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 .
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 8/4/2025, Applicant, on 1/5/2026, amended claims 1, 9 and 15. Claims 1-20 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
1/5/2026 has been entered.
Response to Arguments
Applicant’s arguments filed January 5, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed January 5, 2026.
On Pg.9-12 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims are not directed to an abstract idea. The pending claims are directed to a concrete technological solution to issues that arise when trying to customize a combo of choice products with regular products that takes into consideration a new member's preference and the number of available products, not to an abstract idea. In response, The claims primarily recite the additional element of using computer components to perform each step. The “system”, “processing unit”; “processor-readable medium”, “processor”, “assignment module”, “new member module”; “choice product success ratio module”, and “processor executable code” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
On Pg. 12-14 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states even if the claims are construed to contain an abstract idea, the claim contains at least one element, or combination of elements, sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. In response, Examiner finds the present claims improve an existing business process of product selection and there are currently no functional advancements to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to determine are all, both individually and in combination, computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II). Applicant has not identified any meaningful limitations that would alter this analysis. Therefore, there are no limitations in the present claim that transform the abstract idea such that the claim amounts to significantly more than the abstract idea itself.
On Pg.15 of the Remarks, regarding 35 U.S.C. § 103 rejections, Applicant states the cited references, alone or in combination, teach or suggest each and every element of Applicant's claimed invention. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Quatse is now applied for Claim 1. Please review updated 103 rejection regarding amended claim language..
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to choice product offer analysis.
Claim 1 recites a method for member assignment, Claim 9 recites a method for determining a member limit and Claim 20 recites a system for member assignment, which include receiving data representative of a plurality of choice product success ratios; data representative of a plurality of new member choice product scores; identifying one new member choice product as a function of the plurality of choice product success ratios and the plurality of new member choice product scores; identifying one new member-receiver score of a plurality of new member-receiver scores of the one new member choice product; providing data representative of an identification of the one new member to an assignment module in response to the identification of the one new member-receiver score for an assignment of the one new member to one regular receiver of a plurality of regular receivers or one neutral receiver of a plurality of neutral receivers; and assigning the new member to a receiver based on the provided data representative of the one new member and a predetermined chooser limit (claim 1). Receive the data representative of the identification of one new member, determine whether a member limit of the new member choice product is reached, and assign, when the member limit of the new member choice product is not reached, the new member to the one regular receiver of the plurality of regular receivers or the one neutral receiver of a plurality of regular receivers, and provide data representative of the assignment of the new member to the choice product success ratio module; Receive the data representative of the assignment of the new member, determine whether a member limit of the new member choice product is reached; assign, when the member limit of the new member choice product is not reached based on a predetermined chooser limit, the new member to the one regular receiver of the plurality of regular receivers or the one neutral receiver of a plurality of regular receivers, where an assignment of the new member to the one regular receiver increases a choice product success ratio of one new member choice product, and an assignment of the new member to the one neutral receiver decreases the choice product success ratio of the new member choice product, and provide data representative of the information corresponding to the increase or decrease of the choice product success ratio of the new member choice product to the new member module (claim 9 and claim 15) .
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – marketing & sales activities; managing interactions. The recitation of “system”, “processing unit”; “processor-readable medium”, “processor”, “assignment module”, “new member module”; “choice product success ratio module”, and “processor executable code”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- sales/marketing activities and managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “processing unit”; “processor-readable medium”, “processor”, “assignment module”, “new member module”; “choice product success ratio module”, and “processor executable code” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in customer market analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “processing unit”; “processor-readable medium”, “processor”, “assignment module”, “new member module”; “choice product success ratio module”, and “processor executable code” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-8, 10-14, and 16-20 recite wherein each choice product success ratio of the plurality of choice product success ratios corresponds to one choice product of a plurality of choice products; each choice product success ratio of the plurality of choice product success ratios is determined as a function of a first quantity of existing members of the choice product assigned to a plurality of regular receivers and a second quantity of existing members of the choice product assigned to a plurality of neutral receivers; the function for determining each choice product success ratio of the plurality of choice product success ratios is a quotient between the first quantity and a sum of the first quantity and the second quantity; the function for identifying the one new member choice product does not include at least one new member because of the at least one new