DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 20, 2026, has been entered.
Claims 1, 5, 11, and 19 are amended.
Claims 21-23 are added.
Claims 1, 4, 5, 7, 8, 10, 11, 13, 14, 16-19, and 21-23 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims recite improvement to machine learning. See Remarks p. 11. The Examiner respectfully disagrees. No apparent improvement to machine learning is recited in the claims. Using multiple machine learning algorithms does not impart a functional distinction on the claims, and no apparent improvement to machine learning is recited in the claims. It is understood that terms constituting algorithms are readily combinable, and due to the nature of mathematical algorithms, the use of multiple algorithms in combination is indistinguishable from a singular algorithm that achieves the same purpose. The Examiner has taken this position throughout prosecution. See Final Office Action 12/22/2025 p. 3. The use of a computer to communicate is conventional, and the recited graphical user interface is generic computer hardware.
The Applicant further contends that the present claims do not attempt to manage personal behavior because the claims do not involve a social activity or rules or instructions for a human being to follow. See Remarks p. 12. The Examiner respectfully disagrees. The present claims recite steps for identifying a targeted set of prospective clients that could be implemented mentally or on paper by a human being, but a general purpose computer employing machine learning is recited for implementation. Identifying leads or prospective clients is a social activity. Using the result of one predictive model as input to another predictive model does not provide an improvement in machine learning.
The Applicant further contends that the present claims solve a problem for standardizing financial data. See Remarks p. 13. The Examiner respectfully disagrees. The claims merely recite the idea of standardization, recited at a high level of generality that is insufficient for patentability: “convert the securities data and the household data into a standardized numerical format.” See exemplary independent claim 1. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See MPEP §2106.05(a). Merely reciting the idea of standardization does not provide a manner of achieving the outcome. Moreover, as indicated in the rejection, below, the step is conventional. Contrary to the Applicant’s assertions, the steps of the claims could be implemented mentally or on paper by a human being. However, a general purpose computer employing machine learning is recited in the claims.
The Applicant further contends that the graphical user interface renders the claims eligible. See Remarks p. 14. Again, the Examiner reiterates that the graphical user interface is generic computer hardware that does not provide a practical application or significantly more than the recited abstract idea. The results provided on the graphical user interface could be provided on paper by a human being conducting the recited method.
The Applicant further submits that the claims are subject matter eligible due to lack of conventionality. See Remarks p. 15. In response, the Examiner points out that lack of conventionality does not imply subject matter eligibility. Additional elements outside the scope of the abstract idea have been considered, but they have been found to amount to generic computer hardware operating in a machine learning environment. An abstract idea without significantly more is just that – an abstract idea.
The rejection for lack of subject matter eligibility is maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the independent claims, necessitating further search and consideration of the prior art. A new search returned the Huan reference. Independent claims 1, 11, and 19 now stand rejected as being obvious over Somech in view of Siebel, Martorano, Kumar, and Huang. The Applicant traverses the rejection, and contends that none of the references disclose receiving a client response. See Remarks p. 17. In response, the Examiner points to the rejection, below, which cites ¶[0081]-[0082], [0160]-[0161], and [0298]-[0301] of the Somech reference. Somech discloses selling financial products to clients after offering, which is evidence of a positive response indicative of interest. If a sale occurs, a buyer has accepted an offer, which is an indication of interest.
There are pinpoint citations to support the conclusion of obviousness for the remaining amended language recited in the independent claims. The combination of Huang, Somech and Siebel disclose the various metrics that are reported on the graphical user interface.
The Applicant further contends that the financial knowledge score recited in the independent claims is distinct from the teachings of Somech. See Remarks p. 18. In response, the Examiner points out that the financial knowledge score is defined in the claims as being: “based on a frequency of security trading for the household and a client selectable options trading approval.” See exemplary independent claim 1. Somech reads on this limitation by disclosing that client profiles are ranked based on the frequency of trades and the willingness to use exotic options. See Somech ¶[0089]-[0094].
The Applicant further contends that the references do not teach the use of multiple machine learning models, as claimed. See Remarks p. 18. In response, the Examiner points out that, due to the nature of machine learning algorithms, distinguishing one model from a separate model is a distinction without a difference. As mathematical constructs, algorithms can be easily combined and separated. One algorithm can contain many algorithms as separate terms. Siebel reads on the use of multiple machine learning models by teaching: the use of multiple algorithms in at least ¶[0116].
The Applicant further submits that Siebel’s prediction based on predictive parameters is not based on the price of a security and demand rate. See Remarks p. 19. In response, the Examiner points out that price and demand are predictive parameters.
