DETAILED ACTION
Status of Claims
0. This is a Final office action in response to communication received on June 03, 2026. Claims 1-3, 5, 8-12, 14, 18-25 are pending and examined herein.
Claim Interpretation
1. As per claims 1, 5, and 25, they recite “whether” which is being interpreted as setting forth a contingent limitation in a method claim, which in a method claim fails to positively recite the claim recitation, see MPEP 2111.04 See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) for an analysis of contingent claim limitations in the context of method claims, particularly note "When analyzing the claimed method as a whole, the PTAB determined that giving the claim its broadest reasonable interpretation, "[i]if the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed" (quotation omitted). Schulhauser at 10.".
Claim Rejections - 35 USC § 101
2. 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 1-3, 5, 8-12, 14, 18-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next, using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-3, 5, 8-9, and 24-25 are a method; claims 10-12, 14, 18 are a system; and claims 19-23 are an apparatus. Thus, each claim 1-3, 5, 8-12, 14, 18-25, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-3, 5, 8-12, 14, 18-25 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of preventing customer cancelation of a service provided by a company by analyzing the customer’s past responses to marketing effort to determine whether to present the customer with a retention offer, connect the customer to a customer service agent, or cancel the service based on mathematical evaluation of consumer data to determine a predictive-retention value which is certain methods of organizing human activity and mathematical concepts (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to 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). Further, see MPEP 2106.04(a)(2) II. A-C.
The phrase "Mathematical concepts" applies to mathematical relationships, mathematical formulas or equations, mathematical calculations. Further, see MPEP 2106.04(a)(2) I. A-C. See at least classification model and neural network (per claims 1-2, 4-7, 10-11, 13-16, 19-20).
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES) and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-3, 5, 8-12, 14, 18-25 at least are an interactive voice recognition (IVR) engine, an electronic device, encoding user data as machine learning, electronically connecting (per claims 1, 10, and 19); a processing system, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to (note additionally per claim 10); data stores (per claim 2); login account on company website, website hits (per claim 3). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)) including the encoded user data to utilize machine learning. The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1-2, 11, and their associated disclosure, including the machine learning. The processor executing the "apply it" instruction is further connected to one or more device(s) merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Gathered data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes gathered and transmitted user data to predict whether user will agree to the retention offer based on historical receptiveness to such offers and user profile data which comprises user’s browsing activity on the company website. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content (e.g. retention offer) based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent) which is analyzed/evaluated via machine learning model(s) to output a prediction. The abstract idea is intended to be merely carried out in a technical environment such as collecting/transmitting data via a network such as interactive voice recognition (IVR) and analyzing data via a generic processor to provide appropriate marketing/customer-service action, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 explained above.
Thus, the abstract idea of preventing customer cancelation of a service provided by a company by analyzing the customer’s past responses to marketing effort to determine whether to present the customer with a retention offer, connect the customer to a customer service agent, or cancel the service which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-3, 5, 8-12, 14, 18-25, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of preventing customer cancelation of a service provided by a company by analyzing the customer’s past responses to marketing effort to determine whether to present the customer with a retention offer, connect the customer to a customer service agent, or cancel the service which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);
- US2006/0184386 see “[0024] Consistent with the invention, an enterprise such as a delivery system operator may operate a greeting card service capable of creating and sending a greeting card to a recipient. For example, the USPS may operate a greeting card service as a part of a suite of services. This service, for example, may combine the convenience of the Internet with the effectiveness of traditional hard-copy mail to help strengthen customer loyalty, improve customer retention, cross-sell, up-sell, and effectively follow-up sales or marketing efforts. Output of this service may include full-color 5.times.7 folded, enveloped, and stamped greeting cards. With just a few mouse clicks, a user can open an account, upload images, logos and address lists, and even create a business account. Whether to one recipient or thousands, greeting cards created through the service may be automated and standardized with addresses and proper postage. Created greeting cards may be printed, processed, and entered into an item delivery system, such as the USPS, the next business day, for example. The USPS is exemplary, and other delivery services may be used.
