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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/22/2025 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.
Status of Claims
This action is a Final Action in response to the communications filed on 07/24/2025.
Claims 2 – 3, and 13 – 14, have been cancelled.
Claims 1, 4, 12, 15, and 19 – 20, have been amended.
Applicant’s claims 1, 4, 6 – 12, 15, and 17 – 21 are currently pending in this application.
Objection to claims 4 and 15.
Examiner’s Response to Remarks
Examiner’s Response to Claim Rejections
The § 101 Rejections
The § 103 Rejections
Examiner’s Response to § 101 Rejections
Applicant argues the claims as presented are patentable and recite patentable subject matter; and claim 1, when viewed as a whole, recites patentable subject matter.
Examiner respectfully disagrees. Claim 1 merely recites an abstract idea of certain methods of organizing human activity, commercial interactions. The several steps directed to building an ML model using a number of techniques, including natural language processing, and multi-classifier classification models are merely data analytics. Applicant is simply using the computer as a tool to perform the abstract idea. Each one of these steps can be performed and explained using simple MS Excel Spreadsheets or any other statistical analysis tool to make a prediction regarding a lead conversion. The entire claim 1 is merely performing data analysis to resolve a business problem in sales to predict a lead converting to a sales opportunity. There is no inventive concept here, just mere data analytics to produce a prediction and sending the sales lead conversion prediction to another computer; and the claim does not recite additional elements that amount to significantly more than the judicial exception. Even when viewing claim 1 as a whole, claim 1 does not recite a practical application that improves a field of technology and there is no inventive concept. Claims 12 and 19 are substantially similar and recite the same abstract idea. Accordingly, independent claims 1, 12, and 19 are not patentable.
Examiner’s Response to § 103 Rejections:
Applicant argues that neither Zeng '035 nor Shariff disclose each and every element of the recited claims.
Examiner respectfully disagrees. Applicant recites Zeng does not disclose using historical lead conversion data to predict the likelihood of a sales lead converting to a sales opportunity. Applicant merely uses the phrase likelihood of a sales lead converting to a sales opportunity; and this phrase is not different than Zeng ‘035 phrase taught in ¶ 0041, whether the sales opportunity resulted in a successful sale or not; and further recites the training data is used to predict whether an upsell or cross sell attempt for a particular product will be successful. Zeng ‘035 further teaches in ¶ 0028, profile data may include the employer accounts, and the past sales records and the features of the employer accounts and this profile data is likened to Applicant’s historical lead conversion data. Applicant is merely using different words, however the same prediction occurs. Furthermore, Shariff teaches in col. 53, lines 36 – 43, its promotional system calculating time to close may be a function of (1) the average time to close for each of a plurality of stages of a sales pipeline; (2) stage in sales pipeline, e.g., not wanted, unknown, un-contacted, contacted, negotiating, contract sent, closed; (3) source of merchant, e.g., cold call, web to lead, other; (4) history e.g., ran a promotion, ran a feature using a different promotional system, has not run; (5) category; (6) sub-category; or some combination thereof; and this includes calculating negotiation that is likened to end with a prediction of whether that lead will turn into a potential negotiation. Thus Zeng ‘035 in view of Shariff teach Applicant’s previous independent claim 1.
Applicant has amended independent claims 1, 4, 12, 15, and 19. A new search was necessitated due to the amendments to the independent claims and new art has been applied to the amended claims. Claim 21 is a new claim. Accordingly, claims 1, 4, 6 – 12, 15, and 17 – 21 remain rejected under 35 U.S.C. § 103.
Claim Rejections: 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 4 and 15 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 4 is directed towards a cancelled claim 2. Claim 15 is directed towards a cancelled claim 13. These are in improper dependent form for failing to reference a claim previously set forth.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections: 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, 4, 6 – 12, 15, and 17 – 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 19 recites:
generating, a modeling dataset from a corpus of multi-dimensional historical lead conversion data of lead-identifying features indicative of qualifying leads into sales opportunities;
implementing, a model configured to predict whether a lead will convert to a sales opportunity;
receiving information regarding a new lead and a request for a lead conversion determination;
extracting one or more relevant lead-identifying features from the information regarding the new lead, the one or more relevant lead-identifying features influencing prediction of a lead conversion;
generating a multi-dimensional feature vector including the one or more relevant lead-identifying features;
inputting, by the computing device, the multi-dimensional feature vector to the ML model;
generating a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features;
and sending the lead conversion determination including the prediction to the another computing device.
