Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is sent in response to Applicant’s Communication received on 07/23/2026 for application number 18/386,324.
Response to Amendments
3. The Amendment filed 07/23/2026 has been entered. Claims 1 and 14 have been amended. Claims 1-20 remain pending in the application.
4. Applicant’s amendments to claims 1 and 14 have been fully considered and are persuasive. The amendments provided to overcome the §101 rejection set forth in the previous office action are sufficient. Accordingly, the 35 U.S.C § 101 rejection of claims 1-20 is respectfully withdrawn.
Response to Arguments
Applicant argues that cited references do not disclose the amended features in independent claims 1 and 14. However, the argument is moot since this is a newly presented limitation, thus changes the scope of the claims. However, a newly found reference, Stephan, is applied.
Claim Rejections – 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-5, 7, 8, 11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus et al. (U.S. Patent Application Pub. No. US 20050159996 A1) in view of Stephan et al. (U.S. Patent Application Pub. No. US 20140249872 A1), and further in view of Tellis et al. (U.S. Patent Application Pub. No. US 20140280880 A1).
Claim 1: Lazarus teaches a computer-implemented method for use in a center for classifying customers as being a member of target audience (i.e. For each consumer, a membership function may be defined which describes how strongly the consumer is associated with each merchant segment; para. [0027, 0115]) via operation of a predictive model (i.e. Given the merchant segments, the present invention then creates a predictive model of future spending in each merchant segment, based on transaction statistics of historical spending in the merchant segment by those consumers who have purchased from merchants in the segments, in other segments, and data on overall purchases; para. [0024]), the predictive model being configured to output a score for each of the customers (i.e. The result is a prediction of the amount of money that the consumer is likely to spend in each merchant cluster; para. [0026]) that expresses a probability that the customer will achieve a predicted outcome (i.e. The response rate among the nearest neighbors is aggregated and used as a predictor of the likely response rate for the target consumer. Based on this score for a number of potential target consumers, the marketing effort can be targeted at those consumers most likely to respond favorably, thus improving the efficiency of the marketing campaign; para. [0036]), the method comprising the steps of:
recording previous scores (i.e. the system is used to predict spending, either in future time periods for which there is no actual data as of yet, or in a recent past time period for which data is available and which is used for retrospective analysis. Generally, each account (or consumer) has a profile summarizing the transactional behavior of the account holder; para. [0112]), wherein each of the previous scores comprise a score output by the predictive model in relation to a previous interaction conducted by the center with respect to a previous customer (i.e. The predictive model for each segment is derived from observations of consumer transactions in two time periods: an input time window and a subsequent prediction time window. Data from transactions in the input time window for each consumer (including both segment specific and cross-segment) is used to extract independent variables, and actual spending in the prediction window provides the dependent variable; para. [0110]), and grouping the recorded previous scores within a dataset (i.e. The master files 408 collect and organize the transactions of each consumer from different statement periods into a date ordered sequence of transaction data for each consumer; para. [0107]), wherein the predictive model is trained with machine learning to produce probability scores indicative of probabilities of corresponding customers achieving the predicted outcome based on behaviors exhibited by the customers (i.e. the consumer vectors 106 reveal the consumer's interests in terms of their actual spending behavior; para. [0097]) during interactions with the center (i.e. The predictive model is trained using vector values of a number of customers with respect to a number of market segments. The customers' known spending behavior and/or responses to offers (both positive and negative exemplars) are provided as training data for the predictive model. Based on these data items, the model is trained, using known techniques such as neural network backward propagation techniques, linear regression, and the like; para. [0111, 0293]);
sorting the previous scores of the dataset in order of an value (i.e. the accounts are ranked by score, and assigned a percentile ranking. The result is that for each account there is a percentile ranking value for each of the merchant segments; para. [0348]);
deriving a set of reference quantiles based on the sorted previous probability scores contained in the dataset (i.e. Within each segment, the accounts are ranked by score, and assigned a percentile ranking; para. [0348-0350]), wherein the deriving the set of the reference quantiles comprises dividing the sorted previous scores into a number of equal sized quantiles (i.e. For each merchant segment then, the consumer accounts are ranked by their predicted spending for the segment in the prediction window period. Once the accounts are ranked, they are divided into N (e.g. 20) equal sized bins so that bin 1 has the highest spending accounts, and bin N has the lowest ranking accounts. This identifies the accounts holders that the predictive model for the segment indicated should be are expected to spend the most in this segment. The population is defined as the accounts with percentile ranking of 95% or greater in a current segment; para. [0341, 0350]);