member's choice product success ratio; each new member-receiver score of the plurality of new member-receiver scores is determined from product scores of the new member corresponding choice and neutral products of one receiver of the plurality of regular receivers and the plurality of neutral receivers; each new member-receiver score of the plurality of new member-receiver scores corresponds to one regular receiver of the plurality of regular receivers or one neutral receiver of the plurality of neutral receivers; the identification of the one new member for assignment corresponds to the highest new member-receiver score of the plurality of new member-receiver scores; the member limit of the new member choice product is reached when every regular receiver of the plurality of regular receivers comprising the one new member choice product is full and when every neutral receiver of the plurality of neutral receivers comprising the one new member choice product is full; information corresponding to the new member choice product reaching its member limit is provided to a new member module of the processing unit; reassigning an existing member in the regular receiver assigned to the new member, where the reassignment of the existing member to a second regular receiver of the plurality of regular receivers does not increase or decrease a choice product success ratio of an existing member choice product; reassigning an existing member in the regular receiver assigned to the new member, where the reassignment of the existing member to a neutral receiver of the plurality of neutral receivers decreases a choice product success ratio of an existing member choice product, whereupon information corresponding to the decrease is provided to a new member module of the processing unit for determining an assignment of a next new member; reassigning an existing member in the neutral receiver assigned to the new member, where the reassignment of the existing member to a second neutral receiver of the plurality of neutral receivers does not increase or decrease a choice product success ratio of an existing member choice product and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9 and 15. Regarding Claims, 11, and 13, and the additional element of “processing unit” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). ng is solely used a tool to perform the instructions of the abstract idea..
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 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- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Crites et al., US Publication No. 20080300981 A1, [hereinafter Crites], in view of Belgaied Hassine et al., US Publication No. US 20090234710 A1, [hereinafter Hassine] , in further view of Quatse et al., US Publication No. 20050010472 A1, [hereinafter Quatse].
Regarding Claim 1,
Crites teaches
A method for determining an assignment of a new member in a dynamic distributional system, comprising: receiving, by a processing unit including at least one processor coupled to a non- transitory processor-readable medium storing processor-executable code, data representative of a plurality of choice product success … (Crites Par. 5-“ In general, in one aspect, the invention features a method and software encoded on computer-readable medium. The method includes selecting a first combination of offers from a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and determining whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, one or more of the offers of the first combination are modified to generate a second combination of offers that complies with the second set of rules. However, if the first combination passes the second set of rules, the first combination is returned as a solution.; Par. 7-10; Par. 28;Par. 287-288) ;
receiving, by the processing unit, data representative of a plurality of new member choice product scores (Crites Par. 6-“ In general, in another aspect, the invention features a system including memory configured to store a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and one or more processors configured to: select a first combination of offers from the list; and determine whether the first combination of offers passes a second set of rules belonging to a second, different category of rules.);
identifying, by the processing unit, one new member-receiver score of a plurality of new member-receiver scores of the one new member choice product, providing data representative of an identification of the one new member to an assignment module of the processing unit in response to the identification of the one new member-receiver score for an assignment of the one new member to one regular receiver of a plurality of regular receivers or one neutral receiver of a plurality of neutral receivers. (Crites Par. 24-“ Campaign optimization begins with a set of marketing offers, which can include advertisements and promotional offers, and a pool of potential recipients (also referred to as "customers"), which may include existing customers and new individuals. The offers are produced independently of the optimization process and typically have a number of financial characteristics (costs, delivery channel, risk, etc.). For each of the offers, the process calculates a score that represents the relative "value" of assigning any particular offer to any individual customer. The score can take a number of forms, including the probability that the customer will respond to the offer or an expected value of the offer for the customer. The potential list of contacts is the "proposed contact list" and may include all permutations or a subset of permutations (i.e., an external system may apply eligibility rules to the list to reduce the number of proposed contacts to consider for optimization).”)
Crites teaches offer analysis and the feature is expounded upon by Hassine:
receiving... plurality of choice product success ratios (Hassine Par. 120-121-“Choice Rate (Offer): the observed number of times a given offer has been purchased by the customer divided by the number of times it has been exposed/presented to the customer. Choice Rate is related to a given period of time and to a given customer segment. Example: two offers A, B may be proposed and the customer has a no-choice (“Loss”) alternative. “;Par. 163-“Realization Rate (Offer): the number of final invoiced sales recorded for a given offer divided by the number of orders recorded for that given offer. The Realization Rate may be inferior to 100% due to cancellations and modifications of orders. In the case of Contract Agreements it may also be superior to 100%, when actual sales/orders exceed initial expectations.”; Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.”)