The limitations of Claim 1, under its broadest reasonable interpretation, recites certain methods of organizing human activity related to commercial interactions that includes sales activities or behaviors, and business relations but for the recitation of generic computer components (e.g., a machine learning model, a computing device, natural language process, and a multi-classifier classification model); and uses a computer as a tool to perform certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). For example, evaluating a modeling dataset from a corpus of multi-dimensional historical lead conversion data of lead-identifying features indicative of qualifying leads into sales opportunities; evaluating a model configured to predict whether a lead will convert to a sales opportunity; observing information regarding a new lead and a request for a lead conversion determination; observing one or more relevant lead-identifying features from the information regarding the new lead, the one or more relevant lead-identifying features influencing prediction of a lead conversion; evaluating a multi-dimensional feature vector including the one or more relevant lead-identifying features; evaluating a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features and sending the lead conversion determination including the prediction all are commercial interactions, and particularly sales activities and business relations. Thus, amended independent claim 1 recites the abstract idea, certain methods of organizing human activity.
Applicant’s claim 1 recites mathematical concepts, and particularly mathematical relationships. See MPEP § 2106.04(a)(2)(I). For example, building a model by: generating, a modeling dataset from a corpus of multi-dimensional historical lead conversion data of lead-identifying features indicative of qualifying leads into sales opportunities; evaluating a model configured to predict whether a lead will convert to a sales opportunity; evaluating a model a plurality of training samples, each training sample of the plurality of training samples evaluated from the modeling dataset, each training sample of the plurality of training samples to adjust weights in the model, wherein evaluating the model includes inputting different portions of the modeling dataset and comparing predictions of lead conversions with actual target values of the training samples to adjust weights in the ML model; observing information regarding a new lead and a request for a lead conversion determination from another computing device; observing one or more relevant lead-identifying features from the information regarding the new lead, the one or more relevant lead-identifying features influencing prediction of a lead conversion; evaluating a multi-dimensional feature vector including the one or more relevant lead-identifying features; inputting the multi-dimensional feature vector to the model; and evaluating using the model a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features all constitutes training a learning model; and training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. Accordingly claim 1 recites mathematical concepts.
The limitations of claims 12 and 19, respectively, substantially recite the same subject matter of claim 1 and include the abstract ideas identified above, with additional elements such as in claim 12 one or more processors, one or more non-transitory machine-readable mediums, a computing device, a machine learning model, a computing device, natural language process, and a multi-classifier classification model, and in claim 19 a non-transitory machine-readable medium, one or more processors, a computing device, a machine learning model, a computing device, natural language process, and a multi-classifier classification model which are generic computer components as per Applicant’s Specifications shown below:
“[0023]Turning now to the figures, Fig. 1A is a block diagram of an illustrative network environment 100 for intelligent lead conversion 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 500, which is further described below with respect to Fig. 5.”
and thus are not practically integrated nor significantly more.
The dependent claims encompass the same abstract ideas as well. For instance, claim 4 is directed towards observing the model includes a random forest; claim 6 is directed towards observing the one or more relevant lead-identifying features… new lead; claim 7 is directed towards observing one or more relevant lead-identifying features… individual responsible for the new lead; claim 8 is directed towards observing one or more relevant lead-identifying features…of a geographic region associated with the new lead; claim 9 is directed towards observing the one or more relevant lead-identifying features…a source that generated the new lead; claim 10 is directed towards observing one or more relevant lead-identifying features… a product focus associated with the new lead; claim 11 is directed towards receiving information regarding the lead and sending the prediction; claim 15 is directed towards observing the model includes a random forest; claim 17 is directed towards observing the one or more relevant lead-identifying features…one of a customer associated with the new lead, an individual responsible for the new lead, a geographic region associated with the new lead, a source that generated the new lead, a product focus associated with the new lead; claim 18 is directed towards information of the new lead is received remotely, and remotely sending the prediction; claim 20 is directed towards observing a model includes random forest; and claim 21 is directed towards observing the historical lead conversion data of lead-identifying features is a customer/account, a lead contact, a lead owner, a lead source, a region, campaign type, a solution type, and a product focus. 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, and a multi-classifier classification model; claim 12 recites the additional elements computing device, one or more non-transitory machine-readable mediums, one or more processors, a machine learning model, a computing device, natural language process, and a multi-classifier classification model; claim 19 recites the additional elements a computing device, one or more non-transitory machine-readable mediums, one or more processors, a machine learning model, a computing device, natural language process, and a multi-classifier classification model. The additional elements of a computing device, one or more non-transitory machine-readable mediums, one or more processors, a machine learning model, a computing device, natural language process, and a multi-classifier classification model are considered generic computer components (see at least Spec. ¶¶ 0013 and 0023), illustrate the broader concepts and illustrative network environment. Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (e.g., a 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). See. MPEP 2106.05. 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. Thus, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. The additional elements of a computing device, one or more non-transitory machine-readable mediums, one or more processors, a machine learning model, a computing device, natural language process, and a multi-classifier classification model are considered generic computer components performing generic computer functions 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.
