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 .
Introduction
The following is a non-final Office Action in response to Applicant’s submission filed on 5/28/2024. Currently claims 1-20 are pending and claims 1, 8, 15 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/29/2024 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Siebel et al. (US 20220405775 A1)
Regarding claims 1, 8, 15, Siebel discloses a system (Siebel ABS - A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives) comprising: a processor; and a memory comprising computer program code, the memory and the computer program code (Siebel Fig. 1) configured to cause the processor to: receive an entity identifier of an entity via an entity identifier prompt on a user interface (UI) ( Siebel Fig. 26 – Siebel ¶403 - These controls 2604 enable a user to search the relationship intelligence information (such as based on keyword, connections, or ideal contacts) and create new relationship intelligence information (such as new people or nodes, events, or relationships)); present an icon representing the entity identifier and a current performance data value of the entity in a portion of the UI associated with a current entity class of the entity (Siebel Fig. 22A – 2216); receive a proposed entity class for the entity (Siebel Fig. 22B – 2218 -Siebel ¶384 – An accelerated opportunities section 2218 provides information about opportunities {i.e different/proposed classes} that might be completed earlier than their human-based predictions))); provide the received proposed entity class for the entity to an entity regressor model as input; generate a proposed performance data value using the entity regressor model and based on the proposed entity class (Siebel Fig. 22B – 2218, 2219 - Siebel ¶217 - In some cases, the classifier machine learning model can be trained on previously-won and previously-lost opportunities, possibly using both static and time series features. The regressor machine learning model can be used to determine expected close dates for open opportunities. In some cases, the regressor machine learning model can be trained only on previously-won opportunities, possibly using both static and time series features. Combinations of the outputs from the classifier and regressor machine learning models can therefore be used to identify the probabilities of open opportunities being won within certain timeframes, such as before specified closing dates or within specified date ranges for the opportunities); and automatically move the icon representing the entity identifier to a portion of the UI associated with the proposed entity class and the proposed performance data value (Siebel ¶124 - Examples of automated data transmission operation actions may include transmitting a stream of optimized data to a remote data store or display, dynamically reconfiguring a website based on a specified use case insight).
Regarding claims 2, 9, 16, Siebel discloses the computer program code are configured to further cause the processor to automatically perform an entity class action of the proposed entity class in association with the entity (Siebel ¶124 - As a particular example, one or more automated electronic communication actions may be triggered).
Regarding claims 3, 10, 17, Siebel discloses the entity class action of the proposed entity class includes: determining one or more entity interactions associated with the proposed entity class; generating a schedule data structure for the determined one or more entity interactions, wherein the schedule data structure includes a performance datetime associated with each of the one or more entity interactions; and automatically performing an entity interaction of the one or more entity interactions at the performance datetime with which the entity interaction is associated in the generated schedule data structure (Siebel ¶23 - Using the predicted probabilities to apply the at least one of the one or more use case insights may include initiating one or more automated electronic communication actions including at least one of: scheduling a calendar event or virtual meeting with a customer; generating an electronic communication or social media posting; triggering an online digital marketing campaign; instructing a message for an automated chatbot; and pushing a digital alert message to a mobile device).
Regarding claims 4, 11, 18, Siebel discloses the portion of the UI associated with the proposed entity class and the proposed performance data value includes an ordered list of icons representing entity identifiers, wherein the ordered list of icons is ordered based on performance data values of entities with which the icons representing entity identifiers are associated (Siebel Fig. 22B – 2218); and wherein automatically moving the icon representing the entity identifier to the portion of the UI associated with the proposed entity class and the proposed performance data value includes ((Siebel ¶124 - Examples of automated data transmission operation actions may include transmitting a stream of optimized data to a remote data store or display, dynamically reconfiguring a website based on a specified use case insight) inserting the icon representing the entity identifier into the ordered list of icons representing entity identifiers based on comparisons of the proposed performance data value to the performance data values of entities with which the icons representing entity identifiers are associated (Siebel ¶257 - The NBO machine learning model 808 here is therefore used to identify and prioritize the best new opportunities that should be pursued by representatives...Using this type of information, the NBO machine learning model 808 can generate propensity scores, each of which identifies a probability that a specific customer will obtain a specific product or service. The NBO machine learning model 808 can also rank the propensity scores {i.e. ordered list}, which allows representatives to focus on the opportunities that have better likelihoods of being won).
Regarding claims 5, 12, 19, Siebel discloses the computer program code are configured to further cause the processor to train the entity regressor model, the training comprising: training a first prospective model of a first regressor model type using a training data set including performance data and entity class data; training a second prospective model of a second regressor model type using the training data set; generating a first test output of the first prospective model; generating a second test output of the second prospective model; and selecting the first prospective model as the entity regressor model based on a comparison of the first test output and the second test output indicating that the first prospective model is more accurate than the second prospective model (Siebel ¶224 - The snapshot feature list 518 is used to train a classifier model 520 and a regressor model 522. As noted above, the classifier model 520 is trained to estimate the probability of successfully winning an opportunity without regard to timing, and the regressor model 522 is trained to estimate the closing date for the opportunity. Outputs of the models 520, 522 can therefore be used to generate a probability 524 of successfully winning an opportunity within a specified timeframe. In some embodiments, the classifier model 520 represents a logistic regression classifier model, and the regressor model 522 represents a generalized linear model with a gamma distribution. The models 520, 522 can undergo model validation 526 to ensure that they appear to be operating accurately based on the generated probabilities 524, such as by comparing the generated probabilities 524 to the known outcomes {i.e. test} from the subsets 506 and 508. If validated, the models 520, 522 can be used as a validated compound model representing the opportunity-level machine learning model 402).
Regarding claims 6, 13, 20, Siebel discloses the memory and the computer program code are configured to further cause the processor to: receive user feedback in response to automatically moving the icon representing the entity identifier to the portion of the UI associated with the proposed entity class (Siebel ¶260 - However the feedback is provided, the feedback itself may represent additional knowledge that can be used by the NBO machine learning model 808 in making future recommendations or in retraining the NBO machine learning model 808); and train the entity regressor model using the received user feedback (Siebel ¶235 - The labels 708 and features 710 are used here to train the aggregate-level machine learning model 404, which may represent a regressor model).
Regarding claims 7, 14, Siebel discloses determine a plurality of performance data value changes associated with performance data values of the entity based on changing the entity to the proposed entity class using the entity regressor model (Siebel Fig. 22B – 2218 – Siebel ¶384 – An accelerated opportunities section 2218 provides information about opportunities {i.e different classes} that might be completed earlier than their human-based predictions)); and display the determined plurality of performance data value changes on a portion of the UI near the icon representing the entity identifier (Siebel Fig. 22B – 2218).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Puri et al. (WO 2024047546 A1)
Ettl et al. (US 20170061463 A1)
Deb et al. (US 20150006135 A1)
Manthey et al. (US 20140278737 A1)
and
T. Mori, et al. “Design method of GUI using genetic algorithm," 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 2010, pp. 3200-3204 [online], [retrieved on 2026-03-21]. Retrieved from the Internet <https://ieeexplore.ieee.org/document/5642282?source=IQplus >
The pieces of prior art are cited because they all disclose variations on sales and customer interfaces and using machine learning in order to optimize both results and displays.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624