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 . 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.
Status of the Claims
The pending claims in the present application are claims 1-5, 7-14, and 16-20 of the Amendment filed 27 October 2025 (hereinafter referred to as the “Amendment”).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1-5 and 7-11 constitutes a machine under 35 USC 101, the “system” of claims 12-14 and 16-19 also constitutes a machine under the statute, and the “process” of claim 20 constitutes a method under the statute. Accordingly, claims 1-5, 7-14, and 16-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using independent claim 1 as an example, the claim recites the following abstract idea limitations:
“... for predicting the probability that an entity will purchase a product within a future time period, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... receive input data in the form of entries, each entry comprising, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an entity identifier that identifies an entity that is a potential purchaser of a product, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a product identifier that identifies a product that the entity associated with the entity identifier might purchase based on an interest event that is indicative of the product being relevant to the entity, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a time period identifier that specifies a past time period measured backward from a prescribed date of interest to an interest event date corresponding to the date the interest event associated with the entry occurred, and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an intensity value indicative of the degree to which the product associated with the product identifier is deemed relevant to the entity associated with the entity identifier, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... employ a ... technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized, ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... generate a final matrix only using the input data entries, said final matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... employ the ... technique to create a separate final prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product within the future time period using the final matrix, and using the last-modified control parameters established in creating the initial prediction model for each product as input or if no modification was made to a control parameter in creating the initial prediction model for a product employing the initially-used control parameter as the input for that control parameter, and foregoing validation; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... for each product, using the input data, apply the finalized prediction model associated with that product to estimate the probability that an entity will purchase the product within the future time period; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... establish a list of entities, the products they are predicted to purchase and the probability of the purchases.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: mathematic relationships, formulas, equations, and/or calculations, in the form of logistic regression, elastic net regularization, and matrix math, which fall(s) under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: fundamental economic principles or practices, including market research; commercial interactions, including advertising, marketing, or sales activities or behaviors (associated with, among other things, predicting product purchases); and managing personal behavior or relationships or interactions between people (associated with, among other things, potential purchase transactions between entities such as customers and retailers), which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “receive” step), and evaluation, judgment, and/or opinion (e.g., the recited “generate,” “employ,” “apply,” and “establish” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use independent claim 1 as an example, the claim recites the following additional element limitations:
The claimed “predicting” is performed by “A system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... a purchase probability predictor comprising one or more computing devices, and a purchase probability prediction computer program having a plurality of sub-programs executable by said computing device or devices, wherein the sub-programs configure said computing device or devices to ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “technique” includes “supervised machine learning” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of independent claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, and mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-5 and 7-11, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein said interest event that is indicative of the product being relevant to the entity comprises one of the entity expressing an interest in the product in a communication or a third party mentioning the entity and the product in a communication” of claim 2, the “wherein the intensity value indicative of the degree to which the product associated with the product identifier is deemed relevant to the entity associated with the entity identifier in an entry comprises the number of times an interest event in the input data is associated with the same entity and product” of claim 3, the “wherein ... generating a matrix from a portion of the input data entries comprises: mapping each input data entry onto a timeline based on the entry’s time period identifier; splitting the timeline so that a prescribed percentage of the entries closest to the prescribed date of interest are designated as test entries and the remaining entries are designated as training entries; and for the portion of the timeline comprising training entries, stepping a time window of a prescribed size over the timeline starting at the time corresponding to the mapped entry having largest time period identifier and moving forward in time a prescribed stride amount with each successive step, and at each step of the time window, creating an entity identifier and product identifier pair for each entry mapped onto the timeline that falls within the current time window step, and assigning each created pair to a different location in the matrix and associate a time window identifier assigned to the current time window step with the created pair whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is not already assigned to a location in the matrix, and whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is