Dependent claims 4, 6 – 11, 15, 17 – 18, and 20 – 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, 4, 6 – 12, 15, and 17 – 21 are not patent eligible.
Claim Rejection: 35 U.S.C. § 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 6 – 12, 15, and 17 – 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Kumar; Vivek et al. (U.S. Publication No. 2020/0401932) hereinafter “Kumar” in view of Zeng, Wenrong et al. (U.S. Publication No. 2017/034,5035) hereinafter “Zeng ‘035” in view of Shariff, Shafiq et al. (U.S. Patent No. 10,108,974) hereinafter “Shariff”.
Claims 1, 12, and 19:
building a machine learning (ML) model by: generating, by a computing device, a modeling dataset from a corpus of multi-dimensional historical lead conversion data of lead-identifying features indicative of qualifying leads into sales opportunities using a natural language process; Kumar teaches in ¶ 0032, training process may automatically learn patterns in the historical examples that lead to successful and unsuccessful outcomes. The trained model may map the learned patterns to probabilities of the corresponding outcomes. Kumar teaches in ¶ 0081, sentiment analyzer may use natural language processing and/or trained classifiers to classify the sentiment of an event. Kumar teaches in ¶ 0082, the training process forms feature vectors using the sentiment information and/or the tags from the event. Kumar teaches in ¶ 0112, a list of pending sales opportunities and associated attributes. Each row corresponds to a different sales opportunity and is likened to multi-dimensional. The opportunity attributes include win probability column for displaying the probability score of the opportunity, name column for displaying the opportunity name, account column for displaying the target entity for the opportunity, amount column for displaying the amount of money involved in the sales opportunity, close date column identifying a timeframe for closing the sale, sales stage column to display the current stage of the sales cycle for the opportunity, recommended actions column for presenting actions that are recommended to be executed.
implementing, by the computing device, a multi-classifier classification model configured to predict whether a lead will convert to a sales opportunity; Kumar teaches in ¶ 0097, the trained model may be used to predict that a targeted message will result in positive engagement with a 75% probability score. As another example, the trained model may be used to predict that a particular patch will correct performance degradation with an 90% probability score. The predictions are made based on learned feature patterns from the training dataset. Kumar teaches in ¶ 0098, multiple actions may be evaluated for a single opportunity where multiple actions may be likened to multi-classifier classification model. For example, one action might involve sending a message to contact A and another sending the same message to contact B. In this example, separate feature vectors may be formed for each action, where one of the features includes the contact. The classifications and/or probability scores might be the same or different, depending on the learned patterns from the training dataset. Kumar teaches in ¶ 0116, insights may include, but are not limited to, identifying a particular social media platform for posting a message that results in the highest positive engagement, suggesting a particular contact that has the highest likelihood of accepting a proposal, and indicating that an opportunity is likely no longer viable, where insights predict whether a lead will convert to a sales opportunity;
training, at the computing device, a the multi-classifier classification model using a plurality of training samples, each training sample of the plurality of training samples generated from the modeling dataset, each training sample of the plurality of training samples to adjust weights in the multi-classifier classification model, wherein training the multi-classifier classification model includes inputting different portions of the modeling dataset and comparing predictions of lead conversions with actual target values of the training samples to adjust weights in the ML model; Kumar teaches in claim 3, the sentiment information is generated using a trained classification model that classifies the at least one current event as having a positive sentiment, a negative sentiment, or a neutral sentiment. Kumar teaches in ¶ 0107, The tuning process adjusts the model weights for the identified features based on the feedback. A model weight in this context is a weighting value that is indicative of how predictive a given feature is to a classification. If the feedback indicates that the feature is not relevant, then the model weight may be adjusted downward. Conversely, the model weight may be increased if the feedback is positive.