determining one or more action thresholds, the one or more action thresholds defining a range of target quantiles within the set of reference quantiles (i.e. the population is defined as the accounts with percentile ranking of 95% or greater in a current segment; para. [0350, 0372]);
receiving an output of a probability score from the predictive model, based on data indicative of behavior (i.e. Once a trained model 1303 is available, predicted response 1304 for a customer can be generated based on vector values 1304 for the customer in a number of merchant segments; para. [0292]) exhibited by a first customer (i.e. generate 310 for each consumer, an amount of predicted spending in each merchant segment in a desired prediction time period; para. [0114]) that is interacting (i.e. The predicted period may be an actual future period or a current (e.g. recently ended) period for which actual spending is available; para. [0114]);
comparing the score of the first customer to the previous probability scores contained within the quantiles of the set of reference quantiles to determine a matching quantile therewithin for the probability score of the first customer (i.e. Within each segment, the accounts are ranked by score, and assigned a percentile ranking. The result is that for each account there is a percentile ranking value for each of the merchant segments; para. [0348, 0367]); and
determining if the matching quantile is within the range of target quantiles and, based thereon, selectively classifying the first customer as being a member of the target audience (i.e. A financial institution can specify a targeted population for a segment by specifying a population count for the segment, for example, the top 1000 accounts holders, or the top 10% account holders in a segment; para. [0372]).
Lazarus does not explicitly teach for used in a contact center; probability scores; recording previous probability scores, wherein each of the previous probability scores comprise a probability score output by the predictive model in relation to a previous interaction conducted by the contact center with respect to a previous customer, during interactions with the contact center; sorting the previous scores of the dataset in order of an increasing value; based on data indicative of behavior exhibited by for a first customer that is interacting with the contact center during a current interaction.
However, Stephan teaches for use in a contact center (i.e. in contact center environments such as contact center 100, it is desirable to predict the likelihood of some specific outcome occurring in association with a customer interaction, be it a telephone-based interaction, a web-based interaction, or other type of electronic interaction over the PSTN 102 or Internet 104; para. [0020]), the predictive model being configured to output a probability score (i.e. the result is a prediction score between zero and one that indicates the likelihood (or probability) that the identified outcome will occur in association with the current interaction with the specific customer; para. [0071]) for each of the customers (i.e. after predicting an outcome of an interaction with a first customer in the customer group, a contact center may repeat at least some of the method steps above for a second, different customer in the customer group; para. [0073]) that expresses a probability that the customer will achieve a predicted outcome (i.e. The method 400 next proceeds to block 410 where an outcome associated with future interactions with, action, or response from customers in the customer group is identified as a target of a predictive model. For example, it may be useful for a company (which could include a sole proprietor) to predict, after or during the current or next interaction with a customer in the group, whether the customer will be satisfied as a result of the interaction, whether the customer will terminate his or her account, whether the customer will purchase a product, whether a customer will pay an outstanding bill, whether a customer is a fraudster, and whether a customer will initiate additional subsequent interaction sessions regarding the same issue, or a combination thereof; para. [0067, 0071]), the method comprising the steps of:
recording previous probability scores, wherein each of the previous probability scores comprise a probability score output by the predictive model in relation to a previous interaction conducted by the contact center with respect to a previous customer (i.e. prediction data predicting the outcome of an interaction … where individual profiles for all customers in the customer group are created. Each customer profile contains all biographical data about a specific customer and also contains all interaction data associated with previous interactions with the customer … the result is a prediction score between zero and one … the predicted outcome data generated in block 418 may be added to the customer profile associated with the specific customer; para. [0065, 0066, 0071, 0072]), and grouping the recorded previous probability scores within a dataset (i.e. where individual profiles for all customers in the customer group are created. Each customer profile contains all biographical data about a specific customer and also contains all interaction data associated with previous interactions with the customer. Each