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 05/08/26.
Claims 1-20 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, a baseline conversion prediction corresponding to a baseline 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, a baseline conversion prediction corresponding to a baseline 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 baseline conversion prediction from the neural network classifier, the transportation metric function by varying the baseline transportation metric while holding the plurality of inputs constant to generate a plurality of probabilities of receiving transportation requests, and by determining a relationship between the baseline transportation metric and the plurality of probabilities of receiving transportation requests” also seems to be missing steps.
Regarding the 101 rejection below, the amended claims 1, 8 and 15 recite limitations that are simply “apply it”. For instance, claim 1 recites: “utilizing an offline transportation model”, “utilizing an elasticity model”, “utilizing a neural network classifier of the elasticity model”, “utilizing an elasticity estimation layer of the elasticity model”, etc. Utilizing is just using the neural network and such the amended claims amount 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).
In light of these notes, the amended claims, do not overcome previously presented rejection under 101. As is discussed below. This note is intended as a conversation starter to help applicants understand the examiner’s perspective.
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, to generate a prediction classification of a prospective transportation request based on a plurality of inputs including origin coordinates and destination coordinates, a baseline conversion prediction corresponding to a baseline transportation metric; and determining, to determine a likelihood of receiving a transportation request based on the baseline conversion prediction, the transportation metric function by varying the baseline transportation metric while holding the plurality of inputs constant to generate a plurality of probabilities of receiving transportation requests, and by determining a relationship between the baseline transportation metric and the plurality of probabilities of receiving transportation requests; 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: 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, 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, utilizing an elasticity estimation layer of the elasticity model trained, 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 claim 5 recites “further comprising generating, by a feature generator model, one or more request features for the transportation request information based on the set of predicted future transportation requests, the one or more request features comprising at least one of a shadow price, an incentive cost, a marginal cost of transportation, or a provider supply requirement, wherein generating the transportation metric comprises generating the transportation metric based on the one or more request features and the transportation metric function”. Dependent claims 6 recites “wherein generating the transportation metric comprises selecting, from the transportation metric function and based on the one or more optimization parameters, a transportation metric value corresponding to the target effect”. Dependent claims 7 recites “further comprising, after providing the response to the requester device: receiving a transportation request corresponding to the transportation request information; updating transportation system state data based on the transportation request, the transportation system state data comprising at least one of provider availability, provider supply, expected request volume, or provider pay budget; and generating a subsequent transportation metric for a subsequent transportation request based on the updated transportation system state data wherein the transportation request information”. 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 2 recites “generating training data by perturbing transportation metric values for transportation requests associated with one or more geocoded areas; measuring changes in requester conversion corresponding to the perturbed transportation metric values; generating a ground truth transportation metric function based on the measured changes in requester conversion; comparing a predicted transportation metric function of the elasticity model with the 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.” Dependent claim 3 recites “further comprising enforcing a monotonic constraint on the elasticity estimation layer by constraining the transportation metric function such that, for increasing transportation metric values generated while holding the plurality of inputs constant, the plurality of probabilities of receiving transportation requests are monotonically non-increasing with respect to increases in the transportation metric”. Dependent claim 4 recites “receiving, by the transportation matching system from the requester device, a transportation request corresponding to the transportation request information; matching the requester device with an autonomous vehicle in response to the transportation request; updating, based on matching the requester device with the autonomous vehicle, provider- availability data for a geographic area corresponding to the origin; and using the updated provider-availability data to generate a subsequent transportation metric for a subsequent transportation request”. In these claims, “elasticity model”, “elasticity estimation layer”, “utilizing the elasticity model, a classifier configured to, an elasticity estimation layer configured to”, “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: 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, 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, utilizing an elasticity estimation layer of the elasticity model trained, 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 - withdrawn
In the most recent filings, applicants have amended independent claims 1-20. Please see the Remarks dated 04/30/26 OA appendix document (pages 4-5), Interview Summary dated 04/30/26 and Remarks dated 05/08/26, especially on pages 18-21 from applicants why the 103 rejection is overcome are persuasive.
