DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recently filed claims by applicants on 12/05/25.
Claims 1-3, 8, and 15 are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
Note:
In independent claims 1, 8 and 15, amended claims recite “generating, utilizing a neural network classifier of the elasticity model trained to generate a prediction classification of a prospective transportation request based on a plurality of inputs including origin coordinates and destination coordinates, predictions of receiving transportation requests for a particular transportation metric; and …. providing, by the transportation matching system to the requester device, a response to the transportation request information comprising the generated transportation metric.” Here, the claims are simply making predictions of transportation request demand and then providing a response with the transportation metric. It is unclear what providing a response…comprising a transportation metric means. Is it simply a message containing the transportation metric? For instance, a user requests transportation and the system calculates how long it will be before the transportation service arrives. Then the transportation metric is the arrival time of the transportation. However, once the user selects the transportation the number of vehicles available for the next request changes and the claim is not looping back into the model as a way for that information to be considered in the future calculation for the next time a request is received. It seems like the claim may be missing some steps.
Similarly, since the calculation of the transportation metric is not being used in the system for modifying the model itself in any way for future forecasting. The steps of making a prediction and calculating the likelihood in the claim limitations “generating, utilizing a neural network classifier of the elasticity model trained to generate a prediction classification of a prospective transportation request based on a plurality of inputs including origin coordinates and destination coordinates, predictions of receiving transportation requests for a particular transportation metric; and determining, utilizing an elasticity estimation layer of the elasticity model trained to determine a likelihood of receiving a transportation request based on the prediction classification of the prospective transportation request, probabilities of receiving transportation requests based on the predictions from the neural network classifier” also seems to be missing steps.
In light of these notes, the amended claims, do not overcome previously presented rejections under 101 and 103. As is discussed below. This note is intended as a conversation starter to help applicants understand the examiner’s perspective. Applicants are welcome to call the examiner to discuss this further.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-7 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 8-14 is/are directed to a computer program product which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 15-20 is/are directed to a system which is a statutory category.
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Claim 1 is directed to an abstract idea, as evidenced by claim limitations “receiving, transportation request information from a requester device, the transportation request information comprising an origin, a destination, and a time; generating, a set of predicted future transportation requests corresponding to the origin, the time, and a geocoded area defining the destination of the transportation request information; determining, parameters trained to generate transportation metric functions from features extracted from the set of predicted future transportation requests, a transportation metric function for achieving a target effect specific to the origin, the time, and the geocoded area defining the destination of the transportation request information by: generating, a prediction classification of a prospective transportation request based on a plurality of inputs including origin coordinates and destination coordinates, predictions of receiving transportation requests for a particular transportation metric; and determining, a likelihood of receiving a transportation request based on the prediction classification of the prospective transportation request, probabilities of receiving transportation requests based on the predictions; determining one or more optimization parameters associated with the transportation request information, the one or more optimization parameters corresponding to the target effect specific to the origin, the time, and the geocoded area defining the destination; generating, utilizing the transportation metric function, a transportation metric based on the transportation request information and the one or more optimization parameters; and providing, a response to the transportation request information comprising the generated transportation metric.”
These claim limitations, under their broadest reasonable interpretation, belong to the grouping of “certain methods of organizing human activity”. Managing allocation of transportation providers for one or more human entities involves managing personal behavior or interaction between people. This is organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —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).
Independent Claims 8 and 15 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim only recites “A method for managing transportation services comprising: by a transportation matching system, by the transportation matching system, by the transportation matching system, utilizing an offline transportation model, utilizing an elasticity model, by the transportation matching system, by the transportation matching system to the requester device; A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computer device to; A system for managing transportation services comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: utilizing a neural network classifier of the elasticity model trained to generate, utilizing an elasticity estimation layer of the elasticity model trained to determine, from the neural network classifier, by a transportation matching system, utilizing an offline transportation model, utilizing an elasticity model comprising, utilizing a neural network classifier of the elasticity model trained to generate, utilizing an elasticity estimation layer of the elasticity model trained to determine, from the neural network classifier, by the transportation matching system, by the transportation matching system to the requester device”, such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
As a result, claims 1, 8 and 15 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception.