[0033] System 100 may also transmit data by methods and processes other than, or in combination with, network 120. These methods and processes may include, but are not limited to, transferring data via, diskette, CD ROM, flash memory sticks, facsimile, conventional mail, an interactive voice response system (IVR), or via voice over a publicly switched telephone network.”
- US2022/0013112 see “[0057] Accordingly, in various embodiments, the call handler 155 may place a call in a queue if there are no suitable agents available to handle the call, and/or the call handler 255 may route the call to an interactive voice response system (e.g., server) (“IVR”) (not shown) to play voice prompts. In particular embodiments, these prompts may be defined to be in a menu type structure and the IVR may collect and analyze responses from the party in the form of dual-tone multiple frequency (“DMTF”) tones and/or speech. In addition, the IVR may be used to further identify the purpose of the call, such as, for example, prompting the party to enter account information or otherwise obtain information used to service the call. Further, in particular embodiments, the IVR may interact with other components such as, for example, a data store 275 to retrieve or provide information for processing the call. In other configurations, the IVR may be used to only provide announcements.
[0059] In addition to receiving inbound communications, the contact center may also originate communications to parties, referred to herein as “outbound” communications. For instance, in particular embodiments, the call handler 255 may be a dialer, such as a predictive dialer, that originates outbound calls at a rate designed to meet various criteria. Here, the call handler 255 may include functionality for originating calls, and if so, this functionality may be embodied as a private automatic branch exchange (“PBX” or “PABX”). In addition, the call handler 255 may directly interface with voice trunks using facilities 216c, 216d , 216e to the PSTN 215 and/or Internet provider 223a, 223b for originating calls. After the calls are originated, the call handler 155 may perform a transfer operation to connect the calls with agents, a queue, or an IVR. Furthermore, in various embodiments, the call handler 255 may make use of one or more algorithms to determine how and when to dial a list of numbers so as to minimize the likelihood of a called party being placed in a queue while maintaining target agent utilization.
[0246] For instance, many contact centers make use of an interactive voice response systems, or IVRs. As previously noted, an IVR may collect and analyze responses from a party in the form of speech. For instance, an IVR may be used to identify the purpose of a call, such as, for example, prompting the party to enter account information or otherwise obtain information used to service the call. By identifying the purpose of a call, the IVR may then provide the party on the call with needed information (without involving an agent) or route the call appropriately. Here, the IVR's ability to identify the purpose of a call is paramount to the IVR taking the proper action. That is to say, the IVR's ability to identify the party's intention for the call is paramount to the IVR taking the proper action. Therefore, capturing the semantic and non-semantic characteristics of the call along with the corresponding relationships between the characteristics can enable the IVR to better identify the party's intention for the call.” [similarly here user's data is received and based on analysis whether targeted promotions are to be provided or not is determined]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution promotions are communicated or displayed to user on an interface if one or more condition(s )is/are met]; and
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
(0) Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."; i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."; (ix) Pub. No.: US20100041365 "Artificial intelligence techniques typically can apply advanced mathematical algorithms--e.g., decision trees, neural networks, regression analysis, principal component analysis (PCA) for feature and pattern extraction, cluster analysis, genetic algorithm, and reinforced learning--to historic and/or current data associated with systems 100, 200, 300, 400, and 500 to facilitate rendering an inference(s) related to the systems 100, 200, 300, 400, and 500."; and (x) NPL: Bagging Classifier, Jul 21, 2025, geeksforgeeks, https://web.archive.org/web/20250721162130/https://www.geeksforgeeks.org/machine-learning/what-is-bagging-classifier/.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Examiner’s Reason(s) For Withdrawal Of Prior Art Based Rejection
3. Claims 1, 2, 6, 10-11, 15, and 19 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Sakamoto et al. (Pub. No.: US 2023/0275973) referred to hereinafter as Sakamoto, in view of Sekar et al. (Pub.No.: US2021//0201238A1) referred to hereinafter as Sekar, and in view of Koushik et al. (Pub. No.: US2025/0131449) referred to hereinafter as Koushik.
