DETAILED ACTION
This rejection is in response to Amendments filed on 01/28/2026.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Arguments
Applicant’s arguments, see pages 14-15, filed 01/28/2026, with respect to 35 U.S.C. 112(b) rejection to claims 1-20 have been fully considered and are persuasive. The 5 U.S.C. 112(b) rejection to claims 1-20 has been withdrawn.
Applicant's arguments filed 01/28/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 16-17 of remarks filed 01/28/2026 that the claims are patent eligible because they recite specific details of gathering, sharing, processing data using a device before inputting data into machine learning model which imposes meaningful limits on practicing the abstract idea, Examiner respectfully disagrees.
In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP §§ 2106.05(b), 2106.05(c), 2106.04(d), and 2106.05(f).
The courts have also identified limitations that did not integrate a judicial exception into a practical application: merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
A specific way of achieving a result of gathering, sharing, processing data that is inputted is not a stand-alone consideration in Step 2A Prong Two. The device is merely used as a tool to perform the abstract idea. Therefore, the mere use of a device to implement steps of gathering, sharing, and processing data prior to inputting it into a machine learning model does not integrate the judicial exception into a practical application.
With respect to applicant’s arguments on pages 17-18 of remarks filed 01/28/2026 with respect to the prior art not teaching information about a number of items from an original set of items associated with the order that were replaced during fulfillment, Examiner respectfully disagrees.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-10 are directed to a method and claims 11-19 are directed to a computer program product comprising a non-transitory computer readable storage medium, and claim 20 is directed to a system which falls within one of the four statutory categories of invention(process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes 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). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
obtaining order data with information about an order placed by a user…;
gathering, …, communication data exchanged between …the user and …a picker who is fulfilling the order by:
gathering a set of images of items taken by the picker … during a fulfillment process for the order and shared, …and during the fulfillment process, with …the user, and …to identify a set of items depicted by the set of images;
gathering fulfillment data describing a fulfillment process for the order, the fulfillment data including information about a number of items from an original set of items associated with the order that were replaced by the picker during the fulfillment process;
…the order data, the communication data including information about the set of items depicted by the set of images, and the fulfillment data including the information about the number of items from the original set of items that were replaced during the fulfillment process to predict the tip amount;
identifying information about a sentiment of the user in relation to the fulfillment process;
receiving information about an original tip amount the user provided for fulfilling the order before the fulfillment process for the order was completed;
responsive to the information about the sentiment of the user, generating, based on the predicted tip amount and the original tip amount, …
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interactions by reciting obtaining order data, gathering fulfillment data and images, predicting a tip amount, identifying a sentiment of the user in relation to the fulfillment process, receiving information about an original tip amount, and responsive to the information about the sentiment of the user, generating a tip adjustment amount. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (Claim 1);
…of an online system; …, via a network,… a device associated with the user and a device associated with a picker …:… via the device associated with the picker…, via the network …, with the device associated with the user, and applying one or more computer-vision techniques to the set of images…; accessing a tip prediction model, wherein the tip prediction model is a machine-learning model trained… ; applying the tip prediction model…; …a user interface signal …; sending, via the network, the user interface signal to the device associated with the user, wherein sending the user interface signal including the information about the tip increase amount causes a user interface of the device associated with the user to display the tip increase amount prompting the user to increase the original tip amount to at least the predicted tip amount (Claims 1, 11, and 20).
via the network and from the device associated with the user,…(Claim 2);
via a network and from the device associated with the picker, (Claims 3 and 12);
via a network (Claims 4 and 13);
…, via the network and from at least one of the device associated with the user or the device associated with the picker, … (Claims 5 and 14);
… at the online system … with the online system …; and training the tip prediction model using the generated training data to generate an initial set of parameters of the tip prediction model (Claims 6 and 15);
…at the online system; and training the tip prediction model using the gathered training data to generate an initial set of parameters of the tip prediction model (Claims 7 and 16);
the tip increase amount that is displayed at the user interface of the device associated with the user; and re-training the tip prediction model by updating, using the collected feedback data, a set of parameters of the tip prediction model (Claims 8 and 17);
… via the network … to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to display (Claims 9 and 18);
displaying the user interface further comprises: …and … via the network … to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to further display the generated message along with the tip increase amount (Claims 10 and 19);
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: (Claim 11);
A computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising: (Claim 20).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (Claim 1);
…of an online system; …, via a network,… a device associated with the user and a device associated with a picker …:… via the device associated with the picker…, via the network …, with the device associated with the user, and applying one or more computer-vision techniques to the set of images…; accessing a tip prediction model, wherein the tip prediction model is a machine-learning model trained… ; applying the tip prediction model…; …a user interface signal …; sending, via the network, the user interface signal to the device associated with the user, wherein sending the user interface signal including the information about the tip increase amount causes a user interface of the device associated with the user to display the tip increase amount prompting the user to increase the original tip amount to at least the predicted tip amount (Claims 1, 11, and 20).
