DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendments and remarks filed on 7 May 2026.
Claims 1, 3, 9 and 16 have been amended.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendments fail to correct the objection to the “recommended action” and additionally created a 112 issue. See new grounds of rejection set forth below as necessitated by the amendments to the claims.
Applicant’s amendments are insufficient to overcome the 101 rejections previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Applicant’s amendments are insufficient to overcome the 103 rejections previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Response to Arguments
Applicant’s arguments filed on 7 May 2026 have been fully considered but are not persuasive.
Regarding the 101, applicant argues that the claims specifically recite calculating, generating and determining using machine learning algorithms and therefore differentiate the claims from being a part of any method of organizing human activity. Examiner respectfully disagrees. The calculating, generating a score and determining scores and recommendations are all part of the identified abstract idea and are merely performing “using” a machine learning algorithm. Performing a step “using” machine learning or a machine learning algorithm without any details as to how that algorithm functions or realizing an improvement in the machine learning process itself is not sufficient to demonstrate eligibility under 101. The use of a computer element or machine learning algorithm in a generalized fashion does not meaningfully limit the otherwise abstract claims. In order for the addition of a machine or programmed element to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed step to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e. through the utilization of machine learning or a computer for performing calculations/making determinations, etc. The instant application’s claims do not incorporate specific technical functions or steps but instead are merely applied by a computer, i.e. “using” machine learning.
Applicant argues that the amended claim language including “feature engineering” cannot be interpreted as human activities. Examiner respectfully disagrees. Feature engineering is a broad concept that involves creating, selection and/or transforming raw data for use in model performance. It is considered part of data pre-processing commonly utilized in machine learning. Claiming feature engineering that is applied “by the processor set” does not set forth any specific function that could not be performed as part of the instructions for organizing human activity in the overall action recommendation process. Additionally, feature engineering can be considered a mental process since it is merely an analysis or manipulation of data that could be done the same way mentally or manually to combine attributes, create to variables, adjust features, select features, extract features to reduce dimensionality, scale features, etc. Without explicitly details the step is considered abstract and is merely applied by the processors which does not integrate the recited abstract idea into a practical application nor does it amount to significantly more.
Applicant argues that the claims amount to significantly more because the claim is directed to an improvement in the technical field of cloud resource management, specifically to provision of a technical solution. Examiner respectfully disagrees. The claims are considered directed to a method of organizing human activity, mental processes and mathematical concepts. The abstract concepts are considered to be merely applied by a computer and not meaningfully integrated into a practical application nor do they amount to significantly more. The claim limitations are merely applied by the processor set using machine learning algorithms and do not improve the functioning of the computer or realize an improvement in any other technology. Merely using machine learning is not sufficient to demonstrate 101 eligibility. The broad limitation of performing a first action of a set of ranked recommended actions is not indicative of an improvement to the functioning of a computer or any other technology or technical field, such as cloud resource management. There is no control or even recitation of cloud resources in the claim language. The specification in at least [0078] describes that the recommended actions could include targeted interventions or strategies that affect customer satisfaction such as retention strategies, discounts, support, engagement plans, personalized offers, service improvements, engagement initiatives, targeted communications, etc. This does not demonstrate any sort of next level control or cloud resource management that is sufficient to transform the claim into a patent eligible invention.
The 101 rejection is respectfully maintained and updated below as necessitated by the amendments to the claims.
Regarding the 103, applicant argues that none of the previously cited references teach the amended claim limitations in their entirety. Examiner respectfully disagrees. The amendments have necessitated updated grounds of rejection under 103. Ronen specifically teaches a model selection step where the machine learning model is selected based on the characteristics of the data. The 103 rejections are respectfully maintained, see updated grounds of rejection set forth below as necessitated by the amendments to the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 11, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the first ranked set of recommended actions", Claims 11 and 17 recite “the first ranked set of recommended action”. There is insufficient antecedent basis for these limitations in the claims. The independent claims introduce a first ranked set of recommended action items. It is unclear if the first ranked set of recommended actions or the first ranked set of recommended action are referring to the action items of the independent claims or other recommended actions. Clarification and correction are required.
