DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
Claims 1-20 are pending.
Applicant provided information disclosure statement.
Allowable Subject Matter
Claims 2, 9, and 16 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Saraf Merritt (US10542148B1) in further view of Sheridan (US20090132662A1) in further view of Chandra (US20210042800A1) in further view of Muncy (US20120179476A1) who teaches auto-lead data format. However, with respect to exemplary claim 2, 9, and 16, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 2, 9, and 16.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-20 recite a Judicial Exception. Exemplary independent claim 15 and similarly claims 1 and 8 recite the limitations of
…training…model using customer data, sales agent data, and completed transaction data, to predict a likelihood of a successful transaction between: a customer associated with a customer profile, and a sales agent associated with a sales agent profile, wherein the likelihood is predicted based on comparing the customer profile to the sales agent profile; receiving…a first customer profile corresponding to a first customer, wherein the first customer profile comprises confidential information and nonconfidential information; receiving…a plurality of sales agent profiles each corresponding to a respective sales agent of a plurality of sales agents; receiving, for each of the plurality of sales agent profiles, one or more previously completed transactions made by the corresponding sales agent; determining, based on the confidential information by inputting the first customer profile and the plurality of sales agent profile into the…model, a first sales agent, of the plurality of sales agents, that has a high likelihood of making a successful transaction with the first customer, wherein the first sales agent is determined based on a similarity between the first customer profile and the one or more previously completed transactions made by the first sales agent; generating an excerpt of the first customer profile omitting the confidential information; sending…the excerpt, of the first customer profile, and an identification of the first sales agent; receiving…feedback indicating whether the first sales agent made the successful transaction with the first customer; and storing…a mapping between the first customer profile and a first sales agent profile corresponding to the first sales agent; and adjusting…the confidential information in the first customer profile, and the mapping, the…model.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of training, predicting, comparing, receiving, determining, inputting, generating, sending, storing, and adjusting data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses simply training a model, predicting a likelihood of a transaction between a customer and agent that includes comparing their profiles, receiving customer and agent data, inputting the customer and agent data in a model to determine which agent has a high likelihood of making a successful transaction, generating an excerpt, sending the excerpt, receiving feedback regarding if the transaction was successful, storing the customer and agent mapping, and adjusting the model based on the feedback. These steps are mere data manipulation step that do not require a computer. For example, a company manager can receive and analyze customer and agent data by inputting the data into a model to determine likelihood of a transaction. A company manager would also be able to adjust that model based on feedback received and to generate an excerpt showing the agent and customer. Determining if a sale will occur between an agent and customer is not a novel concept and the claimed invention is merely automating a manual process.
In addition, the claims deal with customers and agents with respect to sales. The Specification in para 0002 also talks about providing different services to customers.
These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (sales activity, fundamental economic principles or practices; business relations, interactions between people).
It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of CBR machine learning model, first computing device, first database, second computing device, second database, system, processors, and non-transitory computer-readable medium.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using 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.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe details about the excerpt such as using an ADF format to generate it. The dependent claims further describe what the nonconfidential and confidential information comprise such as demographic information and financial information.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites CBR machine learning model, first computing device, first database, second computing device, and second database.
Claim 8 recites system, CBR machine learning model, first computing device, first database, second computing device, and second database.
Claim 15 recites non-transitory computer-readable medium, processors, CBR machine learning model, first computing device, first database, second computing device, and second database.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations as see in para 0025.
When looking at the additional elements in combination, the Applicant’s specification merely states general purpose computer configurations as seen in para 0025. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-8, 10-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Merritt (US10542148B1) in further view of Sheridan (US20090132662A1) in further view of Chandra (US20210042800A1).
Regarding claim 15, and similarly claim 1 and 8, Merritt teaches
A non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, cause performance of actions comprising (See col 5-6 the processor in communication with the non-transitory machine-readable memory and the call routing module executes a set of instructions instructing the processor to)
A system comprising: a first computing device; and a second computing device; wherein the first computing device is configured to (See fig. 1) This shows a system and this shows a plurality of computing devices such as item 102, 162, and 164.
