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 .
Status of Claims
This Office action is in response to correspondence received June 17, 2025.
Claims 1, 17, and 19 have been amended. Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claims 1, 17, and 19, which are similar in scope:
continuously monitoring one or more consumer computing devices; detecting, based on the continuous monitoring, a plurality of transactions between a consumer computing device of the one or more consumer computing devices and an enterprise organization computing device; storing the plurality of transactions in a transaction history associated with the consumer computing device; assigning a score to each transaction of the plurality of transactions; receiving a consumer concern requesting assistance with a transaction; receiving a token comprising details that describe the consumer concern and the consumer computing device; determining whether the consumer concern corresponds to at least one transaction within the transaction history associated with the consumer computing device; based on determining the consumer concern corresponds to at least one transaction within the transaction history, determining whether the transaction is similar to at least one previously received transaction; based on determining the transaction is different from previously received transactions, generating a solution to the consumer concern; and transmitting instructions to initiate communication with the consumer computing device to resolve the consumer concern, wherein transmitting the instructions to initiate communication causes the enterprise organization to initiate communication and transmit the generated solution for execution resolve the consumer concern.
The steps, above, are a certain method of organizing human activity, commercial interactions, because the steps are for after sales customer support. Such an activity is similar to marketing or sales activities or behaviors, or business relations, two of the examples (inclusive examples) in MPEP 2106.04(a) for commercial interactions. Performing post sales customer service would be treated as a commercial interaction as it makes up a part of “sales activities or behaviors,” being related to sales and something which is done to ameliorate customer concerns during a sale. It is noted that a token under a broadest reasonable interpretation in light of the specification is a data representation of a transaction, so it could be a serial number or file number, neither of which is technical and if it were technical, is applied and therefore would be fairly interpreted as a Therefore, Applicant has recited an abstract idea in the independent claims.
The additional elements in claim 1 are:
at a computing device including one or more processors and memory:
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
The additional elements in claim 17 are:
A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
The additional elements of claim 19 are:
One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
This judicial exception is not integrated into a practical application because the additional elements in combination are, under a broadest reasonable interpretation in light of the specification, instructions to apply generic computers to the abstract idea. Claims 1, 17, and 19 are similar in scope but recite method, system, and CRM claims, and the body of the claims has information going from and to a consumer computing device and an enterprise organization computing device. Sending information to and from computers, where the information is the abstract idea steps, is instructing computers to be applied to the abstract idea. See MPEP 2106.05(f)(2).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, the combination of elements is apply it and apply it elements are not significantly more than the abstract idea.
The additional elements in claim 1 are:
at a computing device including one or more processors and memory:
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
The additional elements in claim 17 are:
A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
The additional elements of claim 19 are:
One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to
“from/to a/the consumer/enterprise organization computing device”
Transmitting to/initiating communication with the consumer computing/enterprise organization computing device.
Dependent claims 2-15 further define the abstract idea. Limitations such as determining scores and likelihoods are analysis steps that further define the commercial interaction and have no technical elements that would be analyzed as additional elements. Any additional elements claims are recited, like the independent claims, as apply it elements and therefore are not a practical application or significantly more.
Claims 16, 18, and 20 recite making a phone call which is an additional element of apply it instructions because making a phone call is something a generic computer can do (VoIP) or a generic computer in the form of a smartphone. Therefore this additional element along with the generic enterprise and consumer computers sending/receiving information are not a practical application or significantly more.
Therefore, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6, 8-12, and 15-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shimpi et al., US 12058219 B1 (“Shimpi”).
Per claims 1, 17, and 19, which are similar in scope, Shimpi teaches A method comprising: at a computing device including one or more processors and memory: continuously monitoring one or more consumer computing devices in col 5 ln 47-56: “Context processor 150 may be in electronic communication with information database 110 and the Information Channels, and may receive account activity data associated with a customer in response to internal system 101 receiving a communication (e.g., a telephone call) from the customer. Context processor 150 may also be in electronic communication with real-time stream processor 135, which may retrieve real time account activity data from the Information Channels in response to receiving a communication from a customer.” Being in electronic communication and receiving account data in response to receiving a communication teaches continuously monitoring. This is because the systems are in electronic communication, therefore they are coupled without interruption (continuous) and the actions occur in response to receiving a communication, which can only happen through continuous monitoring. This is also taught in col 22 ln 26-31: “Moreover, where the micro-app desires an input from a user, the micro-app may be configured to request a response from the operating system which monitors various hardware components and then communicates a detected input from the hardware to the micro-app.” The monitoring is continuous as there is no interruption taught.