already assigned to a location in the matrix, associating a time window identifier assigned to the current time window step with the entity identifier and product identifier pair corresponding to the same interest event as the created pair” of claim 4, the “wherein ... generating a matrix from a portion of the input data entries, further comprises splitting the timeline so that 30% of the entries closest to the prescribed date of interest are designated as test entries and the remaining 70% of the entries are designated as training entries” of claim 5, the “further comprising ... eliminating, during the creation of the initial prediction models, probability estimates for entities that are known to already have the product or a product from a same category of products as the product” of claim 7, the “wherein ... generating a final matrix from the input data entries comprises: mapping each input data entry onto a timeline based on the entry’s time period identifier; stepping a time window of a prescribed size over the timeline starting at the time corresponding to the mapped entry having largest time period identifier and moving forward in time a prescribed stride amount with each successive step, and at each step of the time window, creating an entity identifier and product identifier pair for each entry mapped onto the timeline that falls within the current time window step, and assigning each created pair to a different location in the final matrix and associate a time window identifier assigned to the current time window step with the created pair whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is not already assigned to a location in the final matrix, and whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is already assigned to a location in the final matrix, associating a time window identifier assigned to the current time window step with the entity identifier and product identifier pair corresponding to the same interest event as the created pair” of claim 8, the “further comprising ... eliminating the input data entries deemed likely to be inaccurate, prior to ... generating the matrix, said eliminating comprising: identifying outlier entries in the input data using a seasonal ESD test on the time period identifiers and intensity values of the input data; and eliminating identified outlier entries from the input data up to a prescribed percentage of the entries” of claim 9, the “further comprising ... eliminating, after ... applying the finalized prediction model associated with each product to the input data to estimate the probability that an entity will purchase the product within the future time period, probability estimates for entities that are known to already have the product, or a product from a same category of products as the product, to establish a revised list of entities, the products they are predicted to purchase and the probability of that purchase” of claim 10, and the “wherein the future time period is 200 days starting from the prescribed date of interest” of claim 11). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system” of claims 2-5 and 7-11, the “sub-program” of claims 4, 5, and 7-10, and the “executing the sub-program for” of claims 9 and 10). Accordingly, claims 2-5 and 7-11 also are rejected as ineligible under 35 USC 101.
Regarding claims 12-14 and 16-19, while the claims are of different scope relative to claims 1-5 and 7-10, the claims recite limitations similar to the limitations of claims 1-5 and 7-10. As such, the rejection rationales applied to reject claims 1-5 and 7-10 also apply for purposes of rejecting claims 12-14 and 16-19. Claims 12-14 and 16-19 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding independent claim 20, while the claim is of different scope relative to independent claims 1 and 12, the claim recites limitations similar to the limitations of claims 1 and 12. As such, the rejection rationales applied to reject claims 1 and 12 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected as ineligible under 35 USC 101.
Examiner Comments
While claims 1-5, 7-14, and 16-20 are rejected as ineligible under 35 USC 101, no prior art rejections are being asserted against the claims. This is because the claims appear to distinguish over the prior art of record. The closest prior art of record is U.S. Pat. App. Pub. No. 2010/0049538 A1 to Frazer et al. (hereinafter referred to as “Frazer”). Frazer discloses, “The method 100 also includes predicting the likelihood of the occurrence of a future event 112. In retail situations, the future event many times is the purchase of another product” (para. [0044]), “In addition to predicting that an event will occur, in some embodiments of the invention, a time frame in which the event will occur is also predicted. In one embodiment, predicting the likelihood of the occurrence of a future event 112 is generally done as a risk factor over a number of selected times. This is referred to as predicting the time to the event. The risk factor is set for the various time frames. The time frames can be as short or as long as desired. For example, the time frame may be a second, or it may be several days. The risk factor is based on the risk that the action takes place over the time frame” (para. [0045]), “For each time frame, a propensity matrix including one or more customers and at least one product is formed. Several propensity matrices will be produced for each of the future time slots. This data is input to the selection module 440. The selection module 440 selects from among the best times to make a recommendation to the consumer. The selection module 440 can also be thought of as an optimization module for timing recommendations that will be the most effective in causing the future event” (para. [0052]), and “Traditional modeling frameworks in statistical pattern recognition and machine learning, such as classification and regression, seek optimal causal or correlation based mapping from a set of input features to one or more target values” (para. [0076]). Generally speaking, these disclosures read on elements of the pending claims, including the recited “purchase probability prediction,” “time period,” “matrix,” and “machine learning” limitations of independent claim 1. Frazer does not, however, disclose or suggest, the matrices receiving past time periods (in that Frazer appears to associate future time slots with matrices, not past ones), or the use of machine learning in conjunction with such matrices. As such, the disclosures of Frazer do not read on the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of claim 1. Claim 1, therefore, distinguishes over Frazer.
