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 final Office action in response to Applicant’s submission filed on 7/20/2026. Currently claims 1-20 are pending and claims 1, 8, 15 are independent. Claims 1, 7, 8, 15 have been amended from the previous claim set dated 5/28/2024. No claims have been added or cancelled.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Siebel et al. (US 20220405775 A1) in view of Washam et al. (US 11410111 B1)
Regarding claims 1, 8, 15 (Amended), 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); 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 (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).
Siebel lacks receiving a proposed entity class for the entity, the proposed entity class being a different entity type than an entity type of a current entity class assigned to the entity; generate a proposed performance data value for a metric of the entity that would result from the entity being the proposed entity class.
Washam, from the same field of endeavor, teaches receiving a proposed entity class for the entity, the proposed entity class being a different entity type than an entity type of a current entity class assigned to the entity; generate a proposed performance data value for a metric of the entity that would result from the entity being the proposed entity class (Washam Figs 4 - 312 - Washam COL 18 ROW 40 - In a manner similar to that illustrated in FIG. 3B, computing system 240 may, in response to user 221 completing the information in input fields 312 {i.e. proposed class} and selecting button 414A, may cause a user interface presenting a predicted future cash flow, based on information provided within input fields 312 of FIG. 4A, to be presented within window 305).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the business information methodology/system of Siebel by including the business metric analysis techniques of Washam because Washam discloses “using machine learning techniques to predict optimal, appropriate, or projected values for business metrics (Washam COL 1 ROW 8)”. Additionally, Siebel further details that “The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective (Siebel ABS)” so it would be obvious to consider including the additional business metric analysis techniques that Washam discloses because it would augment the predictions included within Siebel by helping determine the appropriate/optimal metric to predict which would optimize the business objective.
Regarding claims 2, 9, 16, Siebel in view of Washam 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 in view of Washam 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 in view of Washam 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 in view of Washam 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 in view of Washam 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 in view of Washam 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).
Response to Arguments
Applicant's arguments filed 7/2/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the 35 USC § 102 rejections on the previous Office action, Applicant amended the independent claims to further limit the claims with respect to analyzing the impact of changing entity classes. In light of this amendment, Examiner agrees that the original reference did not teach this, however the amendment necessitated a further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Washam as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Conclusion
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 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
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