generating, by the computing device, a multi-dimensional feature vector including the one or more relevant lead-identifying features; Kumar teaches in ¶ 0098, feature vectors may be formed for each action; multi-dimensional is taught above where row corresponds to a different sales opportunity
inputting, by the computing device, the multi-dimensional feature vector to the ML model; Kumar teaches in ¶ 0091, the training process formed a feature vector from sentiment and non-sentiment information extracted from the found events
extracting, by the computing device, one or more relevant lead-identifying features from the information regarding the new lead, the one or more relevant lead-identifying features influencing prediction of a lead conversion; Kumar teaches in ¶ 0059, outcome of an action taken for a given opportunity may serve as feedback for training a machine learning model; Kumar teaches in ¶ 0061, add and/or delete opportunities on demand and/or in response to detected events;
Kumar teaches a trained model, feature vectors, row corresponds to a different sales opportunity, a classification model, sentiment, the GUI is updated in real-time as recommendation engine produces new opportunity insights, discover a new news article that is relevant to a particular opportunity, recommendation engine reevaluate the opportunity to update the score based on the new information, frontend interface updates and refresh the GUI presented to the end user to highlight the new insight, sending an electronic message to one or more recipients, Kumar does not explicitly sending, by the computing device, the lead conversion determination. However, Zeng ‘035 teaches the following:
generating, by the computing device using the ML model, a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features; Zeng ‘035 teaches in ¶ 0043, one example machine learning approach includes constructing, at the server and from the training data set, multiple decision trees to output an indication of whether upselling or cross-selling a given product to a given employer account has a likelihood of success (e.g., a P(sale) value) exceeding a predetermined threshold (e.g., 0.8 or 0.85);
and sending, by the computing device, the lead conversion determination including the prediction to the another computing device; Zeng ‘035 teaches in ¶ 0073, the instructions may be transmitted or received over the network using a transmission medium via a network interface device (e.g., a network interface component included in the communication components); lead conversion determination is taught above.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 to assist businesses in transmission of features and the determined product (Zeng ‘035, Spec. 0018).
Kumar teaches a trained model, feature vectors, row corresponds to a different sales opportunity, a classification model, sentiment, the GUI is updated in real-time as recommendation engine produces new opportunity insights, discover a new news article that is relevant to a particular opportunity, recommendation engine reevaluate the opportunity to update the score based on the new information, frontend interface updates and refresh the GUI presented to the end user to highlight the new insight, sending an electronic message to one or more recipients, and Zeng ‘035 sending, by the computing device, the lead conversion determination. Neither Kumar nor Zeng ‘035 explicitly teach receiving from the promotional system a sales lead. However, Shariff teaches the following:
receiving, by the computing device, information regarding a new lead and a request for a lead conversion determination from another computing device; Shariff teaches in col. 19, lines 5 – 6, receiving from the promotional system a sales lead that may be likened to a new lead, and calculating a probability of closing a sale lead that may be likened to a lead conversion determination; Shariff teaches in col. 5, lines 46 – 47, receiving the data from another computing device, where the data received may be likened to information and a request received; Shariff further teaches in col. 22, line 67 and col. 23, lines 1 – 27, these computer program instructions may also be stored in a non-transitory computer-readable storage memory… a general purpose computer may be provided with an instance of the processor;
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12);
Claims 4 and 15:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Kumar further teaches the following:
wherein the multi-classifier classification model; Kumar teaches above in claims 1, 12, and 19, the sentiment information is generated using a trained classification model that classifies the at least one current event as having a positive sentiment, a negative sentiment, or a neutral sentiment.
Kumar teaches trained classification model, and Kumar does not explicitly teach random forest modeling. However, Zeng ‘035 teaches the following:
includes a random forest; Zeng ‘035, teaches in ¶ 0046 example machine learning techniques includes random forests.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 to assist businesses in transmission of features and the determined product (Zeng ‘035, Spec. ¶ 0018).