A method comprising (See abstract-Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents) This shows a method.
Training…model ( See col. 13- the predictive modeling module 110 trains a logistic regression model 114 with the full set of features of the ACXIOM database.) This shows a model used by the system is trained with data. This model is used for predicting likelihood of a favorable outcome (i.e. sale transaction).
However Merritt doesn’t teach that this model is a cased based reasoning machine learning model, however Sheridan teaches a case-based reasoning (CBR) machine learning model (See para 0039- These machine learning techniques may include one or more of neural networks, cluster analysis, case-based reasoning, induction, or any other suitable machine learning techniques.) This teaches a cased based reasoning machine learning model.
Merritt and Sheridan are analogous art because they are from the same problem-solving area of call centers and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Merritt’s invention by incorporating the method of Sheridan because Merritt would also be able to implement a machine learning model when routing calls. This would ensure accurate routing is done based on the customer and agent data. The routing would also become better over time as machine learning models become more robust overtime. In addition, the system of Merritt would also be able to handle more complex data with respect to the machine learning model.
Merritt further teaches using customer data, sales agent data, and completed transaction data, to predict a likelihood of a successful transaction between: a customer associated with a customer profile, and a sales agent associated with a sales agent profile (See fig. 2) (See col 14-17 At step 204, the method retrieves customer profile data from a customer relationship management (CRM) database, such as CRM database 122) (See col 14-17 At step 206, the method retrieves from an agent database (e.g., agent profile database 126), agent profile data for each of a plurality of agents of the contact center. Agent profile data may include, for example, agent ID, basic agent profile information, agent status data, agent sales history, and agent skills) (See col 14-17 At step 210, the method calculates an outcome prediction for a set of call routing options by applying the predictive model determined at step 208, to values of model variables. Each call routing option includes the identified customer, and one of the agents.) (See col 14-17 At step 212, for the outcome predictions calculated for the series of call routing options during the recent time interval at step 210, the method determines whether one of these outcome predictions satisfies favorable outcome criterion included in the predictive model. In an embodiment, the favorable outcome criterion provides a measurable criterion for successful achievement of a defined favorable outcome. An example of a favorable outcome criterion is a minimum acceptable likelihood of completion of a defined sales transaction.) (See col. 3-4- In an embodiment, the method routes the identified customer to one of the plurality of agents only after identifying an agent for which the calculated outcome prediction satisfies a favorable outcome criterion. For example, a given outcome prediction may represent likelihood of a given agent's closing a sale to the identified customer). This shows customer data, agent data, and completed transaction data is received. This data is received from the customer and agent profiles. This is to determine a likelihood of a sale occurring (i.e. favorable outcome).
wherein the likelihood is predicted based on comparing the customer profile to the sales agent profile (See col. 14-17- At step 210, the method calculates an outcome prediction for a set of call routing options by applying the predictive model determined at step 208, to values of model variables. Each call routing option includes the identified customer, and one of the agents. In an embodiment, the method applies the predictive model to values of the model variables over a recent time interval.) This shows the data from the customer profile and agent profile is compared as a pair to determine which one will provide the favorable outcome (i.e. sales transaction).
receiving, by a first computing device from a first database, a first customer profile corresponding to a first customer (See figure 1) (See col 14-17 At step 204, the method retrieves customer profile data from a customer relationship management (CRM) database, such as CRM database 122) This shows a first computing device such as item 102 receives customer profile data from a first data base such as item 122.
wherein the first customer profile comprises confidential information and nonconfidential information (See col. 8-9-Customer profile data 124 includes, for example, records of individual customer account information such as customer name, address information, telephone number information, credit card number, and other data fields to assist in customer care and value-added promotions related to the organization's set of customer accounts.) (See col. 15-16-Retrieved customer profile data also can include customer demographic and psychographic characteristics that may affect affinity of given agents to that customer (customer-agent affinity)) This shows the customer data includes confidential data such as credit card data and nonconfidential data such as customer demographic data.
