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 .
DETAILED ACTION
This communication is a Non-Final Office Action in response to communications received on 4/14/26.
Claims 1, 7, 11, 17, 20 have been amended.
Therefore, Claims 1-20 are now pending and have been addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/14/26 has been entered.
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.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-10 are directed to a system, claim 20 is directed to a non-transitory medium and claims 11-19 are directed to a method. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 11 and 20 recite methods that
order data including a plurality of orders; parsing the order data to determine an order status for each of the plurality of orders; wherein each order status comprises one of a plurality of values, wherein each of the plurality of values indicate a positive order or negative order; identify at least one a negative order from the order data based on the order status; determine, based on the historical customer data, a number of previous negative orders over a predetermined amount of time; compare the number of previous negative orders to a maximum negative order threshold and, based on the comparison: when the number of previous negative orders is less than the maximum negative order threshold: input the order data and generate, in real-time, appeasement data for the negative order; when the number of previous negative orders is equal to or greater than the maximum negative order threshold: generate an appeasement error associated with the negative order, and store the appeasement error.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as database, a computing device comprising at least one processor, an electronic device, a user interface, non-transitory medium (Claims 1, 11, 20)), the claims are directed to providing an appeasement offer to user for negative order. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing an appeasement offer. In particular, the claims only recites the additional element – database, a computing device comprising at least one processor, an electronic device, a user interface, at least one machine learning model, database, non-transitory medium (Claim 20). The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component and merely add 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). Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application. The limitation of “training at least one machine learning model based on historical customer data…retrain the trained at least one trained machine learning model based on the order data and the appeasement data” is simply application of a computer model, itself an abstract idea. Furthermore, such training/retraining and applying of a model is no more than putting data into a black box machine learning model operation, devoid of technological implementation and application details. Therefore, Each step requires a generic computer to perform generic computer functions. In addition, limitations reciting data gathering such as “receiving the order data..“ is insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en bane), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371-72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)). Also, the limitations reciting “storing historical customer data and order data associated with a customer of a retailer; transmit , in real-time, the appeasement data…display of an appeasement offer associated with appeasement data to the customer” are merely a post-solution step of storing/transmitting/displaying data output—a nominal addition to the claim that does not meaningfully limit the claim. Therefore, step (h) is an insignificant extra-solution activity. See MPEP 2106.05(g).
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using 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, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; provide an appeasement offer to user for negative order. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the database, a computing device comprising at least one processor, an electronic device, a user interface, machine learning model, non-transitory medium ,these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0051]-[0052]details “ processor of a general purpose computer, [0055] processor can store data in memory, [0010] device having a user interface to display data. [0018], [0067] training/retraining model” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data, which fall under well-understood, routine and conventional functions of generic computers. As discussed in Step 2A, Prong Two above, the recitations of “receiving steps” and “storing/transmitting/display steps” amount to receiving or transmitting data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and are well-understood, routine and conventional limitations that amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself.
Dependent claims 2-10, 11-19 add additional limitations, for example but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1 and 11.
Claims 2-3, 12-13 recites generating, based on the historical customer data, a user appeased score; generating, based on the order data, an order score; combining the user appeased score and the order score to generate an order appeased score; comparing the order appeased score to one or more thresholds to generate a comparison; generating an award affinity score based on the comparison, the award affinity score being associated with an affinity of the customer towards an appeasement type; and
generating, based on the appeasement type, one or more appeasements to be offered to the customer; wherein one or more weights are applied to the order score. These limitations are abstract idea of organizing human activity and mathematical calculations (generating user appeased score, order score, affinity score). The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception
Claims 4-5, 14-15 recites comparing the appeasement data to one or more thresholds to generate a threshold comparison; based on the threshold comparison, modifying the appeasement data to generate subsequent appeasement data; and comparing the subsequent appeasement data to one or more thresholds to generate a subsequent threshold comparison further narrows the abstract idea of claim 1, 11. The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception
Claims 6,8-10, 16, 18-19 recite microbatching the plurality of orders within the time period; in response to identification of the negative order, generating in real-time, on the user interface of the electronic device, an interactive appeasement display; in response to identification of the negative order, automatically generating and transmitting an electronic correspondence to the electronic device of the customer; wherein the order data is received concurrently from a plurality of sources. The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element (as addressed for claims 1, 11) amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 7, 17 recites receiving redemption data associated with the customer redeeming the appeasement offer associated with the appeasement data; and refining at least one trained machine learning models based on the redemption data. The limitation of “train/retrain one or more models” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning model operation, devoid of technological implementation and application details. Therefore, Each step requires a generic computer to perform generic computer functions. The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)).