detecting, based on the continuous monitoring, a plurality of transactions between a consumer computing device of the one or more consumer computing devices and an enterprise organization computing device in col 2 ln 50 – col 3 ln 2: “To expedite the process of communicating with the customer, predictive communication system 120 comprised in internal system 101 may have the capability of accessing and analyzing account activity data (reflecting activity a customer has conducted through his or her customer profile and/or transaction account, which may reflect activity trends) in order to predict the intent of the customer in initiating the customer communication (i.e., the reason for the customer's telephone call or other communication).” See also col 4 ln 15-34: “ In various embodiments, internal channels 84 may comprise various communication channels available to the customer to communicate with internal system 101 and the associated entity (e.g., a transaction account issuer). For example, internal channels 84 may comprise account activity data related to customer communications between internal system 101 and the customer through a customer profile, a mobile device and/or application, electronic messaging (e.g., email, instant messaging, social media channels, video calls or the like), telephone calls, and/or the like.” See also: col 5 ln 47-56: “Context processor 150 may be in electronic communication with information database 110 and the Information Channels, and may receive account activity data associated with a customer in response to internal system 101 receiving a communication (e.g., a telephone call) from the customer. Context processor 150 may also be in electronic communication with real-time stream processor 135, which may retrieve real time account activity data from the Information Channels in response to receiving a communication from a customer.” The underlined sections are specifically taught above and therefore this teaches based on the continuous monitoring.
Shimpi then teaches storing the plurality of transactions in a transaction history associated with the consumer computing device in col 3 ln 18 – 41: “ In various embodiments, information database 110 may comprise hardware and/or software capable of storing data. For example, information database 110 may comprise a server appliance running a suitable server operating system (e.g., MICROSOFT INTERNET INFORMATION SERVICES or, “IIS”) and having database software (e.g., ORACLE) installed thereon. In various embodiments, information database 110 may store account activity data and customer profile information associated with a customer profile, which is associated with a customer. In various embodiments, information database 110 may store numerous customer profiles associated with numerous customers. The customer profile may be associated with and/or comprise multiple transaction accounts (i.e., the customer utilizes numerous transaction instruments, each of which may be associated with a transaction account). Customer profile information may comprise information about the customer and/or her transaction history such as, merchants transacted with, types of transactions, type of transaction account, frequency of transactions, number of transactions, timing of transactions demographic information, personal information (e.g., gender, race, religion), social media posts, social media comments about the customer, pictures of the customer, video of the customer, or any other information.”
Shimpi then teaches assigning a score to each transaction of the plurality of transactions in col 5 ln 57 – col 6 ln 11: “Context processor 150, via predictive model 154 comprising analysis rules, may analyze the account activity, including real-time insights 156, to determine ranking of the intent insights comprised in the account activity data. The analysis rules of predictive model 154 may be based on prior customer servicing interactions (e.g., the number, frequency, topics, of such interactions), communication channels, and other information used to train predictive model 154 to determine priority ranking of intent insights.”
Shimpi then teaches receiving, from the consumer computing device, a consumer concern requesting assistance with a transaction in col 9 ln 19-35: “With combined reference to FIGS. 1A, 1B, and 2, in accordance various embodiments, a method 200 for predicting a customer communication intent is depicted. In various embodiments, a customer having a customer profile associated and/or comprised in internal system 101 may initiate a communication with internal system 101. Such customer communications will be referred to as telephone calls herein, however, the customer communication may be any communication (e.g., in real time), such as instant messaging, or other types of electronic messaging, video chat, and/or the like. In various embodiments, internal system 101 may have transmitted a notification to the customer profile instructing the customer to contact internal system 101. In response to the customer initiating a customer communication with internal system 101, internal system 101 and/or predictive communication system 120 may receive the customer communication (step 202).”
Shimpi then teaches receiving, from the enterprise organization computing device, a token comprising details that describe the consumer concern and the consumer computing device in col 9 ln 36-55: “Internal system 101, in response to receiving the customer communication, may identify the customer profile (step 204) associated with the customer initiating the customer communication. In various embodiments, internal system 101 may identify the customer profile by receiving a customer profile identifier (e.g., a password, social security number, biometric (e.g., fingerprint, voice print or facial recognition from a video call), or the like) and matching the customer profile identifier with a saved identifier. In various embodiments, the customer profile identifier may be a telephone number, in which case, internal system 101 may automatically match the telephone number from which the telephone call is received with a stored telephone number associated with the customer profile, thereby identifying the customer profile and authenticating the identity of the customer. In various embodiments, the customer profile identifier may be requested of customer by internal system 101, and input by the customer. The system may require the customer to answer additional security questions or enter additional identifying data.”