The Applicant additionally alleges that the profit determination taught by Somech does not include a determination of revenue. See Remarks p. 19. In response, the Examiner points out that profit is calculated using revenue. The average deal size taught by Somech also appears to suggest revenue.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1, 4, 5, 7, 8, 10, 11, 13, 14, 16-19, and 21-23 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1, 4, 5, 7, 8, 10, 11, 13, 14, 16-19, and 21-23 are all directed to one of the four statutory categories of invention, the claims are directed to identifying a targeted set of prospect clients (as evidenced by the preamble of exemplary independent claim 1), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: [1] “receive a dataset having securities data and household data;” [2] “convert the securities data and the household data into a standardized numerical format;” [3] “generate . . . a financial knowledge score for a predicted level of household knowledge;” [4] “determine . . . a model score for each household;” [5] “filter the households based on the model score;” [6] “determine a prioritization score for each remaining household;” [7] “prioritize the remaining households based on the prioritization score;” [8] “display . . . a list of the prioritized remaining households;” [9] “generate and transmit an automated electronic communication;” [10] “receive a client response;” and [11] “generate and display . . . a date . . . securities owned by the client . . . and a selectable input.” Step [1] and [3]-[11] are steps for managing personal behavior related to the abstract idea of identifying a targeted set of prospect clients that, when considered alone and in combination, are part of the abstract idea of identifying a targeted set of prospect clients. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of identifying a targeted set of prospect clients. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes identifying and prioritizing leads to purchase hard to sell securities.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a memory, processor, and user interface in independent claim 1; a user interface in independent claim 11; and a computer readable medium with instructions, and a user interface in independent claim 19). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, but the abstract idea of identifying a targeted set of prospect clients is generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than an abstract idea. See MPEP §2106.05(h). Step [2], identified in the paragraph, above, merely recites a step for converting data into a standardized format that amounts to insignificant extrasoution activity. The step is a well-known step that is tangentially related to the invention, and the step is necessary and/or preferred in data aggregation. See MPEP §2106.05(g). The claims require no more than a generic computer (a memory, processor, communication, bus, and user interface/display in independent claim 1; a user interface/display in independent claim 11; and a computer readable medium with instructions, and a user interface/display in independent claim 19) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Furthermore: an element that is found to amount to insignificant extrasolution activity in step 2A of the subject matter eligibility analysis must be evaluated in step 2B to determine whether the step amounts to more than what is well-understood, routine, and conventional. Converting data into a standardized format is well-understood, routine, and conventional; as evidenced by newly cited ¶[0078]-[0081] of US 20180060965 A1 to Kumar. Therefore, that step does not provide significantly more than the recited abstract idea. The claims are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, 4, 5 , 8, 10, 11, 13, 17-19, and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20090292649 A1 to Somech et al. (hereinafter ‘SOMECH’), US 20150058196 A1 to Martorano et al. (hereinafter ‘MARTORANO’), and US 20220405775 A1 to Seibel et al. (hereinafter ‘SIEBEL’), US 20180060965 A1 to Kumar (hereinafter ‘KUMAR’), and US 20200004887 A1 to Huang et al. (hereinafter ‘HUANG’).
Claim 1 (Currently Amended)
SOMECH discloses a system for automatically initiating communications with a targeted set of prospect clients from a pool of existing clients (see ¶[0298]; rank and follow up on leads), the system comprising: a memory storing computer readable instructions (see abstract; a memory with instructions); a display (see ¶[0314]; I/O devices, including displays); and processing circuitry in communication with the memory and the display via at least one communication bus (see ¶[0313]; a processor coupled to memory elements through a system bus), the processing circuitry configured to execute the computer readable instructions (see again abstract; a processor) to cause the system to,
receive a dataset having securities data (see ¶[0074]; define a financial instrument and generate reports about financial instruments) and household data (see ¶[0081] and [0187]; a profile about a client. An e-mail address and a postal address).
SOMECH does not specifically disclose, but MARTORANO discloses, the securities data including hard-to-borrow (HTB) securities (see abstract and ¶[0008]-[0009]; securities for short sale orders that are difficult to fill are deemed “hard to borrow)”
SOMECH does not specifically disclose, but SIEBEL discloses, a demand rate for each HTB security of the HTB securities (see ¶[0009]; calculating a CRM metric associated with at least one of: customer satisfaction, customer churn, customer retention, demand forecasting, and product forecasting),
SOMECH further discloses the household data including attributes specific to households owning one or more HTB securities (see again ¶[0081]-[0082]] and [0187]; a profile about a client, including past executed transactions. An e-mail address and a postal address. See also ¶[0113]-[0114], [0214], [0228], [0289-[0292]; purchase history. Understand the relationship between offerings and know which purchase may lead to another one), and each household including at least one client associated with at least one existing account owning at least one HTB security of the HTB securities (see again ¶[0117]-[0118]; generating predictive data corresponding to the client, for example, any suitable "pattern" data representing a behavioral pattern of the client with respect to FI transactions, e.g., a percent of first offers accepted by the client, a percent of offers declined by the client, and the like),
SOMECH does not specifically disclose, but KUMAR discloses, convert the securities data and the household data into a standardized numerical format (see ¶[0078]-[0081]; capture investor and financial account information and convert it into a standard format).
SOMECH does not specifically disclose, but SIEBEL discloses, generate, with a first machine learning model (see abstract; artificial intelligence and model-driven software architecture), a financial knowledge score for a predicted level of household financial knowledge for each household (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171)
SOMECH discloses based on a frequency of security trading for the household and a client selectable options trading approval (see ¶[0089]-[0094]; client profile may include currencies the client is willing to use exotic options and assets the client may only use vanilla options; and the frequency the client trades).
