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 .
Claim Rejections - 35 USC § 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.
Claims 1, 5-9, 11, 13-14, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Malaviya et al (Pub. No.: US 20180330390 A1), hereafter Malaviya in view of Vakhutinsky et al (Pub. No.: US 20240020716 A1), hereafter Vakhutinsky and Shi et al (US 20210073672 A1), hereafter Shi.
Regarding claims 1, 14, and 20, Malaviya teaches A system, computer implemented method, and non-transitory computer-readable medium (system and method, P0005, P0049. Machine-readable medium stored on a disk, P0050): at least one memory storing instructions; a network interface; and at least one hardware processor interoperably coupled with the network interface and the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising (memory, processor, and display, P0049): receiving, via the network interface and for a user who is interacting with a particular item of a provider, a set of data relating to the user and the particular item (Data pertaining to users is collected including spending patterns and internet browsing patterns, P0058); generating a customized recommendation for the user that specifies a customized offer for the user to acquire the particular item (Enhanced targeting system may provide marketing and or sales information based on user data pertaining to spending patterns, internet browsing patterns, or more, P0054, P0058), wherein the generating comprises: inputting the set of data to a predictive model that generates an output probability specifying a likelihood that the user will obtain an item of the provider (enhanced targeting system takes input including user data pertaining to spending patterns, internet browsing patterns, or more, P0058, P0067 discusses likelihood of specific events occurring including purchases); obtaining, from the predictive model and in response to the input set of data, a first model output specifying a particular likelihood that the user will obtain the particular item (Enhanced targeting system outputs the likelihood that an event happens, the event including the sale of an item to a user, P0067-P0068); … identifying, using a clustering model and based on the first model output …, a first cluster from among a plurality of clusters, wherein each of the plurality of clusters indicates one or more attributes corresponding to users in the cluster (Fuzzy K-means clustering is used to generate clusters in the form of a list 112 corresponding to the likelihood of items corresponding to the clusters being sold, P0135-P0136).
Malaviya does not appear to explicitly teach “generating, based on the identified first cluster, the customized recommendation for the user to obtain the particular item; and transmitting, via the network interface and to a device corresponding to the user, the customized recommendation”.
Vakhutinsky teaches generating, based on the identified first cluster, the customized recommendation for the user to obtain the particular item (Offer optimization model 1104 generates offers for potential customers based on the probability of users in specific clusters being customers according to a predictive model, P0126); and transmitting, via the network interface and to a device corresponding to the user, the customized recommendation (Offer optimization model 1104 presents offers to users in real time, P0126. Offers may be delivered on devices and servers coupled together over a network, P0029).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Malaviya and Vakhutinsky before them, to include Vakhutinsky’s specific teaching of generating offers for potential customers based on a predictive model in Malaviya’s method of targeted marketing associated with a population of assets. One would have been motivated to make such a combination of generating offers for potential customers based on a predictive model (see Vakhutinsky P0126) and generating a list of potential buyers with a predictive model to create offers for customers (see Malaviya P0063, P0093, P0109).
Malaviya in view of Vakhutinsky does not appear to explicitly teach: “computing, based on the first model output, scores for a first set of features of the predictive model, wherein a score for a particular feature represents a degree to which the particular feature contributed to the first model output… and the scores for the first set of features”.
Shi teaches computing, based on the first model output, scores for a first set of features of the predictive model, wherein a score for a particular feature represents a degree to which the particular feature contributed to the first model output… and the scores for the first set of features (feature impact scores indicating importance of a feature in relation to a model output are calculated by comparing outputs of a model with the feature and replacing the feature, P0005, P0006, P0042).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Malaviya, Vakhutinsky, and Shi before them, to include Shi’s specific teaching of computing feature impact scores in Malaviya’s method of targeted marketing associated with a population of assets. One would have been motivated to make such a combination of feature impact scores dependent upon model outputs (see Shi P0005, P0006, P0042) and selecting a model based on input characteristics/features to determine a prediction list representing the likelihood of satisfying an objective function (see Malaviya P0062-P0063).
Regarding claims 5 and 18, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claims 1 and 14 as outlined above. Vakhutinsky further teaches generating, based on the identified first cluster and the scores for the first set of features, the customized recommendation (Offer optimization model 1104 generates offers for potential customers based on the probability of users in specific clusters being customers according to a predictive model, P0126).
Regarding claims 6 and 19, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claims 1 and 14 as outlined above. Malaviya further teaches wherein the clustering model comprises a k-means clustering algorithm (Clustering algorithm is a fuzzy k-means clustering algorithm, P0135).
Regarding claim 7, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claims 1 and 14 as outlined above. Vakhutinsky further teaches training the predictive model using a set of training data and a corresponding set of labels, wherein the set of training data includes a plurality of sets of data relating to multiple users and items with which the multiple users interacted, and each label in the corresponding set of labels identifies whether a user of the multiple users acquired a respective item (Predictive model is trained using historic data 752. Historic data 752 includes initial offers, adjusted offers, customer offer selections, and customer offer rejections, P0118).
Regarding claim 8, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Vakhutinsky further teaches wherein the set of data relating to the user and the particular item is obtained at a particular point during a lifecycle for acquisition of the particular item and wherein the particular point during the lifecycle includes (1) a point in the lifecycle when the user makes an initial request for information regarding the particular item or (2) a point in the lifecycle when the user has submitted an application requesting an offer for the particular item (A dataset including a customer’s initial choice in items may be used to reformulate the model, P0109).