customer profile may be cumulative of all biographical and interaction data collected, recorded, and/or received about a customer and associated with such customer via a unique identifier … the data contained in all of the customer profiles of the customers in the group is aggregated and fed into predictive analytics software; para. [0066, 0068]); wherein the predictive model is trained with machine learning (i.e. Once an outcome to be predicted is identified, the method 400 proceeds to block 412 where a predictive model operable to predict the likelihood of the identified outcome occurring is built using all the data collected about the customers in the customer group. Specifically, in one embodiment, the data contained in all of the customer profiles of the customers in the group is aggregated and fed into predictive analytics software that creates a binary logistic regression model based on the input data; para. [0068]) to produce probability scores indicative of probabilities of corresponding customers achieving the predicted outcome (i.e. The regression model identifies the variables within the profile data that influence whether the identified outcome will occur or not in the context of a future interaction with customers in the customer group … the result is a prediction score between zero and one that indicates the likelihood (or probability) that the identified outcome will occur in association with the current interaction with the specific customer; para. [0068, 0067, 0071]) based on behaviors exhibited by the customers during interactions with the contact center (i.e. Interaction data can include structured logistical data and also unstructured data, such as behavioral data describing the customer. For example, structured telephony history data may include customer history data such as call length, call origination, hold time, transfer data, conferencing data, third-party connection data, reason for call, number and frequency of all previous calls from the customer, and average length and gross minutes of all calls associated with the customer; para. [0065, 0068]);
receiving an output of a probability score from the predictive model (i.e. the result is a prediction score between zero and one that indicates the likelihood (or probability) that the identified outcome will occur in association with the current interaction with the specific customer; para. [0071]), based on data indicative of behavior exhibited by for a first customer (i.e. interaction data values corresponding with the customer's personality and the number of distress events are generated by the analysis of the interaction. In this regard, the control system 142 and/or the analytics system 160 may generate a transcript of the interaction and apply a linguistic-based behavior model to determine the personality of the customer and apply a distress identification technique to determine the number of stress events that occurred during the interaction; para. [0069]) that is interacting with the contact center during a current interaction (i.e. Depending on the variables identified as relevant to predicting the identified outcome, any different and/or additional interaction data may be generated from the current customer interaction. Additionally, although the analysis of the current interaction may occur after the interaction terminates, as described above, in certain embodiments, the analysis and generation of interaction data may occur in real-time during the interaction itself; para. [0069]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Lazarus to include the feature of Stephan. One would have been motivated to make this modification because using standardized multi-channel customer interaction data and behavioral assessment data provides more accurate predictive models for predicting customer outcomes.
However, Tellis teaches sorting the scores of the dataset in order of an increasing value (i.e. a typical way to compute a percentile is to first sort all of the data points in ascending order, i.e., smallest data points to the largest … The set or array of data buckets are typically arranged in an ascending order such that all of the data buckets span a full range of data values expected to be received for a particular data set or data type, e.g., from the lowest data value to the highest data value; para. [0005, 0019, 0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus and Stephan to include the feature of Tellis. One would have been motivated to make this modification because it provides sorting data points in ascending order and then identifying the percentile according to the position of a data point within that ordered set.
Claim 2: Lazarus, Stephan, and Tellis teach the method of claim 1. Lazarus further teaches wherein the first customer is selectively classified as a member of the target audience by: classifying the first customer as a member of the target audience if the matching quantile is found to be within the range of target quantiles; and classifying the first customer as not being a member of the target audience if the matching quantile is found to be outside of the range of target quantiles (i.e. The segment predominant population identifies those individuals as members of a segment who, relative to their own spending, are predicted to spend the most in the segment. For example, assume a consumer whose predicted spending in a segment is $20.00, which gives the consumer a percentile ranking of 75th percentile. If the consumer's percentile ranking in every other segment is below the 75th percentile, then the consumer is selected in this population for this segment; para. [0348, 0367]).