SOMECH does not specifically disclose, but SIEBEL discloses, determine, with a second machine learning model different than the first machine learning model (see abstract; artificial intelligence and model-driven software architecture. See also ¶[0116]; a machine learning model with one or more algorithms.),
SOMECH further discloses a model score for each household of the households based on the standardized securities data, the standardized household data, and the financial knowledge score for the household generated by the first machine learning model, the model score representing a likelihood of the household participating in a securities lending program (see ¶[0256] and [0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter. Query the clients who are most likely to conduct the defined trade), and
filter the households based on the model score for each household and a threshold score to generate remaining households (see ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score),
SOMECH does not specifically disclose, but SIEBEL discloses, determine a prioritization score for each remaining household of the remaining households based on a price of the at least one HTB security, a household quantity of the at least one HTB security, and the demand rate of the at least one HTB security (see ¶[0020], [0024], [0259]-[0260], [0303]-[0304], [0309], and [0437] and Table 3 and Fig. 13; calculate a lead score based on a probability to buy. Features include buying patterns, transaction history, quantity, and price. Volume of shares being traded. Market cap. See also ¶[0009] and [0425]-[0431[; demand forecasting and lead prioritization to assess lead quality and determine predispositions to buy. The lead score is based on a likelihood of a particular customer obtaining a product based on historical sales records),
SOMECH further discloses the prioritization score for each remaining household is an estimated household revenue potential per month if the household participates in the securities lending program (see ¶[0275]-[0286]; rank the clients based on expected profits based on exposures/targets and investment goals. Examiner Note: a difference in time units (year v. months) does not provide a functional, patentable distinction),
prioritize the remaining households based on the prioritization score for each remaining household of the remaining households (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter), and
automatically generate and display a first graphical user interface on the display (see ¶[0168]-[0169]; generate reports to allow a sales person to access information in the financial instrument systems), the first graphical user interface including a list of the prioritized remaining households having prospect clients from the pool of existing clients (see again ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score) and selectable inputs to ascertain account information about the prioritized remaining households (see ¶[0241] and [0265]; provide the salesperson with a suitable report or list of portfolios having the upcoming event. A report of offered trades to be generated),
in response to the generated graphical user interface, automatically generate and transmit an automated electronic communication to a client associated with an existing client account in the household having the highest prioritization score (see ¶[0020], [0163], [0173]. and [0275]; determine one or more client-specific parameters of a trade to be offered based on the client profile. Identify one or more potential clients to be offered a trade. Rank clients and second the client any suitable customized or formatted trade idea); and
receive a client response of the automated electronic communication from the client, the client response indicating an interest in participating in the securities lending program (see ¶[0081]-[0082], [0160]-[0161], and [0298]-[0301]; after offering, providing and/or selling the financial product to the client. Sell the financial product to the client. The salespeople can increase their close ratio on leads and close more deals).
SOMECH does not specifically disclose, but HUANG discloses, in response to receiving the client response (see ¶[0037]; keep track of which individuals and accounts are of interest with respect to selling a particular product or service), automatically generate and display a second graphical user interface on the display, the second graphical user interface including a date of the most recent initiated communication with the client (see ¶[0037]; contact record for an individual in transaction management system 240 may include a first name, last name, job, an email address, a date of first contact with the individual, a date of most recent contact with the individual. See also ¶[0026]; a user interface).
SOMECH further discloses specific HTB securities owned by the client (see abstract and ¶[0144] & [0291]; receive portfolio data corresponding to financial instrument portfolios associated with a plurality of clients. A list of clients who have accepted more than 30% of offers. The client may specify that he would like to be updated at any time a new asset is supported for trading).
SOMECH does not specifically disclose, but SIEBEL discloses, and a selectable input to update account information for the client (see ¶[0036], [0371] and [0410]-[0411]; outputs include updated data. Controls may optionally be provided for updating the current stage of the opportunity and for editing the information about the opportunity. See also ¶[0090], [0111] & [0116]-[0117]; The AI-based techniques can use various machine learning approaches to supplement manually-input traditional CRM data with a wide variety of additional enterprise data sources. User input through a keyboard).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. MARTORANO discloses securities for orders that are difficult to fill that are deemed “hard to borrow.” It would have been obvious for one of ordinary skill in the art at the time of invention to include the hard to borrow securities as taught by MARTORANO in the system executing the method of SOMECH with the motivation to follow up on leads for difficult to fill orders.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using CRM metrics including client data and profile data, and updates profile data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of leads with artificial intelligence and updating of profiles as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads using profile (see ¶[0007]) and account information (see ¶[0297]). KUMAR discloses account automation and integration that includes converting profile and account data into a standard format. It would have been obvious to include the standard format as taught by KUMAR in the system executing the method of SOMECH with the motivation to use a well-known format.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. HUANG discloses identifying potential customers or leads that includes records of the data of most recent contact with an individual. It would have been obvious for one of ordinary skill in the art at the time of invention to include the data of most recent contact with an individual as taught by HUANG in the system executing the method of SOMECH with the motivation to follow up on leads.