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 an abstract idea without significantly more.
Independent claims 1, 9 and 16 recite a method, program product and system that as a whole illustrate a certain method of organizing human activity because the claims recite feature engineering data to generate/determine a set of inputs, calculating sentiment and weightage scores using an algorithm chose based on a format of the feature engineered data, generating a customer scores for customer satisfaction, determining an attrition risk score and ranked set of recommendations, notifying a user of the attrition risk score and performing a first action of the ranked set of recommended actions. This methodology demonstrates a commercial interaction for business relations and advertising, marking or sales activities. The application is for data driven prioritized customer retention and at least [0077] describes how the scores affecting customer satisfaction are utilized to assess attrition risk to identify high risk customers who may need targeted actions such as retention strategies or personalized offers to improve satisfaction and prevent churn. [0078] describes that the recommended actions to be performed include specific targeted interventions or strategies including retention strategies like providing discounts or tailored engagement plans, personalized offers, service improvements, engagement initiatives, etc. The feature engineering to generate a set of inputs can be considered a mental process in that it is a pre-processing of data, without any specific details, that merely analyzes data to generate or determine a set of inputs, and thus could be performed the same way mentally or manually using pencil and paper. The step is merely an evaluation type function performed “by the processor”. The calculating a sentiment score, weightage score, customer score and attrition risk score could also all be interpreted as mathematical functions. ML algorithms are used to for the calculations, generation and determination and are described in the specification in at least [0077] as Cox Proportional Hazards model, XGBoost, Shapley Additive exPlanations and other mathematical models/algorithms. The mere nominal recitation of a processor and using machine learning algorithms by a processor does not take the claim limitations out of the methods of organizing human activities grouping. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims as a whole merely describe how to generally apply the concept of feature engineering, performing calculations, generations, and determinations by a processor or processor set using algorithms chosen based on a format and performing recommended actions which are marketing or business strategies in a computer environment. The claimed computer components, algorithm and processor set are recited at a high level of generality and are merely invoked as tools to perform the analytic retention process. Receiving by a processor and notifying a user are also recited at a high level of generality and illustrate insignificant extra solution activity since they demonstrate mere data gathering and transmission. Implementing the abstract idea on a generic processor and using a machine learning algorithm that is described as math is not a practical application of the abstract idea and does not meaningfully integrate the recited abstraction into a practical application. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept. For the receiving and notifying steps that were considered extra solution activity in Step 2A, these have been re-evaluated in step 2B and determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the processor is anything other than a generic, off the shelf computer component and the Symantec, TLI and OIP Techs courts decisions in MPEP 2106.05d indicate that the mere collection, receipt and transmission of data over a network are well-understood, routine and conventional functions when claimed in a merely generic manner, as they are here.
Dependent claims 2-8, 10-15 and 17-20 include all of the limitations of the independent claims and therefore recite the same abstract idea. The claims merely narrow the method of organizing human activity for a commercial interaction, business relation or managing behavior by describing rules for generating a state and set of ranked recommended actions to be executed in response to a trigger comprising a last action item, describe the data forming a basis for scoring and recommending action items, describe customer content data, describe additional analytics as a basis for recommendations, and the rules for ranking recommended actions. The claims recite additional limitations for receiving feedback and second inputs at a high level of generality illustrating further insignificant extra solution activity, data gathering. When re-evaluated in step 2B these steps are determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the processor is anything other than a generic, off the shelf computer component and the Symantec, TLI and OIP Techs courts decisions in MPEP 2106.05d indicate that the mere collection, receipt and transmission of data over a network are well-understood, routine and conventional functions when claimed in a merely generic manner, as they are here.
Accordingly, Claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Null et al. (US 11,436,647) in view of Siebel et al. (US 2022/0405775) further in view of Ronen et al. (US 2025/0054615).