Claim 3: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus further teaches comprising the step of: in response to the first customer being classified as a member of the target audience, triggering a response action be taken in relation to the first customer (i.e. The targeting engine 422 allows the financial institution to specify targeted populations for each (or any) merchant segment, to enable selection of the targeted population for receiving predetermined promotional offers; para. [0371]).
Claim 4: Lazarus, Stephan, and Tellis teach the method of claim 3. Lazarus further teaches wherein the response action comprises an automated action taken by automated systems of the center as a response to the first customer being classified as a member of the target audience (i.e. The targeting engine 422 allows the financial institution to specify targeted populations for each (or any) merchant segment, to enable selection of the targeted population for receiving predetermined promotional offers; para. [0371]).
Lazarus does not explicitly teach the contact center.
However, Stephan further teaches wherein the response action comprises an automated action taken by automated systems of the contact center (i.e. the contact center control system 142 and/or the analytics system 160 may automatically take some action based on the calculated score; para. [0049]) as a response to the first customer being classified (i.e. if the prediction score calculated in block 252 is 0.8 or higher on a scale from zero to one, it is predicted that the customer will be satisfied, but if the prediction score is below 0.8, it is predicted that the customer will not be satisfied; para. [0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Lazarus to include the feature of Stephan. One would have been motivated to make this modification because using standardized multi-channel customer interaction data and behavioral assessment data provides more accurate predictive models for predicting customer outcomes.
Claim 5: Lazarus, Stephan, and Tellis teach the method of claim 4. Lazarus does not explicitly teach detecting the interaction involving the first customer, the interaction comprising the first customer interacting with a contact center resource over a communication channel; recording behavioral data of behavior exhibited by the first customer during the current interaction; and providing the behavioral data to the predictive model as inputs.
However, Stephan further teaches detecting the interaction involving the first customer (i.e. a recording of a current interaction involving a specific customer in the customer group is received; para. [0069]), the interaction comprising the first customer interacting with a contact center resource over a communication channel (i.e. Various specific types of communications contemplated through one or more of these channels include, without limitation, telephone calls, facsimile transmissions, voice over IP (“VoIP”) calls, video conferences, pre-recorded video clips, written essays or other texts, in-person meetings, internet-based interactions, email, SMS data (e.g., texts), tweets, instant messages, web-form submissions, smartphone app interactions, social media data, and web content data (including but not limited to internet survey data, blog data, microblog data, discussion forum data, and chat data), etc. In various aspects, real-time communication, such as voice, video, or both, is preferably included. It is contemplated that these communications may be transmitted by and through any type of telecommunication device and over any medium suitable for carrying data. For example, the communications may be transmitted by or through telephone lines, cable, or wireless communications. As shown in FIG. 1, the contact center 100 of the present disclosure is adapted to record varying communications and data formats that represent an interaction that may occur between a customer (or caller) and a contact center agent during fulfillment of a customer and agent transaction; para. [0018, 0046]); recording behavioral data of behavior exhibited by the first customer during the current interaction (i.e. During the course of the interaction, the contact center control system 142 and/or the analytics system 160 collects new data, preferably real-time or near real-time, associated with the variables identified as relevant to predicting the identified outcome; para. [0046]); and providing the behavioral data to the predictive model as inputs (i.e. Once the data values corresponding with the relevant variables have been standardized in block 220, the method proceeds to block 222 where the standardized real-time data values are applied to the predictive model to calculate a likelihood that the identified outcome will occur in association with the current customer interaction; para. [0035, 0043, 0048]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Lazarus to include the feature of Stephan. One would have been motivated to make this modification because using standardized multi-channel customer interaction data and behavioral assessment data provides more accurate predictive models for predicting customer outcomes.