Claim 4 (Previously Presented)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 1.
SOMECH does not specifically disclose, but MARTORANO discloses, wherein the existing client account in the household having the highest prioritization score is a first existing client account, wherein the household having the highest prioritization score includes a second existing client account of the pool of existing accounts and the client (see ¶[0008]; for thinly traded securities and other securities that are deemed "hard to borrow" short sale orders may be difficult to fill given a certain net position for that security across an entity. This is especially true in cases where multiple traders are acting independently but trading through and on behalf of a single entity that needs to meet certain regulations).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. MARTORANO discloses securities for orders that are difficult to fill that are deemed “hard to borrow;” where the securities are more difficult to borrow when multiple traders are acting independently but trading on behalf of the same entity. It would have been obvious for one of ordinary skill in the art at the time of invention to include the hard to borrow securities as taught by MARTORANO in the system executing the method of SOMECH with the motivation to follow up on leads for difficult to fill orders.
Claim 5 (Currently Amended)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 1.
SOMECH further discloses wherein the system is further caused to generate a report with the client response of the automated electronic communication (see ¶[0158]-[0159] and [0168]; a performance analysis report per client. Perform suitable sales data and reporting operations.).
Claim 8 (Original)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 1.
SOMECH does not specifically disclose, but SIEBEL discloses, wherein the machine learning model includes a decision tree machine learning algorithm (see ¶[0120]; a tree model may identify which feature makes a primary contribution to a model).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using models that include tree models. It would have been obvious for one of ordinary skill in the art at the time of invention to include the tree model as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 10 (Previously Presented)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 1.
SOMECH does not specifically disclose, but SIEBEL discloses, wherein the system is further caused to train the first machine learning model based on the received dataset (see ¶[0115]; the model orchestrator function 242 may also be used to train or retrain (if needed) one or more machine learning models used by one or more of the functions 226-238, such as through the use of historical data).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using machine learning models that are trained with historical data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of leads with artificial intelligence trained using historical data as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 11 (Currently Amended)
SOMECH discloses a method for automatically initiating communications with a targeted set of prospect clients from a pool of existing clients (see ¶[0298]; rank and follow up on leads).
SOMECH does not specifically disclose, but SIEBEL discloses, the method comprising: receiving, at a first machine learning model (see abstract; artificial intelligence and model-driven software architecture).
SOMECH further discloses a dataset having securities data (see ¶[0074]; define a financial instrument and generate reports about financial instruments), and household data (see ¶[0081] and [0187]; a profile about a client. An e-mail address and a postal address),
SOMECH does not specifically disclose, but MARTORANO discloses, the securities data including hard-to-borrow (HTB) securities (see abstract and ¶[0008]-[0009]; securities for short sale orders that are difficult to fill are deemed “hard to borrow.”
SOMECH does not specifically disclose, but SIEBEL discloses, a demand rate for each HTB security of the HTB securities (see ¶[0009]; calculating a CRM metric associated with at least one of: customer satisfaction, customer churn, customer retention, demand forecasting, and product forecasting),
SOMECH further discloses the household data including attributes specific to households owning one or more HTB securities (see again ¶[0081]-[0082]] and [0187]; a profile about a client, including past executed transactions. An e-mail address and a postal address. See also ¶[0113]-[0114], [0214], [0228], [0289-[0292]; purchase history. Understand the relationship between offerings and know which purchase may lead to another one), and each household including at least one client associated with at least one existing account owning at least one HTB security of the HTB securities (see again ¶[0117]-[0118]; generating predictive data corresponding to the client, for example, any suitable "pattern" data representing a behavioral pattern of the client with respect to FI transactions, e.g., a percent of first offers accepted by the client, a percent of offers declined by the client, and the like),
SOMECH does not specifically disclose, but KUMAR discloses, converting the securities data and the household data into a standardized numerical format (see ¶[0078]-[0081]; capture investor and financial account information and convert it into a standard format).
SOMECH does not specifically disclose, but SIEBEL discloses, generating, with a second machine learning model different than the first machine learning model (see abstract; artificial intelligence and model-driven software architecture. See also ¶[0116]; a machine learning model with one or more algorithms.), a financial knowledge score for a predicted level of household financial knowledge for each household generated by the second machine learning model (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171).
SOMECH discloses based on a frequency of security trading for the household and a client selectable options trading approval (see ¶[0089]-[0094]; client profile may include currencies the client is willing to use exotic options and assets the client may only use vanilla options; and the frequency the client trades).
SOMECH does not specifically disclose, but SIEBEL discloses, determining, with the first machine learning model (see abstract; artificial intelligence and model-driven software architecture).
