DETAILED ACTION
This final action is in reply to the response to the reply and amendment, filed 22 April 2025, which was filed in response to the non-final action, dated 12 January 2025.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 27 May 2025 complies with 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner. The initialed 1449 Form is enclosed herewith.
Response to Amendments
Claims 1, 3-5, 8-9, 11-13, 16-22 are pending. Claims 1, 8, 9, 16, 17-22 have been amended and claims 6, 7, 14, and 15 have been canceled.
Applicant’s amendments to claims 11, 12 and 13 necessitated new objections, as discussed below.
With regard to the 35 U.S.C. 101 rejection of independent claims 1 and 9 (pgs. 5-20, Action), Applicant has amended the independent claims to require “obtaining, by a hardware processor, a real-time factor related to the plurality of routes “ and contends that, the addition of this element constitutes significantly more than the judicial exception (pgs. 7, reply). The examiner finds this contention unpersuasive.
First, the examiner interprets the “obtaining ... a real-time factor related to the plurality of routes” additional element under the broadest reasonable interpretation as merely data gathering, as discussed below.
Secondly, taken alone, the additional elements, further discussed below, do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP 2106.05).
Finally, with regard to Step 2B, the independent claims 1 and 9 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into practical application.
Accordingly, the rejection of claims 1, 3-5, 8-9, 11-13, 16-22 under 35 U.S.C. 101 is maintained, as discussed below.
With regard to the rejections under 35 USC 103 (pgs. 21-47, Action), Applicant’s amendments to claims 1 and 9, have rendered the rejection of claims 1, 3-5, 8, 9, 11-13, and 16-22, moot. Upon further search and consideration, the new grounds of rejection under 35 U.S.C. 103 are: claims 1 and 9 in view of Wansley, Schlesinger and Gearhart; claims 3, 8, 11 and 16 in view of Wansley, Schlesinger, Gearhart, Fuji and Wan; claims 4 and 12 in view of Wansley, Schlesinger, Gearhart and Duale; claims 5 and 13 in view of Wansley, Schlesinger, Gearhart and Fuji; claims 17, 20 and 22 in view of Wansley, Schlesinger, Gearhart, Fujii, and Woodward; claims 18 and 22 in view of Wansley, Schlesinger, Gearhart, Fujii, Woodward and Wilczynski; and claim 19 in view of Wansley, Schlesinger, Gearhart, Fujii, Woodward and Dai, as discussed below.
Applicant points out that with regard to claim 17 the limitation “wherein an initial setting for the interactive control is based on the trade-off determined by the learning model,” applicant correctly points out that there were no citations to this limitation (pgs. 9, Reply). The action below corrects this omission (see claim 17, mapping below).
Claim Objections
Claims 11-13 are objected to because of the following informalities.
Claims 11, 12 and 13 recite “plurality of candidate navigation routes”. It is unclear whether these recitations are the same or different from “a plurality of candidate navigation routes” recited in claim 9.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 8-9, 11-13, 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
• STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
• STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
o STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
o STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
o STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1, 3-9, 11-22 are directed toward non-statutory subject matter as shown below.
STEP 1: Do the claims fall within one of the statutory categories? Yes, because claims 1 and 9 are directed toward a method (claim 1) and system (claim 9) which fall within one of the statutory categories.
STEP 2A (PRONG 1): Are the claims directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claims 1 and 9 are directed to an abstract ideas.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
1. Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
2. Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
3. Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
As per claims 1 and 9, the method (claim 1) and the system (claim 9) are mental processes that can be performed in the mind and, therefore, an abstract idea. In particular, claims 1 and 9 recite the abstract ideas:
“specify a trade-off between the difficulty levels of a plurality of route and time for selecting navigation routes ... trade-off corresponding to a function route difficulty... ,”
“generating, by the processing hardware, a plurality of candidate navigation routes between the starting location and the destination for the user using the real-time factor, ” and
“applying the trade-off to constrain selections of the plurality of candidate navigation routes to identify a preferred navigation route.” These recitations merely consist of specifying trade-offs between the difficulty levels of a plurality of route and time for selecting navigation routes, generating a plurality of candidate navigation routes between the starting location and the destination using the real-time factor, and applying the trade-off to constrain sections of the plurality of candidate navigation route to identify a preferred navigation route. This is equivalent to a person specifying trade-offs between the difficulty levels of a plurality of route and time for selecting navigation routes, generating a plurality of candidate navigation routes between the starting location and the destination using the real-time factor, and applying the trade-off to constrain sections of the plurality of candidate navigation route to identify a preferred navigation route. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person, mentally specifies trade-offs between the difficulty levels of a plurality of route and time for selecting navigation routes, generates a plurality of candidate navigation routes between the starting location and the destination using the real-time factor, and applies the trade-off to constrain sections of the plurality of candidate navigation route to identify a preferred navigation route. The mere nominal recitations that the user specifies trade-offs at the “user interface” (claim 1, 9), the real-time factor is received by “processing hardware,” (claim 1), that the plurality of candidate navigation routes is generated by “a hardware processor” (claim 1), that generating a plurality of candidate navigational routes and applying the trade-off are caused by a “computing-system” including “computer-readable memory storing ... instructions,” when executed by “one or more processors,” (claim 9), does not take the limitations out of the mental process grouping.