The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Subject matter free of prior art
Claims 1, 4-11, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bates (US 2023/0316343 A1) in view of Miziolek et al. (US 12,450,561 B2) and Koushik (US 2025/0131449 A1)
Regarding Claims 1, 11 and 20. Bates (US 2023/0316343 A1) discloses the system, comprising:
Bates discloses at least one processor; a non-transitory computer readable medium storing instructions ([0015] The system includes: at least one memory; and at least one processor. [0018]memory) that, when executed, cause the at least one processor to:
Bates discloses receive, from a database, order data and historical customer data associated with a customer of a retailer (Fig 3 #40,merchant system include #74 customer and transaction data, [0227] Data storage devices may also store customer and transaction data 74 such as customer names, addresses, contact information, target potential customers, transaction details (order data), and so on., [0104] transaction histories (historical customer data), [0222] [0372] The transaction processing system 350 can include one or more processors located across any number of systems or devices, and at any number of locations.), wherein the order data is within a time period, the order data including a plurality of orders ([0015] receive or access data associated with a transaction between a customer and a merchant. Determine, from at least one of customer information and merchant information in the data associated with the transaction, a donation amount and a location associated with the transaction, Fig 6A #104 identifying a merchant and a transaction, [0179] transaction detail: Card Holder BIN number “1111”; Transaction Amount: $104.00; Transaction Date: Jan. 1, 2004; Transaction Time: 00:00:12; Merchant: “A”; and Card Holder ID: 1. Fig 24 shows customer transactions and trends from time period, [0227] Data storage devices may also store customer and transaction data 74 such as customer names, addresses, contact information, target potential customers, transaction details (order data), [0893] At 6601, the system can be configured to receive or access transaction data. In some embodiments, the transaction data can, at a minimum, include a transaction amount, a transaction date/time, a cardholder identifier and a merchant identifier. For example, a transaction may be for $50 with a merchant associated with identifier X at store Q on Jan. 1, 2015, at 10 am Eastern Standard Time with a customer associated with identifier A.);
Bates discloses parse the order data to determine an order status for each of the plurality of orders ([0885] identifying merchant location information based on transaction data (e.g. MIDs) and merchant profile information, and comparing it with the catchment areas of the charities supported by the cardholder.[0886] determining whether a transaction falls within a donation catchment area (order status) can be based on a transaction data field which indicates whether the payment transaction was an in-store transaction, a telephone transaction or a web transaction. [0893] At 6601, the system can be configured to receive or access transaction data. In some embodiments, the transaction data can, at a minimum, include a transaction amount, a transaction date/time, a cardholder identifier and a merchant identifier. For example, a transaction may be for $50 with a merchant associated with identifier X at store Q on Jan. 1, 2015, at 10 am Eastern Standard Time with a customer associated with identifier A.); wherein each of the plurality of values indicate a negative order ([0332] negative customer feedback)
Bates discloses identify a negative order from the order data, the negative order associated with the customer ([0332] producUservices/merchants based on negative customer feedback, e.g., if the feedback indicates that the producUservices/merchants is unsatisfactory, or below average, or otherwise negative. [0595]trends alerts observed by the loyalty system 26 such as slow time and gap in demographics, negative feedback trends (e.g. x times of negative feedback received within timeframe y, or in a more generic way such as ‘Change in review feedback rating’, [0432] negative feedback received via reviews, social media platforms, [0439] generating new trend as soon as new transaction data or feedback is available).;
Bates discloses generate, in real-time, appeasement data for the negative order, the appeasement data being associated based on the historical customer data ([0331] Recommendation engine 60 may recommend incentives based on the determined customer sentiment. [0332]recommending incentives (appeasement) for particular producUservices/merchants based on negative customer feedback, e.g., if the feedback indicates that the producUservices/merchants is unsatisfactory, or below average, or otherwise negative, [0534] creating an event driven incentive to address negative feedback. [0533] The event driven incentive tailored to address negative feedback, [0593] a merchant may set up automated reward for ‘negative feedback’ and when the merchant receives a new instance of negative feedback a reward is sent out on the merchant's behalf. There may be a ‘history’ section where the merchant sees when and why a reward was sent on his behalf.); and
Bates discloses transmit, in real-time, the appeasement data to an electronic device having a user interface for display of an appeasement offer associated with the appeasement data to the customer. (Fig 18 target reward customers who have given negative reviews [0801] a reward may offer to a targeted customer a donation of 20% of his/her next transaction to a charity rather than a default donation rate of 3%. [0593]a merchant may set up automated reward for ‘negative feedback’ and when the merchant receives a new instance of negative feedback a reward is sent out on the merchant's behalf. There may be a ‘history’ section where the merchant sees when and why a reward was sent on his behalf., [0623] a negative feedback reward triggered when a cardholder completes a review and responds with dislike);
Bates does not teach wherein each order status comprises one of a plurality of values, wherein each of the plurality of values indicate a positive order or negative order; identify at least one a negative order from the order data based on the order status; determine, based on the historical customer data, a number of previous negative orders over a predetermined amount of time; compare the number of previous negative orders to a maximum negative order threshold and, based on the comparison: when the number of previous negative orders is less than the maximum negative order threshold: input the historical customer data and the order data into at least one machine learning model and generate, in real-time, appeasement data for the negative order; when the number of previous negative orders is equal to or greater than the maximum negative order threshold: generate an appeasement error associated with the negative order, and store the appeasement error within the database.