SOMECH further discloses a model score for each household of the households based on the standardized securities data, the standardized household data, and the financial knowledge score for the household, the model score representing a likelihood of the household participating in a securities lending program, and the financial knowledge score for the household, the model score representing a likelihood of the household participating in a securities lending program [sic] (see ¶[0256] and [0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter. Query the clients who are most likely to conduct the defined trade), and
filtering the households based on the model score for each household and a threshold score to generate remaining households (see ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score),
SOMECH does not specifically disclose, but SIEBEL discloses, determining a prioritization score for each remaining household of the remaining households based on a price of the at least one HTB security, a household quantity of the at least one HTB security, and the demand rate of the at least one HTB security (see ¶[0020], [0024], [0259]-[0260], [0303]-[0304], [0309], and [0437] and Table 3 and Fig. 13; calculate a lead score based on a probability to buy. Features include buying patterns, transaction history, quantity, and price. Volume of shares being traded. Market cap. See also ¶[0009] and [0425]-[0431[; demand forecasting and lead prioritization to assess lead quality and determine predispositions to buy. The lead score is based on a likelihood of a particular customer obtaining a product based on historical sales records).
SOMECH further discloses the prioritization score for each remaining household is an estimated household revenue potential per month if the household participates in the securities lending program (see ¶[0275]-[0286]; rank the clients based on expected profits based on exposures/targets and investment goals. Examiner Note: a difference in time units (year v. months) does not provide a functional, patentable distinction),
SOMECH further discloses, prioritizing the remaining households based on the prioritization score for each remaining household of the remaining households (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter), and
automatically generating and displaying a first graphical user interface on a display (see ¶[0168]-[0169]; generate reports to allow a sales person to access information in the financial instrument systems), the first graphical user interface including a list of the prioritized remaining households having prospect clients from the pool of existing clients (see again ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score) and selectable inputs to ascertain account information about the prioritized remaining households (see ¶[0241] and [0265]; provide the salesperson with a suitable report or list of portfolios having the upcoming event. A report of offered trades to be generated),
in response to the generated graphical user interface, automatically generating and transmitting an automated electronic communication to a client associated with an existing client account in the household having the highest prioritization score (see ¶[0020], [0163], [0173]. and [0275]; determine one or more client-specific parameters of a trade to be offered based on the client profile. Identify one or more potential clients to be offered a trade. Rank clients and second the client any suitable customized or formatted trade idea),
receiving a client response of the automated electronic communication from the client, the client response indicating an interest in participating in the securities lending program (see ¶[0081]-[0082], [0160]-[0161], and [0298]-[0301]; after offering, providing and/or selling the financial product to the client. Sell the financial product to the client. The salespeople can increase their close ratio on leads and close more deals).
SOMECH does not specifically disclose, but HUANG discloses, in response to receiving the client response (see ¶[0037]; keep track of which individuals and accounts are of interest with respect to selling a particular product or service), automatically generate and display a second graphical user interface on the display, the second graphical user interface including a date of the most recent initiated communication with the client (see ¶[0037]; contact record for an individual in transaction management system 240 may include a first name, last name, job, an email address, a date of first contact with the individual, a date of most recent contact with the individual. See also ¶[0026]; a user interface).
SOMECH further discloses, specific HTB securities owned by the client (see abstract and ¶[0144] & [0291]; receive portfolio data corresponding to financial instrument portfolios associated with a plurality of clients. A list of clients who have accepted more than 30% of offers. The client may specify that he would like to be updated at any time a new asset is supported for trading).
SOMECH does not specifically disclose, but SIEBEL discloses, and a selectable input to update account information for the client (see ¶[0036], [0371] and [0410]-[0411]; outputs include updated data. Controls may optionally be provided for updating the current stage of the opportunity and for editing the information about the opportunity. See also ¶[0090], [0111] & [0116]-[0117]; The AI-based techniques can use various machine learning approaches to supplement manually-input traditional CRM data with a wide variety of additional enterprise data sources. User input through a keyboard).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. MARTORANO discloses securities for orders that are difficult to fill that are deemed “hard to borrow.” It would have been obvious for one of ordinary skill in the art at the time of invention to include the hard to borrow securities as taught by MARTORANO in the system executing the method of SOMECH with the motivation to follow up on leads for difficult to fill orders.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using CRM metrics including client data and profile data, and updates profile data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of leads with artificial intelligence and updating of profiles as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads using profile (see ¶[0007]) and account information (see ¶[0297]). KUMAR discloses account automation and integration that includes converting profile and account data into a standard format. It would have been obvious to include the standard format as taught by KUMAR in the system executing the method of SOMECH with the motivation to use a well-known format.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. HUANG discloses identifying potential customers or leads that includes records of the data of most recent contact with an individual. It would have been obvious for one of ordinary skill in the art at the time of invention to include the data of most recent contact with an individual as taught by HUANG in the system executing the method of SOMECH with the motivation to follow up on leads.
Claim 13 (Previously Presented)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 11.
SOMECH does not specifically disclose, but MARTORANO discloses, wherein the existing client account in the household having the highest prioritization score is a first existing client account, wherein the household having the highest prioritization score includes a second existing client account of the pool of existing accounts and the client (see ¶[0008]; for thinly traded securities and other securities that are deemed "hard to borrow" short sale orders may be difficult to fill given a certain net position for that security across an entity. This is especially true in cases where multiple traders are acting independently but trading through and on behalf of a single entity that needs to meet certain regulations).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. MARTORANO discloses securities for orders that are difficult to fill that are deemed “hard to borrow;” where the securities are more difficult to borrow when multiple traders are acting independently but trading on behalf of the same entity. It would have been obvious for one of ordinary skill in the art at the time of invention to include the hard to borrow securities as taught by MARTORANO in the system executing the method of SOMECH with the motivation to follow up on leads for difficult to fill orders.