Similarly dependent claims 2-7, 9-14 and 16-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recites “wherein generating the transportation metric based on the transportation request comprises utilizing the elasticity model to generate a transportation metric that produces the target effect.” Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 3 and 4 recites “utilizing the elasticity model, a classifier configured to, an elasticity estimation layer configured to” in the claim limitations “wherein utilizing the elasticity model to generate the transportation metric function comprises determining, via the elasticity model, a probability of session conversion for a transportation mode in relation to a potential change in the transportation metric based on the origin, the destination, and the time of the transportation request information” in claim 3 and “further comprising: receiving, by the transportation matching system from the requester device, a transportation request corresponding to the transportation request information; and matching the requester device with an autonomous vehicle in response to the transportation request” in claim 4. In these claims, “utilizing the elasticity model, a classifier configured to, an elasticity estimation layer configured to” and “by the transportation matching system from the requester device” “autonomous vehicle” are an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A method for managing transportation services comprising: by a transportation matching system, by the transportation matching system, by the transportation matching system, utilizing an offline transportation model, utilizing an elasticity model, by the transportation matching system, by the transportation matching system to the requester device; A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computer device to; A system for managing transportation services comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: utilizing a neural network classifier of the elasticity model trained to generate, utilizing an elasticity estimation layer of the elasticity model trained to determine, from the neural network classifier, by a transportation matching system, utilizing an offline transportation model, utilizing an elasticity model comprising, utilizing a neural network classifier of the elasticity model trained to generate, utilizing an elasticity estimation layer of the elasticity model trained to determine, from the neural network classifier, by the transportation matching system, by the transportation matching system to the requester device” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0125]: general purpose computer. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 8 and 15 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-7, 9-14 and 16-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 8 and 15. As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2017/0339237) Memon; Amir Hussain, further in view of (US 2019/0057476) Zhang et al. and (US 2018/0060738) Achin et al.
As per claims 1, 8 and 15:
Regarding the claim limitations below, Memon shows:
A method for managing transportation services comprising (Memon: [0043]):
Regarding the claim limitations below, Memon shows:
A non-transitory computer readable medium comprising instructions that, when executed by at least one processor (Memon: [0028]: processors, memories), cause a computer device to (Memon: [0095]):
Regarding the claim limitations below, Memon shows:
A system for managing transportation services comprising (Memon: Fig. 1, [0023]-[0024]) at least one processor (Memon: [0028]: processors, memories); and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor (Memon: [0095]), cause the system to:
Regarding the claim limitations below, Memon shows:
receiving, by a transportation matching system, transportation request information from a requester device, the transportation request information comprising an origin, a destination, and a time (Memon: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.);
Regarding the claim limitations below, Memon in view of Achin shows:
generating, utilizing an offline transportation model, a set of predicted future transportation requests corresponding to the origin, the time, and a geocoded area defining the destination of the transportation request information
Memon: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
Even though Memon shows the ability to estimate fare based on demand, Memon does not explicitly show predicting future requests as is discussed in the claim above. Achin shows the ability to forecast and as such, the ability to predicting future requests limitation at least in [0004]: forecasting, [0060]: predicting future values of a target, [0137]: The outer loop provides a test set for both comparing a given model to other models and calibrating each model's predictions on future samples, [0171]: it may be possible to increase the speed of future prediction calculations.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A));
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
“determining, utilizing an elasticity model comprising parameters trained to generate transportation metric functions from features extracted from the set of predicted future transportation requests, a transportation metric function for achieving a target effect specific to the origin, the time, and the geocoded area defining the destination of the transportation request information by:”
Memon shows “wherein determining … based on the origin, the destination, and the time of the transportation request information”: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
However, Memon does not explicitly show “from predicted transportation requests”. Reference Zhang shows “from predicted transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds.
This claim limitation above is shown in applicants’ specification as follows recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows elasticity model in paragraphs [0021] and [0065].
Regarding the claim limitations above, applicants originally submitted specification shows target effect at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows target effect or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower.
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show transportation metric as the claim limitation is understood based on the specification. Memon also does not explicitly show an elasticity model.
However, Achin shows the transportation metric at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows elasticity model [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A).
Memon does not explicitly show “from predicted transportation requests”. Reference Zhang shows “from predicted transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds. Zhang also shows “autonomous vehicle” in [0025] The vehicle type associated with the transportation service request can indicate a type of the requested service vehicle. For example, the vehicles type may include a taxi car (e.g., DiDi Taxi™), an ordinary car (e.g., DiDi Express™), a luxury car (e.g., DiDi Premier™), a bus (e.g., DiDi Bus™ and DiDi Minibus™), etc. It is contemplated that, the service vehicles may also include a type of autonomous vehicles.