Miziolek et al. (US 12,450,561 B2) teaches wherein each order status comprises one of a plurality of values, wherein each of the plurality of values indicate a positive order or negative order (Col 10 lines 58-64 The features associated with the order may include (but are not limited to) a number of items in the order, a number of categories that the items belong to, a number of orders for the customer that were delivered late within a recent time period, whether the customer has posted a negative review in the past, whether the customer has posted a positive review in the past, a percentage of orders for the customer that were delivered late, etc.); identify at least one a negative order from the order data based on the order status (Col 10 lines 58-67 The features associated with the order may include (but are not limited to) a number of items in the order, a number of categories that the items belong to, a number of orders for the customer that were delivered late within a recent time period, whether the customer has posted a negative review in the past, whether the customer has posted a positive review in the past, a percentage of orders for the customer that were delivered late, etc. For example, if the customer has experienced frequent late deliveries, the lateness value of the order may be adjusted to be higher because it is undesirable for a customer to proportionately receive late deliveries. Col 12 lines 20-25 FIG. 4A, when order 402 is received, the order fulfillment engine 306 is configured to predict or associate a delivery time 408 with the order 402. The compensation module 322 is configured to determine a compensation value 404 (also referred to as a base compensation value) for the order 402. Col 12 lines 57-65 FIG. 4B illustrates an example process 400B of predicting an amount of lateness time (negative order) that the order 402 will be fulfilled. As illustrated in FIG. 4B, the delivery time lateness model 316 receives information related to the order 402, the compensation value 404, an amount of time passing 406 since the order was received, and/or any other relevant information, and predicts an amount of lateness time 408 based on the received information.); determine, based on the historical customer data, a number of previous negative orders over a predetermined amount of time (Col 10 lines 58-64 The features associated with the order may include (but are not limited to) a number of items in the order, a number of categories that the items belong to, a number of orders for the customer that were delivered late within a recent time period (negative orders), whether the customer has posted a negative review in the past, whether the customer has posted a positive review in the past, a percentage of orders for the customer that were delivered late, etc); compare the number of previous negative orders to threshold and, based on the comparison: when the number of previous negative orders is less than the maximum negative order threshold (Col 10 lines 65-67, Col 11 lines 1-12 The features associated with the order may include (but are not limited to) a number of items in the order, a number of categories that the items belong to, a number of orders for the customer that were delivered late within a recent time period (negative order threshold), whether the customer has posted a negative review in the past, whether the customer has posted a positive review in the past, a percentage of orders for the customer that were delivered late, etc. For example, if the customer has experienced frequent late deliveries, the lateness value of the order may be adjusted to be higher because it is undesirable for a customer to proportionately receive late deliveries. As another example, if the customer has posted a negative review, it is more likely that the customer would post another negative review after experiencing a late delivery. The lateness value of the order may also be adjusted to be higher because a negative review would result in negative publicity. The compensation module 322 can use the outputs of the delivery time lateness model 316 and the lateness impact model 317 to adjust a compensation value of an order.): input the order data into at least one machine learning model (Col 11 lines 9-16The compensation module 322 can use the outputs of the delivery time lateness model 316 and the lateness impact model 317 to adjust a compensation value of an order. In some embodiments, the compensation module 322 proposes a plurality of boost amounts. For each of the plurality of boost amounts, the delivery time lateness model 316 is caused to predict a revised amount of lateness time that the order would be delivered late if the compensation value is increased by the boost amount. Col 16 lines 15-33 determining the lateness value based in part on the predicted amount of lateness time is also performed by applying a machine learning model (also referred to “a second machine learning model”) trained based on data associated with historical orders and customers. The second machine learning model is trained to take input features associated with the order and input features associated with the customer (e.g., historical orders associated with the customer, whether the customer has submitted positive or negative reviews, etc.). and generate, in real-time, appeasement data (Col 16 lines 38-45 The online concierge system 102 sends 790 the increased compensation value to the client device of each of the one or more available fulfillment agents, causing the order to be accepted sooner by a fulfillment agent to thereby boost order delivery time)