Claim 14 (Previously Presented)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 11.
SOMECH further discloses further comprising generating a report with a client response of the automated electronic communication (see ¶[0158]-[0159] and [0168]; a performance analysis report per client. Perform suitable sales data and reporting operations.).
Claim 17 (Original)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 11.
SOMECH does not specifically disclose, but SIEBEL discloses, wherein the machine learning model includes a decision tree machine learning algorithm (see ¶[0120]; a tree model may identify which feature makes a primary contribution to a model).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using models that include tree models. It would have been obvious for one of ordinary skill in the art at the time of invention to include the tree model as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 18 (Previously Presented)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 11.
SOMECH does not specifically disclose, but SIEBEL discloses, further comprising training the first machine learning model based on the received dataset (see ¶[0115]; the model orchestrator function 242 may also be used to train or retrain (if needed) one or more machine learning models used by one or more of the functions 226-238, such as through the use of historical data).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using machine learning models that are trained with historical data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of leads with artificial intelligence trained using historical data as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 19 (Currently Amended)
SOMECH discloses a non-transitory computer readable medium storing computer readable instructions (see abstract ¶[0041]; an information storage medium and instructions), which when executed by processing circuitry (see again abstract; a processor), causes a system including the processing circuitry to:
receive a dataset having securities data (see ¶[0074]; define a financial instrument and generate reports about financial instruments), and household data (see ¶[0081] and [0187]; a profile about a client. An e-mail address and a postal address),
SOMECH does not specifically disclose, but MARTORANO discloses, the securities data including hard-to-borrow (HTB) securities (see abstract and ¶[0008]-[0009]; securities for short sale orders that are difficult to fill are deemed “hard to borrow.”
SOMECH does not specifically disclose, but SIEBEL discloses, a demand rate for each HTB security of the HTB securities (see ¶[0009]; calculating a CRM metric associated with at least one of: customer satisfaction, customer churn, customer retention, demand forecasting, and product forecasting),
SOMECH further discloses, the household data including attributes specific to households owning one or more HTB securities (see again ¶[0081]-[0082]] and [0187]; a profile about a client, including past executed transactions. An e-mail address and a postal address. See also ¶[0113]-[0114], [0214], [0228], [0289-[0292]; purchase history. Understand the relationship between offerings and know which purchase may lead to another one), and each household including at least one client associated with at least one existing account owning at least one HTB security of the HTB securities (see again ¶[0117]-[0118]; generating predictive data corresponding to the client, for example, any suitable "pattern" data representing a behavioral pattern of the client with respect to FI transactions, e.g., a percent of first offers accepted by the client, a percent of offers declined by the client, and the like),
SOMECH does not specifically disclose, but KUMAR discloses, convert the securities data and the household data into a standardized numerical format (see ¶[0078]-[0081]; capture investor and financial account information and convert it into a standard format).
SOMECH does not specifically disclose, but SIEBEL discloses, generate, with a first machine learning model (see abstract; artificial intelligence and model-driven software architecture), a financial knowledge score for a predicted level of household financial knowledge for each household (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171)
SOMECH discloses based on a frequency of security trading for the household and a client selectable options trading approval (see ¶[0089]-[0094]; client profile may include currencies the client is willing to use exotic options and assets the client may only use vanilla options; and the frequency the client trades).