Regarding claim 9, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Malaviya further teaches determining accuracy of the predictive model at predetermined time intervals (Performance of models is evaluated over time, including accuracy of predictions made by models, P0044, P0064); and triggering a re-training of the predictive model in response to determining that the accuracy does not satisfy a predetermined threshold (After training is complete, the model may be retrained after adjustments are made to the model to determine if the changes have improved the predictive performance of the modified prediction models, P0061).
Regarding claim 11, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Vakhutinsky further teaches detecting one or more user operations of the user in response to the customized recommendation (Customers receive offers and their response, lack of response, or rejection of the offer is stored in database 17, P0046); and generating, based on the one or more user operations, training data for re-training at least one of the predictive model or the clustering model (Customers response, lack of response, or rejection of offer is provided for retraining the model, P0046).
Regarding claim 13, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Malaviya further teaches wherein the particular item comprises a financial product (Item may include home loans or mortgage-based securities, P0143-P0147), and wherein the first set of features comprise at least one of a desired interest rate of the user, an interest rate of the financial product, applied loan amount of the user, a desired processing time of the user for an application requesting an offer for the financial product, a credit score of the user, or a location of the user (List of features associated with a client includes loan data information, P0136).
Regarding claim 21, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Vakhutinsky further teaches wherein the predictive model is a machine learning model (Prediction model used for upselling may be a machine learning model, P0002, P0004, P0033).
Claims 2-4 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Malaviya in view of Vakhutinsky and Shi and further in view of Narayanam et al (Pub. No.: US 20240320538 A1), hereafter Narayanam.
Regarding claims 2 and 15, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claims 1 and 14 as outlined above.
Malaviya in view of Vakhutinsky and Shi does not appear to explicitly teach “wherein the scores for the first set of features comprises Shapley values for the first set of features”.
Narayanam teaches wherein the scores for the first set of features comprises Shapley values for the first set of features (Scores are based on Shapley values, P0018).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Malaviya, Vakhutinsky, Shi, and Narayanam before them, to include Narayanam’s specific teaching of basing scores on Shapley values in Malaviya’s method of targeted marketing associated with a population of assets. One would have been motivated to make such a combination of basing scores on Shapley values (see Narayanam P0018) and generating scores by applying a predictive model to demonstrate the likelihood of satisfying an objective function (see Malaviya P0063).
Regarding claims 3 and 16, Malaviya in view of Vakhutinsky and Shi and further in view of Narayanam teaches the limitations of claims 2 and 15 as outlined above. Narayanam further teaches identifying, based on historical data of Shapley values for a plurality of features, the first set of features from among the plurality of features (Attributes may be identified based on Shapley values that are computed using an evidence set for an attribute or record, P0018).
Regarding claims 4 and 17, Malaviya in view of Vakhutinsky and Shi and further in view of Narayanam teaches the limitations of claims 3 and 16 as outlined above. Malaviya further teaches reducing a first subset of features to a second subset of features using a correlation analysis that correlates one or more features within the first subset of features, wherein the second subset of features is the first set of features (A correlation matrix is calculated indicating the correlation between attribute 83 (first subset of features) and attributes 82 (second subset), P0262, figure 31).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Malaviya in view of Vakhutinsky and Shi and further in view of Tejima (Pub. No.: US 20230035501 A1), hereafter Tejima.
Regarding claim 10, Malaviya in view of Vakhutinsky and Shi teaches the limitations of claim 1 as outlined above. Malaviya in view of Vakhutinsky does not appear to explicitly teach “comparing actual outcomes indicating whether particular users acquired items with corresponding predicted outputs generated by the predictive model indicating whether the particular users will acquire the items; and triggering a re-training of the predictive model in response to determining that the actual outcomes differ from the predicted outputs by a predetermined threshold”.
Tejima teaches comparing actual outcomes indicating whether particular users acquired items with corresponding predicted outputs generated by the predictive model indicating whether the particular users will acquire the items (Actual and predicted demand of customers is compared, P0107); and triggering a re-training of the predictive model in response to determining that the actual outcomes differ from the predicted outputs by a predetermined threshold (Customer data continues to be collected until the difference between actual and predicted demand is above a predetermined threshold. At this point, data collection pauses and the model is retrained, P0107).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Malaviya, Vakhutinsky, Shi, and Tejima before them, to include Tejima’s specific teaching of determining the difference between actual demand and predicted demand of customers and only collecting data until the difference exceeds a threshold in Malaviya’s method of targeted marketing associated with a population of assets. One would have been motivated to make such a combination of determining the difference between actual demand and predicted demand of customers and only collecting data until the difference exceeds a threshold (see Tejima P0107) and comparing predicted changes in evaluation of items on sale with actual changes in valuation to determine performance of predictive models (see Malaviya P0060).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240338386 A1 (Panda et al) teaches a method for artificial intelligence/machine learning (AI/ML)-based modeling including automatically retraining a model in the event that the difference between the actual outputs and predicted outputs of a machine learning model exceeds a predetermined threshold.
US 20230351421 A1 (Bhatnagar et al) teaches a predictive model used to gauge a customer’s relationship with a business based on metrics including product-usage metrics, interaction-frequency metrics, and net-promoter scores.
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/I.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141