In the most recent amendment, the amended claims of independent claims 1, 8 and 15 (underlined) in combination with the previously presented claim limitations overcome previously presented prior art rejection, please see reasons below for more detail:
Independent claim 1: “A method for managing transportation services comprising:
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;
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;
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:
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, a baseline conversion prediction corresponding to a baseline 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 baseline conversion prediction from the neural network classifier, the transportation metric function by varying the baseline transportation metric while holding the plurality of inputs constant to generate a plurality of probabilities of receiving transportation requests, and by determining a relationship between the baseline transportation metric and the plurality of probabilities of receiving transportation requests;
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, 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; and
providing, by the transportation matching system to the requester device, a response to the transportation request information comprising the generated transportation metric.”
The above limitations are shown in the specification in PGPub: in Fig. 2 and related text ([0060]-[0078]) and Fig. 3 and related text ([0088]-[0100]).
In light of the amendments and the description in the specification, previously presented rejection is not applicable any more.
In light of applicants’ arguments and the amendments filed by applicants to previously presented claims in light of the originally filed disclosure the previously made rejection under 35 U.S.C. 103 has been withdrawn for following reasons:
None of the references cited - (US 2017/0339237) Memon; Amir Hussain, further in view of (US 2019/0057476) Zhang et al. and (US 2018/0060738) Achin et al. show the claim limitations discussed above in light of the specification.
Even though, Reference Memon shows 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. Reference Memon shows at least in Fig. 7, 705 and [0080]: fare estimate 705 based upon the provided pickup and destination locations 710. Memon shows 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
Even though Memon shows the ability to estimate fare based on demand, Memon does not explicitly show predicting future requests. Reference Memon also does not explicitly show “transportation metric”, “an elasticity model” and “predictions of receiving transportation requests”, “enforcing a monotonic constraint on the elasticity estimation layer”, “mode of transportation” . As such, the reference does not show all the above claim limitations.
Reference Achin shows the ability to forecast: 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. 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).
Even though Achin shows forecasting, Achin does not explicitly show “predictions of receiving transportation requests”. As such, the reference does not show all the above claim limitations.
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. Zhang reference shows “mode of transportation” 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. However, the reference does not show all the above claim limitations.
*Additionally, the prior art made of record and not relied upon is considered pertinent to applicant's disclosure; however, the reference does not show the above claim limitations:
NPL Reference:
Reference Yong et al. shows 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
(Abstract) 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. However, the reference does not show the claim limitations above.
Foreign Reference:
Reference (EP 3683742 A1) Lehoux V et al. shows: (Abstract) 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.
None of the prior art of record, taken individually or in combination, teach, interalia, the claimed invention as detailed in independent claims 1, 8 and 15, wherein the novelty of the claimed invention is in the combination of limitations and not in any single limitation.
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) 15-18 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 “these amended limitations do more than merely make predictions and provide a result. The claims recite a concrete computational process in which the neural network classifier generates a baseline conversion prediction, the elasticity estimation layer uses that baseline conversion prediction to determine a transportation metric function, the baseline conversion prediction is varied while other model inputs are held constant, and the resulting probabilities are used to determine the transportation metric function. This is not merely an abstract idea of pricing, transportation services or organizing human activity. Instead, the claims apply a specifically structured model workflow to transform transportation request information and predicted future request features into a transportation metric function that is then used to generate a transportation metric for a requester device. The amended claims therefore provide a practical application because they improve how the transportation matching system generates transportation metrics….. This produces more tailored and accurate transportation metrics at a detailed route level, rather than merely applying a conventional pricing concept on a generic computer.” (Please see remarks for details)
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
Please see Note above and the 101 rejection above. As discussed above under step 2A prong 2, 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.
Applicant’s Argument #2
Applicants argue on page(s) 15-18 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 “The amended claims also do more than merely instruct a generic computer to apply an abstract idea. See MPEP § 2106.04(d). The neural network classifier and elasticity estimation layer are not recited as generic computer components performing routine data processing. They are specifically recited as cooperating components of the elasticity model, with the neural network classifier generating a baseline conversion prediction and the elasticity estimation layer using that baseline conversion prediction to generate a transportation metric function through controlled variation of the transportation metric while holding the plurality of inputs constant. This recited model architecture and data flow provide more than a generic instruction to "apply" prediction or pricing on a computer.” (Please see remarks for details)
Response to Argument #2
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
Please see Note above and the 101 rejection above. As discussed above under step 2A prong 2, the claim limitations argued by applicants 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.
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.
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/N.N.P/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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