SOMECH does not specifically disclose, but SIEBEL discloses, determine, with a second machine learning model different than the first machine learning model (see abstract; artificial intelligence and model-driven software architecture. See also ¶[0116]; a machine learning model with one or more algorithms.), a model score for each household of the households based on the standardized securities data, the standardized securities data, the standardized household data, and the financial knowledge score for the household generated by the first machine learning model, the model score representing a likelihood of the household participating in a securities lending program, and the financial knowledge score for the household, the model score representing a likelihood of the household participating in a securities lending program (see ¶[0256] and [0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter. Query the clients who are most likely to conduct the defined trade), and
filter the households based on the model score for each household and a threshold score to generate remaining households (see ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score),
SOMECH does not specifically disclose, but SIEBEL discloses, determine a prioritization score for each remaining household of the remaining households based on a price of the at least one HTB security, a household quantity of the at least one HTB security, and the demand rate of the at least one HTB security (see ¶[0020], [0024], [0259]-[0260], [0303]-[0304], [0309], and [0437] and Table 3 and Fig. 13; calculate a lead score based on a probability to buy. Features include buying patterns, transaction history, quantity, and price. Volume of shares being traded. Market cap. See also ¶[0009] and [0425]-[0431[; demand forecasting and lead prioritization to assess lead quality and determine predispositions to buy. The lead score is based on a likelihood of a particular customer obtaining a product based on historical sales records),
SOMECH further discloses the prioritization score for each remaining household is an estimated household revenue potential per month if the household participates in the securities lending program (see ¶[0275]-[0286]; rank the clients based on expected profits based on exposures/targets and investment goals. Examiner Note: a difference in time units (year v. months) does not provide a functional, patentable distinction),
prioritize the remaining households based on the prioritization score for each remaining household of the remaining households (see ¶[0275]-[0286] and Fig. 1; rank the clients, for example, according to one or more of the following parameters, which may be included, for example, as part of client data 171 (FIG. 1) and/or client profile 173 (FIG. 1): expected profits for next year based on holdings and exposures/targets; counterparty exposure to the bank; past year overall profitability (including trading-desk and sales desk impacts); client's investment goals; client's risk-tolerance profile; client's exposures and hedging policy per asset type; past year sales to the bank; past year margins charged; average deal size; number of deals; and/or any other suitable parameter),
automatically generate and display a first graphical user interface on a display (see ¶[0168]-[0169]; generate reports to allow a sales person to access information in the financial instrument systems), the first graphical user interface including a list of the prioritized remaining households (see again ¶[0298]; Salespeople need only follow up on leads that meet a predefined threshold score), and selectable inputs to ascertain account information about the prioritized remaining households (see ¶[0241] and [0265]; provide the salesperson with a suitable report or list of portfolios having the upcoming event. A report of offered trades to be generated),
in response to the generated graphical user interface, automatically generate and transmit an automated electronic communication to a client associated with an existing client account in the household having the highest prioritization score (see ¶[0020], [0163], [0173]. and [0275]; determine one or more client-specific parameters of a trade to be offered based on the client profile. Identify one or more potential clients to be offered a trade. Rank clients and second the client any suitable customized or formatted trade idea); and
receive a client response of the automated electronic communication from the client, the client response indicating an interest in participating in the securities lending program (see ¶[0081]-[0082], [0160]-[0161], and [0298]-[0301]; after offering, providing and/or selling the financial product to the client. Sell the financial product to the client. The salespeople can increase their close ratio on leads and close more deals).
SOMECH does not specifically disclose, but HUANG discloses, in response to receiving the client response (see ¶[0037]; keep track of which individuals and accounts are of interest with respect to selling a particular product or service), automatically generate and display a second graphical user interface on the display, the second graphical user interface including a date of the most recent initiated communication with the client (see ¶[0037]; contact record for an individual in transaction management system 240 may include a first name, last name, job, an email address, a date of first contact with the individual, a date of most recent contact with the individual. See also ¶[0026]; a user interface).
SOMECH further discloses, specific HTB securities owned by the client (see abstract and ¶[0144] & [0291]; receive portfolio data corresponding to financial instrument portfolios associated with a plurality of clients. A list of clients who have accepted more than 30% of offers. The client may specify that he would like to be updated at any time a new asset is supported for trading).
SOMECH does not specifically disclose, but SIEBEL discloses, and a selectable input to update account information for the client (see ¶[0036], [0371] and [0410]-[0411]; outputs include updated data. Controls may optionally be provided for updating the current stage of the opportunity and for editing the information about the opportunity. See also ¶[0090], [0111] & [0116]-[0117]; The AI-based techniques can use various machine learning approaches to supplement manually-input traditional CRM data with a wide variety of additional enterprise data sources. User input through a keyboard).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. MARTORANO discloses securities for orders that are difficult to fill that are deemed “hard to borrow.” It would have been obvious for one of ordinary skill in the art at the time of invention to include the hard to borrow securities as taught by MARTORANO in the system executing the method of SOMECH with the motivation to follow up on leads for difficult to fill orders.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that prioritizes leads using CRM metrics including client data and profile data, and updates profile data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of leads with artificial intelligence and updating of profiles as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads using profile (see ¶[0007]) and account information (see ¶[0297]). KUMAR discloses account automation and integration that includes converting profile and account data into a standard format. It would have been obvious to include the standard format as taught by KUMAR in the system executing the method of SOMECH with the motivation to use a well-known format.
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. HUANG discloses identifying potential customers or leads that includes records of the data of most recent contact with an individual. It would have been obvious for one of ordinary skill in the art at the time of invention to include the data of most recent contact with an individual as taught by HUANG in the system executing the method of SOMECH with the motivation to follow up on leads.
Claim 21 (New)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the non-transitory computer readable medium as set forth in claim 19.
SOMECH does not specifically disclose, but SIEBEL discloses, wherein the system is further caused to train the first machine learning model and the second machine learning model based on the client response indicating an interest in participating in the securities lending program (see ¶[0192]; training one or more machine learning models that can handle any user-specified close date(s). In some embodiments, this may be handled as follows. Let Y represent an outcome of an opportunity, where Y equals one if the opportunity is won and Y equals zero otherwise. Let X represent the number of days until the opportunity concludes (as either a win or a loss). Let q represent the query date given by a user or the number of days until the end of a quarter or other time period. For any value of q, the OS machine learning model 304 can output the joint probability p(X<q, Y=1). To model this, the joint probability can be broken out using the rules of conditional probability, which can be expressed as p(X<q, Y=1)=p(X<q|Y=1)×p(Y=1). In some cases, this can be expressed as p(X<q, Y=1|θ)=p(X<q|θ, Y=1)×p(Y=1|θ), where θ represents one or more features that capture the current state of an opportunity, such as stage age (the length of time that the opportunity has been in its current transaction stage), number of positive-sentiment emails or other communications obtained during a time period (such as within the past month).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that trains a model that evaluates leads based on positive sentiment emails. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training based on positive sentiment emails as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 22 (New)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 11.