As per Claim 1 Null teaches:
A computer-implemented method, comprising:
receiving, by a processor set, a plurality of customer content (Null in at least Col. 5:4-28 describe data from both a customer’s external platform and reputation platform as well as CRM platform providing information about transactions, see also Col. 27:27-59, Col. 35:5-44);
calculating, by the processor set using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of inputs generated from the plurality of customer content (Null in at least Col. 17:1-24, Col. 44:37-67, Col. 48:37-46, Col. 49:56-Col. 50:25 describe calculating a sentiment score and weighted scores for customer content using machine learning);
generating, by the processor set using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of inputs generated from the plurality of customer content (Null in at least Col. 44:37-Col. 45:9, Col. 46:51-65, Col. 49:4-45 describe generating an overall customer satisfaction score based on sentiment and other weighted ratings and scores);
Null in at least Col. 49: 9-45 describes the ability to generate suggested actions by prioritizing the actions based on impacts. Null does not explicitly recite determining an attrition risk score, a first ranked set of recommended actions, notifying the user of the score and actions to be executed or performing the actions.
However, Siebel teaches an AI based customer relationship management system using model driven software architecture. Siebel further teaches:
feature engineering, by the processor set, data to generate a set of inputs for machine learning algorithms (Siebel in at least [0223] describes preprocessing data to generate a data set that is used to train or as inputs to a machine learning model);
determining, by the processor set using a third machine learning algorithm, a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction (Siebel in at least [0024, 0122, 0124, 0450] describe determining a prioritized list of actions or sales efforts and performing recommendation functions based on the predictions);
notifying a user of the score and the first ranked set of recommended action items to be executed (Siebel in at least [0023-0024, 00094, 0122, 0124, 0261-0262, 0416, 0425] describes communicating scores and recommendations through messages, alerts or other notifications); and
performing, by the processor, a first action of the first ranked set of recommended action items (Siebel in at least [0024, 0122, 0124, 0450] describe the ability to automate actions that are prioritized recommended actionable items or activities).
It would be obvious to one of ordinary skill in the art to modify the ability to generate scores for customer content where suggested actions are prioritized to include techniques for ranking recommended actions items effecting customer satisfaction, notify users of scores and recommendations and perform the recommended actions because each of the elements were known but not necessarily combined as claimed. The technical ability existed to combine the elements as claimed and the result of the combination is predictable since each of the elements performs the same function as it did independently. By prioritizing and communicating recommendations and scores and performing those suggested actions automatically, the combination improves the likelihood that representatives achieve at least one or more objectives while optimizing customer loyalty (Siebel [0024. 0122]).
Neither Null nor Siebel explicitly feature engineering extracted data from customer content to generate a set of inputs for a ML algorithm trained on consumer content, that the algorithm used is chosen based on the format of the feature engineered data, nor do they recite an attrition risk score. However, Ronen teaches an information management system and method that generates operations management reports based on monitored environments. Ronen further teaches:
feature engineering, by the processor set, data extracted from the plurality of customer content to generate a set of inputs for machine learning algorithms trained on consumer content (Ronen in at least [0270, 0389, 0596] describes preprocessing data, cleansing, transformation, statistical analysis and other techniques including data cleaning, normalization, feature selection and splitting the dataset into training and testing subsets, demonstrating techniques tailored to handle specific challenges of large scale data)
using a first machine learning algorithm chosen based on a format of the feature engineered data (Ronen in at least [0389-0391] describes the model selection steps where the appropriate machine learning model is selected based on the nature of the problem and the characteristics of the dataset, i.e. the format of the data, see also [0270 and 0596])
determining, by the processor set using a third machine learning algorithm, an attrition risk score (Ronen in at least [0810, 0809, 0670, 0656, 0642, 0603, 0598, 0010-0012, 0014] describe attrition risk scores being calculated using machine learning algorithms along with satisfaction ratings and other performance reviews and indicators)
It would be obvious to one of ordinary skill in the art to modify the ability to generate scores for customer content where suggested actions are prioritized/ranked and executed to include techniques for feature engineering/data pre-processing, choosing a model based on data format, and generating a attrition risk score because each of the elements were known but not necessarily combined as claimed. The technical ability existed to combine the elements as claimed and the result of the combination is predictable since each of the elements performs the same function as it did independently. By incorporating feature engineering/pre-processing, model selection and attrition risk into the scoring and evaluating of customer content the combination enables businesses and organizations to prioritize retention efforts and implement targeted strategies to prevent departures or customer churn.