identifying, by the processing unit, one new member choice product as a function of the plurality of choice product success ratios and the plurality of new member choice product scores (Hassine Par. 120-121;Par. 67-77-“The implementation of a CCRM system typically involves the following steps: [0068] a Gather and store transaction data (customer characteristics and preferences, context, offers presented, customer choices . . . ) for each customer interaction; [0069] a Produce reports (such as historical exposure and choice rates by offer and customer segment) to help define predictions of choice probability for future transactions; [0070] Adapt the Transaction Management system to be "CCRM compliant" in terms of customer data collection, sales screen displays and sales process logic; [0071] Define Business Rules for the optimization of transactions: objective function (expected revenue, expected margin, conversion rate), constraints and system parameters (such as "Value of Learning"); [0072] Integrate CCRM Optimizer with the Transaction Manager system and other Enterprise systems (such as Costing . . . ) in order to score offers according to their choice probability and expected value; [0073] Adjust sales procedures and define the right incentives for the sales agents and partners to improve the use of the system; [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”);
and assigning the new member to a receiver based on the provided data representative of the one new member …(Hassine Claim 32 -select and apply said disaggregated customer choice model to derive choice probabilities of offers for a new customer in accordance with a segment assigned to said customer based on known characteristics and preferences of said customer .)
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Crites in view of Hassine teach offer analysis and the feature is expounded upon by Quatse:
… whereupon information corresponding to the increase or decrease is provided to a new member module of the processing unit for determining an assignment of a next new member. (Quatse Par. 137; Claim 18-19“ A method of adjusting the distribution of limited quantities of promotional offers from a plurality of promotional offers to a plurality of customers comprising: providing, for each combination of customer and promotional offer from said pluralities, a measure of the acceptance probability that the customer will accept the promotional offer; presenting the measures of acceptance probabilities for an individual customer in a graphical display, wherein said graphical display includes a plurality of graphic elements, one said graphic element being associated with each said measure of acceptance probability provided for said individual customer at least for the highest ranking of said measures; enabling adjustment of said measures of acceptance probability by movement of the associated graphic elements; and selecting a limited quantity of offers from said plurality of offers for distribution to said individual customer, wherein said limited quantity of offers are selected substantially in descending order of said measures of acceptance probabilities as adjusted in said enabling step. ;19-graphical display comprises a bar chart, said graphic elements comprise individual bars of said bar chart, and said movement comprises dragging said bars to lengthen and shorten them and thereby increase and decrease the associated measure of acceptance probability.”);
…and a predetermined chooser limit (Quatse Par. 48- FIG. 7 is a flowchart illustrating an embodiment of a method for calculating the average SKU Group probabilities given any form of customer marketing segmenting. The operational definition of Market Segmenting as used herein is the classification of customers into mutually exclusive groups having similar marketing characteristics according to predefined intentions, inclusion rules, methods, or algorithms. Although the example described here refers to a specific form of Market Segmenting, the invention is not intended to be limited to any particular form of segmenting.)
Crites , Hassine and Quatse are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites in view of Hassine, as taught by Quatse, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites in view of Hassine with the motivation of improve targeting precision while simplifying the declaration of targeting information (Quatse Par.16).
Regarding Claim 2, Crites in view of Hassine in further view of Quatse teach The method of claim 1,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein each choice product success ratio of the plurality of choice product success ratios corresponds to one choice product of a plurality of choice products (Hassine Par. 120-121-“Choice Rate (Offer): the observed number of times a given offer has been purchased by the customer divided by the number of times it has been exposed/presented to the customer. Choice Rate is related to a given period of time and to a given customer segment. Example: two offers A, B may be proposed and the customer has a no-choice (“Loss”) alternative. “;Par. 163-“Realization Rate (Offer): the number of final invoiced sales recorded for a given offer divided by the number of orders recorded for that given offer. The Realization Rate may be inferior to 100% due to cancellations and modifications of orders. In the case of Contract Agreements it may also be superior to 100%, when actual sales/orders exceed initial expectations.”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 3, Crites in view of Hassine in further view of Quatse teach The method of claim 1,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein each choice product success ratio of the plurality of choice product success ratios is determined as a function of a first quantity of existing members of the choice product assigned to a plurality of regular receivers and a second quantity of existing members of the choice product assigned to a plurality of neutral receivers. (Hassine Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.” Par. 120-121;Par. 163; Par. 240).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 4, Crites in view of Hassine in further view of Quatse teach The method of claim 3,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein the function for determining each choice product success ratio of the plurality of choice product success ratios is a quotient between the first quantity and a sum of the first quantity and the second quantity. (Hassine Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.” Par. 120-121;Par. 163; Par. 240).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 5, Crites in view of Hassine in further view of Quatse teach The method of claim 1,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein the function for identifying the one new member choice product does not include at least one new member because of the at least one new member's choice product success ratio. (Hassine Par. 151-153- Optimization: the process of finding the offer/offer instance/offer set/offer sequence that maximizes a pre-defined objective function of the transaction (such as the expected value or the conversion probability) given different constraints (such as for example: minimum exposure, minimum conversion, minimum value). Par. 640-644; Par. 1153-“ They may be produced only for offers, offer instances, offer sets or offer sequences that have already been presented to the customer in the past and for which historical choice rates can be calculated. This excludes in principle new offers or existing offers or sets/sequences that have never been presented.”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 6,