Claim 6:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of a customer associated with the new lead; Shariff teaches in col. 18, lines 52 – 62, the probability to close represents a probability of a merchant committing their inventory, such as signing a contract, where the merchant may be a new customer to the contract and the merchant committing inventory may be a relevant lead identifying feature.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 7:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of an individual responsible for the new lead. Shariff teaches in col. 7, lines 27 – 50, … Impressions are therefore provided to “consumers,” including, but not limited to, a client, customer, purchaser, shopper, user of the promotional system or the like who may be in the position to or does exchange value for one or more instruments under the terms defined by the one or promotions, where consumer attributes in the customer profile using the promotional system may likened to relevant lead-identifying features and the customer being responsible for the new lead. For example, and using the aforementioned running shoes company as the example provider, an individual … The consumer attributes may be inputted into the consumer's profile by the consumer, or collected by components within the promotion system and inputted into the consumer's profile.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 8:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of a geographic region associated with the new lead; Shariff teaches in col. 7, lines 66 – 67, and col. 8, and line 1, the offer which is likened to a relevant lead-identifying feature, may further include a location and/or a hyper-local region (e.g., a location of Chicago, Ill. and/or hyper-local region of Wrigleyville) where Chicago, Ill may likened to a geographic region associated with the new lead.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 9:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of a source that generated the new lead; Shariff teaches in col. 55, lines 56 – 60, the process for assigning a merchant to a resource may include a weighted random allocation, in some examples, a weighted random allocation gives every resource a probability for receiving the new lead.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 10:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of a product focus associated with the new lead; Shariff teaches in col. 54, lines 26 – 28, … sales resources may be provided a target that may be based on the % of that division's assets (accounts) that they own and a target for new leads.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claims 11 and 18:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the information regarding the new lead is received from a remote computing device, and further wherein the sending the prediction is to the remote computing device; Zeng ‘035 teaches in ¶ 0027, the application server module is configured to provide, as a digital transmission, indicia of the one or more features and the determined product. Zeng ‘035 teaches in ¶ 0053, at operation 340, the server provides indicia of the one or more features, the determined product, and the specific employer account. In some cases, the indicia are provided as a digital transmission to the third party server 130 (e.g., a mail server or mobile messaging server, for transmission to a sales representative as an email message or mobile device message). In some cases, the indicia are provided as a digital transmission to one of the client machines 110 or 112 for display thereat. A user of the client machine 110 or 112 (e.g., a sales representative) may access the client machine 110 or 112 to prepare for a sales call to the business associated with the specific employer account. After operation 340, the method 300 ends.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 17:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein the one or more relevant lead-identifying features includes a feature indicative of one of a customer associated with the new lead, an individual responsible for the new lead, a geographic region associated with the new lead, a source that generated the new lead, or a product focus associated with the new lead; Shariff teaches in col. 18, lines 52 – 62, the probability to close represents a probability of a merchant committing their inventory, such as signing a contract. Probability to close and time to close may be based on in combination, but not limited to, a lead source, such as for example a “warm lead” versus a “cold call”, quality of the lead source, merchant attributes, stage of close, interface time, talk time, past feature history data, past feature performance(s), high prior satisfaction, an upcoming expiration of an offer, whether the merchant is a new or existing customer, and/or whether the merchant has adopted other services.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 20:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Shariff further teaches the following:
wherein a multi-classifier classification model includes a random forest, wherein the random forest is trained using the modeling dataset generated from a corpus of historical lead conversion data of an organization; Zeng ‘035 teaches in ¶ 0041, … the determination of operation 330 is made via machine learning using a training data set. The training data is used to predict whether an upsell or cross sell attempt for a particular product will be successful, represented as a probability, as a function F of G. In other words P(sale)=F(G). In the training data set, the P(sale) value is represented with S, which is 0 if there was no sale, or 1 if there was a sale.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine techniques for enhancing actionable opportunities through machine learning of Kumar with a server accessing, a plurality of features of the customer accounts stored with the professional networking service using machine learning to determine products to upsell or cross-sell, and customer accounts which would be most receptive to the upsell or cross-sell attempt of Zeng ‘035 with systems, methods and computer readable media for managing a sales pipeline concerning leads of Shariff to assist businesses with determining a sales lead and sending the sales leads (Shariff, Spec. col. 19, lines 4 – 12).
Claim 21:
Kumar, Zeng ‘035, and Shariff teach claims 1, 12, and 19. Kumar further teaches the following:
wherein the historical lead conversion data of lead-identifying features is a customer/account, a lead contact, a lead owner, a lead source, a region, campaign type, a solution type, and a product focus; Kumar teaches in ¶ 0061 a customer account; Kumar teaches in ¶ 0061 contact information for interacting with a target entity; a lead owner Kumar teaches in ¶ 0061 opportunities are generated by users; a lead source Kumar teaches in ¶ 0061 lead information identifying how an opportunity arose; a region Kumar teaches in ¶ 0070 a match for a sales opportunity may be detected if a target entity has expanded into more regions; campaign type.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Sukumar, Sudeep (U.S. Publication No. 2014/0358638) discloses data in one or more data repositories managed by a business software framework used by a sales organization can be used in creating a set of profiles corresponding to customers of the sales organization.
- Reference Anderson, Van et al. (U.S. Publication No. 2022/0164755) discloses systems and methods are provided for generating, processing and distributing leads, the system comprising a leads processing engine for receiving customer requests, creating leads based upon the customer requests, determining a best available agent or agents for each lead from a pool of available agents based upon one or more selected factors, and offering and/or sending each lead to the best available agent or agents.
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.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
10/30/2025
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625