STEP 2A (PRONG 2): Do the claims recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
• an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
• an additional element adds insignificant extra-solution activity to the judicial exception; and
• an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1 and 9 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into practical application. Claims 1 and 9 further recite the additional elements:
“obtaining, by a hardware processor, a real-time factor related to the plurality of routes;” and
“receiving, by the processing hardware, an indication of a starting location and a destination from the user.” These additional elements further limit the abstract idea without integrating the abstract idea into practical application or significantly more. In particular, the “obtaining ... a real-time factor … ,“ and “receiving ... an indication of a starting location ... “ steps are recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data) and amount to mere data gathering, a form of insignificant extra-solution activity added to the judicial exception per MPEP 2106.05(g), because the steps characterize pre solution activity, such as an individual obtaining traffic data from observation and receiving (mentally formulating) a starting and a destination location.
Claims 1 and 9 still further include the additional elements “processing hardware” (claim 1, Ln. 3, 10), “computer-system” (claim 9, Ln. 1), “computer-readable memory storing ... instructions,” (claim 9, Ln. 3) and “one or more processors” (claim 9, Ln. 3). These elements are not sufficient to amount to significantly more than the judicial exception because they fail to integrate the exception into practical application. The mere inclusion of instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the system accomplishes obtaining route data and determining a quantitative metric by “processing hardware” caused by “a computer system” including “computer-readable memory storing ... instructions” when executed by “one or more processors”, i.e., via computers. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated in the practical application. The “processing hardware,” “computer system,” “computer-readable memory storing ... instructions,” and the “one or more processors” merely describes how to generally “apply” the otherwise metal judgements in a generic or general purpose computing environment. The processing hardware, computer system, computer readable memory storing ... instructions, and one or more processors are recited at a high level of generality and merely automate the storing, extracting and creating steps.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, claims 1 and 9 do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
• adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1 and 9 do not recite any specific limitation or combination of limitations that are well-understood, routine, conventional (WURC) activity in the field. Obtaining and receiving data are fundamental, i.e., WURC, activities performed by servers, such as servers, cloud servers, computers operating on databases, and processors executing instructions stored on computer-readable memory, such as the processing hardware recited in claim 1, and the computer system recited in claim 9. Further, applicant’s specification does not provide any indication that the obtaining and receiving activities of the system are performed using anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data (which is one example listed in applicant’s disclosure as a remedial action) is a well understood, routine, and conventional function.
Thus, since claims 1 and 9 are: (a) directed toward abstract ideas; (b) do not recite additional elements that integrate the judicial exception into practical application; and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 and 9 are directed to non-statutory subject matter.
Claim 3, recites the additional element:
receiving, by the processing hardware, a time constraint parameter indicative of a time by which the user must arrive at the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 3 further recites the additional element:
wherein generating the navigation route includes applying the time constraint parameter to further constrain selections of the plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 4, recites the additional element:
receiving, by the processing hardware, a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 4 further recites the additional element:
wherein generating the navigation route includes applying the carpooling parameter to further constrain selections of the plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 5, recites the additional element:
receiving, by the processing hardware, a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 5 further recites the additional element:
wherein generating the navigation route includes applying the time-of-travel parameter to further constrain selections of the plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 8, recites the abstract idea:
determining the function further using indications of other users' preferences regarding the trade-off, for routes between the starting location and the destination.
This limitation represents a concept performed in the human mind (including an observation, judgement and opinion) and therefore does not serve to integrate the judicial exception into a practical application, or does it amount to significantly more.
Claim 11, recites the additional element:
receive a time constraint parameter indicative of a time by which the user must arrive at the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 11 further recites the additional element:
to generate the navigation route, the instructions cause the system to apply the time constraint parameter to further constrain selections of plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. In particular, “generat[ing] the navigation route .... “ adds insignificant extra solution activity to the judicial exception, per MPEP 2106.05(g), because it characterizes post solution activity, such as an individual mentally generating a route based on the mental evaluation of navigation criteria.
Claim 12, recites the additional element:
receive a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 12 further recites the additional element:
wherein to generate the navigation route, the instructions cause the system to apply the carpooling parameter to further constrain selections of plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 13, recites the additional element:
receive a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination ... .
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception. This additional element is also recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 13 further recites the additional element:
wherein to generate the navigation route, the instructions apply the time-of-travel parameter to further constrain selections of plurality of candidate navigation routes.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 16, recites the abstract idea:
determine the function further using indications of other users` preferences regarding the trade-off, for routes between the starting location and the destination.
This limitation represents a concept performed in the human mind (including an observation, judgement and opinion) and therefore does not serve to integrate the judicial exception into a practical application, or does it amount to significantly more.
Claims 17 and 21, recite the additional element:
train a machine learning model using the route data and the route segment data to determine the trade-off between the difficulty levels of the route segments and time in selection of navigation routes by the user, the trade-off corresponding to a function that expresses an amount of time by which a first route the user travelled between a pair of locations differs from a second route the user travelled between the pair of locations as a function of route segment difficulty.
This limitation represents a concept performed in the human mind (including an observation, judgement and opinion) and therefore does not serve to integrate the judicial exception into a practical application, or does it amount to significantly more.
Further, the additional element “training a machine learning model to determine quantitative metric of trade-off … “ is merely an instruction to perform the abstract idea of “determining” the quantitative metric on a computer, per MPEP 2106.06(d), MPEP 2106.05(f). This additional element merely recites particular inputs and outputs (i.e., inputs-amount of time to travel between two locations; outputs-trade-off between difficulty levels), and does not recite elements as to how the machine learning model is trained. Unlike Example 39 (see, “Subject Matter Eligibility Examples: Abstract Ideas”, https://www.uspto.gov/sites/default/files/documents/101_examples_37to42_20190107.pdf, last visited, 9-1-2023), the additional element does not recite details with regard to how the data is manipulated, nor how any “training” data would be collected or created.