The method of claim 1, wherein each new member-receiver score of the plurality of new member-receiver scores is determined from product scores of the new member corresponding choice and neutral products of one receiver of the plurality of regular receivers and the plurality of neutral receivers (Crites Par. 5-6-“ In general, in another aspect, the invention features a system including memory configured to store a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and one or more processors configured to: select a first combination of offers from the list; and determine whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, the one or more processors modify one or more of the offers of the first combination to generate a second combination of offers that complies with the second set of rules. If, however, the first combination passes the second set of rules, the one or more processors return the first combination as a solution.”).
Regarding Claim 7, Crites in view of Hassine in further view of Quatse teach The method of claim 1,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein each new member-receiver score of the plurality of new member-receiver scores corresponds to one regular receiver of the plurality of regular receivers or one neutral receiver of the plurality of neutral receivers.. (Hassine Par. 170; Par. 67-78- The implementation of a CCRM system typically involves the following steps: [0068] a Gather and store transaction data (customer characteristics and preferences, context, offers presented, customer choices . . . ) for each customer interaction; [0069] a Produce reports (such as historical exposure and choice rates by offer and customer segment) to help define predictions of choice probability for future transactions; [0070] Adapt the Transaction Management system to be "CCRM compliant" in terms of customer data collection, sales screen displays and sales process logic; [0071] Define Business Rules for the optimization of transactions: objective function (expected revenue, expected margin, conversion rate), constraints and system parameters (such as "Value of Learning"); [0072] Integrate CCRM Optimizer with the Transaction Manager system and other Enterprise systems (such as Costing . . . ) in order to score offers according to their choice probability and expected value; [0073] Adjust sales procedures and define the right incentives for the sales agents and partners to improve the use of the system; [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 8,
The method of claim 1, wherein the identification of the one new member for assignment corresponds to the highest new member-receiver score of the plurality of new member-receiver scores. (Crites Par. 9-“ The combinations of offers may be organized in the list in descending order according to scores associated with the combinations of offers such that the first combination of offers has a score that is higher than the scores of the other combinations in the list. The second set of rules may be ordered by the most restrictive first; and each rule of the second set may be applied, one by one and in order, to the first combination. The list of combinations of offers that pass a first set of rules may be organized as a tree structure, and the next highest scoring combinations of offers may be generated one at a time and on-demand; and stored in the tree structure.”).
Regarding Claim 9,
Crites teaches
A method for determining a member limit in a dynamic distributional system of a plurality of receivers, comprising: receiving, by a processing unit including at least one processor coupled to a non- transitory processor-readable medium storing processor-executable code, data representative of an identification of a new member for an assignment to one regular receiver of a plurality of regular receivers or one neutral receiver of a plurality of regular receivers, where the identification of the new member is determined as a function of one member- receiver score of a plurality of member-receiver scores of one new member choice product of the new member; (Crites Par. 287-288;Par.5- 6-“ ] In general, in one aspect, the invention features a method and software encoded on computer-readable medium. The method includes selecting a first combination of offers from a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and determining whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, one or more of the offers of the first combination are modified to generate a second combination of offers that complies with the second set of rules. However, if the first combination passes the second set of rules, the first combination is returned as a solution.; Par. 61);
determining, by the processing unit, whether a member limit of the new member choice product is reached…; (Crites Par. 27-“ The computer system 10 also includes automated campaign management software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82);
and assigning, by the processing unit, when the member limit of the new member choice product is not reached, the new member to the one regular receiver of the plurality of regular receivers or the one neutral receiver of a plurality of regular receivers,… (Crites Par. 27-“ The computer system 10 also includes automated campaign management software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82);
Crites teaches offer analysis and the feature is expounded upon by Hassine:
... where an assignment of the new member to the one regular receiver increases a choice product success ratio of one new member choice product, and an assignment of the new member to the one neutral receiver decreases the choice product success ratio of the one new member choice product, … (Hassine Par. 120-121; Par. 74-78- [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”; Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.”)