Reference Memon and Reference Zhang are analogous prior art to the claimed invention because the references generally relate to field of location-based transportation services. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
This claim limitation is shown in applicants’ specification as follows “transportation metric” recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows “an elasticity model” in paragraphs [0021] and [0065].
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
generating, utilizing a neural network classifier of the elasticity model trained to generate a prediction classification of a prospective transportation request based on a plurality of inputs including origin coordinates and destination coordinates, predictions of receiving transportation requests for a particular transportation metric
Memon shows “generating, utilizing a neural network classifier of the elasticity model, … for a particular transportation metric”: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user. Memon also shows: “including origin coordinates and destination coordinates” in [0078] FIG. 5 illustrates user interface 400 including shared user location data of a selected user. For example, location services module 129 receives a user selection of Sterling Archer 405 (e.g., within user interface 400 as illustrated in FIG. 4) and, in response, transmits the current physical location of Sterling Archer 405 (or additional detail regarding the current physical location of Sterling Archer 405) to the mobile device 104 associated with the user's account. For example, location services module 129 transmits the location data such that the mobile device 104 presents map 505 at a closer zoom level to illustrate a more precise current physical location 510 of Sterling Archer 405. In one embodiment, the location data includes an address, name of a location, GPS coordinates, or another indication of the current physical location of the selected user. For example, map 505 includes label 515 indicating that Sterling Archer 405 is currently at a physical location 510 that is near or coincides with “Headquarters.” [0054] At block 326, location services module 129 optionally receives a fare estimate from the third party server. For example, if location services module 129 transmits both a pickup address and a destination address to the third party server (e.g., to bring the first user to the second user or vice versa), the third party server may respond with a fare estimate prior to or as a part of completing the car service request.
However, Memon does not explicitly show “predictions of receiving transportation requests”. Reference Zhang shows “predictions of receiving transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds.
Reference Memon and Reference Zhang are analogous prior art to the claimed invention because the references generally relate to field of location-based transportation services. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
This claim limitation is shown in applicants’ specification as follows “transportation metric” recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows “an elasticity model” in paragraphs [0021] and [0065].
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price. Achin also shows: “utilizing a neural network classifier of the elasticity model trained to generate a prediction classification of” [0056] In some embodiments, the modeling techniques may be assigned to families of modeling techniques. The familial classifications of the modeling techniques may be assigned by a user (e.g., based on intuition and experience), assigned by a machine-learning classifier (e.g., based on processing steps common to the modeling techniques, data indicative of the results of applying different modeling techniques to the same or similar problems, etc.), or obtained from another suitable source. The tools for assessing the similarities between predictive modeling techniques may rely on the familial classifications to assess the similarity between two modeling techniques. In some embodiments, the tool may treat all modeling techniques in the same family as “similar” and treat any modeling techniques in different families as “not similar”. In some embodiments, the familial classifications of the modeling techniques may be just one factor in the tool's assessment of the similarity between modeling techniques.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
determining, utilizing an elasticity estimation layer of the elasticity model trained to determine a likelihood of receiving a transportation request based on the prediction classification of the prospective transportation request, probabilities of receiving transportation requests based on the predictions from the neural network classifier
Memon shows “determining, utilizing an elasticity estimation layer of the elasticity model, … of receiving transportation requests based on the predictions from the neural network classifier”: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
However, Memon does not explicitly show “from predicted transportation requests”. Further, Memon does not explicitly show “a probability of session conversion” as is recited in the claim.
Reference Zhang shows “from predicted transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds. Zhang further shows “determine a likelihood” at least in [0061] In some embodiments, after the second transportation request is generated by request generation unit 108, the first transportation request may be kept active. Thus both the first and the second transportation requests may be active until either one is processed, or responded by a service vehicle. As the first and the second transportation requests are usually placed in different queues (e.g., carpool queue v. non-carpool queue), keeping both of them in processing can increase the chance of the requests being responded by an available service vehicle as soon as possible. [0081] In some embodiments, after the second transportation request is generated, the first transportation request may be kept active. Thus both the first and the second transportation requests may be active until either one is processed, or responded by a service vehicle. As the first and the second transportation requests are usually placed in different queues (e.g., carpool queue v. non-carpool queue), keeping both of them in processing can increase the chance of the requests being responded by an available service vehicle as soon as possible.