via the network and from the device associated with the user,…(Claim 2);
via a network and from the device associated with the picker, (Claims 3 and 12);
via a network (Claims 4 and 13);
…, via the network and from at least one of the device associated with the user or the device associated with the picker, … (Claims 5 and 14);
… at the online system … with the online system …; and training the tip prediction model using the generated training data to generate an initial set of parameters of the tip prediction model (Claims 6 and 15);
…at the online system; and training the tip prediction model using the gathered training data to generate an initial set of parameters of the tip prediction model (Claims 7 and 16);
the tip increase amount that is displayed at the user interface of the device associated with the user; and re-training the tip prediction model by updating, using the collected feedback data, a set of parameters of the tip prediction model (Claims 8 and 17);
… via the network … to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to display (Claims 9 and 18);
displaying the user interface further comprises: …and … via the network … to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to further display the generated message along with the tip increase amount (Claims 10 and 19);
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: (Claim 11);
A computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising: (Claim 20).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheutlin et al. (US Pub. No. 20230298076 A1, hereinafter “Zheutlin”) in view of Lauka et al. (US Patent No. 11301919 B1, hereinafter “Lauka”).
Regarding claims 1, 11, and 20
Zheutlin discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising (Zheutlin, [0012]: processor and computer readable storage medium):
obtaining order data with information about an order placed by a user of an online system; gathering, via a network, communication data exchanged between a device associated with the user and a device associated with a picker who is fulfilling the order by:…gathering fulfillment data describing a fulfillment process for the order,… (Zheutlin, [0020]: receiving a service request from a user, wherein a service is to be provided according to the service request by a service provider and accessing data for the service request; [0021]: users and/or consumers may request services through online platforms; [0049]: data for service request includes travel duration and physical exertion of service provider and user satisfaction; [0046]: receive and/or access data received from a mobile device of user and service provider; [0051]: real time data may be received from at least a computing device associated with the service provider, which may be capable of transmitting data; [0049]: real time data includes user satisfaction; [0056]: receive and/or access data user (e.g., consumer) satisfaction and/or feedback from a smart wearable device associated with the user));
accessing a tip prediction model, wherein the tip prediction model is a machine-learning model trained to predict a tip amount that is likely to lead to satisfaction of a picker associated with the online system who fulfilled the order; applying the tip prediction model to the order data, the communication data…, and the fulfillment data…, to predict the tip amount (Zheutlin, [0050]: determine gratuity for service request utilizing a set of gratuity recommendation rules and/or one or more machine learning models; [0051] The gratuity recommendation rules determines the baseline gratuity amount based on data related to the service request such as real time data received from service provider device; [0052]: determine gratuity based on ranking and satisfaction of service; [0053]: utilize one or more machine learning models in determining the gratuity for the service request; [0040]: service provider preferences as to gratuity; [0021]: A service provider may be an employee of the company providing the online platform, independent contractor, freelance worker, and/or other person willing to provide the service requested by the user and/or consumer; [0022] Depending on the service provided a user and/or consumer may be prompted within the online platform to provide a tip and/or gratuity in addition to the charge for the requested service; [0027]: gratuity determined based on travel and physical exertion of service provider; [0028]: gratuity based on service provider preferences);
identifying information about a sentiment of the user in relation to the fulfillment process (Zheutlin, [0052]: user ranking for services (e.g. timeliness, user satisfaction) used to determine gratuity recommendation);
