DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The preliminary amendment filed on 06/30/2025 has been entered and fully considered.
Claims 1-20 have been canceled.
Claims 21-40 have been newly added.
Claims 21-40 are pending in Instant Application.
Priority
Examiner acknowledges Applicant’s claim to priority benefits of U.S. Application No. 18/396,134, filed on December 26, 2023, which is a continuation of U.S. Application No. 17/808,238, filed on June 22, 2022, which issued at U.S. Patent No. 11,887,483, which is a continuation of U.S. Application No. 15/809,618, filed on November 10, 2017, which issued as U.S. Patent No. 11,386,789.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 09/10/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Novak et al. (USPGPub 2013/0246207) in view of Arian et al. (USPGPub 2021/0182993). As per claim 21, Novak discloses a computer-implemented method comprising: generating a training dataset comprising historical digital provider device incentives and measured provider device activity resulting from the historical digital provider device incentives (see at least paragraph 0033; wherein the historical record can include requester data 111 and provider data 113 received at particular dates and times previously received by system 100. In some examples, the historical data can also be used to approximate the amount of requesters and the amount of service providers at a particular geographic region at a certain time and/or date. In this manner, information can be collected, stored, and maintained for future use (e.g., for price adjustment at a later time or for predicting future supply and demand for the service)); generating an input training vector from the measured provider device activity of the training dataset (see at least paragraph 0028; wherein the requester management 120 and the provider management 130 each receives requester data 111 and provider data 113, respectively, handles the data, processes the data to determine relevant information for adjusting prices, and stores the data in one or more memory resources (e.g., in one or more databases 140 that are accessible by components of system 100)); and a predictive request model to generate provider device incentives from provider- requestor device gaps (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand) by: generating, utilizing the predictive request model, a predicted driver incentive from the input training vector (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand. The rules and models database can include rules for limiting the increase or decrease in price adjustment (e.g., set a ceiling and/or a floor). For example, a rule can place a limit on the price for the service to be adjusted by preventing the price from being adjusted to more than three times the default price (e.g., max increasing adjustment is 3.times.). Similarly, the limit can prevent the price from being adjusted to less than 0.5 times the default price (e.g., max decreasing adjustment is 0.5.times.)); and tuning the predictive request model based on the predicted driver incentive and one or more of the historical digital provider device incentives from the training dataset (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand…see at least paragraph 0036; wherein the price adjustment 150 can apply one or more rules or models 143 in order to determine whether to adjust a price for the service. For example, a model or metric that the price adjustment 150 can use to adjust the price for a service can be based on a utilization parameter). Novak does not explicitly mention training a predictive request model. However Arian does disclose: training a predictive request model (see at least paragraph 0125; wherein machine learning algorithms are used for training prediction models and/or making prediction). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Arian with the teachings as in Novak. The motivation for doing so would have been to improve the prediction accuracy, see Arian paragraph 0099. As per claims 22, 29, and 36, Arian discloses training the predictive request model by training parameters of an artificial neural network model based on the predicted driver incentive and one or more of the historical digital provider device incentives from the training dataset (see at least paragraph 0125; wherein the algorithms utilize statistical modeling to generate predictions or estimates about the user or user behavior and/or responsiveness to various incentive or informational offers. In some embodiments, machine learning algorithms are used for training prediction models and/or making predictions…see at least paragraph 0130; wherein the machine learning algorithm is selected from an artificial neural network). As per claims 23, 30, and 37, modified Novak discloses further comprising generating the training dataset by: transmitting a digital provider device incentive to a provider device; and monitoring user interactions with the provider device to determine the measured provider device activity (see at least paragraph 0052; wherein the adjusted prices are provided to the requester devices and/or the provider devices (step 230). The applications that are running on the requester and provider devices can use data corresponding to the adjusted prices in order to display a pricing user interface (as part of the application) to the respective users and service providers. The requesters and service providers can be notified of the current adjusted price (relative to the default price) so that the parties can choose to order the service and provide the service, respectively, at the adjusted price. The one or more requesters can order the service at the adjusted price (by interacting with the application) and one or more service providers can accept the order to fulfill the service request (again, by interacting with the application). In this manner, the price adjustment system can arrange services between parties and dynamically adjust the prices for the services). As per claims 24, 31, and 38, modified Novak discloses generating the input training vector from the measured provider device activity of the training dataset by generating a vector representation of a geographic area for a historical provider device incentive (see at least paragraph 0031; wherein the requester management 120 and the provider management 130 can provide parsed data 135 to the price adjustment 150 that includes information about the current time, the current location of the requesting devices 170 and the provider devices 180 (e.g., where the service provider is currently located to determine if he or she is available for fulfilling service requests to those in a particular geographic area), the current state of the service provider or the service provider's vehicle, the type of vehicles that are being requested, and/or the requested destination or service locations of the requesters. For example, the price for a transportation service (pickup and drop off) can be adjusted for different users and different service providers (e.g., drivers) based on the current time and the current location of the requesting devices 170 and the provider devices 180). As per claims 25 and 32, modified Novak discloses generating the input training vector from the measured provider device activity of the training dataset by generating a vector representation of a time period for a historical provider device incentive (see at least paragraph 0024; wherein using requester data 111 received from the requesting devices 170 and provider data 113 received from the provider devices 180, system 100 can determine current, up-to-date information about supply and demand conditions in order to properly set or adjust a price for a service. In some examples, the requester data 111 can be used (in part) to determine the current number and/or the current location of requesters for the service (e.g., this can represent the demand for the service) at a given time. Similarly, the provider data 113 can be used (in part) to determine the current number and/or the current location of available service providers capable of fulfilling the service at the given time (e.g., this can represent the supply for fulfilling the service)). As per claims 26, 33, and 39, modified Novak discloses generating the predicted driver incentive by generating, utilizing the predictive request model, at least one of an provider device incentive value or a provider device incentive type (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand. The rules and models database can include rules for limiting the increase or decrease in price adjustment (e.g., set a ceiling and/or a floor). For example, a rule can place a limit on the price for the service to be adjusted by preventing the price from being adjusted to more than three times the default price (e.g., max increasing adjustment is 3.times.). Similarly, the limit can prevent the price from being adjusted to less than 0.5 times the default price (e.g., max decreasing adjustment is 0.5.times.)). As per claims 27, 34, and 40, Arian discloses monitoring an additional digital provider device incentive and additional provider device activity resulting from the additional digital provider device incentive (see at least paragraph 0125; wherein the algorithms utilize statistical modeling to generate predictions or estimates about the user or user behavior and/or responsiveness to various incentive or informational offers. In some embodiments, machine learning algorithms are used for training prediction models and/or making predictions…see at least paragraph 0130; wherein the machine learning algorithm is selected from an artificial neural network); and re-training the predictive request model utilizing the additional digital provider device incentive and the additional provider device activity (see at least paragraph 0128; wherein a machine learning algorithm can be applied to new or updated data to be re-trained or optimized to generate a new prediction model. In some embodiments, a machine learning algorithm or model is re-trained periodically). As per claim 28, Novak discloses a system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor (see at least paragraph 0017; wherein one or more embodiments described herein may be implemented through the use of instructions that are executable by one or more processors. These instructions may be carried on a computer-readable medium), cause the system to: generate a training dataset comprising historical digital provider device incentives and measured provider device activity resulting from the historical digital provider device incentives (see at least paragraph 0033; wherein the historical record can include requester data 111 and provider data 113 received at particular dates and times previously received by system 100. In some examples, the historical data can also be used to approximate the amount of requesters and the amount of service providers at a particular geographic region at a certain time and/or date. In this manner, information can be collected, stored, and maintained for future use (e.g., for price adjustment at a later time or for predicting future supply and demand for the service)); generate an input training vector from the measured provider device activity of the training dataset (see at least paragraph 0028; wherein the requester management 120 and the provider management 130 each receives requester data 111 and provider data 113, respectively, handles the data, processes the data to determine relevant information for adjusting prices, and stores the data in one or more memory resources (e.g., in one or more databases 140 