Claims 17 and 21 further recite the additional elements:
wherein the first route includes route segments of a first difficulty level, and the second route includes route segments of a second difficulty level;
wherein an initial setting for the interactive control is based on the trade-off determined by the machine learning model and
wherein the training of the machine learning model includes using feedback from drivers.
These additional elements fail to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claims 18 and 22, recites the additional element:
wherein the training of the machine learning model includes using road geometry.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 19, recites the additional element:
wherein the training of the machine learning model includes using a number of accidents reported for corresponding road segments.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
Claim 20, recites the additional element:
wherein the training of the machine learning model includes using road types.
This additional element fails to integrate the exception into a practical application, nor does it amount to significantly more than the judicial exception.
As such, claims 1, 3-9 and 11-22 are rejected under 35 U.S.C. 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
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 is incorrect, any correction of the statutory basis 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 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 non-obviousness.
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 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication Number 2014/0058672 to Wansley et al. (hereafter Wansley) in view of U.S. Patent Publication Number 2017/0328725 to Schlesinger et al. (hereafter Schlesinger) and U.S. Patent Publication 2015/0051829 to Gearhart et al. (hereafter Gearhart).
As per claim 1, Wansley discloses [a] method for generating navigation routes (see at least Wansley, Abstract, disclosing computer-implemented methods for calculating a travel route based on navigational preferences and travel history of a user), the method comprising: ... (1) ... ; ... (2) ... ;
receiving, by the processing hardware, an indication of a starting location and a destination from the user (see at least Wansley, Fig. 3, showing step 330 calculating, in response to the received request, the travel route from the origin location to the destination location based on the stored navigational preferences and the stored travel history of the user; and [0058] disclosing that the travel route from the origin location to the destination location is calculated based on the user's navigational preferences and stored travel history) ; ... (3) ... ;
generating, by the processing hardware, a plurality of candidate navigation routes between the starting location and the destination for the user, (see at least Wansley, [0092] discussing the examples ... of possible travel routes are analyzed and ranked in accordance with the user's stored navigational preferences stored in the travel history; and [0098] disclosing that server 170 can provide for display the various remaining travel routes); and ... (4) ... ; ... (5) ... . But, Wansley does not explicitly teach the following limitations taught in Schlesinger and Gearhart:
(1) providing, via a user interface, an interactive control for a user (see at least Gearhart, [0029] disclosing that the user may input information using any input means of the electronic device 115; [0037])
(2) to specify a trade-off between difficulty levels of a plurality of routes and time for selecting navigation routes (see at least Schlesinger, [0047] disclosing that outine characteristics of a user include user preferences, such as cuisine preferences, brand preferences, road preferences, driving preferences, movie preferences, music preferences, parental status (i.e., whether the user is a parent), demographic information (e.g., age, gender, marital status, the user being engaged to be married, the user being married, the user being single, literacy/education, employment status, occupation, residence location), routinely visited venues (e.g., user hubs), and many more. Examples of sporadic characteristics of a user include the user being sick, the user craving fast food, the user being late for work, the user diverging from or contradicting an expected tracked routine, the user being on vacation (a user being on vacation may have a higher preference for more scenic routes than otherwise), specific personal events of the user, such as a wedding (a user that has an event to attend may have a stronger preference for fast routes),
the trade-off corresponding to a function of route difficulty the user prefers to accept to save time (see at least Schlesinger, [0063] disclosing that routing engine 260 can account for the various trade-offs in preferences that may be required to determine optimal routes for particular users. Many drivers have preferences for their preferred driving routes. In various implementations, routing engine 260 can account for these preferences, which may vary from user to user and based on other context (e.g., semantic characteristics of users and/or route components) surrounding a user request for a route … further disclosing that routing engine 260 can account for these preferences, which may vary from user to user and based on other context (e.g., semantic characteristics of users and/or route components) surrounding a user request for a route. Furthermore, routing engine 260 can account for variation to the importance of these various factors from user to user; [0090] ; [0091]; [0092]; disclosing time … weather conditions <difficulty>; [0088]; [0094] disclosing that Estimated times to traverse can in addition or instead be utilized to determine route preference subscores for a route preference score. For example, routing engine 260 can forecast the corresponding route preference of route preference subscores for one or more route components using an estimated time to traverse for one or more route components);
(3) obtaining, by a hardware processor, a real-time factor related to the plurality of routes (see at least Gearhart, [0024] disclosing that the route presentation 10 may provide real-time or live information, such as traffic, weather, and event information; [0042]);
(4) generating ... a plurality of candidate navigation routes between the starting location and the destination for the user using the real-time factor (see at least Gearhart, [0042] disclosing that based on the user information and/or any other relevant information, such as traffic information, weather information, event information retrieved from any databases, such as a mapping database server 145, an optimal route or routes may be calculated and saved in memory. The route(s) calculated at step 310 may be presented to the user via the electronic device 110 and/or may be saved in memory for any adjustment based on any other user inputs prior to presentation to the user); and
(5) applying the trade-off to constrain selections of the plurality of candidate navigation routes to identify a preferred navigation route (see at least Schlesinger, [0047]; [0067).
Wansley, Schlesinger and Gearhart are analogous art to claim 1 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of obtaining route data, to (1) provide an interactive control for a user, to specify a trade-off between difficulty levels of a plurality of routes and time for selecting navigation routes, and have the trade-off corresponding to a function of route difficulty the user prefers to accept to save time, (3) obtain a real-time factor related to the plurality of routes, (4) generate a plurality of candidate navigation routes between the starting location and the destination for the user using the real-time factor and (5) apply the trade-off to constrain selections of the plurality of candidate navigation routes to identify a preferred navigation route , as disclosed in Schlesinger and Gearhart, with a reasonable expectation of success. Doing so would provide the benefit of having the route plan that accounts for trade-offs in preferences users may consider to arrive at an optimal route (see at least Schlesinger, [0002]).