receiving information about an original tip amount the user provided for fulfilling the order before the fulfillment process for the order was completed; responsive to the information about the sentiment of the user, generating, based on the predicted tip amount and the original tip amount, and sending, via the network, the user interface signal to the device associated with the user, wherein sending the user interface signal including the information about the tip increase amount causes a user interface of the device associated with the user to display the tip increase amount prompting the user to increase the original tip amount to at least the predicted tip amount (Zheutlin, [0049]: determining a baseline gratuity amount specific to the user and updated as the user requests additional services. The baseline gratuity amount may be dynamically adjusted based on user satisfaction related to the service request as the service provider starts the service requested by the user (e.g., consumer); [0051]: automatically adjust the baseline gratuity in pre-determined amounts as recommendation and based on user feedback; [0032]: user being enabled to input feedback relating to the service into the online platform (e.g., including details such as whether or not the service was satisfactory which may influence a user's tip and/or gratuity determination); [0061]: display the recommended gratuity to the user (e.g., consumer) in the gratuity recommendation user interface 118 with breakdown as to how the gratuity recommendation may have been determined including adjustments prior to approving the gratuity for processing; [0062]: present the user (e.g., consumer) one or more prompts based on the real time data which may enable the user (e.g., consumer) to manually increase the gratuity amount based on the data received, accept and/or deny a recommended increase according to the gratuity recommendation rules, amongst other prompts which may be presented to the user in the gratuity recommendation user interface 118; [0059]: the user (e.g., consumer) may receive a prompt on user interface with a recommended percentage increase in the baseline gratuity based on adverse weather conditions, based on the user's increased percentage in the baseline gratuity in similar weather conditions in the past).
Zheutlin does not teach:
gathering a set of images of items taken by the picker via the device associated with the picker during a fulfillment process for the order and shared, via the network and during the fulfillment process, with the device associated with the user, and applying one or more computer-vision techniques to the set of images to identify a set of items depicted by the set of images;
the fulfillment data including information about a number of items from an original set of items associated with the order that were replaced by the picker during the fulfillment process;… including information about the set of items depicted by the set of images … including the information about the number of items from the original set of items that were replaced during the fulfillment process.
However, Lauka teaches:
gathering a set of images of items taken by the picker via the device associated with the picker during a fulfillment process for the order and shared, via the network and during the fulfillment process, with the device associated with the user, and applying one or more computer-vision techniques to the set of images to identify a set of items depicted by the set of images (Lauka, Figs. 5A & 5B, C8, L60-67: picker discovers that item requested in order is not available; C9, L1-35: picker takes multiple images of areas with multiple item substitutions. Image analysis identifies items in the images and sends substitution message with items in the images to customer requesting a response; C6, L30-55: text recognition software and image processing software applied to images);
the fulfillment data including information about a number of items from an original set of items associated with the order that were replaced by the picker during the fulfillment process;… including information about the set of items depicted by the set of images … including the information about the number of items from the original set of items that were replaced during the fulfillment process (Lauka, C9, L50-67: substituted items accepted by customer are added to pickers list to complete the order or a new order; C10, L1-35: new order can be generated with substituted items accepted by customer; C9, L1-35: multiple images of areas with multiple item substitutions; C11, L15-32: total number of substitutions).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the gathering of data of Zheutlin with gathering and sharing images and a number of items from an original set of items associated with the order that were replaced by the picker as taught by Lauka because the results of such a modification would be predictable. Specifically, Zheutlin would continue to teach gathering of data except that now gathering and sharing images and a number of items from an original set of items associated with the order that were replaced by the picker is taught according to the teachings of Lauka in order to improve efficiency of order fulfillment. This is a predictable result of the combination. (Lauka, C2, L1-55).
Regarding claim 2
The combination of Zheutlin and Lauka teaches the method of claim 1, wherein obtaining the order data comprises: receiving, via the network and from the device associated with the user, information about a set of items that were originally ordered by the user (Zheutlin, [0020]: receiving a service request from a user, wherein a service is to be provided according to the service request by a service provider and accessing data for the service request; [0021]: users and/or consumers may request services through online platforms (e.g. food delivery); [0053]: order a meal).