Koushik (US 2025/0131449 A1) teaches when the number of previous negative orders is equal to or greater than the threshold: generate an appeasement message. ([0019] when a particular customer has an established pattern of requesting appeasements on every placed order, appeasement control system 110 may set an appeasement allowance (maximum threshold) associated with an account of the customer to limit the overall expenditure on appeasements for the customer. [0036] determines one or more actions to take in response, including, for example, approving appeasement requests, rejecting appeasement requests. Validation module 204 may analyze customer data 220, order data 222, and CSR data 226, including appeasement history and appeasement patterns of customers, customer clusters, and CSRs, to validate or reject a particular appeasement request or detected appeasement situation. Validation module 204 may also determine an appeasement allowance (threshold) to apply to a particular appeasement request or detected appeasement situation., [0094] When validation module 204 determines not to address the appeasement request, at activity 704, validation module 204 synthesizes a rationale for rejection of the appeasement request and method 700 ends. In embodiments, user interface module 214 of appeasement control system 110 transmits the rejection and rationale to the customer., [0101] validation module 204 may determine to set an appeasement allowance for the customer based on the appeasement history of the customer, which may be a limit on the value of individual appeasement offers, a limit on appeasement value across a specified time frame, or any other limit on the possible appeasement offers presented to the customer., [0015] customer data, including appeasement history and customer service interactions, determine to limit appeasement offers, determine appeasement allowances for customers, reject appeasement requests from customers or customer service representatives (CSRs), or reassign appeasement requests to different CSRs.). Koushik teaches train one or more models to generate the appeasement data based on the order data and the historical customer data ([0075] To personalize the one or more contextual appeasement offers, contextual offers module 206 may tailor the one or more appeasement offers to the particular needs or preferences of the customer. In embodiments, contextual offers module 206 mines data around a recent order of a customer or an order corresponding to an appeasement situation, as well as an order history of the customer, to derive the one or more contextual appeasement offers., [0076] recommendation module 208 dynamically calculates an acceptance score representing the likelihood of the appeasement offer resolving the appeasement situation for each of the one or more appeasement offers. , [0050] Reinforcement learning model 234 comprises an AI or ML model trained to perform reinforcement learning. In embodiments, reinforcement learning model 234 uses input of feedback data 232, including user feedback and customer feedback, to predict the future likelihood of acceptance for a particular appeasement offer or type of appeasement offer)
Roy (US11,954,692) teaches mitigating user dissatisfaction with a product; generate, based on the order data, an order score (Col 7 lines 31-38collect, monitor, and/or analyze user interaction data received from IoT devices 120 and/or IoT network 150 to determine usage patterns of the user with respect to the given product. Machine learning module 110 may score the user interaction data (e.g. range 1-100, percentage, etc.) similarly to the user sentiment data in order to assess the user's initial usage pattern with the product); combine the scores (Col 11 lines 63-67, Col 122 lines 1-3the system may score the user interaction data based on a percentage or range, while the user sentiment data may be given a positive (e.g. +1), negative (−1), or neutral (0) score for detected sentiment associated with each word voiced by the user in relation to the product in order to generate an overall score.); compare the score to one or more thresholds to generate a comparison (Col 7 lines 38-49 Machine learning module 110 may utilize the collected user interaction data score in combination with user sentiment data score to generate a satisfaction threshold specific to a user and the product. machine learning module 110 may continuously monitor user interaction data and user sentiment data related to the given product to be measured against the satisfaction threshold. If the satisfaction threshold has been exceeded, the system will determine the user is dissatisfied with the product., Col 9 lines 55-67 the system may utilize a scoring model to score both the user interaction data and the user sentiment data to generate the satisfaction threshold. For example, the user may initially use a new product at a high rate (e.g., resulting in a high score for user interaction data) and have positive feelings (e.g., resulting in a high sentiment score) toward the product after the initial purchase leading the system to generate a high satisfaction threshold. Col 10 lines 20-24 the second set of user interaction data and the second set of user sentiment data may be provided a score and that score will be compared to the scores used to generate the satisfaction threshold
Gingras (US 11,403,658) discloses dynamically controlling the selective presentation of post-transaction offers based on an indicator that is determined from historical response data. The trust indicator 316 (also referred to as a trust score) may be used by the e-commerce platform 100 to manage post-transaction offers associated with the online store 138. A trust indicator 316 may, for example, be calculated using a weighted aggregation of customer response data, as discussed further below. The trust indicator 316 can be compared against a defined criteria, such as a trust threshold 354 at the post-transaction offer manager 350, to automatically and dynamically control whether future post-transaction offers should be permitted at the online store 138. The trust indicator 316 may be a metric or label that is associated with the online store 138, and that represents the positivity (or negativity) of customers' responses to post-transaction offers provided by the online store 138.