Claim 7: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus further teaches wherein: the equal sized quantiles of the set of reference quantiles comprises each of the quantiles of the set of reference quantiles having an equal number of the previous probability scores (i.e. For each merchant segment then, the consumer accounts are ranked by their predicted spending for the segment in the prediction window period. Once the accounts are ranked, they are divided into N (e.g. 20) equal sized bins so that bin 1 has the highest spending accounts, and bin N has the lowest ranking accounts. This identifies the accounts holders that the predictive model for the segment indicated should be are expected to spend the most in this segment; para. [0341]); the matching quantile comprises the quantile within the set of reference quantiles determined to have the previous probability scores that are closest in value to the probability score of the first customer (i.e. Segment Predominant Scores Account Statistics Table: All open accounts with at least one purchase transaction are scored (predicted spending) for all of the segments. Within each segment, the accounts are ranked by score, and assigned a percentile ranking. The result is that for each account there is a percentile ranking value for each of the merchant segments; para. [0348]); and the predictive model comprises a neural network (i.e. The predictive models may be a neural network; para. [0024]).
Claim 8: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus further teaches wherein the step of recording the previous probability scores calculated previously by the predictive model for the previous customers comprises: recording the previous probability scores that occurred within one or more previous operational units defined in relation to a present operational unit of the center (i.e. To create the segment models, the consumer transaction data is organized into groups of observations. Each observation is associated with a selected end-date. The end-date divides the observation into a prediction window and an input window. The input window includes a set of transactions in a defined past time interval prior to the selected end-date (e.g. 6 months prior). The prediction window includes a set of transactions in a defined time interval after the selected end-date (e.g. the next 3 months); para. [0261]).
Stephan further teaches recording the previous probability scores that occurred within one or more previous operational units defined in relation to a present operational unit of the contact center (i.e. it may be desired to determine the likelihood of a customer purchasing a product in view of customer interactions recorded in the past six months, rather than all customer interactions ever recorded. Additionally, benchmark data sets may be based on other criteria besides time periods. The method 200 next proceeds to block 218 where a current customer interaction is initiated between an agent associated with the contact center 100 and a customer; para. [0045, 0046, 0065, 0066]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Lazarus to include the feature of Stephan. One would have been motivated to make this modification because using standardized multi-channel customer interaction data and behavioral assessment data provides more accurate predictive models for predicting customer outcomes.
Claim 11: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus further teaches comprising the step of determining the number of quantiles of the set of reference quantiles (i.e. Once the accounts are ranked, they are divided into N (e.g. 20) equal sized bins so that bin 1 has the highest spending accounts, and bin N has the lowest ranking accounts; para. [0341]).
Claims 14-18 are similar in scope to Claims 1-3, 5, 6 and are rejected under a similar rationale.
7. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lazarus in view of Stephan, Tellis, and further in view of Singman et al. (U.S. Patent Application Pub. No. US 20220159046 A1).
Claim 6: Lazarus, Stephan, and Tellis teach the method of claim 5. Lazarus does not explicitly teach wherein the interaction comprises a web session in which the first customer interacts with a website; and wherein the response action comprises offering, via the web session, the first customer a live chat or voice session with an agent.
However, Singman teaches wherein the interaction comprises a web session in which the first customer interacts with a website; and wherein the response action comprises offering, via the web session, the first customer a live chat or voice session with an agent (i.e. The method includes receiving a request from the user web application to communicate with the entity, establishing a communication session between the user web application and an automated agent, obtaining contextual information associated with the communication session, the contextual information including information provided by the user web application, determining that a user of the user web application should be connected with a live agent, and in response to the determining, connecting the user web application with a terminal of an agent associated with the entity. The method further includes providing the live agent associated with the entity with the contextual information; para. [0018]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus, Stephan, and Tellis to include the feature of Singman. One would have been motivated to make this modification because it allows the system to escalate a website interaction to a live agent when appropriate.