Reference Memon and Reference Zhang are analogous prior art to the claimed invention because the references generally relate to field of location-based transportation services. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
This claim limitation is shown in applicants’ specification as follows “transportation metric” recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows “an elasticity model” in paragraphs [0021] and [0065].
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price. Achin also shows ““a probability of session conversion” as is recited in the claim (in [0058]: characteristics of a dataset include statistical properties of the dataset's variables, including, without limitation, the number of total observations; the number of unique values for each variable across observations; the number of missing values of each variable across observations; the presence and extent of outliers and inliers; the properties of the distribution of each variable's values or class membership; cardinality of the variables; etc. In some embodiments, characteristics of a dataset include relationships (e.g., statistical relationships) between the dataset's variables, including, without limitation, the joint distributions of groups of variables; the variable importance of one or more features to one or more targets (e.g., the extent of correlation between feature and target variables); the statistical relationships between two or more features (e.g., the extent of multicollinearity between two features); etc.)
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
determining one or more optimization parameters associated with the transportation request information, the one or more optimization parameters corresponding to the target effect specific to the origin, the time, and the geocoded area defining the destination (Regarding the claim limitations above, applicants originally submitted specification shows “target effect” at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows “target effect” or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower. Memon: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user);
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
generating, by the transportation matching system utilizing the transportation metric function, a transportation metric based on the transportation request information and the one or more optimization parameters (Memon: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user. Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. Memon also shows optimization parameters in Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A);
and
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
providing, by the transportation matching system to the requester device, a response to the transportation request information comprising the generated transportation metric (Figs. 6-7, [0080]-[0082]).
As per claims 2, 9 and 16:
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
elasticity model by: comparing a predicted transportation metric function of the elasticity model with a ground truth transportation metric function to determine a measure of loss; and adjusting the parameters of the elasticity model to reduce the measure of loss for a subsequent training iteration
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. Memon also shows optimization parameters in Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price. Achin also shows: “the elasticity model with a ground truth transportation metric function to determine a measure of loss” [0327] However, there is a concern that second-order models may be systematically less accurate for predictions than the source model. Some embodiments of techniques for reducing (e.g., minimizing) any loss of accuracy associated with moving from a source model to a second-order model (and, in some cases, for generating second-order models with greater accuracy than their source models) are described below. [0332] One concern is that any errors in the first-order model may be compounded or magnified when building a second-order model, thereby systematically reducing the accuracy of second-order models. First, the inventors have recognized and appreciated that there is a question of whether this is true empirically. Second, if it is true in some cases, the inventors have recognized and appreciated that using a more accurate first-order model is likely to reduce the loss of accuracy. For example, because blended models (as described, for example, toward the end of the Section titled “Modeling Space Exploration Engine”) are sometimes more accurate than any single model, fitting a second-order model to a blend of first-order models may reduce any loss of accuracy associated with second-order modeling. [0333] The inventors have empirically determined that the concerns about the accuracy of second-order models are largely misplaced. Tests of the system on 381 datasets resulting in 1195 classification and 1849 regression first-order models were performed. For the tested classification models, 43% of second-order models were less accurate than the corresponding first-order models, but no more than 10% worse according to the log loss measure of accuracy. Thirty percent of the second-order models were actually more accurate than the first-order models. In only 27% of the cases was the second-order model more than 10% less accurate than the first-order model. Approximately one-third of these cases (roughly 9% of the total population) occurred when the dataset was very small. Another one-third of these cases (again, roughly 9% of the total population) occurred when the first-order model was very accurate at less than 0.1 log loss and the second order model was still also very accurate at less than 0.1 log loss. Thus, in over 90% of the cases with an adequately large data set, the second-order model was either within 10% of the first-order model or the second-order models was very accurate by absolute standards. In 41% of the cases, the best second-order model was derived from a blend of first-order models.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations above, applicants originally submitted specification shows “target effect” at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows “target effect” or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower.
As per claims 3, 10 and 17:
Regarding the claim limitations below:
further comprising enforcing a monotonic constraint on the elasticity estimation layer such that the probabilities of receiving transportation requests are monotonically non-increasing with respect to changes in the transportation metric wherein utilizing the elasticity model to generate the transportation metric.