As per claim 9, similar to claim 1, Wansley discloses [a] computing system (see at least Wansley, [0101] disclosing computer system 500) comprising:
one or more processors; and a computer-readable memory storing thereon instructions that, when executed by the one or more processors (see at least Wansley, [0101] disclosing the computer system 500 may be implemented with one or more processors; and [0102] disclosing that Computer system 500 can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory 504), cause the computing system to: ... (1) ... ; ... (2) ... ;
receive an indication of a starting location and a destination from the user (see at least Wansley, Fig. 3, showing step 330 calculating, in response to the received request, the travel route from the origin location to the destination location based on the stored navigational preferences and the stored travel history of the user; and [0058]) ; ... (3) ... ;
generate a plurality of candidate navigation routes between the starting location and the destination for the user, (see at least Wansley, [0092]); and ... (4) ... ; ... (5) ... . But, Wansley does not explicitly teach the following limitations taught in Schlesinger and Gearhart:
(1) provide, via a user interface, an interactive control for a user (see at least Gearhart, [0029]; [0037])
(2) to specify a trade-off between difficulty levels of a plurality of routes and time for selecting navigation routes (see at least Schlesinger, [0047]),
the trade-off corresponding to a function of route difficulty the user prefers to accept to save time (see at least Schlesinger, [0063]; [0090] ; [0091]; [0092]; [0088]; [0094]);
(3) obtain a real-time factor related to the plurality of routes (see at least Gearhart, [0024]; [0042]);
(4) generate a plurality of candidate navigation routes between the starting location and the destination for the user using the real-time factor (see at least Gearhart, [0042] disclosing that based on the user information and/or any other relevant information, such as traffic information, weather information, event information retrieved from any databases, such as a mapping database server 145, an optimal route or routes may be calculated and saved in memory. The route(s) calculated at step 310 may be presented to the user via the electronic device 110 and/or may be saved in memory for any adjustment based on any other user inputs prior to presentation to the user); and
(5) applying the trade-off to constrain selections of the plurality of candidate navigation routes to identify a preferred navigation route (see at least Schlesinger, [0047]; [0067).
Wansley, Schlesinger and Gearhart are analogous art to claim 9 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of obtaining route data, to (1) provide an interactive control for a user, to specify a trade-off between difficulty levels of a plurality of routes and time for selecting navigation routes, and have the trade-off corresponding to a function of route difficulty the user prefers to accept to save time, (3) obtain a real-time factor related to the plurality of routes, (4) generate a plurality of candidate navigation routes between the starting location and the destination for the user using the real-time factor and (5) apply the trade-off to constrain selections of the plurality of candidate navigation routes to identify a preferred navigation route , as disclosed in Schlesinger and Gearhart, with a reasonable expectation of success. Doing so would provide the benefit of having the route plan that accounts for trade-offs in preferences users may consider to arrive at an optimal route (see at least Schlesinger, [0002]).
Claims 3, 8, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger and Gearhart as applied to claims 1 and 9 above, and further in view of U.S. Patent Publication Number 2018/0058875 to Wan et al. (hereafter Wan).
As per claim 3, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 1, as discussed above. Wansley further discloses the limitation:
wherein generating the navigation route includes applying the ... parameter to further constrain selections of route segments (see at least Wansley, [0062] disclosing that each route segment may be affected by environmental factors. For example, street conditions such as the length of a route segment, complexity (e.g., number of turns), and traffic speed may affect the desirability of the route segment, and consequently the travel path as a whole; and [0063] disclosing that based on the affinity values and the environmental factors, the travel routes are ordered from most desirable to least desirable). But neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitation taught in Wan:
receiving, by the processing hardware, a time constraint parameter indicative of a time by which the user must arrive at the destination limitation (see at least Wan, [0049] disclosing that the candidate route 214 can correspond to an arrival time estimate 216, a candidate distance 218, a route decomposition profile 220, or a combination thereof; [0050] disclosing that the arrival time estimate 216 can include a calculated prediction of a time corresponding to arrival of a traveler at a specific corresponding location along the candidate route 214 or of a duration of travel necessary to reach the specific corresponding location; and [0051]) ... .
Wansley, Schlesinger, Gearhart and Wan are analogous art to claim 3 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Wan is directed to a navigation system with a user-preference analysis mechanism (see at least Wan, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of using a time constraint parameter indicative of a time by which the user must arrive at the destination to constrain the route segment selection, as disclosed in Wan, with a reasonable expectation of success. Doing so would provide the benefit of additional navigational information from other users (see at least Wan, [0003]).
As per claim 8, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 1, as discussed above. But, neither Wansley, Schlesinger nor Gearhart explicitly teach the limitation taught in Wan:
further using indications of other users' preferences regarding the difficulty levels, for routes between the starting location and the destination (see at least Wan, [0034] disclosing that the navigation system 100 can be used by a system user 108, further participants 110, or a combination thereof; [0035] disclosing that the further participants 110 can include multiple people or entities accessing or utilizing the navigation system 100 or one or more devices therein. The further participants 110 can include the people or entities different and separate from the system user 108; and [0051] disclosing that the navigation system 100 can calculate the arrival time estimate 216 based on historical data or the average speed using all users, a grouping of users including the user grouping 208, specific to the system user 108, or a combination thereof).
