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 is a Final Action on the merits in response to the claims filed on 01/29/2026.
Claims 1, 12, and 17 have been amended.
Claims 1, 3, 5 – 12, 14, 16 – 17, 19, and 21, are currently pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/29/2026, has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner. The initialed and dated copy of Applicant’s IDS form, 1449, is attached to the instant Office Action.
Response to Remarks
Examiner’s Response to Remarks.
The § 101 Rejection
The § 103 Rejection
Examiner’s Response to The 101 Rejections.
Applicant argues the Office Action does not establish that the claims (as previously presented) are directed to an abstract idea.
Examiner respectfully disagrees. Applicant’s claim 1 recites the abstract idea, certain methods of organizing human activity commercial interactions. Applicant’s claim 1 merely receives, analyzes and displays the predicted data. Claim 1 recites building a model using an NLP; the claim further recites implementing a multi-target deep neural network that is merely providing multiple predictions, a sales opportunity outcome and sales opportunity duration and is merely evaluating data. The multi-target deep neural network is trained to predict a sales opportunity outcome and a sales opportunity duration using a plurality of training samples; and this is merely evaluating data. Claim 1 further receives information, and further evaluates relevant features influencing predictions, and this is merely correlation analysis of the data. This correlation information is used as input data for the machine learning model to predict a first output and second output, simultaneously, and sends the predictions for assigning a priority to the new sales opportunity. However, every step of the claim is merely an abstract idea that is certain methods of organizing human activity, evaluating data, and is coupled with a final step of sending the predicted information for assignment in sales activities.
Applicant argues claim 1 recites a particular machine-learning implementation but Applicant does not provide a connection between the ML model and the deep neural network and do not recite a particular predictive model using the modeling dataset that shows particular arrangement with the independent variables in Fig. 2., Applicant’s Drawings. Applicant’s Drawings Fig. 3., is merely a standard and routine drawing of a deep neural network, adds its recited independent variables as inputs, and adds its opportunity outcome and opportunity durations, which are the predicted outcomes at the output layer. Applicant’s claim 1 is merely appending well-understood, routine, conventional activities previously known to the industry of deep neural network modeling, recited at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); see https://botpenguin.com/glossary/deep-neural-network Fig. 12.2., Deep network architecture with multiple layers; see https://www.altexsoft.com/blog/deep-learning/ Typical architecture of deep neural networks. The additional elements of a machine learning model, a computing device, natural language process, and a multi-target deep neural network do not integrate the judicial exception into a practical application. Claim 1 as a whole does not integrate the judicial exception into a practical application; and there are no additional elements recited in the claim beyond the judicial exception, as we have here with no inventive concept. There is no improvement to the computer nor is there any improvement to a technological area when running Applicant’s single multi-target deep neural network with Applicant’s modeling dataset; as better results can be produced by running two separate regression models using Applicant’s modeling dataset with a first regression model providing a first prediction and a second regression model providing a second prediction. The only benefit to using Applicant’s multi-target deep neural network versus two separate single output ML models is recited in Applicant’s Specification ¶ 0027, “The use of the multi-target ML model to output the two predictions simultaneously may provide benefits over using a combination of two separate single output ML models. For example, training two single output ML models may take longer and be more computationally expensive than training the multi-target ML model in accordance with implementations of this disclosure. As another example, training the multi-target ML model in accordance with implementations of this disclosure may optimize for the multiple targets (e.g., two targets) together which may improve the accuracy of the output predictions compared to optimizing for a single target as in the case of using single output ML models.” However, the courts have made it very clear that “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provides an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Applicant is merely resolving a business problem, providing zero technological improvement to the computer, and providing zero improvement to a technological area. Applicant’s claim 1 is merely an abstract idea that collects data, analyzes and trains the data, and displays the predicted results from the data analysis.
Claims 12 and 17 are similar to claim 1 and also recite the same abstract idea and are not integrated into a practical application. All dependent claims inherit the same deficiencies as the independent claims. Accordingly all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to the § 103 Rejections
Applicant argues claims 1, 3, 5-12, 14, 16-17, 19, and 21, under 35 U. S.C. § 103, are eligible over Shariff, Shafiq et al. (U.S. Patent No. 10,108,974) in view of Zeng, Wenrong et al. (U.S. Publication No. 2017/0345035) in view of Wang, Qi, et al. "Deep Bayesian multi-target learning for recommender systems." arXiv preprint arXiv:1902. 09154 (2019) in view of Kumar, Vivek et al. (U.S. Publication No. 2020/0401932); and that none of the references, taken separately or as the alleged combination, recite every feature of the claims.
Examiner agrees. Examiner’s art individually or in combination teaches color coded, correlations, multi-label classification, multiple sub-categories and data analysis with prediction modeling and identifies variables; however, Examiner’s art does not teach correlation heatmap, principal component analysis individually nor in combination. Accordingly, rejection under 35 U.S.C. § 103 is removed.