Memon shows “wherein determining … based on the origin, the destination, and the time of the transportation request information”: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
However, Memon does not explicitly show “from predicted transportation requests”. Further, Memon does not explicitly show “a probability of session conversion” as is recited in the claim.
Reference Zhang shows “from predicted transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds.
Reference Memon and Reference Zhang are analogous prior art to the claimed invention because the references generally relate to field of location-based transportation services. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
This claim limitation is shown in applicants’ specification as follows “transportation metric” recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows “an elasticity model” in paragraphs [0021] and [0065].
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show “transportation metric” as the claim limitation is understood based on the specification. Memon also does not explicitly show “an elasticity model”.
However, Achin shows the “transportation metric” at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows “an elasticity model” [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price. Achin also shows ““a probability of session conversion” as is recited in the claim (in [0058]: characteristics of a dataset include statistical properties of the dataset's variables, including, without limitation, the number of total observations; the number of unique values for each variable across observations; the number of missing values of each variable across observations; the presence and extent of outliers and inliers; the properties of the distribution of each variable's values or class membership; cardinality of the variables; etc. In some embodiments, characteristics of a dataset include relationships (e.g., statistical relationships) between the dataset's variables, including, without limitation, the joint distributions of groups of variables; the variable importance of one or more features to one or more targets (e.g., the extent of correlation between feature and target variables); the statistical relationships between two or more features (e.g., the extent of multicollinearity between two features); etc.). Achin shows: “enforcing a monotonic constraint on the elasticity estimation layer“ [0049] In some embodiments, a machine-executable template includes metadata describing attributes of the predictive modeling technique encoded by the template. The metadata may indicate one or more data processing techniques that the template can perform as part of a predictive modeling solution (e.g., in a pre-processing step, in a post-processing step, or in a step of predictive modeling algorithm). These data processing techniques may include, without limitation, text mining, feature normalization, dimension reduction, or other suitable data processing techniques. Alternatively, or in addition, the metadata may indicate one or more data processing constraints imposed by the predictive modeling technique encoded by the template, including, without limitation, constraints on dimensionality of the dataset, characteristics of the prediction problem's target(s), and/or characteristics of the prediction problem's feature(s). [0081] In some embodiments, exploration engine 110 determines the suitability of a predictive modeling procedure for a prediction problem based, at least in part, on one or more attributes of the predictive modeling procedure, including (but not limited to) the attributes of predictive modeling procedures described herein. As just one example, the suitability of a predictive modeling procedure for a prediction problem may be determined based on the data processing techniques performed by the predictive modeling procedure and/or the data processing constraints imposed by the predictive modeling procedure. [0240] (1) Interface<−> Analytic+Data. The left-most column of flow 710 first transforms the user's raw dataset and modeling requirements into a refined dataset and list of computation jobs, then coalesces and delivers the results to the user in a format he can easily comprehend. So the goals and constraints flow from Interface Services 620 to Analytic Services 630, while progress and exceptions flow back. In parallel, raw datasets and user annotations flow from Interfaces Services 620 to Data Services 650, while trained models and their performance metrics flow back. At any point, the user can initiate changes and force adjustments by the Analytic Services 630 and Data Services 650 layers. Note that in addition to this dynamic circular flow, there are also more traditional linear interactions (e.g., when Interface Services 620 retrieves system status from Analytic Services 640 or static content from Data Services 650).
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A).
As per claims 4, 11 and 18:
Regarding the claim limitations below, Memon in view of Zhang and Achin shows:
further comprising:
receiving, by the transportation matching system from the requester device, a transportation request corresponding to the transportation request information; and
matching the requester device with an autonomous vehicle in response to the transportation request
This claim limitation above is shown in applicants’ specification as follows recited in the claim at least in [0017]-[0019], Fig. 5, # 500. Particularly, the specification recites: [0021]: the transportation matching system can train and utilize an elasticity model to generate a transportation metric function for a received transportation request. [0110] As shown in FIG. 5, the elasticity model 502 includes various components such as the classifier 506 and the elasticity estimation layer 508. As illustrate, the transportation matching system 102 can implement a classifier 506 in the form of a gradient boosted trees classifier with a binary cross entropy (log loss) loss function. Further the specification shows elasticity model in paragraphs [0021] and [0065].