Claim Objected To And The Examiner’s Reason(s) For Claims Overcoming Prior Art: However the below noted previously relied upon and discovered prior art references were insufficient to establish a prima facie case of obviousness against the recitation of the claims being objected to. As such, claims 3-5, 7-9, and 12-14, and 16-19 were objected to as being dependent upon a rejected base claim, but would have been allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
*Previously noted
The Examiner previously noted the above listed relied upon prior art references, which appeared to be the closest. Furthermore, the Examiner also discovered the following:
(i) Chakrabarty et al. (Patent No.: US 9,800,727) referred to hereinafter as Chakrabarty see col 1 lines 50-62 note "a customer may use his computing device to navigate to the organization's website and search or click for information that may be responsive to his needs—resulting in the generation of clickstream data associated with the customer's browsing session. Such clickstream data contains a myriad of information, such as URLs, search queries, metadata, and the like that is potentially relevant to future interactions with the same customer. Upon obtaining some information (or being unable to fully obtain the desired information) from the website, the customer may initiate a voice call to the call center of the organization for additional assistance."
(ii) Pub. No.: US20160352900 see [0037] “FIG. 3 is a block diagram of an example flow for an interaction between a customer 302 and the agent 127(1-n). The customer 302 contacts the contact center 15 with a question or an issue, etc. In the banking example the customer 302 may wish to transfer money and/or close an account. The customer 302 can interact with the correct knowledge base for self-service, e.g., using IVR 119, and/or interact with the available agent 127(1-n) that is informed about the subject that the customer 302 is asking about. The orchestration server 133 routes the interaction to the correct agent 127(1-n). For some interactions the analytics server 137 parses the interaction to determine intention, e.g., customer needs, determine sentiment e.g., neutral, positive, negative, or a scored sentiment, and determine topics, e.g., money, bank, transfer. The analytics server 137 can tag certain key phrases, e.g., that the customer is ‘cancelling’ their account, and track messages to the customer, e.g., a message about lowering rates. The customer 302 can provide an explicit score to a survey, e.g., during and/or after the interaction completes. In other cases, the customer may have selected “cancel account” as a requested service in the IVR 119. In that case the analytics server 137 need not parse the text. If the contact center 15 connects the customer to an agent 127(1-n) then the customer can explain the requested service to the agent 127(1-n). In both the IVR 119 and agent 127(1-n) scenarios the “service type” can be attached to the interaction record as attribute. Therefore, the intention analytics can go beyond the coarse-grain service type classification. The orchestration server 133 can store the interaction and corresponding survey results with the conversation management server 120 and/or universal contact server 132.”
(iii) Patent No.: US 12518201 B1 "When selecting the first and second attributes at blocks 108 and 110, the computer system can apply other types of models in addition to the time-restricted entity analysis model. For example, the computer system can train one or more models that predict the day of the week or the hour of the day during which the target action is more likely to occur, independently of the time of year or the variation in attributes that are correlated with the target action at different times of year. These day-of-week or hour-of-day models can be combined with a time-restricted entity analysis model examining longer time periods (such as a month) to more accurately predict whether the target action will occur at a specific time. In an example where the target action is a customer placing a call to a mobile network provider to cancel the customer's subscription, the time-restricted entity analysis model is used to identify particular attributes of customers who are likely to cancel their subscriptions in a given month. The day-of-week and time-of-day models can be used in conjunction with this time-restricted model to predict, for example, that a customer with the particular attributes who is calling at 4 pm on Saturday is highly likely to cancel his subscription, but that another customer with the particular attributes who is calling at 10 am on Sunday is highly unlikely to cancel her subscription."
(iv) Patent No.: US7,406,426 see Abstract “automated system and method for customer management deploys customer databases to profile customer service requests for distribution to appropriately assigned agent representatives. The representatives adopt particular roles according to customer care volume, inquiry type, time of day and other customer management needs. Consumer profiles may be accessed in real time to combine customer care events with cross-selling and other promotions related to the consumer's transaction history and other factors.”
(v) R. Vadakattu, B. Panda, S. Narayan and H. Godhia, "Enterprise subscription churn prediction," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 2015, pp. 1317-1321, doi: 10.1109/BigData.2015.7363888.