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Crites in view of Hassine teach offer analysis and the feature is expounded upon by Quatse:
…based on a predetermined chooser limit (Quatse Par. 48- FIG. 7 is a flowchart illustrating an embodiment of a method for calculating the average SKU Group probabilities given any form of customer marketing segmenting. The operational definition of Market Segmenting as used herein is the classification of customers into mutually exclusive groups having similar marketing characteristics according to predefined intentions, inclusion rules, methods, or algorithms. Although the example described here refers to a specific form of Market Segmenting, the invention is not intended to be limited to any particular form of segmenting.)
… whereupon information corresponding to the increase or decrease is provided to a new member module of the processing unit for determining an assignment of a next new member. (Quatse Par. 137; Claim 18-19“ A method of adjusting the distribution of limited quantities of promotional offers from a plurality of promotional offers to a plurality of customers comprising: providing, for each combination of customer and promotional offer from said pluralities, a measure of the acceptance probability that the customer will accept the promotional offer; presenting the measures of acceptance probabilities for an individual customer in a graphical display, wherein said graphical display includes a plurality of graphic elements, one said graphic element being associated with each said measure of acceptance probability provided for said individual customer at least for the highest ranking of said measures; enabling adjustment of said measures of acceptance probability by movement of the associated graphic elements; and selecting a limited quantity of offers from said plurality of offers for distribution to said individual customer, wherein said limited quantity of offers are selected substantially in descending order of said measures of acceptance probabilities as adjusted in said enabling step. ;19-graphical display comprises a bar chart, said graphic elements comprise individual bars of said bar chart, and said movement comprises dragging said bars to lengthen and shorten them and thereby increase and decrease the associated measure of acceptance probability.”);
Crites , Hassine and Quatse are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites in view of Hassine, as taught by Quatse, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites in view of Hassine with the motivation of improve targeting precision while simplifying the declaration of targeting information (Quatse Par.16).
Regarding Claim 10, Crites in view of Hassine in further view of Quatse teach The method of claim 9,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein the member limit of the new member choice product is reached when every regular receiver of the plurality of regular receivers comprising the one new member choice product is full and when every neutral receiver of the plurality of neutral receivers comprising the one new member choice product is full. (Hassine Par. 32; Par. 116; Par. 301-304- “Real Availability Restriction”: in this case the offer cannot be proposed due to lack of corresponding resources (an example is an airline fare product that cannot be proposed to the customer when capacity of the flight is sold out).”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 11,
The method of claim 10, wherein information corresponding to the new member choice product reaching its member limit is provided to a new member module of the processing unit (Crites Par. 27-“ The computer system 10 also includes automated campaign management software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82).
Regarding Claim 12, Crites in view of Hassine in further view of Quatse teach The method of claim 9, further comprising:…
Crites in view of Hassine teach offer analysis and the feature is expounded upon by Quatse:
reassigning an existing member in the regular receiver assigned to the new member, where the reassignment of the existing member to a second regular receiver of the plurality of regular receivers does not increase or decrease a choice product success ratio of an existing member choice product. (Quatse Par. 60-61“ It is a revision method for manually overriding the distribution of promotional offers after the distribution list for all offers and all customers is assembled. In cases where the targeting computations and methods of the invention lead to final distributions that are unexpected and in some ways undesired by the user, the user is able to view a display such as a bar chart of the distribution of offers and modify the distribution appropriately. The identifier and/or name of each promotional offer of the Master List appears on the horizontal axis of the bar chart display 55. The height of each bar shows the number of the given offers distributed. The user can click on any bar, and drag it to a greater or smaller distribution. An adjusted coefficient appears in the score of the offer for all customers and the new total is calculated.”);
Crites , Hassine and Quatse are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites in view of Hassine, as taught by Quatse, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites in view of Hassine with the motivation of improve targeting precision while simplifying the declaration of targeting information (Quatse Par.16).