Wansley, Schlesinger, Gearhart and Wan are analogous art to claim 8 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Wan is directed to a navigation system with a user-preference analysis mechanism (see at least Wan, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of, in response to determining that the route data is insufficient for determining the quantitative metric without additional data, determining the quantitative metric further using indications of other users' preferences regarding the first property and the second property, for routes between the starting location and the destination, as disclosed in Wan, with a reasonable expectation of success. Doing so would provide the benefit of additional navigational information from other users (see at least Wan, [0003]).
As per claim 11, similar to claim 3, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 9, as discussed above. Wansley further discloses the limitation:
wherein the instructions further cause the system to: ... generate the navigation route, the instructions cause the system to apply the time constraint parameter to further constrain selections of route segments (see at least Wansley, [0062]; and [0063]). But, neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitation taught in Wan:
receive a time constraint parameter indicative of a time by which the user must arrive at the destination (see at least Wan, [0049]; [0050]; and [0051]) ... .
Wansley, Schlesinger, Gearhart and Wan are analogous art to claim 11 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Wan is directed to a navigation system with a user-preference analysis mechanism (see at least Wan, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of using a time constraint parameter indicative of a time by which the user must arrive at the destination to constrain the route segment selection, as disclosed in Wan, with a reasonable expectation of success. Doing so would provide the benefit of additional navigational information from other users (see at least Wan, [0003]).
As per claim 16, similar to claim 8, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 9, as discussed above. But, nether Wansley, Schlesinger nor Gearhart explicitly teach the limitation taught in Wan:
further using indications of other users' preferences difficulty levels and time, for routes between the starting location and the destination (see at least Wan, [0034]; and [0051]).
Wansley, Schlesinger, Gearhart and Wan are analogous art to claim 16 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Wan is directed to a navigation system with a user-preference analysis mechanism (see at least Wan, [0001]).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of, in response to determining that the route data is insufficient for determining the quantitative metric without additional data, determining the quantitative metric further using indications of other users' preferences regarding the first property and the second property, for routes between the starting location and the destination, as disclosed in Wan, with a reasonable expectation of success. Doing so would provide the benefit of additional navigational information from other users (see at least Wan, [0003]).
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger and Gearhart as applied to claims 1 and 9 above, and further in view of U.S. Patent Publication Number 2019/0162546 Duale et al. (hereafter Duale).
As per claim 4, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 1, as discussed above. Wansley further discloses the limitation:
wherein generating the navigation route includes applying ... parameter to further constrain selections of route segments (see Wansley, [0062] disclosing that each route segment may be affected by environmental factors. For example, street conditions such as the length of a route segment, complexity (e.g., number of turns), and traffic speed may affect the desirability of the route segment, and consequently the travel path as a whole; and [0063] disclosing that based on the affinity values and the environmental factors, the travel routes are ordered from most desirable to least desirable). But, neither Wansley, Schlesinger nor Gearhart explicitly teach the limitation, disclosed in Duale, which teaches a comparable method where it is known to add:
receiving, by the processing hardware, a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination (see at least Duale, [0087] disclosing that the method further includes computing a sum of travel parameter for route segments using the waypoints for carpooling and combining the carpool users into new users (as described earlier), at 875. The method further includes computing the sum of the travel parameters for route segments using the waypoints for carpooling, at 875. In one or more examples, multiple combinations of the waypoints for carpooling are determined, and the sums for each respective combination is computed. The method further includes outputting the optimal route based on the sums computed, at 880. For example, the route with the minimum sum is the optimal route. The travel parameter being optimized may be the travel distance, travel duration or the like) ... .
Wansley, Schlesinger, Gearhart and Duale are analogous art to claim 4 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Duale is directed to navigation systems which provide route planning in travel and transport and provide an optimized route plan for multiple travelers with the same destinations (see Duale, [0001]).
Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination to constrain the route segment selection, as disclosed in Duale, with a reasonable expectation of success. The results would have been predicable to one of ordinary skill.
As per claim 12, similar to claim 4, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 9, as discussed above. Wansley further discloses the limitation:
wherein to generate the navigation route, the instructions cause the system to apply ... parameter to further constrain selections of route segments (see Wansley, [0062]; and [0063]). But, neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitation taught in Duale, which teaches a comparable method where it is known to add:
wherein the instructions cause the system to: wherein the instructions cause the system to: receive a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination (see at least Duale, [0087]) ... .
Wansley, Schlesinger, Gearhart and Duale are analogous art to claim 12 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Duale is directed to navigation systems which provide route planning in travel and transport and provide an optimized route plan for multiple travelers with the same destinations (see Duale, [0001]).
Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a carpooling parameter indicative of whether carpooling is available to the user between the starting location and the destination to constrain the route segment selection, as disclosed in Duale, with a reasonable expectation of success. The results would have been predicable to one of ordinary skill.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger and Gearhart as applied to claims 1 and 9 above, and further in view of U.S. Patent Publication Number 2011/0145290 to Fujii et al. (hereafter Fujii).
As per claim 5, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 1, as discussed above. Wansley further discloses the limitation:
wherein generating the navigation route includes applying ... parameter to further constrain selections of route segments (see at least Wansley, [0062] disclosing that each route segment may be affected by environmental factors. For example, street conditions such as the length of a route segment, complexity (e.g., number of turns), and traffic speed may affect the desirability of the route segment, and consequently the travel path as a whole; and [0063] disclosing that based on the affinity values and the environmental factors, the travel routes are ordered from most desirable to least desirable). But, neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitation taught in Fujii:
receiving, by the processing hardware, a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination ... (see Fujii, Fig. 5, showing searching algorithm 554, Minimum time <interpreted as an amount of time>; [0037] disclosing that examples of such a route searching algorithm, there are enumerated a shortest distance ( distance priority) algorithm, a minimum time (time priority) algorithm, a toll road priority algorithm, a right tum avoidance algorithm, etc.).