(vi) N. Katyal and S. Jain, "Optimizing Campaign Effectiveness: Identifying Target Customers via Recommender Engine," 2025 7th International Symposium on Computational and Business Intelligence (ISCBI), Macau, China, 2025, pp. 103-107, doi: 10.1109/ISCBI64586.2025.11015413.
*Being noted initially in view of the updated search
- US2020/0322662 see [0148] Once the telemetry data is collected, the data may be processed using an AI predictive model 2130. In one embodiment, the AI predictive model 2130 may use a gradient boosting machines 2131 method for making a composition of decision trees. In another embodiment, neural networks 2132 may be used for handling time components. Results 2140 may indicate a probability of service cancellation 2141 and a probability of a particular customer calling the support center 2142.
[0149] Subscribers may be ranked on their probability of cancelling service 2141 or a probability of calling the support center 2142. Subscribers with highest rank may be reached out to by telephone or email programmatically or via phone operators or help support. Other offers may be provided to the subscribers via email, text message or may be made available on the users set top box and/or OTT player application. Other electronic methods may be employed as well.
[0174] "FIG. 26 is an illustration 2600 of the churn reduction impact of quality improvements. As can be seen on the left hand side of FIG. 26, an initial high risk group 2610 may have a 35% probability churn. After 6 months of providing quality improvements, a smaller portion of those users who remain are still likely to churn. For example, the remaining high risk group 2620 may have a 40% probability churn. However, the high risk churn list becomes smaller. For example, there may be a 82.3% reduction in the size of the high risk churn list 2621. At the same time, the group of those willing to speak with a service agent may become larger. For example, among the high risk group 2620, there may be a 14.7% increase in willingness to speak with a service agent 2622."
- US2022/0013112 see [0057] Accordingly, in various embodiments, the call handler 155 may place a call in a queue if there are no suitable agents available to handle the call, and/or the call handler 255 may route the call to an interactive voice response system (e.g., server) (“IVR”) (not shown) to play voice prompts. In particular embodiments, these prompts may be defined to be in a menu type structure and the IVR may collect and analyze responses from the party in the form of dual-tone multiple frequency (“DMTF”) tones and/or speech. In addition, the IVR may be used to further identify the purpose of the call, such as, for example, prompting the party to enter account information or otherwise obtain information used to service the call. Further, in particular embodiments, the IVR may interact with other components such as, for example, a data store 275 to retrieve or provide information for processing the call. In other configurations, the IVR may be used to only provide announcements.
[0059] In addition to receiving inbound communications, the contact center may also originate communications to parties, referred to herein as “outbound” communications. For instance, in particular embodiments, the call handler 255 may be a dialer, such as a predictive dialer, that originates outbound calls at a rate designed to meet various criteria. Here, the call handler 255 may include functionality for originating calls, and if so, this functionality may be embodied as a private automatic branch exchange (“PBX” or “PABX”). In addition, the call handler 255 may directly interface with voice trunks using facilities 216c, 216d, 216e to the PSTN 215 and/or Internet provider 223a, 223b for originating calls. After the calls are originated, the call handler 155 may perform a transfer operation to connect the calls with agents, a queue, or an IVR. Furthermore, in various embodiments, the call handler 255 may make use of one or more algorithms to determine how and when to dial a list of numbers so as to minimize the likelihood of a called party being placed in a queue while maintaining target agent utilization.
[0174] Depending on the embodiment, the ensemble may be based on any one of several different techniques. The first of these techniques is bagging. Bagging involves combining the results via majority voting so that the class (emotion) that receives the most votes is selected as the class (emotion) for the particular instance. Here, bagging would involve combining the semantic result and the non-semantic result by giving each result a vote in determining whether a particular utterance segment contains an emotion.
[0175] However, a problem with using bagging in this instance is when each result provides a different prediction. For example, the question becomes what should be the selected prediction for a particular utterance segment in an instance when the semantic model predicts the utterance segment contains the emotion anger and the non-semantic model predicts the utterance segment contains the emotion sadness? Such an instance may be handled differently depending on the embodiment. For example, in one embodiment, the prediction for the non-semantic model may be used because this model is considered to be more reliable because a speaker has a harder time manipulating non-semantic features with respect to expressing an emotion. While in another embodiment, each of the results may be weighted based on the accuracy of each model. Here, the accuracy for each model can be based on, for example, historical performance in correctly identifying emotions expressed by parties in segments of audio recordings.