Regarding claims 3 and 12
The combination of Zheutlin and Lauka teaches the method of claim 1, wherein gathering the fulfillment data further comprises: receiving, via the network and from the device associated with the picker, at least one of a set of features for a set of items picked by the picker at a location of a retailer, information about a time the picker spent on checking-out at the location of the retailer, or information about an effort made by the picker during a drop-off phase of the fulfillment process (Zheutlin, [0021]: food delivery; [0051]: The real time data may be received from at least a computing device associated with the service provider, one or more smart wearable devices associated with the service provider, vehicles and/or other means of transportation associated with the service provider which may be capable of transmitting data; [0052]: user may rank timeliness highest for food delivery requests; [0027]: travel duration of the service provider, physical exertion of the service provider, weather conditions, amongst other real time data related to the service request).
Regarding claims 4 and 13
The combination of Zheutlin and Lauka teaches the method of claim 1, wherein gathering the fulfillment data further comprises: receiving, via the network, at least one of information about a traffic during delivery of the order to a delivery location of the user, or information about a weather event during delivery of the order to the delivery location (Zheutlin, [0021]: food delivery; [0027]: travel duration of the service provider, physical exertion of the service provider, weather conditions, amongst other real time data related to the service request; [0048]: Data that may be accessed from the one or more publicly available resources which may include, but is not limited to including weather conditions; [0059]: adverse weather conditions).
Regarding claims 5 and 14
The combination of Zheutlin and Lauka teaches the method of claim 4, wherein gathering the fulfillment data further comprises: receiving, via the network and from at least one of the device associated with the user or the device associated with the picker …(Zheutlin, [0046]: receive and/or access data received from a mobile device of user and service provider; [0051]: real time data may be received from at least a computing device associated with the service provider, which may be capable of transmitting data; [0049]: real time data includes user satisfaction; [0056]: receive and/or access data user (e.g., consumer) satisfaction and/or feedback from a smart wearable device associated with the user).
Lauka teaches:
information about a new items that were added to the order during the fulfillment process (Lauka, C9, L50-67: substituted items accepted by customer are added to pickers list to complete the order or a new order; C10, L1-35: new order can be generated with substituted items accepted by customer; C9, L1-35: multiple images of areas with multiple item substitutions; C11, L15-32: total number of substitutions).
The motivation to combine Zheutlin and Lauka is the same as set forth above in claim 1.
Regarding claims 6 and 15
The combination of Zheutlin and Lauka teaches the method of claim 1, further comprising: gathering information about a set of tip increase amounts for a set of orders placed at the online system during a defined time period; gathering information about sentiments of a group of pickers in relation to the set of tip increase amounts; generating training data using the information about the set of tip increase amounts and the information about sentiments; and training the tip prediction model using the generated training data to generate an initial set of parameters of the tip prediction model (Zheutlin, [0058]: increase baseline gratuity based on factors; [0054] utilize a linear regression machine learning model in determining the gratuity for the service request based on the user's (e.g., consumers) past gratuities; [0051]: The gratuity recommendation rules may be pre-determined parameters by which the baseline gratuity may be determined and/or the baseline gratuity may be adjusted in pre-determined amounts and/or percentage change adjustments which may be dynamically adjusted for each user based on feedback received from the user; [0032] the user being enabled to input feedback relating to the service into the online platform (e.g., including details such as whether or not the service was satisfactory based on conditions). After inputting the feedback related to the service, the user may further indicate which factors should primarily influence the determination to recommend a tip and/or gratuity amount; [0057]: utilize one or more machine learning techniques in training the neural network based on the data; [0021]: online platforms).