Wansley, Schlesinger, Gearhart and Fujii are analogous art to claim 5 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii relates to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger, and Gearhart, to provide the benefit of having the generation of the navigation route includes applying a parameter to further constrain selections of route segments, and receiving a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination, as disclosed in Fujii, with a reasonable expectation of success. Doing so would provide the benefit of having present a route that meets a user's preference even in a place where the user has not traveled before (see at least Fujii, [0008]).
As per claim 13, similar to claim 5, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 9, as discussed above. Wansley further discloses the limitation:
wherein to generate the navigation route, the instructions apply ... parameter to further constrain selections of route segments (see at least Wansley, [0062] disclosing that each route segment may be affected by environmental factors. For example, street conditions such as the length of a route segment, complexity (e.g., number of turns), and traffic speed may affect the desirability of the route segment, and consequently the travel path as a whole; and [0063] disclosing that based on the affinity values and the environmental factors, the travel routes are ordered from most desirable to least desirable). But, neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitation taught in Fujii:
wherein the instructions further cause the system to: receive a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination ... (see at least Fujii, Fig. 5, showing searching algorithm 554, Minimum time <interpreted as an amount of time>; [0037] disclosing that examples of such a route searching algorithm, there are enumerated a shortest distance ( distance priority) algorithm, a minimum time (time priority) algorithm, a toll road priority algorithm, a right tum avoidance algorithm, etc.).
Wansley, Schlesinger, Gearhart and Fujii are analogous art to claim 13 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii relates to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Wansley, Schlesinger, and Gearhart, to provide the benefit of having the generation of the navigation route includes applying a parameter to further constrain selections of route segments, and receiving a time-of-travel parameter indicative of a time at which the user is to travel between the starting location and the destination, as disclosed in Fujii, with a reasonable expectation of success. Doing so would provide the benefit of having present a route that meets a user's preference even in a place where the user has not traveled before (see at least Fujii, [0008]).
Claims 17, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger and Gearhart as applied to claims 1 and 9 above, and further in view of Fujii and U.S. Patent Publication Number 2016/0202074 to Woodard et al. (hereafter Woodard).
As per claim 17, the combination of Wansley, Schlesinger and Gearhart discloses all of the limitations of claim 1, as discloses above. Schlessinger further disclose the following limitations:
training a machine learning model using route data and route segment data to determine quantitative metric of a trade-off between the difficulty levels of the route segments and time in selection of navigation routes by the user (see at least Schlesinger, [0067] a routing condition comprises a threshold value for one or more of the semantic characteristics and/or preferences, which a user may set or may be machine learned <i.e. trained >; [0072] … preference weight factors …; [0073] disclosing that a preference weight metric can correspond to one or more machine learning models. For example, each preference weight metric could correspond to a respective machine learning model. Thus the preference weights, as with other weights, can be machine learned. In some cases, a machine learning model can define inputs which collectively correspond to a route preference; [0092] <weather, accidents, interpreted as difficulty level>) ... ;
wherein an initial setting for the interactive control is based on the trade-off determined by the machine learning model (see at least Schlesinger, [0073] disclosing that each metric described herein, such as a preference weight metric can correspond to one or more machine learning models. For example, each preference weight metric could correspond to a respective machine learning model. Thus the preference weights, as with other weights, can be machine learned. In some cases, a machine learning model can define inputs which collectively correspond to a route preference. For example, at least some inputs of a machine learning model can correspond to sensor data from one or more user devices provided in association with the user. This can include various inferred semantic characteristics (e.g., user characteristics 232); [0076]; Fig. 5 showing the step 530 of determining route scores of the routes based on route preferences , [0134] disclosing that method 500 includes determining route scores of the routes based on route preferences. For example, routing engine 260 can determine routes scores based on route preferences of routing factors 262. The route scores can be generated using any suitable approach) ... . But neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitations taught in Fujii and Woodward:
the trade-off corresponding to a function that expresses an amount of time by which a first route the user travelled between a pair of locations differs from a second route the user travelled between the pair of locations as a function of route segment difficulty (see at least Fujii, Fig. 5, showing searching algorithm 554, Minimum time <interpreted as an amount of time>; [0037] disclosing that examples of such a route searching algorithm, there are enumerated a shortest distance ( distance priority) algorithm, a minimum time (time priority) algorithm, a toll road priority algorithm, a right tum avoidance algorithm <interpreted as segment difficulty>, etc.; [0051]-[0054] disclosing metrics; Fig. 4A, showing a first route and a second route between an origin and a destination where the links (segments) are different); [0047] disclosing that shown in FIG. 4A, in case where a link series of the actual travel route is 1, 2, 3, 4 and 5 and a link series of the travel route obtained as the search result is 1, 6, 7 and 8, it is judged or determined that a mismatch has occurred at a node N1);
wherein the first route includes route segments of the first difficulty level, and the second route includes route segments of a second difficulty level (see at least Fujii, Fig. 5, showing searching algorithm 554, minimum time <interpreted as an amount of time>; [0037] disclosing that examples of such a route searching algorithm, there are enumerated a shortest distance ( distance priority) algorithm, a minimum time (time priority) algorithm, a toll road priority algorithm, a right tum avoidance algorithm, etc.; [0051]-[0054] disclosing metrics; Fig. 4A, showing a first route and a second route between an origin and a destination where the links (segments) are different); [0047] disclosing that shown in FIG. 4A, in case where a link series of the actual travel route is 1, 2, 3, 4 and 5 and a link series of the travel route obtained as the search result is 1, 6, 7 and 8, it is judged or determined that a mismatch has occurred at a node N1); and
wherein the training of the machine learning model includes using feedback from drivers (see at least Woodard, [0031] disclosing that the historical trip data 106 may be collected in a variety of ways, but in general may be collected from devices of users, such as the user 110, that are actually traveling about a region. This can be accomplished by collecting data reported from any suitable type of mobile computing device (e.g., a mobile phone, navigation unit of a vehicle, etc.). In some embodiments, the computing device 108 of FIG. 1 may comprise one of these mobile computing devices that reports location-based measurements and data over the network 130 as the computing device 108 (carried by the user 110) moves about a region).