Searching also turned up CA Pat. App. Pub. No. 2 619 667 A1 to Lazarus et al. (hereinafter referred to as “Lazarus”). While Lazarus discloses, “Predictive modeling of consumer financial behavior” (Abstract), “For each merchant segment a predictive model is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods” (Abstract), and various matrices (see Tables 6, 8, and 9), the matrices relate to co-occurrences as opposed to times, and their use with machine learning is not disclosed or suggested. As such, the disclosures of Lazarus do not read on the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of independent claim 1. Claim 1, therefore, distinguishes over Lazarus.
Searching also turned up Vieira, Armando. “Predicting online user behaviour using deep learning algorithms.” arXiv prepring arXiv:1511.06247 (2015) (hereinafter referred to as “Vieira”). While Vieira is similar to the claimed invention in that it discloses predicting online user behaviour using deep learning algorithms (see Abstract), and involves the use of various matrices (see, e.g., pp. 5, 6, and 10-14), Vieira does not appear to disclose or suggest the specific steps associated with the matrix, machine learning, and related inputs, of the claimed invention (e.g., the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of independent claim 1). Claim 1, therefore, distinguishes over Vieira.
The statements above apply also to independent claims 12 and 20, which recite limitations similar to those of independent claim 1. Claims 2-5 and 7-11, 13, 14, and 16-19 also distinguish over the closest prior art of record at least due to their dependency from one of claims 1 and 12.
Response to Arguments
On pp. 12-24 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, with respect to Step 2A, Prong One of the eligibility analysis, the applicant highlights the recited “receive input data in the form of thousands of entries ...” and “generate a matrix from a portion of the input data entries ...” limitations of independent claim 12. (Amendment, pp. 13 and 14.) The applicant takes the position that t is not reasonably feasible for a person to generate the claimed matrix from thousands of entries, and thus, claims 12-14 and 16-19 cannot be characterized as a mental process, and so do not include an abstract idea.
The examiner finds the arguments above unpersuasive. The examiner’s view is that there is no set limit on how many inputs the human mind can receive, or to the size of the matrix an individual can generate using the individual’s mind, a pen, and some paper. For example, a matrix with 100 rows and 100 columns provides cells for 10,000 inputs, and could be represented on several pages of graph paper. The ease of performing such acts increases with the number of human minds involved. Furthermore, the applicant’s arguments do not address the other enumerated groupings of abstract ideas that have been asserted. For at least these reasons, the examiner maintains that the claims fail to meet the criteria for eligibility of Step 2A, Prong One.
On pp. 14-21 of the Amendment, with respect to Step 2A, Prong Two of the eligibility analysis, the applicant highlights steps associated with generating initial and final models (see Amendment, pp. 16-19), and argues that the modification of the supervised machine learning technique to generate the final prediction model constitutes an improvement to the technique (see Amendment, p. 19). Along the same lines, the applicant argues that the claims recite technological improvements to the claimed supervised machine learning technique technology, not just the mere use of the technology, as evidenced by the advantageous modification to the supervised learning technique described above. (See Amendment, p. 20.) The applicant emphasizes the eligibility rationale from McRo. (See id.) The applicant also argues that, with reference to MPEP 2106.05(a)(I), it is not reasonable to hold the view that a person would be able to perform the recited “create a separate final prediction model ...” limitation. (See Amendment, p. 21.)
The examiner finds the arguments above unpersuasive. Using independent claim 1 as an example, the claim recites, “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model ...” and “employ the supervised machine learning technique to create a separate final prediction model ...” The way the claim reads, supervised machine learning is used (employed), not improved or otherwise modified. Thus, the claims do not specify any improvements to technology or a technical field, as models for establishing probabilities of purchases are non-technological and non-technical. Unlike the situation in McRo, where the claimed steps involve improvements to computer animation, the claimed steps in the present application involve improving purchase probability prediction models, which an abstract idea. Purchase probability prediction modelling involves mathematics-based steps that any individual can process mentally with the aid of pen and paper. For at least these reasons, the claims fail to meet the criteria for eligibility at Step 2A, Prong Two.