Shimpi then teaches determining whether the consumer concern corresponds to at least one transaction within the transaction history associated with the consumer computing device in col 10 ln 5-30: “Context processor 150, utilizing real-time insights 156, customer feedback 152, and the analysis rules of predictive model 154, may analyze the account activity data (step 208). As discussed herein, predictive model 154 may analyze the account activity data by ranking the intent insights comprised in the account activity data by priority (or the likelihood that an action reflected by a certain intent insight is the reason for the customer communication). Again, intent insights may be pieces of data that reflect past and/or recent actions (including communications) by the customer or related to the customer whether thru the customer profile in internal system 101 or through external channels 86. For example, predictive model 154 may rank the intent insights chronologically, with the most recent intent insight (reflecting the most recent action taken by the customer) ranked first. For example, if the customer tried most recently to replace a card, or internal system 101 instructed the customer to initiate a customer communication with internal system 101, an intent insight reflecting such action would be ranked highest. In various embodiments, the analysis rules of predictive model 154 may cause ranking of intent insights in any suitable manner (e.g., by type, wherein certain types of intent insights are ranked higher, such as fraud-related insights, or by chronology and type, and/or the like). Accordingly, predictive model 154 may create a ranked list of intent insights.”
Shimpi then teaches based on determining the consumer concern corresponds to at least one transaction within the transaction history, determining whether the transaction is similar to at least one previously received transaction in col 7 ln 35 – col 8 ln 7: “As an example, arbitration engine 184 may recognize that an intent insight reflecting an account payment is always conducted on a certain day of the month by the customer calling into internal system 101. Therefore, if the customer calls internal system 101 on that day of the month, the intent insight reflecting account payment may be ranked higher than other intent insights having different characteristics. By analyzing the ranking of the intent contexts, arbitration engine 184 may determine an intent prediction (i.e., the most likely reason for a customer's communication (e.g., telephone call)). Arbitration engine 184 may transmit the intent prediction to communication decision engine 186.”
Shimpi then teaches based on determining the transaction is different from previously received transactions, generating a solution to the consumer concern in col 8 ln 7-37: “Communication decision engine 186, in various embodiments, may receive the intent prediction from arbitration engine 184. Communication decision engine 186 may be configured to route the customer communication (e.g., telephone call) to the appropriate service system 188. A service system 188 may be the communication that the customer receives, in response to initiating a customer communication. Accordingly, communication decision engine 186 may analyze the intent prediction and match the intent prediction with an appropriate service system 188 to respond to the customer communication. For example, if the intent prediction reflects a prediction that the customer wishes to request a replacement transaction instrument, communication decision engine 186 may route the customer communication to an automated (voice) response system that is specifically for card replacement. The intent prediction and routing to an appropriate service system 188 may occur before the customer has to take any action after initiating a communication with internal system 101 (except, in some cases, providing a customer identifier to confirm the customer's identity). As another example, communication decision engine 186 may recognize that an intent prediction is related to a predicted action by the customer that cannot be completed by certain service systems 188 (e.g., automated service systems 188), such as to address fraud on a transaction account. Accordingly, communication decision engine 186 may route the customer communication to a service system 188 allowing person-to-person communication. In various embodiments, there may be a service system 188 for each action to be completed by the customer, or multiple actions may be associated with a service system 188.” The service system teaches the solution. For example, it could be replace a card, or route the customer to person-to-person communication. It is based on the insight and therefore where an insight is ranked higher than one that is temporally closer, that teaches “based on determining a transaction is different than previous transactions” because under a broadest reasonable interpretation, the transaction is not like the transactions that are temporally closer and is therefore “different” than “previous” (temporally closer) transactions. See
col 7 ln 35 – col 8 ln 7: “Arbitration engine 184 may also determine that an intent insight of the plurality of intent insights received by communication response system 170 may be a priority insight. As examples of priority insights, the business rules in arbitration engine 184 for prioritizing intent insights may cause intent insights related to fraud to rank highest, or certain correlations may cause an intent insight to be ranked higher than another that is temporally closer to the customer's communication.” The rest of the paragraph (see cols 7 and 8) describes analyzing transactions based on the time of the month which necessitates previous transactions as the pattern is based on previous transactions in the month.