receiving, by the first computing device …a plurality of sales agent profiles each corresponding to a respective sales agent of a plurality of sales agents (See col 14-17 At step 206, the method retrieves from an agent database (e.g., agent profile database 126), agent profile data for each of a plurality of agents of the contact center. Agent profile data may include, for example, agent ID, basic agent profile information, agent status data, agent sales history, and agent skills) This shows a first computing device such as item 102 receives agent profile data. This is received from database as seen in figure 2.
Even though Merritt teaches receiving agent profile data, it is not clear that it is received from a second computing device. However another section of Merritt teaches from a second computing device (See fig. 1 item 162 and 164) This shows that a second computing device such as an agent device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the agent profile data be received from the agent device. This would ensure the agent is providing the most up to date data about themselves. This would make the system of Merritt more sophisticated since item 102 would be able to get data from more than just databases but real time data from agent devices.
Merritt further teaches receiving, for each of the plurality of sales agent profiles, one or more previously completed transactions made by the corresponding sales agent (See col. 15-16- Agent profile data 126 includes, for example, agent ID, basic agent profile information, agent status data, agent sales history, and agent skills) The sales history data corresponds to previously completed transactions. This data is received by the system from the plurality of databases seen in fig. 1.
Merritt further teaches determining, based on the confidential information by inputting the first customer profile and the plurality of sales agent profile into the…model, a first sales agent, of the plurality of sales agents, that has a high likelihood of making a successful transaction with the first customer (See step 210 in figure 2) (See col. 14-17- At step 210, the method calculates an outcome prediction for a set of call routing options by applying the predictive model determined at step 208, to values of model variables. Each call routing option includes the identified customer, and one of the agents. In an embodiment, the method applies the predictive model to values of the model variables over a recent time interval… At step 212, for the outcome predictions calculated for the series of call routing options during the recent time interval at step 210, the method determines whether one of these outcome predictions satisfies favorable outcome criterion included in the predictive model. In an embodiment, the favorable outcome criterion provides a measurable criterion for successful achievement of a defined favorable outcome. An example of a favorable outcome criterion is a minimum acceptable likelihood of completion of a defined sales transaction… at step 216 the method routes the customer call to the call routing option (contact center agent) that satisfies the favorable outcome criterion. If at step 216 more than one contact center agent satisfies the favorable outcome criterion, as between these call center agents the call is routed to the contact center agent with the maximum outcome prediction.) This step is based on step 204 of receiving confidential information from customers’ profile. The system is trying to find an agent that would result in a favorable outcome (i.e. sales transaction. )
wherein the first sales agent is determined based on a similarity between the first customer profile and the one or more previously completed transactions made by the first sales agent; (See fig. 2) The agent data which includes the sales history is used with the customer data to determine similarity between the customer and agent that will result in a desired outcome (i.e. sale transaction). (See col. 15-16-Agent sales history may include aggregate sales productivity metrics, as well as distributed performance metrics such as sales metrics by product types, by customer types, etc. For example, given agents may have a particularly strong sales history with certain types of customer, and/or in selling or servicing certain types of products. Agent skills may include training and certifications, and licenses received. )
generating an excerpt of the first customer profile omitting the confidential information; sending, by the first computing device and to the second computing device, the excerpt, of the first customer profile, and an identification of the first sales agent (See figure 4) This shows an excerpt of the customer that is shown to the agent. This information doesn’t include confidential information since it shows the matching factors. Item 410 also doesn’t have to include confidential information since this data can include scripts for the agent (See col. 18 Customer data pane 410 also may display scripts for the agent to obtain additional customer profile data from the customer. ) Figure 4 would also show identification of the sales agent since it would show agent-customer matching factors with scores for them such as the agent skills. This information is sent by the first computing device such as item 102 to the agent device which corresponds to item 162.