As per Claim 2 Null does not explicitly recite but Siebel further teaches:
generating a current customer state and the first ranked set of recommended action items to be executed in response to a triggering event (Siebel in at least [0023, 0122, 0124, 0192] describe determining or capturing the current state of an opportunity or transaction and actions that may be triggered based on other actions or events).
Siebel is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 3 Null does not explicitly recite but Siebel further teaches
wherein the triggering event comprises an execution of at least one action item of the first ranked set of recommended actions (Siebel in at least [0023, 0122, 0124, 0192] describe determining or capturing the current state of an opportunity or transaction and actions that may be triggered based on other actions or events).
Siebel is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 4 Null does not explicitly recite but Siebel further teaches:
wherein the first ranked set of recommended action items are determined based on a combination of the plurality of customer content, the sentiment scores, and the weightage scores (Siebel in at least [0024, 0122, 0124, 0450] describe determining a prioritized list of actions or sales efforts and performing recommendation functions based on the predictions).
Siebel is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
Neither Null nor Siebel explicitly recite an attrition risk score. However, Ronen further teaches:
wherein the attrition risk score is determined based on a combination of the plurality of customer content, the sentiment scores, and the weightage scores (Ronen in at least [0810, 0809, 0670, 0656, 0642, 0603, 0598, 0010-0012, 0014] describe attrition risk scores being calculated using machine learning algorithms along with satisfaction ratings and other performance reviews, weights and indicators).
Ronen is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 5 Null does not explicitly recite but Siebel further teaches:
wherein the plurality of customer content comprises customer communications, demographics data, platform analytics, and historical recommendations (Siebel in at least [0023, 0034, 0051, 0069, 0096, 0108, 0110, 0117, 0123-0124, 0127-0129, 0186] describe content and data including communications, demographics, platform data and historical data).
As per Claim 6 Null further teaches:
performing a root cause analysis in response to the score exceeding a threshold; and recommending a second ranked set of recommended action items based on the root cause analysis (Null in at least Col. 20:10-67 describes performing a more directed analysis for a root cause for changes to scores and identifying the biggest opportunities for improvement upon to match a benchmark).
Neither Null nor Siebel explicitly recite an attrition risk score. However, Ronen further teaches:
the attrition risk score (Ronen in at least [0810, 0809, 0670, 0656, 0642, 0603, 0598, 0010-0012, 0014] describe attrition risk scores being calculated using machine learning algorithms along with satisfaction ratings and other performance reviews, weights and indicators).
Ronen is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 7 Null further teaches:
receiving feedback data and second data inputs related to an entity based on a customer response to the first ranked set of recommendation actions items (Null in at least Col. 3:22-Col. 4:50, Col. 28:10-45, Col. 37:15-32 describes receiving feedback and additional inputs based on a response to suggested actions or other options or actions).
As per Claim 8 Null further teaches:
wherein the first action of the first ranked set of recommended action items comprises a highest ranking action (Null in at least Col. 44:1-29 describes the ability to prioritize and rank issues and insights by points impacts or best in class).
As per Claim 9-20 the limitations are substantially similar to those set forth in Claims 1-8 and are therefore rejected based on the same reasons and rationale set forth in the rejections of Claims 1-8 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sivajothi et al. (US 2026/0012465) Systems and processes for enhancing cyber security and optimizing software repositories using feature engineering and the integration of a trained AI model.
Zion et al. (US 2020/0380416) Machine Learning Pipeline Optimization demonstrating a process of modeling methods organized in racks of a machine learning pipeline where feature engineering is utilized.
WYPER et al. (US 2023/0214837) System for processing a transaction at a point of engagement where a feature engineering state includes machine learning models that leverage preprocessed enhanced data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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, Rutao Wu can be reached on 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623
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