Regarding Claim 13, Crites in view of Hassine in further view of Quatse teach The method of claim 9, further comprising:…
Crites in view of Hassine teach offer analysis and the feature is expounded upon by Quatse:
reassigning an existing member in the regular receiver assigned to the new member, where the reassignment of the existing member to a neutral receiver of the plurality of neutral receivers decreases a choice product success ratio of an existing member choice product, whereupon information corresponding to the decrease is provided to a new member module of the processing unit for determining an assignment of a next new member.. (Quatse Par. 60-61; Claim 19- “ wherein said graphical display comprises a bar chart, said graphic elements comprise individual bars of said bar chart, and said movement comprises dragging said bars to lengthen and shorten them and thereby increase and decrease the associated measure of acceptance probability.”);
Crites , Hassine and Quatse are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites in view of Hassine, as taught by Quatse, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites in view of Hassine with the motivation of improve targeting precision while simplifying the declaration of targeting information (Quatse Par.16).
Regarding Claim 14, Crites in view of Hassine in further view of Quatse teach The method of claim 9,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
reassigning an existing member in the neutral receiver assigned to the new member, where the reassignment of the existing member to a second neutral receiver of the plurality of neutral receivers does not increase or decrease a choice product success ratio of an existing member choice product. (Hassine Par. 1059-1063- “(O2)=P(O2')=1/2, thus P(O2)/P(O2')=1; If the choice set is now {O1,O2,O2}, the IIA Property states that the precedent ratios of probability remain unchanged and we should have: P(O1)/P(O2)=P(O2)/P(O2')=P(O1)/P(O2')=1, thus leading to: P(O1)=P(O2)=P(O2')=1/3; This last result is in contradiction with the intuition that adding O2' to the choice set will not change the preferences between the two resorts but instead lead to the following probabilities”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 15,
Crites teaches
A dynamic distributional system of a plurality of products, comprising: a processing unit at least one processor coupled to a non-transitory processor-readable medium storing processor-executable code and having a new member module, an assignment module, and a choice product success ratio module, where the new member module is configured to perform the following functions by the processing unit: receive data representative of a plurality of choice product success…, (Crites Par. 5-“ In general, in one aspect, the invention features a method and software encoded on computer-readable medium. The method includes selecting a first combination of offers from a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and determining whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, one or more of the offers of the first combination are modified to generate a second combination of offers that complies with the second set of rules. However, if the first combination passes the second set of rules, the first combination is returned as a solution.; Par. 7-10; Par. 28;Par. 287-288) ;
receive data representative of a plurality of new member choice product scores (Crites Par. 6-“ In general, in another aspect, the invention features a system including memory configured to store a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and one or more processors configured to: select a first combination of offers from the list; and determine whether the first combination of offers passes a second set of rules belonging to a second, different category of rules.);
identify one new member-receiver score of a plurality of new member- receiver scores of the one new member choice product, and provide data representative of an identification of one new member to the assignment module in response to the identification of the one new member-receiver score. (Crites Par. 24-“ Campaign optimization begins with a set of marketing offers, which can include advertisements and promotional offers, and a pool of potential recipients (also referred to as "customers"), which may include existing customers and new individuals. The offers are produced independently of the optimization process and typically have a number of financial characteristics (costs, delivery channel, risk, etc.). For each of the offers, the process calculates a score that represents the relative "value" of assigning any particular offer to any individual customer. The score can take a number of forms, including the probability that the customer will respond to the offer or an expected value of the offer for the customer. The potential list of contacts is the "proposed contact list" and may include all permutations or a subset of permutations (i.e., an external system may apply eligibility rules to the list to reduce the number of proposed contacts to consider for optimization).”)
the assignment module is configured to perform the following functions by the processing unit: receive the data representative of the identification of one new member, determine whether a member limit of the new member choice product is reached…, and assign, when the member limit of the new member choice product is not reached, the new member to the one regular receiver of the plurality of regular receivers or the one neutral receiver of a plurality of regular receivers,… and the choice product success ratio module is configured to perform the following functions by the processing unit : receive the data representative of the assignment of the new member, determine whether a member limit of the new member choice product is reached; assign, when the member limit of the new member choice product is not reached, the new member to the one regular receiver of the plurality of regular receivers or the one neutral receiver of a plurality of regular receivers,; (Crites Par. 287-288;Par.5- 6-“ ] In general, in one aspect, the invention features a method and software encoded on computer-readable medium. The method includes selecting a first combination of offers from a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and determining whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, one or more of the offers of the first combination are modified to generate a second combination of offers that complies with the second set of rules. However, if the first combination passes the second set of rules, the first combination is returned as a solution.; Par. 61; Par. 27-“ The computer system 10 also includes automated campaign management software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82Par. 27-“ The computer system 10 also includes automated campaign management ; software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82);
Crites teaches offer analysis and the feature is expounded upon by Hassine:
... choice product success ratio, (Hassine Par. 120-121-“Choice Rate (Offer): the observed number of times a given offer has been purchased by the customer divided by the number of times it has been exposed/presented to the customer. Choice Rate is related to a given period of time and to a given customer segment. Example: two offers A, B may be proposed and the customer has a no-choice (“Loss”) alternative. “;Par. 163-“Realization Rate (Offer): the number of final invoiced sales recorded for a given offer divided by the number of orders recorded for that given offer. The Realization Rate may be inferior to 100% due to cancellations and modifications of orders. In the case of Contract Agreements it may also be superior to 100%, when actual sales/orders exceed initial expectations.”; Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.”)