8. Claims 9, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus in view of Stephan, Tellis, and further in view of Bayrak et al. (U.S. Patent Application Pub. No. US 20130060587 A1).
Claim 9: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus further teaches wherein the step of recording the previous probability scores comprises: determining a sliding lookback window in relation to a current operational unit, wherein the sliding lookback window comprising a predetermined number of previous operational units occurring just prior to the current operational unit (i.e. To create the segment models, the consumer transaction data is organized into groups of observations. Each observation is associated with a selected end-date. The end-date divides the observation into a prediction window and an input window. The input window includes a set of transactions in a defined past time interval prior to the selected end-date (e.g. 6 months prior); para. [0261]); and recording, as the previous probability scores, each of the probability scores calculated by the predictive model during the sliding lookback window (i.e. Segment Predominant Scores Account Statistics Table: All open accounts with at least one purchase transaction are scored (predicted spending) for all of the segments. Within each segment, the accounts are ranked by score, and assigned a percentile ranking. The result is that for each account there is a percentile ranking value for each of the merchant segments; para. [0348]);
Lazarus does not explicitly teach wherein the operational unit comprises one of: a minute; an hour; a shift; a day; and a week.
Bayrak teaches wherein the step of recording the previous probability scores comprises: determining a sliding lookback window in relation to a current operational unit (i.e. different levels of data availability for different customers/phone types/time slots are addressed by balancing an overall baseline best time to contact (BTTC) score with customer-specific information. If the outbound call history is limited (or completely absent) for a given customer (at a given phone type during a given time slot), the method in one embodiment places most (or all) of the weight on the overall baseline rate. The more customer-specific information is available for a given phone-type/time slot combination, the more weight is given to that information. The resulting weighted, customer specific BTTC score is then adjusted using inbound call information. An inbound effect is estimated for customers that actively called the call center during a certain time slot using a given phone type. Typically, if a customer contacted the call center during a given time slot, this increases the probability of reaching the customer with an outbound call during that time slot; para. [0024-0026]), wherein the sliding lookback window comprising a predetermined number of previous operational units occurring just prior to the current operational unit (i.e. The scale of “best time” may depend on the channel used to contact the customer. For phone calls, the relevant time scale may be “time of the day”, for instance, described by the one-hour time slots; para. [0020, 0039]); and recording, as the previous probability scores, each of the probability scores calculated by the predictive model during the sliding lookback window (i.e. As an example application, call centers are described in connection with the methodology of the present disclosure in one embodiment. In one embodiment, the methodology of the present disclosure uses customer specific historical call patterns to determine the best time to contact a customer. For each customer in the system, the method generates a matrix with scores indicating the chance of reaching the customer at a certain phone type (home, cell, work, other) during a given time slot as shown in FIG. 1; para. [0024]); wherein the operational unit comprises one of: a minute; an hour; a shift; a day; and a week (i.e. The scale of “best time” may depend on the channel used to contact the customer. For phone calls, the relevant time scale may be “time of the day”, for instance, described by the one-hour time slots; para. [0020, 0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus, Stephan, and Tellis to include the feature of Bayrak. One would have been motivated to make this modification because it improves the effectiveness of outreach to selected customers by using historical interaction data.
Claim 10: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus does not explicitly teach recording, as the previous probability scores, a predetermined number of a most recent of the previous probability scores calculated by the predictive model.
Bayrak teaches recording, as the previous probability scores, a predetermined number of a most recent of the previous probability scores calculated by the predictive model (i.e. if only the most recent 1 year of data is used, then L=‘Today’-1 year; para. [0024, 0045-0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus, Stephan, and Tellis to include the feature of Bayrak. One would have been motivated to make this modification because it improves the effectiveness of outreach to selected customers by using historical interaction data.
Claim 19 is similar in scope to Claim 10 and is rejected under a similar rationale.
9. Claims 12, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus in view of Stephan, Tellis, and further in view of Neskey et al. (U.S. Patent Application Pub. No. US 20240370569 A1).
