route.
However Cahan teaches wherein, for each candidate navigation route (a proposed travel itinerary [Cahan, 0022]), the risk level indicates risk to the health status of the user if the user takes the candidate navigation route (a health risk prevalence level map illustrating the proposed travel itinerary is displayed in the graphical user interface. In one embodiment, the health risk prevalence level map includes a plurality of health risk prevalence level maps displayed in a layered arrangement in the graphical user interface, each individual prevalence level map associated with one health risk [Cahan, 0022]; [Cahan, 0041]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the determination of risk factors for calculating navigation routes invention of the combination of Dean and Assouad, to include the user medical features of Cahan.
One would have been motivated to make this modification to better assess risk factors by assessing the medical condition of the users in such a manner that presents useful risk management data to the user.
Claim 15, having the same deficiencies of claim 1, is likewise rejected.
Claim 2. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 1. Dean further teaches further comprising: receiving an indication of at least one of a first location or a second location, wherein the first location corresponds to at least one of a present user location or a departure location, and wherein the second location corresponds to a destination (a total path for an AV from a starting point to a destination can be comprised of a sequential set of capability-in-scope lane segments from the starting point to the destination [Dean, 0013]); identifying the at least one candidate navigation route, wherein the identified at least one candidate navigation route is between the first location and the second location (for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination [Dean, 0018]); and selecting, from the identified at least one candidate navigation route, a navigation route for the user based on at least one selection rule (and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]).
Claim 17, having the same deficiencies of claim 2, is likewise rejected.
Claim 3. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 2. The combination further teaches further comprising: determining whether the risk level for the identified at least one candidate navigation route is within a risk tolerance of the user, wherein the risk tolerance is determined based on at least one of at least one characteristic of the user or a preference of the user; and selecting the navigation route for the user that is within the risk tolerance and reduces risk of adversely affecting the health status of the user based on the at least one selection rule (for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination, and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]. The geographical tracking of asymptomatic users is automatically implemented and retroactively evaluated by a digital processor upon any of said asymptomatic users later triggering a said health risk indicator so to track potential retroactive geographical infection transmission from said asymptomatic users [Assouad, 0034]).
Claim 18, having the same deficiencies of claim 3, is likewise rejected.
Claim 5. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 2. Dean further teaches wherein the at least one selection rule specifies that at least one of: the identified at least one candidate navigation route with the lowest risk level is to be selected for the user; or the identified at least one candidate navigation route that has a risk level within the risk tolerance is to be selected, wherein the risk tolerance is determined based on at least one of the at least one characteristic or the preference, and wherein if there is a plurality of identified candidate navigation routes each having a risk level within the risk tolerance, one of the plurality of identified candidate navigation routes is to be selected based on at least one of: which of the plurality of identified candidate navigation routes has a lowest travel time or distance; availability of transport options for each of the plurality of identified candidate navigation routes; or the preference (for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination, and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]).
Claim 20, having the same deficiencies of claim 5, is likewise rejected.
Claim 6. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 2. Dean further teaches wherein, if none of the identified at least one candidate navigation route have a risk level within the risk tolerance, wherein the risk tolerance is determined based on at least one of the at least one characteristic or the preference, the method comprises at least one of: providing an indication that the identified at least one candidate navigation route is not within the risk tolerance; or selecting the navigation route for the user based on the at least one selection rule, wherein the at least one selection rule specifies that at least one of: the identified at least one candidate navigation route with the lowest risk level is to be selected for the user; or the identified at least one candidate navigation route is to be selected based on at least one of: which of the identified at least one candidate navigation route has a lowest travel time or distance; availability of transport options for the identified at least one candidate navigation route; or the preference (the risk factor cost layers comprise an intervention risk factor, including events that indicate the vehicle is incapable of autonomous operation along a given path segment; a bad experience risk factor, including events that indicate hard stops, jerking motions, and other events that impact a passenger experience; and a harmful event risk factor, including collisions involving the vehicle [Dean, 0031]).
Claim 8. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 2. Dean further teaches further comprising generating navigation data corresponding to the selected navigation route (and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]).
Claim 9. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 8. Dean further teaches further comprising causing a navigation application on a user device to load the generated navigation data for use in providing navigation instructions for the user (and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]).
Claim 10. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 1. Cahan further teaches wherein at least one characteristic of the user comprises information regarding at least one medical condition or demographic information associated with the user (an individual health risk probability for succumbing to each health risk is determined for the given individual and all health risks. In addition, a projected health risk prevalence level over time at each one of the plurality of locations within the geographical area is determined for each health risk. the individual health risk probability for succumbing and the projected health risk prevalence level for all health risks, all locations contained in the portion of the geographical of the proposed travel itinerary and the given time duration of the proposed travel itinerary are used to generate the personal health risk vulnerability model [Cahan, 0010]).
Claim 11. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 1. Dean further teaches wherein the risk level for the at least one candidate navigation route is calculated using at least one risk model configured to estimate the risk level for the user along the at least one candidate navigation route, wherein the risk model is trained using a training data set indicative of at least one correlation between a plurality of risk factors and a plurality of characteristics (the path cost calculation system 260 models scores for each of the cost layers, including the travel time and risk factor cost layers, for path segments (i.e., predetermined lengths of a road or lane of a road) in the geographic region. The path cost calculation system 260 can retrieve pre-calculated cost layer scores, either from the data store 240 or from a remote server, and then apply machine learning techniques to update the cost layer scores at run-time. The path cost calculation system 260 periodically updates, or annotates, navigation maps for the geographic region to include the cost layer scores generated from the statistical modelling [Dean, 0046]).