identifying one new member choice product as a function of the plurality of choice product success ratios and the plurality of new member choice product scores (Hassine Par. 120-121; Par. 67-77-“The implementation of a CCRM system typically involves the following steps: [0068] a Gather and store transaction data (customer characteristics and preferences, context, offers presented, customer choices . . . ) for each customer interaction; [0069] a Produce reports (such as historical exposure and choice rates by offer and customer segment) to help define predictions of choice probability for future transactions; [0070] Adapt the Transaction Management system to be "CCRM compliant" in terms of customer data collection, sales screen displays and sales process logic; [0071] Define Business Rules for the optimization of transactions: objective function (expected revenue, expected margin, conversion rate), constraints and system parameters (such as "Value of Learning"); [0072] Integrate CCRM Optimizer with the Transaction Manager system and other Enterprise systems (such as Costing . . . ) in order to score offers according to their choice probability and expected value; [0073] Adjust sales procedures and define the right incentives for the sales agents and partners to improve the use of the system; [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”);
and provide data representative of the assignment of the new member to the choice product success ratio module; (Hassine Par. 120-121; Par. 74-78- [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”; Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.”)
... where an assignment of the new member to the one regular receiver increases a choice product success ratio of one new member choice product, and an assignment of the new member to the one neutral receiver decreases the choice product success ratio of the one new member choice product, … (Hassine Par 120-121;Par. 74-78- [0074] Identify the characteristics influencing customer choices and build segments grouping customers showing consistent choice behavior; [0075] Define choice models and calibrate the models on a sample of historical data. Apply the choice model to predict choice probabilities; [0076] Monitor the success of the CCRM and continually refine the system. The system refinement process includes monitoring the accuracy of the forecasts, periodic updating of the choice models and predictions to reflect new offers; [0077] Identify new factors influencing the choice model and incorporate such factors in the model; [0078] Use CCRM to improve the definition of offerings and their price.”; Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.”)
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Crites in view of Hassine teach offer analysis and the feature is expounded upon by Quatse:
…based on a predetermined chooser limit (Quatse Par. 48- FIG. 7 is a flowchart illustrating an embodiment of a method for calculating the average SKU Group probabilities given any form of customer marketing segmenting. The operational definition of Market Segmenting as used herein is the classification of customers into mutually exclusive groups having similar marketing characteristics according to predefined intentions, inclusion rules, methods, or algorithms. Although the example described here refers to a specific form of Market Segmenting, the invention is not intended to be limited to any particular form of segmenting.)
… and provide data representative of the information corresponding to the increase or decrease of the choice product success ratio of the new member choice product to the new member module. (Quatse Par. 137; Claim 18-19“ A method of adjusting the distribution of limited quantities of promotional offers from a plurality of promotional offers to a plurality of customers comprising: providing, for each combination of customer and promotional offer from said pluralities, a measure of the acceptance probability that the customer will accept the promotional offer; presenting the measures of acceptance probabilities for an individual customer in a graphical display, wherein said graphical display includes a plurality of graphic elements, one said graphic element being associated with each said measure of acceptance probability provided for said individual customer at least for the highest ranking of said measures; enabling adjustment of said measures of acceptance probability by movement of the associated graphic elements; and selecting a limited quantity of offers from said plurality of offers for distribution to said individual customer, wherein said limited quantity of offers are selected substantially in descending order of said measures of acceptance probabilities as adjusted in said enabling step. ;19-graphical display comprises a bar chart, said graphic elements comprise individual bars of said bar chart, and said movement comprises dragging said bars to lengthen and shorten them and thereby increase and decrease the associated measure of acceptance probability.”);
Crites , Hassine and Quatse are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites in view of Hassine, as taught by Quatse, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites in view of Hassine with the motivation of improve targeting precision while simplifying the declaration of targeting information (Quatse Par.16).