Shimpi then teaches and transmitting, to the enterprise organization computing device, instructions to initiate communication with the consumer computing device to resolve the consumer concern in col 8 ln 7 – 38: “As another example, communication decision engine 186 may recognize that an intent prediction is related to a predicted action by the customer that cannot be completed by certain service systems 188 (e.g., automated service systems 188), such as to address fraud on a transaction account. Accordingly, communication decision engine 186 may route the customer communication to a service system 188 allowing person-to-person communication.” See also col 11 ln 14-46: “Each service system 188 may be a communication system to address certain actions (e.g., transaction instrument replacement, account freezing, fraud, balance payment, etc.). For example, if the action is something that can be taken care of over an automated service system 188, communication decision engine 186 may route the customer communication to an automated service system 188”
Then, Shimpi teaches wherein transmitting the instructions to initiate communication with the consumer computing device causes the enterprise organization to initiate communication with the consumer computing device and transmit, to the consumer computing device, the generated solution for execution resolve the consumer concern in col 8 ln 7-37: “Communication decision engine 186, in various embodiments, may receive the intent prediction from arbitration engine 184. Communication decision engine 186 may be configured to route the customer communication (e.g., telephone call) to the appropriate service system 188. A service system 188 may be the communication that the customer receives, in response to initiating a customer communication. Accordingly, communication decision engine 186 may analyze the intent prediction and match the intent prediction with an appropriate service system 188 to respond to the customer communication. For example, if the intent prediction reflects a prediction that the customer wishes to request a replacement transaction instrument, communication decision engine 186 may route the customer communication to an automated (voice) response system that is specifically for card replacement. The intent prediction and routing to an appropriate service system 188 may occur before the customer has to take any action after initiating a communication with internal system 101 (except, in some cases, providing a customer identifier to confirm the customer's identity). As another example, communication decision engine 186 may recognize that an intent prediction is related to a predicted action by the customer that cannot be completed by certain service systems 188 (e.g., automated service systems 188), such as to address fraud on a transaction account. Accordingly, communication decision engine 186 may route the customer communication to a service system 188 allowing person-to-person communication. In various embodiments, there may be a service system 188 for each action to be completed by the customer, or multiple actions may be associated with a service system 188.” See also col 11 ln 28-46: “In response, presentation layer 180 may present the intent prediction (step 214) to the customer (e.g., via a push notification). Such a presentation may comprise an accuracy inquiry, inquiring if the action reflected in the intent prediction is the reason for the customer's call. In response, the customer may indicate “yes” or “no” (in various embodiments, by pressing a button, speaking, or otherwise providing an electronic input to predictive communication system 120), which may be an accuracy response (customer feedback 152). Internal system 101 and/or predictive communication system 120 may receive the accuracy response (step 216), and transmit the results as customer feedback 152 for predictive model 154 to utilize. In response to receiving a positive accuracy response, presentation layer 180 may continue with the same service system 188. In response to receiving a negative accuracy response, presentation layer 180 may re-route the customer communication to another service system 188 (e.g., a selection menu to allow the customer to input the reason for the call).” See also col 13 ln 65 – col 14 ln 34: “In various embodiments, the system and method may include alerting a subscriber when their computer (e.g., web client 192) is offline. The system may include generating customized information and alerting a remote subscriber that the information can be accessed from their computer. The alerts are generated by filtering received information, building information alerts and formatting the alerts into data blocks based upon subscriber preference information. The data blocks are transmitted to the subscriber's wireless device which, when connected to the computer, causes the computer to auto-launch an application to display the information alert and provide access to more detailed information about the information alert. More particularly, the method may comprise providing a viewer application to a subscriber for installation on the remote subscriber computer; receiving information at a transmission server sent from a data source over the Internet, the transmission server comprising a microprocessor and a memory that stores the remote subscriber's preferences for information format, destination address, specified information, and transmission schedule, wherein the microprocessor filters the received information by comparing the received information to the specified information; generates an information alert from the filtered information that contains a name, a price and a universal resource locator (URL), which specifies the location of the data source; formats the information alert into data blocks according to said information format; and transmits the formatted information alert over a wireless communication channel to a wireless device associated with a subscriber based upon the destination address and transmission schedule, wherein the alert activates the application to cause the information alert to display on the remote subscriber computer and to enable connection via the URL to the data source over the Internet when the wireless device is locally connected to the remote subscriber computer and the remote subscriber computer comes online.” This teaches that the enterprise organization initiates communication with the consumer computer.