[0201] Decision trees are probabilistic classifiers that given a set of discrete or continuous features and a labeled training set, the decision tree construction algorithm repeatedly selects a single feature that, according to an information-theoretic criterion (entropy), has the highest predictive value for the classification task in question. The feature queries are arranged in a hierarchical fashion, yielding a tree of questions to be asked of a given data point. The leaves of the tree store probabilities about the class distribution of all samples falling into the corresponding region of the feature space that serve as predictors for unseen samples. The decision tree serves as a prosody model for estimating the posterior probability of a sentence boundary at a given inter-word boundary, based on the extracted prosodic features.
[0204] Thus, the identify sentence boundaries module applies the sentence boundary model to the posterior probabilities and the lexical features in Operation 840. Here, depending on the embodiment, the sentence boundary model is some type of classifier such as a Hidden Markov model or a maximum entropy model. For instance, in particular embodiments, the sentence boundary model is a conditional random fields (CRFs) model. CRFs are a class of statistical modeling method often applied in machine learning and used for structured prediction. Whereas a discrete classifier predicts a class for a single sample without considering neighboring samples, a CRFs can take context into account and predict a sequence of classes for a sequence of input samples.
[0246] For instance, many contact centers make use of an interactive voice response systems, or IVRs. As previously noted, an IVR may collect and analyze responses from a party in the form of speech. For instance, an IVR may be used to identify the purpose of a call, such as, for example, prompting the party to enter account information or otherwise obtain information used to service the call. By identifying the purpose of a call, the IVR may then provide the party on the call with needed information (without involving an agent) or route the call appropriately. Here, the IVR's ability to identify the purpose of a call is paramount to the IVR taking the proper action. That is to say, the IVR's ability to identify the party's intention for the call is paramount to the IVR taking the proper action. Therefore, capturing the semantic and non-semantic characteristics of the call along with the corresponding relationships between the characteristics can enable the IVR to better identify the party's intention for the call.
The claims overcome the above noted prior art references as the Applicant has resolved the claim objection by incorporating allowable claim recitation in the independent claims. Therefore, prior art rejection against claims 1-3, 5, 8-12, 14, 18-25 is hereby withdrawn.
Response to Applicant’s Remarks
4. Claim objection has been withdrawn in view of filed claim amendments.
Regarding 101, the Examiner finds all the apparatus claims are now properly directed to a statutory category in view of the filed claim amendments.
Next, the Applicant argues Step 2A Prong Two, note “Applicant respectfully submits that each of independent Claims 1, 10, and 19, as a whole, integrate the limitations into practical applications to provide a technical solution to a technical problem related to routing cancellation interactions. In particular, Claims 1, 10, and 19 provide technical solutions based on improving routing of cancellation interactions between distinct computer-implemented customer interactive voice recognition (IVR) engines and a live agent connection for customer retention, and are therefore patent eligible under Step 2A, Prong Two” in view of the specification paragraphs [0002]; [0020]-[0026]; and [0028]-[0034], and “Applicant respectfully submits that each of independent Claims 1, 10, and 19, as a whole, integrate the limitations into practical applications to provide a technical solution to a technical problem related to routing cancellation interactions. In particular, Claims 1, 10, and 19 provide technical solutions based on improving routing of cancellation interactions between distinct computer-implemented customer interactive voice recognition (IVR) engines and a live agent connection for customer retention, and are therefore patent eligible under Step 2A, Prong Two.”