Regarding the claim limitations above, applicants originally submitted specification shows target effect at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows target effect or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower.
It should be noted that both transportation metric and an elasticity model are broad claim terms and the above explanation is not recited in the claim limitations from the specification. The claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). However, in order to promote compact prosecution, in light of the specification, the above claim is rejected using an additional reference.
Reference Memon shows at least in Fig. 7, # 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. However, Memon does not explicitly show “gradient boosted trees” as is recited in the specification and as such does not explicitly show transportation metric as the claim limitation is understood based on the specification. Memon also does not explicitly show an elasticity model.
However, Achin shows the transportation metric at least in [0135]: a gradient boosted tree model, [0063]: the price estimation model can include one or more decision trees, also see [0064]. [0123]: The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques. Achin also shows elasticity model [0135]: elastic-net model, [0190] If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects. [0254], [0291], [0302]: shows demand forecasting, [0277]: shows supply forecasting, both affect price.
Reference Memon and Reference Achin are analogous prior art to the claimed invention because the references generally relate to field of estimating/ predicting demand of a resource and the related costs associated with the resource. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Achin, particularly the transportation metric and the elasticity model at least in [0062]-[0064], [0123], [0135], in the disclosure of Reference Memon, particularly in the fare estimating process [0080] in order to provide for a system that generates a price estimation model to be used to perform price estimation and/or optimization as taught by Reference Achin (see at least in [0062]-[0064], [0123], [0135]) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Achin, the results of the combination were predictable (MPEP 2143 A).
Memon does not explicitly show “from predicted transportation requests”. Reference Zhang shows “from predicted transportation requests” at least in [0033] and Fig. 5, S506 and S507: where Zhang shows the sample data can include an origin, a destination, a requested time, a location, a position in a waiting queue, a number of previous requests in the waiting queue of a historical request. The supervised signal can include the actual waiting time of the historical request. Based on the sample data and the supervised signal, device 100 can train a machine learning model, which can further estimate the waiting time according to features of a transportation service request. It is contemplated that, status determination unit 106 can continuously determine the estimated waiting time during the whole process of waiting for a response, to periodically update the estimated waiting time, for example, every five seconds. Zhang also shows “autonomous vehicle” in [0025] The vehicle type associated with the transportation service request can indicate a type of the requested service vehicle. For example, the vehicles type may include a taxi car (e.g., DiDi Taxi™), an ordinary car (e.g., DiDi Express™), a luxury car (e.g., DiDi Premier™), a bus (e.g., DiDi Bus™ and DiDi Minibus™), etc. It is contemplated that, the service vehicles may also include a type of autonomous vehicles.
Reference Memon and Reference Zhang are analogous prior art to the claimed invention because the references generally relate to field of location-based transportation services. Said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
As per claims 5, 12 and 19: Memon shows:
wherein: the target effect increasing a volume of shared transportation requests (Regarding the claim limitations above, applicants originally submitted specification shows “target effect” at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows “target effect” or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower); and
determining the transportation metric function comprises determining a transportation metric that will increase the volume of shared transportation requests (Regarding the claim limitations above, applicants originally submitted specification shows “target effect” at least in [0022]: the transportation matching system can receive an indication of one or more goals or target effects associated with a transportation request. [0028]: producing targeted effects such as establishment of service in new geographic areas and/or increasing requester surplus, among others as described herein. In light of this description, the Memon reference shows “target effect” or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower).
As per claims 6, 13 and 20:
Regarding the claim limitations below:
“wherein: the target effect comprises increasing an incentive for a provider to service a transportation request that indicates a particular destination; and
determining the transportation metric function comprises determining a transportation metric that will increase the incentive for the provider to service the transportation request that indicates the particular destination.”