Claim Rejection: 35 U.S.C. § 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, 3, 5 – 12, 14, 16 – 17, 19, and 21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 12, and 17 recite:
generating a modeling dataset from a corpus of historical sales opportunity and deal closure data;
implementing a first branch configured to predict a sales opportunity outcome and a second branch configured to predict a sales opportunity duration;
predict the sales opportunity outcome and the sales opportunity duration;
receiving information regarding a new sales opportunity;
determining one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration, wherein determining the one or more relevant features comprises performing exploratory data analysis on the information regarding the new sales opportunity including generating a correlation heatmap;
generating a feature vector including the one or more relevant features;
applying a dimensionality reduction operation including principal component analysis (PCS) to the feature vector to produce a reduced-dimension feature vector;
inputting the reduced-dimension feature vector;
simultaneously generating, a first output and a second output, the first output including a first prediction of an opportunity outcome of the new sales opportunity and the second output including a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features;
and sending the first and second predictions for assigning a priority to the new sales opportunity.
Claim 1 under its broadest reasonable interpretation, recites certain methods of organizing human activity; and particularly commercial interactions (i.e., sales activities and business relations), where the claim recites evaluating, a modeling dataset from a corpus of historical sales opportunity and deal closure data; implementing a first branch configured to predict a sales opportunity outcome and a second branch configured to predict a sales opportunity duration; evaluate the sales opportunity outcome and the sales opportunity duration; observing, information regarding a new sales opportunity; evaluating, one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration wherein determining the one or more relevant features comprises performing exploratory data analysis on the information regarding the new sales opportunity; evaluating, a feature vector including the one or more relevant features; applying, a dimensionality reduction operation including principal component analysis (PCS) to the feature vector to produce a reduced-dimension feature vector; inputting the reduced-dimension feature vector; and simultaneously evaluating, a first output and a second output, the first output including a first prediction of an opportunity outcome of the new sales opportunity and the second output including a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features and these all involve sales activities where the claim is prioritizing sales opportunities and deal closures. See MPEP §2106.04(a)(2)(II). Claims 12 and 17 are substantially similar and recites the same subject matter as claim 1. Accordingly, claims 1, 12, and 17 recite certain methods of organizing human activity.
The dependent claims encompass the same abstract ideas as well. For instance, claims 3, 14, and 19 are directed towards observing the multi-output DNN predicting a classification response and regression response; claim 5 is directed towards observing one or more relevant features includes a feature indicative of a customer; claim 6 is directed towards observing the one or more relevant features includes a feature indicative of a type of opportunity associated with the new sales opportunity; claim 7 is directed towards observing the one or more relevant features includes a feature indicative of an individual tasked to close the new sales opportunity; claim 8 is directed towards observing the one or more relevant features includes a feature indicative of a product associated with the new sales opportunity; claim 9 is directed towards observing the one or more relevant features includes a feature indicative of a quantity of a product associated with the new sales opportunity; claim 10 is directed towards observing the one or more relevant features includes a feature indicative of a geographic region associated with the new sales opportunity; claim 11 is directed towards observing the one or more relevant features includes a feature indicative of a deal price associated with the new sales opportunity; claim 16 is directed towards observing the one or more relevant features includes a feature indicative of one of a customer associated with the new sales opportunity, a type of opportunity associated with the new sales opportunity, an individual tasked to close the new sales opportunity, a type of opportunity, a product associated with the new sales opportunity, a quantity of the product associated with the new sales opportunity, a geographic region associated with the new sales opportunity, or deal price associated with the new sales opportunity; and claim 21 is directed towards observing a plurality of interfaces to a plurality of data sources having the sales opportunity and deal closure data; observing updated sales opportunity and deal closure data through the plurality of interfaces; evaluating the modeling dataset with the updated sales opportunity and deal closure data; and evaluating the multi-target deep neural network with updated training samples generated from the updated sales opportunity and deal closure data all involve evaluation and observation of data. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a machine learning model, a computing device, natural language process, a correlation heatmap, and a multi-target deep neural network; in addition to reciting the additional elements of claim 1, claim 12 also recites the additional elements of a computing device, one or more non-transitory machine readable mediums, one or more processors, machine learning model, natural language process, and a multi-target deep neural network; and in addition to reciting the additional elements of claim 1, claim 17 recites additional elements such as reciting a computing device, non-transitory machine-readable storage medium, one or more processors, a machine learning model, a natural language process, and a multi-target deep neural network; however these are generic computer components as per Applicant’s Specifications shown below:
“[0028] Turning now to the figures, Fig. 1A is a block diagram of an illustrative network environment 100 for intelligent sales opportunity outcome prediction, in accordance with an embodiment of the present disclosure. As illustrated, network environment 100 may include one or more client devices 102 communicatively coupled to a hosting system 104 via a network 106. Client devices 102 can include smartphones, tablet computers, laptop computers, desktop computers, workstations, or other computing devices configured to run user applications (or “apps”). In some implementations, client devices 102 may be substantially similar to a computing device 600, which is further described below with respect to Fig. 6.”
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer component (e.g., a processor). In the instant claim 1, the first and second predictions can be obtained where the instant dataset is used when running two separate regression models or running one multi-target ML model. Claim 1 collects data, analyzes and trains data, and displays the predicted results. Furthermore, Applicant’s claim 1 is merely appending well-understood, routine, conventional activities previously known to the industry, recited at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claims are directed to an abstract idea.
Dependent claims 3, 5 – 11, 14, 16, 19, and 21, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1, 3, 5 – 12, 14, and 16 – 17, 19, and 21, are not patent eligible under 35 U.S.C. § 101.
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. 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 Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
05/22/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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