SOMECH does not specifically disclose, but SIEBEL discloses, further comprising training the first machine learning model and the second machine learning model based on the client response indicating an interest in participating in the securities lending program (see ¶[0192]; training one or more machine learning models that can handle any user-specified close date(s). In some embodiments, this may be handled as follows. Let Y represent an outcome of an opportunity, where Y equals one if the opportunity is won and Y equals zero otherwise. Let X represent the number of days until the opportunity concludes (as either a win or a loss). Let q represent the query date given by a user or the number of days until the end of a quarter or other time period. For any value of q, the OS machine learning model 304 can output the joint probability p(X<q, Y=1). To model this, the joint probability can be broken out using the rules of conditional probability, which can be expressed as p(X<q, Y=1)=p(X<q|Y=1)×p(Y=1). In some cases, this can be expressed as p(X<q, Y=1|θ)=p(X<q|θ, Y=1)×p(Y=1|θ), where θ represents one or more features that capture the current state of an opportunity, such as stage age (the length of time that the opportunity has been in its current transaction stage), number of positive-sentiment emails or other communications obtained during a time period (such as within the past month).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that trains a model that evaluates leads based on positive sentiment emails. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training based on positive sentiment emails as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 23 (New)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 1.
SOMECH does not specifically disclose, but SIEBEL discloses, wherein the system is further caused to train the first machine learning model and the second machine learning model based on the client response indicating an interest in participating in the securities lending program (see ¶[0192]; training one or more machine learning models that can handle any user-specified close date(s). In some embodiments, this may be handled as follows. Let Y represent an outcome of an opportunity, where Y equals one if the opportunity is won and Y equals zero otherwise. Let X represent the number of days until the opportunity concludes (as either a win or a loss). Let q represent the query date given by a user or the number of days until the end of a quarter or other time period. For any value of q, the OS machine learning model 304 can output the joint probability p(X<q, Y=1). To model this, the joint probability can be broken out using the rules of conditional probability, which can be expressed as p(X<q, Y=1)=p(X<q|Y=1)×p(Y=1). In some cases, this can be expressed as p(X<q, Y=1|θ)=p(X<q|θ, Y=1)×p(Y=1|θ), where θ represents one or more features that capture the current state of an opportunity, such as stage age (the length of time that the opportunity has been in its current transaction stage), number of positive-sentiment emails or other communications obtained during a time period (such as within the past month).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. SIEBEL discloses an artificial intelligence-based customer relationship management system using model-driven software that trains a model that evaluates leads based on positive sentiment emails. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training based on positive sentiment emails as taught by SIEBEL in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20090292649 A1 to SOMECH et al., US 20150058196 A1 to MARTORANO et al., US 20220405775 A1 to SEIBEL et al., US 20180060965 A1 to KUMAR, and US 20200004887 A1 to HUANG et al. as applied to claims 1 and 5 above, and further in view of US 10242068 B1 to Ross et al. (hereinafter ‘ROSS’).
Claim 7 (Original)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the system as set forth in claim 5.
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG does not specifically disclose, but ROSS discloses, wherein the system is further caused to filter the households based on at least one of existing client input, a total number of assets, and the client response (see claims 1 and 4; filtering, by the first processor of the computer, the lead information to obtain a set of filtered lead information comprising only a subset of leads containing the attribute; assigning, by the first processor of the computer, a score to each attribute associated with each lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria, wherein the attributes comprise liquid assets).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. ROSS discloses filtering leads based on attributes that include liquid assets. It would have been obvious for one of ordinary skill in the art at the time of invention to include filtering based on liquid assets as taught by ROSS in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Claim 16 (Original)
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG discloses the method as set forth in claim 14.
The combination of SOMECH, MARTORANO, SIEBEL, KUMAR, and HUANG does not specifically disclose, but ROSS discloses, wherein filtering the households includes filtering the households based on at least one of existing client input, a total number of assets, and the client response (see claims 1 and 4; filtering, by the first processor of the computer, the lead information to obtain a set of filtered lead information comprising only a subset of leads containing the attribute; assigning, by the first processor of the computer, a score to each attribute associated with each lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria, wherein the attributes comprise liquid assets).
SOMECH discloses financial instrument management that includes a sales force selling securities that ranks clients to follow up on leads. ROSS discloses filtering leads based on attributes that include liquid assets. It would have been obvious for one of ordinary skill in the art at the time of invention to include filtering based on liquid assets as taught by ROSS in the system executing the method of SOMECH with the motivation to rank clients to follow up on leads.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
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, Patricia Munson can be reached at 571-270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
Prosecution Insights