Merritt further teaches and storing…a mapping between the first customer profile and a first sales agent profile corresponding to the first sales agent (See fig. 4) This shows an interface that stores the mapping of the first customer with sales agent with respect to matching factors.
However Merritt doesn’t teach that it is stored in a database, however Merritt teaches in a second database as seen in figure 1 which shows multiple databases.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the agent and customer mapping stored in a database. This would ensure past mappings are stored for future use. For example, if the same customer were to call, the system would already know what agent had a high favorable outcome with them. The system would not need to run further analysis and would merely just have to select that agent to route the call too.
In addition, Merritt already teaches a second computing device such as the agent device as seen in fig. 1, but doesn’t teach receiving feedback, however Sheridan teaches
receiving, from the second computing device, feedback (See para 0054- The recipient's feedback and electronic message 18 may be analyzed by machine learning module 32 using one or more machine learning techniques.) This teaches receiving feedback with respect to a machine learning model.
Merritt and Sheridan are analogous art because they are from the same problem-solving area of call centers and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Merritt’s invention by incorporating the method of Sheridan because Merritt would also be able to implement a machine learning model when routing calls. This would ensure accurate routing is done based on the customer data, agent data, and feedback data. The system of Merritt would also be able to handle more complex data. The routing would also become efficient over time as machine learning models become more robust with the feedback data.
However Sheridan does not teach the feedback data corresponds to successful transaction, however Chandra teaches
indicating whether the first sales agent made the successful transaction with the first customer (See para 0026- In implementations, the feedback component may be configured to receive input from the agent during the second communication phase corresponding to the probability of one or more outcome events, receive input corresponding to the actual occurrence of one or more outcome events, and received input as feedback to the first predictive model or the second predictive model. As disclosed herein, the feedback component may facilitate the training of one or more predictive models.) (See para 0120- probability of an outcome event (e.g., the sale of a product).) This teaches that a feedback is received which includes whether an outcome event occurred. The outcome event being the sale of a product.
Merritt, Sheridan, and Chandra are analogous art because they are from the same problem-solving area of communicating with individuals such as agents and communicating with individuals that include customers. In addition, all arts belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Merritt’s and Sheridan’s invention by incorporating the method of Chandra because Merritt would also be able to implement a machine learning model when routing calls. This would ensure accurate routing is done based on the customer data, agent data, and feedback data. Sheridan would also be able to use the limitation of Chandra because Sheridan would be able to use a first and second predictive model. Having two models would make the system of Sheridan more sophisticated and would ensure the messages in Sheridan are routed properly.
In addition, Merritt doesn’t teach machine learning model being adjusted, however Sheridan teaches
and adjusting, using the feedback…the CBR machine learning model. (See para 0103- At step 312, machine learning module 32 (or another suitable component of system 10) may update analyzer heuristics 28 based on the analysis of the feedback received from the recipient. Updating analyzer heuristics 28 may include taking no action with respect to analyzer heuristics 28, modifying an existing analyzer heuristic 28, adding an analyzer heuristic 28, or deleting an analyzer heuristic 28.) This shows machine learning model is adjusted by updating the analyzer heuristics.
Merritt already teaches the confidential information in the first customer profile, and the mapping as seen in fig. 4 and fig. 2.
Merritt and Sheridan are analogous art because they are from the same problem-solving area of call centers and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Merritt’s invention by incorporating the method of Sheridan because Merritt would also be able to implement a machine learning model when routing calls. This would ensure accurate routing is done based on the customer data, agent data, and feedback data. The system of Merritt would also be able to handle more complex data. The routing would also become efficient over time as machine learning models become more robust with the feedback data. Sheridan would also be able to use the additional data in Merritt to adjust the machine learning model. Having additional data to train the machine learning model would ensure that the machine learning model become more accurate over time.