Claim 12. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 11. Dean further teaches wherein the at least one risk model is configured to estimate the risk level for the user along a plurality of sub-routes of the at least one candidate navigation route, and wherein the risk level calculated for the at least one candidate navigation route is based on a weighted combination of the estimated risk level associated with each of the plurality of sub-routes (the path cost calculation system 260 annotates the navigation maps with an overall value for each path segment, which is a weighted sum of the individual cost layer scores in some implementations. The weights applied to each of the individual cost layers can be determined by the path cost calculation system 260 itself [Dean, 0048]).
Claim 13. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 1. Dean further teaches wherein the at least one risk factor comprises at least one of an environmental risk or presence of at least one infectious agent within the at least one geographical area (an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time [Dean, 0017]).
Claim 16. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the apparatus of claim 15. Dean further teaches wherein the at least one processor is further configured to apply a trained machine-learning model or a non-machine learning model (examples described herein may reference software training techniques that correspond to machine learning, neural networks, artificial intelligence, and the like [Dean, 0019]).
Claim(s) 4, 7, and 19, are rejected under 35 U.S.C. 103 as being unpatentable over Dean et al. (US 20200019175 A1, published: 1/16/2020), Assouad (US 20210244365 A1, published: 8/12/2021), and Cahan et al. (US 20170351831 A1, published: 12/7/2017), and in further view of Nepomuceno et al. (US 20170292848 A1, published: 10/12/2017).
Claim 4. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 3. The combination does not teach further comprising determining whether the risk level for the identified at least one candidate navigation route is within the risk tolerance by: comparing the risk level for each of the identified at least one candidate navigation route with a risk tolerance metric indicative of the at least one candidate navigation route being within the risk tolerance; and in response to determining that the risk level for the identified at least one candidate navigation route is within the risk tolerance, identifying the identified at least one candidate navigation route as being within the risk tolerance and reducing the risk of adversely affecting the health status of the user; or in response to determining that the risk level for the identified at least one candidate navigation route is not within the user's risk tolerance, indicating that the identified at least one candidate navigation route is risky for the user.
However, Nepomuceno teaches further comprising determining whether the risk level for the identified at least one candidate navigation route is within the risk tolerance by: comparing the risk level for each of the identified at least one candidate navigation route with a risk tolerance metric indicative of the at least one candidate navigation route being within the risk tolerance; and in response to determining that the risk level for the identified at least one candidate navigation route is within the risk tolerance, identifying the identified at least one candidate navigation route as being within the risk tolerance and reducing the risk of adversely affecting the health status of the user (FIG. 6 illustrates a computer-implemented method 600 for risk-based route selection according to one embodiment [Nepomuceno, 0081]. Method 600 may then calculate a risk index for the area based upon a comparison between the number of expected collisions and the number of observed collisions (block 606) [Nepomuceno, 0086, FIG. 6]. Method 600 may then select a travel route for a vehicle based upon the calculated risk index (block 608) [Nepomuceno, 0087, FIG. 6]); or in response to determining that the risk level for the identified at least one candidate navigation route is not within the user's risk tolerance, indicating that the identified at least one candidate navigation route is risky for the user (Subsequent to block 708, method 700 may then optionally transmit the selected travel route to an electronic device (e.g., mobile device 110, an on-board computer 114, wearable electronics, or a navigator) associated with a vehicle, operator or passenger of a vehicle, pedestrian, bicyclist, and the likes to facilitate routing or re-routing that avoids traversing the area based upon the risk index, via wireless communication or data transmission over one or more radio links or wireless communication channels (block 710) [Nepomuceno, 0092, FIG. 7]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the determination of risk factors for calculating navigation routes invention of the combination of Dean, Assouad, and Cahan, to include the risk comparison features of Nepomuceno.
One would have been motivated to make this modification to better assess risk factors by comparing them to other values in a manner to present useful risk management data to the user.
Claim 19, having the same deficiencies of claim 4, is likewise rejected.
Claim 7. (Previously Presented): The combination of Dean, Assouad, and Cahan, teaches the method of claim 2. Dean further teaches receiving a user input indicative of the preference; and selecting the navigation route for the user according to the user input (and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle [Dean, 0018]). Dean does not teach further comprising: causing a graphical user interface to display the identified at least one candidate navigation route alongside an indication of a risk level for each of the displayed candidate navigation routes.
However, Nepomuceno teaches further comprising: causing a graphical user interface to display the identified at least one candidate navigation route alongside an indication of a risk level for each of the displayed candidate navigation routes (the on-board computer 114 or mobile device 110 may process the historical traffic data to determine or select a travel route for a vehicle based upon the risk index, and may further generate a virtual navigation map or an alert depicting the area to display on the mobile device 110 or on-board computer 114 or take other actions [Nepomuceno, 0035]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the determination of risk factors for calculating navigation routes invention of the combination of Dean, Assouad, and Cahan, to include the risk comparison features of Nepomuceno.
One would have been motivated to make this modification to better assess risk factors by comparing them to other values in a manner to present useful risk management data to the user.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SETH A SILVERMAN whose telephone number is (571)272-9783. The examiner can normally be reached Mon-Thur, 8AM-4PM MST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached on (571)272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seth A Silverman/Primary Examiner, Art Unit 2145