The Memon reference shows “target effect” or a goal in [0035]: facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system. [0065]: the leader and/or the follower may be moving and, therefore, the route the follower is to take is regularly updated to reflect a new location of the leader and/or follower. Further, Memon shows “incentive” at least in [0035] The content store 146 stores content items associated with user profiles, such as images, videos, and/or audio files. Content items from the content store 146 may be displayed when a user profile is viewed or when other content associated with the user profile is viewed. For example, displayed content items may show images or video associated with a user profile or show text describing a user's status. Additionally, other content items may facilitate user engagement by encouraging a user to expand his connections to other users or entities, to invite new users to the system or to increase interaction with the social network system 130 by displaying content related to users, objects, activities, or functionalities of the social networking system 130. Examples of social networking content items include suggested connections or suggestions to perform other actions, media provided to or maintained by the social networking system 130 (e.g., pictures, videos), status messages or links posted by users to the social networking system, events, groups, pages (e.g., representing an organization or commercial entity), and any other content provided by, or accessible via, the social networking system 130.
As per claims 7 and 14:
Regarding the claim limitations below:
wherein the transportation request information further comprises a mode of transportation, and wherein determining the transportation metric function comprises generating a transportation metric function for achieving the target effect specific to the origin, the time, the geocoded area defining the destination and the mode of transportation.
Memon: Figs. 4-10, [0052]-[0053], [0080]-[0082]: For instance, see Fig. 4, #410 and 420. Related text [0077] shows: Sterling Archer's current physical location 410. Further, Fig. 7, #710 also see related text [0080]: pickup and destination locations 710. [0056]: the notification includes confirmation information such as: an identification of the car service company, identification of the car service vehicle that will pick up the first user, identification of the car service driver that will pick up the first user, and/or an estimated time the car service will pick up the first user.
Memon reference does not explicitly show “mode of transportation”. However, Zhang reference shows the above limitation at least in [0025]-[0027]: where the user has the ability to select the type of transportation, including taxi car (e.g., DiDi Taxi™), an ordinary car (e.g., DiDi Express™), a luxury car (e.g., DiDi Premier™), a bus (e.g., DiDi Bus™ and DiDi Minibus™), etc.
It would have been obvious to one of ordinary skill in the art before the filing of this application for AIA to provide the teachings of Reference Zhang, particularly the determining the estimated waiting time and adjusting the price accordingly at least in [0033] and Fig. 5, S506 and S507, in the disclosure of Reference Memon, particularly in the transportation parameters Figs. 4-10, [0052]-[0053, [0080]-[0083] in order to provide for a system that estimates the cost and waiting time for a customer for a time interval and the for one or more areas in the region as taught by Reference Zhang (see at least in [0033] and Fig. 5, S506 and S507) so that the process of providing location based transportation services can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar location-based transportation services field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Memon in view of Reference Zhang, the results of the combination were predictable (MPEP 2143 A).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Applicant’s Argument #1
Applicants argue on page(s) 11-12 of applicants remarks that the amended claims overcome previously made rejection under 35 U.S.C. 101 (see applicants remarks for more details). Particularly, applicants argue “currently amended independent claims 1, 8, and 15 integrate any alleged judicial exceptions into a practical application. For instance, currently amended independent claims 1, 8, and 15 recite improvements to the functioning of a computer by "determining, utilizing an elasticity model comprising parameters …. This is a practical application of generating transportation metrics in an accurate and computationally efficient manner, and thus the claims are not directed to a judicial exception.”
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
As discussed above, the claim limitations argued by applicants are not practical application, because the additional elements are recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Please see 101 rejection above for more details.
Additionally, the improvements argued by applicants above are business improvements. In Diamond v. Diehr, the technical field of rubber molding is improved by a product that is processed via a technical process. The improvements made are directed to the technical field itself. By contrast, the improvements argued above by applicants are directed to business improvements which are abstract and not directed to any particular technology or technical field.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
Yong et al. A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice. PLOS ONE. Published: January 13, 2016. https://doi.org/10.1371/journal.pone.0146850. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146850
This reference is concerned with paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached.
Foreign Reference:
(EP 3683742 A1) Lehoux V et al. This reference is concerned with determining a first set of possible initial stations, a second set of possible initial stations, a first set of possible final stations and a second set of possible final stations in a multi-modal transportation network. The determination is made such that possible final stations of a second set of possible final stations are stations from which the arrival location is reachable using a second transportation mode at a given second maximum cost, which do not belong to a first set of possible initial stations or to a first set of possible final stations, and which comply with a geometrical constraint defined by a departure location (31) and an arrival location (32). A routing optimization algorithm is performed so as to select, among the itineraries having a main portion from an initial station to a final station each belonging to the first or second sets of possible initial stations or of possible final stations, an optimal itinerary according to a criterion.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624