However, the Examiner respectfully disagrees. The rejection has been updated in view of filed claim amendments. The Applicant is reminded that the claims, as a whole, must be given their broadest reasonable interpretation in light of the as-filed specification without importing limitations from the disclosure into the claims. As set forth in the updated prong two rejection, note “claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-3, 5, 8-12, 14, 18-25 at least are an interactive voice recognition (IVR) engine, an electronic device, encoding user data as machine learning, electronically connecting (per claims 1, 10, and 19); a processing system, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to (note additionally per claim 10); data stores (per claim 2); login account on company website, website hits (per claim 3). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)) including the encoded user data to utilize machine learning. The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1-2, 11, and their associated disclosure, including the machine learning. The processor executing the "apply it" instruction is further connected to one or more device(s) merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Gathered data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes gathered and transmitted user data to predict whether user will agree to the retention offer based on historical receptiveness to such offers and user profile data which comprises user’s browsing activity on the company website. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content (e.g. retention offer) based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent) which is analyzed/evaluated via machine learning model(s) to output a prediction. The abstract idea is intended to be merely carried out in a technical environment such as collecting/transmitting data via a network such as interactive voice recognition (IVR) and analyzing data via a generic processor to provide appropriate marketing/customer-service action, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 explained above.
Thus, the abstract idea of preventing customer cancelation of a service provided by a company by analyzing the customer’s past responses to marketing effort to determine whether to present the customer with a retention offer, connect the customer to a customer service agent, or cancel the service which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea and require further analysis under Step 2B” – thus, there is a clear distinction between improving machine learning and/or IVR and using said technologies as “apply it” instructions, for instance machine learning algorithm is utilized to predict whether a customer is likely to respond to a marketing effort or not and if the customer is likely to respond then routing the customer to a customer service agent e.g. retention department to prevent customer cancelation at a service providing company which generally links the abstract idea to electronic and/or IVR environments. Accordingly, contrary to the Applicant’s assertions, the claims are directed to the abstract idea based on the BRI of the claimed invention as a whole and the unique facts of the instant application are different from DDR. Therefore, the Examiner respectfully maintains the rejection.
Next, the Applicant argues Step2B, in view of amendments that now claim a classification model and IVR as not being well-understood, routine, or conventional (WURC). Once again, the Applicant is reminded that the claims must be given their broadest reasonable interpretation in light of the as-filed specification without importing limitations from the as-filed specification and the analysis is limited to consideration of additional elements considered singularly and in-combination. The evidentiary requirement is limited to insignificant extra-solution activities such as pre-solution and post-solution activities as determined in prong two. Further, evidence for "apply it" instructions and/or generally linking to a technical environment as determined in prong two is not required as noted per 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More - I. THE SEARCH FOR AN INVENTIVE CONCEPT -
B. Examples Of How Courts Conduct The Search For An Inventive Concept - II. ELIGIBILITY STEP 2B: WHETHER THE ADDITIONAL ELEMENTS CONTRIBUTE AN "INVENTIVE CONCEPT" - note "
Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should:
• Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant." That being said, the BRI of classification model is mathematical concepts, however, in view of compact prosecution and in view of user data being encoded, the Examiner also considered it as machine learning (which is not expressly claimed). As explained, under prong two merely applying machine learning for evaluation of user data is insufficient to integrate the abstract idea into practical application, for instance note
(A) Improvement to an algorithm or an abstract idea per SAP v. Investpic: Page 2, line 22 through Page 3, line 13 “Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting.”
(B) Again, machine learning is being applied to an otherwise abstract idea when the claim is properly construed as a whole, for instance see Recentive Analytics v. Fox Corp see page 12, lines 1-4: “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.”;
- Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
- Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
- Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
- Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
- Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
- Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.
Furthermore, there is a clear distinction between improving a technology and generally linking the abstract idea to a technical environment such as a specific communication channel such as IVR as already explained in prong two. Nevertheless, the Examiner has provided use of IVR is considered WURC in the industry in the updated step 2B in view of compact prosecution.
Therefore, the claims fail to set forth any additional element that can be considered significantly more.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant.
THIS ACTION IS MADE FINAL. 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 DIPEN M PATEL whose telephone number is (571)272-6519. The examiner can normally be reached Monday-Friday, 08:30-17:00 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, Waseem Ashraf can be reached at (571)270-1376. 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.
/DIPEN M PATEL/Primary Examiner, Art Unit 3621
Prosecution Insights