Regarding claims 3, 10, and 17 Merritt further teaches
wherein the nonconfidential information of the first customer profile comprises at least one of: demographic information of the first customer; or one or more target transactions associated with the first customer. (See col. 15-16-Retrieved customer profile data also can include customer demographic and psychographic characteristics that may affect affinity of given agents to that customer (customer-agent affinity)) This shows the customer data includes nonconfidential data such as customer demographic data.
Regarding claims 4, 11, and 18 Merritt further teaches
wherein the confidential information of the first customer profile comprises financial information of the first customer. (See col.8- Customer profile data 124 includes, for example, records of individual customer account information such as customer name, address information, telephone number information, credit card number, and other data fields to assist in customer care and value-added promotions related to the organization's set of customer accounts.) This shows the customer data includes confidential data such as credit card data which corresponds to financial data.
Regarding claims 5, 12, and 19 Merritt further teaches
wherein each of the plurality of sales agent profiles comprises at least one of: a demographic attribute of the sales agent; or a sales expertise attribute of the sales agent. (See col. 15-16- Agent profile data may include, for example, agent ID, basic agent profile information, agent status data, agent sales history, and agent skills. Agent status may include information on years of experience, seniority, authorizations, etc. Basic agent profile information can include agent demographic and psychographic characteristics that may affect customer-agent affinity.) This shows agent expertise such as years of experience.
Regarding claims 6, 13, and 20, Merritt and Sheridan teach the limitations of claims 1, 8, and 15, however Merritt further teaches
wherein the determining the first sales agent that has the high likelihood of making the successful transaction with the first customer comprises using the CBR model to: weigh, based on a degree of match between the first customer profile and one or more attributes of the first sales agent profile, each of the one or more attributes; and determine, based on the weighing, a score indicating the high likelihood. (See figure 4) (See col. 18-19 menu box 640 can display detailed aspects of each Agent-Customer Matching Factor considered by the predictive model in weighting that factor in selecting the agent and product that satisfied the favorable outcome criteria.) (See col. 18-19 Each pane includes a score value 440 of the respective Agent-Customer Matching Factor, e.g., a percentage (scores 76, 67, 56, and 55 respectively). A meter indicator icon 450 has a pointer position corresponding to the score) This teaches that a predictive model weighs attributes when determining what agent customer combo would lead to a favorable outcome. Figure 4 also shows a score based on the weighing of these attributes. For example, targeted sales rating is a factor and has a score.
However Merritt doesn’t teach that this model is a cased based reasoning machine learning model, however Sheridan teaches a case-based reasoning (CBR) machine learning model (See para 0039- These machine learning techniques may include one or more of neural networks, cluster analysis, case-based reasoning, induction, or any other suitable machine learning techniques.) This teaches a cased based reasoning machine learning model.
Merritt and Sheridan are analogous art because they are from the same problem-solving area of call centers and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Merritt’s invention by incorporating the method of Sheridan because Merritt would also be able to implement a machine learning model when routing calls. This would have ensured accurate routing is done based on the customer and agent data and the system of Merritt would be able to handle more complex data. The routing would also become better overtime as machine learning models become more robust overtime.
Regarding claim 7 and 14, Merritt and Sheridan teach the limitations of claims 1 and 8 however Merritt further teaches
further comprising receiving, from the second database and for each of the plurality of sales agent profiles, one or more previously completed transactions made by the corresponding sales agent (See col. 15-16- Agent profile data 126 includes, for example, agent ID, basic agent profile information, agent status data, agent sales history, and agent skills) The sales history data corresponds to previously completed transactions. This data is received by the system from the plurality of databases seen in fig. 1.
and wherein the determining the first sales agent that has the high likelihood of making the successful transaction with the first customer comprises using the…model to compare a similarity between the first customer profile and customer profiles associated with the one or more previously completed transactions. (See fig. 2) The agent data which includes the sales history is used with the customer data to determine similarity between the customer and agent that will result in a desired outcome (i.e. sale transaction).
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Muncy (US20120179476A1) Discloses auto-lead data format with respect to sale leads.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625