Regarding Claim 16, Crites in view Hassine in further view of Quatse teach The system of claim 15, …
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein each choice product success ratio of the plurality of choice product success ratios corresponds to one choice product of a plurality of choice products. (Crites Par. 120-121-“Choice Rate (Offer): the observed number of times a given offer has been purchased by the customer divided by the number of times it has been exposed/presented to the customer. Choice Rate is related to a given period of time and to a given customer segment. Example: two offers A, B may be proposed and the customer has a no-choice (“Loss”) alternative. “;Par. 163-“Realization Rate (Offer): the number of final invoiced sales recorded for a given offer divided by the number of orders recorded for that given offer. The Realization Rate may be inferior to 100% due to cancellations and modifications of orders. In the case of Contract Agreements it may also be superior to 100%, when actual sales/orders exceed initial expectations.”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 17, Crites in view of Hassine in further view of Quatse teach The system of claim 15,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein each choice product success ratio of the plurality of choice product success ratios is determined as a function of a first quantity of existing members of the choice product assigned to a plurality of regular receivers and a second quantity of existing members of the choice product assigned to a plurality of neutral receivers. (Hassine Par. 137-“ Exposure Rate (Offer, Offer Sequence, Offer Set): percentage of exposures of an offer during a given period of time for a given customer segment. It is equal to the ratio of the number of times the offer was proposed/presented to customers in a given segment, to the total number of offers proposed to customers in the segment.” Par. 120-121;Par. 163; Par. 240).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 18, Crites in view of Hassine in further view of Quatse teach
The system of claim 15, wherein each new member-receiver score of the plurality of new member-receiver scores is determined from product scores of the new member corresponding choice and neutral products of one receiver of the plurality of regular receivers and the plurality of neutral receivers. (Crites Par. 5-6-“ In general, in another aspect, the invention features a system including memory configured to store a list of combinations of offers that pass a first set of rules belonging to a first category of rules, where the offers in the list of combinations of offers have assigned scores associated with sending the offers to proposed contacts; and one or more processors configured to: select a first combination of offers from the list; and determine whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination violates one or more rules of the second set of rules, the one or more processors modify one or more of the offers of the first combination to generate a second combination of offers that complies with the second set of rules. If, however, the first combination passes the second set of rules, the one or more processors return the first combination as a solution.”).
Regarding Claim 19, Crites in view of Hassine in further view of Quatse teach The method of claim 15,…
Crites teaches offer analysis and the feature is expounded upon by Hassine:
wherein the member limit of the new member choice product is reached when every regular receiver of the plurality of regular receivers comprising the one new member choice product is full and when every neutral receiver of the plurality of neutral receivers comprising the one new member choice product is full. (Hassine Par. 32; Par. 116; Par. 301-304- “Real Availability Restriction”: in this case the offer cannot be proposed due to lack of corresponding resources (an example is an airline fare product that cannot be proposed to the customer when capacity of the flight is sold out).”).
Crites and Hassine are directed to customer offer analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Crites, as taught by Hassine, by utilizing additional metric analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Crites with the motivation of improvement of revenue management with consideration of consumer behavior (Hassine Par. 38).
Regarding Claim 20,
The system of claim 19, wherein information corresponding to the new member choice product reaching its member limit is provided to the new member module. (Crites Par. 27-“ The computer system 10 also includes automated campaign management software 30 that includes contact optimization software 32 that prioritizes offers sent to multiple contacts based on given criteria. The contact optimization software 32 provides a streamlined technique that in some scenarios, executes faster than purely linear programming solutions, and that can find an optimal solution if there are no cross-customer capacity constraints (e.g., limits on the number of customers per offer or number of emails/calls made in any time period) or a nearly optimal solution otherwise.”; Par. 35; Par. 82).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. 7155423 B1 to Josephson et al.- Abstract-“ The system has a seeker (100) acquiring candidates (102) by generating or retrieving the candidates along with their scores according to criteria. A filter (104) is used to locate the promising candidates, where the candidates are passed from the seeker to the filter. Filtered candidates (106) are passed from the filter to a viewer (108), where the viewer permits a user to view trade-offs in multiple scatterplots and selects the candidates interactively.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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