Wansley, Schlesinger, Gearhart, Fujii, and Woodward are analogous art to claim 17 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii is directed to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract). Woodard relates to a system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model trained from historical trip data that is based on location-based measurements reported from mobile devices (see at least Woodard, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified and method, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of having the trade-off correspond to a function that expresses an amount of time by which a first route the user travelled between a pair of locations differ from a second route the user traveled between the pair of locations as a function of route difficulty, having the first route include route segments of the first difficulty level and the second route includes route segments of a second difficulty level, as discussed in Fujii, was a reasonable expectation of success, and having the training of the machine learning model include using feedback from drivers, as disclosed in Woodard, with a reasonable expectation of success. Doing so would provide the benefit of a better user experience due to the accuracy of the travel time predictions (see at least Woodard, [0003]).
As per claim 20, Wansley, Schlesinger, Gearhart, Fujii and Woodard discloses all of the limitations of claim 17, as discloses above. Schlesinger further discloses the limitation:
wherein the training of the machine learning model includes using road types (see at least Schlesinger, [0048] disclosing that Examples of routine characteristics of a location (e.g., a route or route component) include a type or utility category (e.g., a highway, a freeway, a dirt road a surface street, a bike trail, a walking trail, etc.), traffic conditions on at a particular time of day, ongoing constructions, a presence of lane merges, peak traffic times at the location, aggregate or posted driving speeds for users at a location; [0102] disclosing that a route preference corresponds to a preference for main roads, or route components, for suggested routes. Main route components correspond to route components that on aggregate users traverse more frequently than non-main route components. And further that mapping data can indicate which route components or portions thereof correspond to main roads, such as highways, side streets, side roads, and the like. The frequency can be extracted from this road type or other mapping data).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger, Gearhart, Fujii and Woodard, to provide the benefit of having the training of the machine learning model include using road types, as further disclosed in Schlesinger, with a reasonable expectation of success. Doing so would provide the benefit of a better user experience due to the accuracy of the travel time predictions (see at least Woodard, [0003]).
As per claim 21, similar to claim 17, the combination of Wansley, Schlesinger, Gearhart discloses all of the limitations of claim 9, as discloses above. Schlessinger further disclose the following limitations:
training a machine learning model using route data and route segment data to determine quantitative metric of a trade-off between the difficulty levels of the route segments and time in selection of navigation routes by the user (see at least Schlesinger, [0067]; [0072]; [0073]; [0092]) ... ;
wherein an initial setting for the interactive control is based on the trade-off determined by the machine learning model (see at least Schlesinger, [0073]; [0076]; Fig. 5 showing the step 530 of determining route scores of the routes based on route preferences , [0134]) ... . But neither Wansley, Schlesinger nor Gearhart explicitly teach the following limitations taught in Fujii and Woodward:
the trade-off corresponding to a function that expresses an amount of time by which a first route the user travelled between a pair of locations differs from a second route the user travelled between the pair of locations as a function of route segment difficulty (see at least Fujii, Fig. 5, showing searching algorithm 554, Minimum time <interpreted as an amount of time>; [0037]; [0051]-[0054] disclosing metrics; Fig. 4A, showing a first route and a second route between an origin and a destination where the links (segments) are different); [0047]);
wherein the first route includes route segments of the first difficulty level, and the second route includes route segments of a second difficulty level (see at least Fujii, Fig. 5, showing searching algorithm 554, minimum time <interpreted as an amount of time>; [0037]; [0051]-[0054]); and
wherein the training of the machine learning model includes using feedback from drivers (see at least Woodard, [0031]).
Wansley, Schlesinger, Gearhart, Fujii, and Woodward are analogous art to claim 21 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii is directed to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract). Woodard relates to a system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model trained from historical trip data that is based on location-based measurements reported from mobile devices (see at least Woodard, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified and method, as disclosed in Wansley, Schlesinger and Gearhart, to provide the benefit of having the trade-off correspond to a function that expresses an amount of time by which a first route the user travelled between a pair of locations differ from a second route the user traveled between the pair of locations as a function of route difficulty, having the first route include route segments of the first difficulty level and the second route includes route segments of a second difficulty level, as discussed in Fujii, was a reasonable expectation of success, and having the training of the machine learning model include using feedback from drivers, as disclosed in Woodard, with a reasonable expectation of success. Doing so would provide the benefit of a better user experience due to the accuracy of the travel time predictions (see at least Woodard, [0003]).
Claims 18 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger, Gearhart, Fujii and Woodard as applied to claims 17 and 21 above, and further in view of U.S. Patent Publication 2019/0310093 to Wilczynski et al. (hereafter Wilczynski).