Regarding claims 7 and 16
The combination of Zheutlin and Lauka teaches the method of claim 1, further comprising: gathering training data by surveying a group of pickers about a set of tip amounts for a set of fulfillment processes associated with a set of orders placed at the online system; and training the tip prediction model using the gathered training data to generate an initial set of parameters of the tip prediction model (Zheutlin, [0054] The gratuity recommendation program 110 may also utilize a linear regression machine learning model in determining the gratuity for the service request based on the user's (e.g., consumers) past gratuities; [0051]: The gratuity recommendation rules may be pre-determined parameters by which the baseline gratuity may be determined and/or the baseline gratuity may be adjusted in pre-determined amounts and/or percentage change adjustments which may be dynamically adjusted for each user based on feedback received from the user; [0032] the user being enabled to input feedback relating to the service into the online platform After inputting the feedback related to the service, the user may further indicate which factors should primarily influence the determination to recommend a tip and/or gratuity amount; [0057]: utilize one or more machine learning techniques in training the neural network based on the data; [0021]: users and/or consumers may request services through online platforms; [0060]: analyze user feedback utilizing one or more a machine learning model; [0026]: tips from other users and tips for similar services).
Regarding claims 8 and 17
The combination of Zheutlin and Lauka teaches the method of claim 1, further comprising: collecting feedback data with information about a response by the user in relation to the tip increase amount that is displayed at the user interface of the device associated with the user; and re-training the tip prediction model by updating, using the collected feedback data, a set of parameters of the tip prediction model (Zheutlin, [0058]: increase baseline gratuity based on factors; [0059]: user may accept recommended tip adjustment displayed; [0062]: store gratuities accepted in knowledge corpus (database); [0057]: training using data stored in knowledge corpus (database); [0049]: The baseline gratuity amount may be specific to the user and updated as the user requests additional services; [0060]: the gratuity for the service request may be continuously updated utilizing the gratuity recommendation rules and/or one or more machine learning models until the service is completed by the service provider).
Regarding claims 9 and 18
The combination of Zheutlin and Lauka teaches the method of claim 1, wherein identifying the information about the sentiment of the user in relation to the fulfillment process comprises: sending, via the network, a message to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to display the message prompting the user to provide feedback with information about a level of satisfaction by the user in relation to the fulfillment process; and identifying, using the information about the level of satisfaction, the information about the sentiment of the user in relation to the fulfillment process (Zheutlin, [0052]: on user interface, users may directly provide ranking for services (e.g. timeliness, user satisfaction) used to determine gratuity recommendation); [0029]: dynamically adjusting gratuity determinations based on user rankings of a plurality of categories each corresponding to an explanatory variable; [0045]: prompts displayed to the user (e.g., consumer) by the gratuity recommendation program 110 in the gratuity recommendation user interface 118 including rating for a specific service; [0032]: user being enabled to input feedback relating to the service into the online platform (e.g., including details such as whether or not the service was satisfactory which may influence a user's tip and/or gratuity determination).
Regarding claims 10 and 19
The combination of Zheutlin and Lauka teaches the method of claim 1, wherein displaying the user interface further comprises: generating, using the order data and the fulfillment data, a message explaining an effort made by the picker during the fulfillment process; and sending, via the network, the message to the device associated with the user, wherein sending the message causes the user interface of the device associated with the user to further display the generated message along with the tip increase amount (Zheutlin [0061]: display the recommended gratuity to the user (e.g., consumer) in the gratuity recommendation user interface 118 with breakdown as to how the gratuity recommendation may have been determined including adjustments prior to approving the gratuity for processing; [0062]: present the user (e.g., consumer) one or more prompts based on the real time data which may enable the user (e.g., consumer) to manually adjust the gratuity amount based on the data received, accept and/or deny a recommended adjustment according to the gratuity recommendation rules, amongst other prompts which may be presented to the user in the gratuity recommendation user interface such as presenting the user 5 prompts related to inclement weather for 5 different service requests 118; [0059]: the user (e.g., consumer) may receive a prompt with a recommended percentage increase in the baseline gratuity based on adverse weather conditions; [0027]: data related to service request that is used to determine gratuity recommendations includes user (e.g., consumer) satisfaction, travel duration of the service provider, physical exertion of the service provider, weather conditions, amongst other real time data related to the service request).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Pandhi et al . (US Pub. No. 20220358530 A1) related to creating invoices with tipping options, Abrons et al. (US Pub. No. 20220383277 A1) related to generate interactive receipts with the option of a dynamic tip amount, and non-patent literature, “Taxicab tipping and sunlight”, related to how factors like weather alters tipping amounts for taxicab.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LATASHA D RAMPHAL/Examiner, Art Unit 3688 /Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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