However, the prior art fails to teach or suggest at least “determine, based on the historical customer data, a number of previous negative orders over a predetermined amount of time; compare the number of previous negative orders to a maximum negative order threshold and, based on the comparison: when the number of previous negative orders is less than the maximum negative order threshold; input the order data into the trained at least one machine learning model and generate, in real-time, appeasement data for the negative order; retrain the trained at least one trained machine learning model based on the order data and the appeasement data; when the number of previous negative orders is equal to or greater than the maximum negative order threshold: generate an appeasement error associated with the negative order”
The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims.
Response to Arguments
Applicant's arguments filed 4/14/26 have been fully considered but they are not persuasive.
Regarding 101 rejection, Applicant states that claims provide practical application and additional elements reflect an improvement. Examiner has considered all arguments and respectfully disagrees.
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component and merely add 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). Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application. The limitation of “training/retraining ML models” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning model operation, devoid of technological implementation and application details. Therefore, Each step requires a generic computer to perform generic computer functions. In addition, limitations reciting data gathering such as “receiving the order data..“ is insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en bane), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371-72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)). Also, the limitations reciting “storing historical customer data and order data associated with a customer of a retailer; transmit , in real-time, the appeasement data…display of an appeasement offer associated with appeasement data to the customer” are merely a post-solution step of storing/transmitting/displaying data output—a nominal addition to the claim that does not meaningfully limit the claim. Regarding improvement argument, Examiner respectfully disagrees. The specification provides no further detail as to how the claim set achieves such an improvement. MPEP 2106.05(a) recites “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. Examiner notes neither specification nor claims recite how the improvement to processor or technology is achieved. The instant claims are directed to an abstract idea, and does not integrate the abstract idea into a practical application. The additional elements recited in the instant claims are only to generic computing components that implement the abstract idea on a computing environment. As such, it can be interpreted that the instant claims only make the abstract idea more efficient, and there are not actual changes/improvements to any computing components. Furthermore, the information handling system is not a specialized computing device as it merely uses generic computing components that execute instructions to perform the abstract idea. Such a device may be programmed to perform any abstract idea, and is not a particular device.
With regards to Desjardin decision, claims were directed to training a machine learning model, however current claims do not recite any ML model or training of a ML model. The amended claims recite training/retraining one or more models, thus the limitations are recited at high level of generality without any details regarding training/retraining model is achieved or how the model is used to provide appeasement. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Applicant’s arguments with respect to claim(s) 103 rejection have been considered. In view of claim amendments and applicant remarks, 35 U.S.C 103 rejection is withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma (US 8,285,596) discloses system for enhanced retention of customers of a business entity. In one embodiment, a historical data corresponding to a customer is maintained in the CRM system, with the historical data including information on prior interactions between the customer and the business entity. Fig 6C discloses various scoring criteria for customer status
Tietzen (US 2018/0276710) discloses method for incenting a registered customer to conduct a transaction with a registered merchant. The method data mines transaction data between registered merchants and registered customers with an artificial intelligence engine operated by a supercomputer. The method predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant.
Scheibelhut (US 2025/0139137) discloses the online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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