Claim 12: Lazarus, Bayrak, and Tellis teach the method of claim 2. Lazarus does not explicitly teach wherein the step of determining the number of quantiles within the set of reference quantiles comprises: providing a user interface on a device of an end user configured to allow the end user to provide input selecting between a plurality of alternatives that each specify a different number of quantiles for the set of reference quantiles; and receiving an input from the end user via the device indicating a selection of one of the plurality of alternatives; wherein the plurality of first alternatives comprise at least one of the following: 10 quantiles; 100 quantiles; and 1,000 quantiles.
However, Neskey teaches wherein the step of determining the number of quantiles within the set of reference quantiles comprises: providing a user interface on a device of an end user configured to allow the end user to provide input selecting between a plurality of alternatives that each specify a different number of quantiles for the set of reference quantiles; and receiving an input from the end user via the device indicating a selection of one of the plurality of alternatives; wherein the plurality of first alternatives comprise at least one of the following: 10 quantiles; 100 quantiles; and 1,000 quantiles (i.e. As discussed earlier, the user interface 800 may be operable to increase or decrease the number of quantiles (i.e., dots) by adjusting a quantiles slider input component 820, giving the resulting distribution a different appearance. As the quantiles slider input component 820 is moved to decrease the number of quantiles from 100 to 50 in FIG. 8C, such as by moving the quantiles slider input component 820 to the left, the size of each quantile or dot increases, giving the resulting distribution a different appearance. In this manner, the quantiles slider input component 820 can change the appearance of the distribution via the number of quantiles or dots selected. For example, some users may prefer a higher quantity of quantiles to give the resulting distribution a more refined and smoother appearance (e.g., based on user preference); para. [0072]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus, Stephan, and Tellis to include the feature of Neskey. One would have been motivated to make this modification because it allows the system to adjust resolution and computational efficiency.
Claim 13: Lazarus, Stephan, and Tellis teach the method of claim 2. Lazarus does not explicitly teach wherein the step of determining the one or more action thresholds comprises: providing a user interface on a device of an end user configured to allow the end user to provide input identifying a selected quantile within the set of reference quantiles; receiving an input from the end user via the device identifying the selected quantile; and deeming the selected quantile as comprising an action threshold of the one or more action thresholds.
However, Neskey teaches wherein the step of determining the one or more action thresholds comprises: providing a user interface on a device of an end user configured to allow the end user to provide input identifying a selected quantile within the set of reference quantiles; receiving an input from the end user via the device identifying the selected quantile; and deeming the selected quantile as comprising an action threshold of the one or more action thresholds (i.e. As discussed earlier, the user interface 800 may be operable to increase or decrease the number of quantiles (i.e., dots) by adjusting a quantiles slider input component 820, giving the resulting distribution a different appearance. As the quantiles slider input component 820 is moved to decrease the number of quantiles from 100 to 50 in FIG. 8C, such as by moving the quantiles slider input component 820 to the left, the size of each quantile or dot increases, giving the resulting distribution a different appearance. In this manner, the quantiles slider input component 820 can change the appearance of the distribution via the number of quantiles or dots selected. For example, some users may prefer a higher quantity of quantiles to give the resulting distribution a more refined and smoother appearance (e.g., based on user preference); para. [0072]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Lazarus, Stephan, and Tellis to include the feature of Neskey. One would have been motivated to make this modification because it allows the system to adjust resolution and computational efficiency.
Claim 20 is similar in scope to Claim 13 and is rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Dezonno et al. (Pub. No. US 20040098274 A1), predicting transaction outcomes based on monitoring customer and agent interactions in a customer contact center including monitoring a customer and agent interaction for current attributes and analyzing the current attributes and an attribute history to determine an outcome probability for the interaction.
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 extension fee 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 date of this final action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN TRAN whose telephone number is (303)297-4266. The examiner can normally be reached on Monday - Thursday - 8:00 am - 5:00 pm MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Ell can be reached on 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TAN H TRAN/Primary Examiner, Art Unit 2141
Prosecution Insights