that are accessible by components of system 100)); and a predictive request model to generate provider device incentives from provider-requestor device gaps (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand) by: generating, utilizing the predictive request model, a predicted driver incentive from the input training vector (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand. The rules and models database can include rules for limiting the increase or decrease in price adjustment (e.g., set a ceiling and/or a floor). For example, a rule can place a limit on the price for the service to be adjusted by preventing the price from being adjusted to more than three times the default price (e.g., max increasing adjustment is 3.times.). Similarly, the limit can prevent the price from being adjusted to less than 0.5 times the default price (e.g., max decreasing adjustment is 0.5.times.)); and tuning the predictive request model based on the predicted driver incentive and one or more of the historical digital provider device incentives from the training dataset (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand…see at least paragraph 0036; wherein the price adjustment 150 can apply one or more rules or models 143 in order to determine whether to adjust a price for the service. For example, a model or metric that the price adjustment 150 can use to adjust the price for a service can be based on a utilization parameter). Novak does not explicitly mention train a predictive request model. However Arian does disclose: train a predictive request model (see at least paragraph 0125; wherein machine learning algorithms are used for training prediction models and/or making prediction). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Arian with the teachings as in Novak. The motivation for doing so would have been to improve the prediction accuracy, see Arian paragraph 0099. As per claim 35, Novak discloses a non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor (see at least paragraph 0017; wherein one or more embodiments described herein may be implemented through the use of instructions that are executable by one or more processors. These instructions may be carried on a computer-readable medium), cause a computing device to: generate a training dataset comprising historical digital provider device incentives and measured provider device activity resulting from the historical digital provider device incentives (see at least paragraph 0033; wherein the historical record can include requester data 111 and provider data 113 received at particular dates and times previously received by system 100. In some examples, the historical data can also be used to approximate the amount of requesters and the amount of service providers at a particular geographic region at a certain time and/or date. In this manner, information can be collected, stored, and maintained for future use (e.g., for price adjustment at a later time or for predicting future supply and demand for the service)); generate an input training vector from the measured provider device activity of the training dataset (see at least paragraph 0028; wherein the requester management 120 and the provider management 130 each receives requester data 111 and provider data 113, respectively, handles the data, processes the data to determine relevant information for adjusting prices, and stores the data in one or more memory resources (e.g., in one or more databases 140 that are accessible by components of system 100)); and a predictive request model to generate provider device incentives from provider- requestor device gaps (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand) by: generating, utilizing the predictive request model, a predicted driver incentive from the input training vector (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand. The rules and models database can include rules for limiting the increase or decrease in price adjustment (e.g., set a ceiling and/or a floor). For example, a rule can place a limit on the price for the service to be adjusted by preventing the price from being adjusted to more than three times the default price (e.g., max increasing adjustment is 3.times.). Similarly, the limit can prevent the price from being adjusted to less than 0.5 times the default price (e.g., max decreasing adjustment is 0.5.times.)); and tuning the predictive request model based on the predicted driver incentive and one or more of the historical digital provider device incentives from the training dataset (see at least paragraph 0034; wherein the system databases 140 can also include a rules or models database, which store entries corresponding to rules and/or models used by the price adjustment 150 for making dynamic pricing decisions. For example, the models can include relevant economic models for adjusting prices for services based on supply and demand…see at least paragraph 0036; wherein the price adjustment 150 can apply one or more rules or models 143 in order to determine whether to adjust a price for the service. For example, a model or metric that the price adjustment 150 can use to adjust the price for a service can be based on a utilization parameter). Novak does not explicitly mention train a predictive request model. However Arian does disclose: train a predictive request model (see at least paragraph 0125; wherein machine learning algorithms are used for training prediction models and/or making prediction). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Arian with the teachings as in Novak. The motivation for doing so would have been to improve the prediction accuracy, see Arian paragraph 0099.
Conclusion
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/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662
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