As per claim 18, the combination of Wansley, Schlesinger, Gearhart, Fujii and Woodard discloses all of the limitations of claim 17, as discloses above. But, neither Wansley, Schlesinger, Gearhart, Fujii nor Woodard explicitly disclose the following limitation disclosed in Wilczynski:
wherein the training of the machine learning model includes using road geometry (see at least Wilczynski, [0021] disclosing that the cost component may be determined based on a route graph (edge/node weighting). The cost component may reflect a measure of a characteristic of an environment (e.g., geographic characteristic, preference characteristic, difficulty characteristic, risk characteristic) through which the route travels. The characteristic of the environment may be static (e.g., does not change over time) or dynamic (e.g., changes over time)).
Wansley, Schlesinger, Gearhart, Fujii, Woodard and Wilczynski are analogous art to claim 18 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii is directed to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract). Woodard relates to a system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model trained from historical trip data that is based on location-based measurements reported from mobile devices (see at least Woodard, Abstract). Wilczynski relates to systems, methods, and non-transitory computer readable media for flexible route planning, that include a composite cost with at least a temporal cost component and a non-temporal cost component (see at least Wilczynski, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger, Gearhart, Fujii and Woodard, to provide the benefit of having the training of the machine learning model include using road geometry, as disclosed in Wilczynski, with a reasonable expectation of success. Doing so would provide the benefit of a considering non-traditional travel modes and user preferences in the route planning (see at least Wilczynski, [0003]).
As per claim 22, similar to claim 18, the combination of Wansley, Schlesinger, Gearhart, Fujii and Woodard discloses all of the limitations of claim 21, as discloses above. But, neither Wansley, Schlesinger, Gearhart, Fujii nor Woodard explicitly disclose the following limitation disclosed in Wilczynski:
wherein the training of the machine learning model includes using road geometry (see at least Wilczynski, [0021]).
Wansley, Schlesinger, Gearhart, Fujii, Woodard and Wilczynski are analogous art to claim 22 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii is directed to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract). Woodard relates to a system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model trained from historical trip data that is based on location-based measurements reported from mobile devices (see at least Woodard, Abstract). Wilczynski relates to systems, methods, and non-transitory computer readable media for flexible route planning, that include a composite cost with at least a temporal cost component and a non-temporal cost component (see at least Wilczynski, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method, as disclosed in Wansley, Schlesinger, Gearhart, Fujii and Woodard, to provide the benefit of having the training of the machine learning model include using road geometry, as disclosed in Wilczynski, with a reasonable expectation of success. Doing so would provide the benefit of a considering non-traditional travel modes and user preferences in the route planning (see at least Wilczynski, [0003]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Wansley, Schlesinger, Gearhart, Fujii and Woodard as applied to claim 17 above, and further in view of U.S. Patent Publication Number 2019/ 0147540 to Dai et al. (hereafter Dai).
As per claim 19, the combination of Wansley, Schlesinger, Gearhart, Fujii and Woodard discloses all of the limitations of claim 1, as discussed above. But, neither Wansley, Schlesinger, Gearhart, Fujii nor Woodard explicitly disclose the following limitation disclosed in Dai:
wherein the training of the machine learning model includes using a number of accidents reported for corresponding road segments (see at least Dai, [0070] disclosing that the historical vehicle accident occurrence frequency of the user in the sample data may be used as corresponding output data to train the initial vehicle accident occurrence frequency calculation model using the machine learning method; [0110]).
Wansley, Schlesinger, Gearhart, Fujii, Woodard and Duale are analogous art to claim 19 because they are in the same field of navigation systems that calculate and apply qualitative metrics indicative of trade-offs between parameters in navigation based upon a user’s travel history. Wansley is directed to a method for calculating a travel route based on navigational preferences and a user’s travel history (see at least Wansley, Abstract). Schlesinger relates to comparing routes to one or more threshold values and reference routes to determine whether to provide those routes to users, to filter out deficient routes that are unlikely to be acceptable to users, where the threshold values and reference routes of the comparisons can constrain the duration, distance, and/or complexity of the routes (see at least Schlesinger, [0019]). Gearhart relates to systems and methods for providing user customizable break point recommendations for a trip, based in part on user input (see at least Gearhart, [0001]). Fujii is directed to a route searching apparatus that includes a travel route accumulation unit, an algorithm selection unit to select route searching algorithms to meet the preference of a user based on the accumulated travel routes, and a route searching unite to perform a route search up to a destination by using the selected algorithms (see at least Fujii, Abstract). Woodard relates to a system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model trained from historical trip data that is based on location-based measurements reported from mobile devices (see at least Woodard, Abstract). Dai is directed to a method and apparatus for outputting information that includes acquiring at least one personal attribute characteristic of a target user, and determining a user type of the target user under a preset attribute (see Dai, Abstract).
Therefore, it would have been prima facie obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Wansley, Schlesinger, Gearhart, Fujii, and Woodard, to provide the benefit of having the training of the machine learning model include using a number of accidents reported for corresponding road segments, as disclosed in Dai, with a reasonable expectation of success. Doing so would provide the benefit of providing the user with navigation consistent with the user target attributes (see at least Dai, Abstract, [0047]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. Patent Publication Number 2020/0041291 to Dunnette, carpooling [0048]; U.S. Patent Publication Number 2020/0264629 to Maveddat et al. (hereafter Maveddat) first and second routes with different segments [0036], [0047]; and U.S. Patent Publication Number 2021/0081688 to Bazargan et al., carpooling [0097].
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 PATRICK M. BRADY III whose telephone number is (571)272-7458. The examiner can normally be reached Monday - Friday 8:00 am - 5;30 pm.
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/PATRICK M BRADY/ Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/ Supervisory Patent Examiner, Art Unit 3666