On pp. 21-24 of the Amendment, with respect to Step 2B of the eligibility analysis, the applicant argues that the rejected claims recite elements that are not well-understood, routine, or conventional, emphasizing the recited “each entry comprising ...” limitation. (See Amendment, p. 23.) The examiner finds the arguments unpersuasive because the emphasized limitation does not have any additional elements, and the well-understood, routine, or conventional eligibility rationales apply to additional elements. For at least this reason, the claims fail to meet the criteria for eligibility at Step 2B.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant's disclosure. Such prior art includes the following:
U.S. Pat. No. 11,151,486 B1 to Ross et al. discloses, “A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.” (Abstract.)
U.S. Pat. App. Pub. No. 2017/0345035 A1 to Zeng et al. discloses, “a server accesses, for a plurality of customer accounts within a professional networking service, a plurality of features of the customer accounts stored with the professional networking service. The server accesses, for the plurality of customer accounts within the professional networking service, past sales records of products from the professional networking service to the plurality of employer accounts. The server determines, based on the past sales records and based on one or more features from the plurality of features, a product to upsell or cross-sell to a specific customer account. The server provides, as a digital transmission, indicia of the one or more features and the determined product.” (Abstract.)
U.S. Pat. App. Pub. No. 2018/0174088 A1 to Brown discloses, “providing retail salesperson(s) with a sales acceleration system using a mobile computing device, a gamification system, an integration engine that provides access to existing corporate data, media resources communications facilities and management structures and a learning machine that selects products to offer customers based on spend profile, multichannel sales interest, inventory and emotional state to allow salesperson(s) to individualize customer product offerings with the highest predictive probability score for purchase. Notable feature of Invention is the ability to engage salesperson(s) through a game that awards points for each activity completion. Management configures game steps for the enterprise as a whole or for any business unit, department, time of day or any other available criteria in a matrix and weights step completion values according to objectives of area, unit, store or enterprise.” (Abstract.)
U.S. Pat. App. Pub. No. 2019/0188588 A1 to Yang et al. discloses, “for each feature of one or more features of a target sample data, feature values for one or more pseudo-samples are generated using, localized stratified sampling. The one or more pseudo-samples are fed into the trained machine learned model to obtain their prediction values. A piecewise linear regression model is trained using the one or more pseudo-samples and their prediction values, the piecewise linear regression model having two coefficients for each feature, a first coefficient describing prediction change when a corresponding feature value is increased and a second coefficient describing prediction change when a corresponding feature value is decreased. A top positive feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of positive first coefficient or greatest magnitude of negative second coefficient. A top negative feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of negative first coefficient or greatest magnitude of positive second coefficient. A top feature contributor is identified based on a feature of the one or more features of the target sample having a greatest magnitude of a combination of second coefficient and feature value in the target sample data.” (Abstract.)
CA Pat. App. Pub. No. 3028646 A1 to Lal et al. discloses, “Mobile devices with multiple radios create an opportunity for retail venues to present new messaging channels to visitors, even visitors who do not subscribe to or do not activate a venue app. Venue operators are uniquely situated to aggregate data before a visit and to track a user during a visit, because their sole objective is to increase overall venue traffic and conversion to sales, without favoritism among tenants.” (Abstract.)
KR Pat. No. 101813805 B1 to Kim discloses, “a method and an apparatus to predict purchase probability of a user using machine learning. According to the present invention, the method, which is performed in a device including a process and predicts a purchase probability of a user accessing an online store formed with a plurality of webpages, comprises the following steps: converting raw data of a plurality of previous users to generate a plurality of pieces of learning data, wherein the raw data includes webpage navigation path information and purchase information for the online store of the previous users; learning the plurality of pieces of learning data through a machine learning algorithm to generate a purchase probability model; converting the webpage navigation path for the online store with respect to the user to generate at least one piece of input data; and inputting the at least one piece of input data to the machine learning algorithm to predict the purchase probability with respect to the user.” (English-language abstract).
Strandberg, Rickard, and Johan Låås. "A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales." (2019).
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 THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624