Per claim 2, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches assigning the score to each transaction of the plurality of transactions further comprises predicting, for each transaction, a likelihood that consumer computing device will request assistance with the transaction in col 7 ln 10-34: “In various embodiments, presentation layer 180 may comprise an insight parser 182, an arbitration engine 184, a communication decision engine 186, and/or a service system 188. Insight parser 182 may be configured to receive the ranked list of intent insights from servicing layer 172 that was comprised in API 165, read the rank of each intent insight of the ranked list (the rank may be a rank marker placed on each intent insight by predictive model 154), map the intent insights according to each rank, and transmit the mapped, ranked intent insights to arbitration engine 184. For example, mapping the intent insights by insight parser 182 may comprise insight parser 182 notating which intent insight is ranked as the highest priority, and then mapping or ordering the intent insights from there.” See also col 7 ln 35 – col 8 ln 6: “In various embodiments, arbitration engine 184 may implement business rules to determine which intent insights may be the highest ranking. For example, arbitration engine 184 may determine that the closest intent insight in time to the communication by the customer may be the highest ranking intent insight. As an example, a customer may access the associated customer profile online and an attempt to order a replacement transaction instrument, which may not be completed. In response, the customer may then call internal system 101 soon after attempting the online replacement to complete the replacement order”
Per claim 3, Shimpi teaches the limitations of claim 2, above. Shimpi further teaches the score assigned to a transaction, of the plurality of transactions, is within one of: a first level of likelihood, wherein the score within the first level of likelihood indicates the consumer computing device will request assistance with the transaction; or a second level of likelihood, wherein the score within the second level of likelihood indicates the consumer computing device will not request assistance with the transaction in col 11 ln 14-44: “For example, if the action is something that can be taken care of over an automated service system 188, communication decision engine 186 may route the customer communication to an automated service system 188. In response, presentation layer 180 may present the intent prediction (step 214) to the customer (e.g., via a push notification). Such a presentation may comprise an accuracy inquiry, inquiring if the action reflected in the intent prediction is the reason for the customer's call. In response, the customer may indicate “yes” or “no” (in various embodiments, by pressing a button, speaking, or otherwise providing an electronic input to predictive communication system 120), which may be an accuracy response (customer feedback 152). Internal system 101 and/or predictive communication system 120 may receive the accuracy response (step 216), and transmit the results as customer feedback 152 for predictive model 154 to utilize. In response to receiving a positive accuracy response, presentation layer 180 may continue with the same service system 188. In response to receiving a negative accuracy response, presentation layer 180 may re-route the customer communication to another service system 188 (e.g., a selection menu to allow the customer to input the reason for the call).”
Per claim 4, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches generating the token, wherein the generating comprises determining whether the consumer computing device corresponds to a unique consumer identifier, and wherein the token is used to: transmit the details that describe the consumer concern and the consumer computing device; and confirm receipt of the consumer concern in col 9 ln 36 – col 10 ln 4: “" In various embodiments, the customer profile identifier may be requested of customer by internal system 101, and input by the customer. The system may require the customer to answer additional security questions or enter additional identifying data.
In response to identifying the customer profile, internal system and/or predictive communication system 120 may access the account activity data (step 206) associated with the customer profile (and/or a spouse account activity, supplemental account activity, etc). "
Per claim 6, Shimpi teaches the limitations of claim 4, above. Shimpi further teaches based on determining the consumer computing device corresponds to the unique consumer identifier, requesting, from the consumer computing device, a code that corresponds to the unique consumer identifier, wherein the code comprises at least one of: a password that corresponds to the unique consumer identifier; or a personal identification number (PIN) that corresponds to the unique consumer identifier in col 9 ln 36-55: “In various embodiments, internal system 101 may identify the customer profile by receiving a customer profile identifier (e.g., a password, social security number, biometric (e.g., fingerprint, voice print or facial recognition from a video call), or the like) and matching the customer profile identifier with a saved identifier.”
Per claim 8, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches the details that describe the consumer concern may consist of at least one of: a transactional issue that the consumer computing device experienced; an enterprise organization service with which the consumer computing device experienced the transactional issue; or an enterprise organization program within which the consumer computing device experienced the transactional issue in col 10 ln 48 – col 11 ln 13: “As a further example, intent insights reflecting matters of fraud or transaction account decline may be prioritized higher (i.e., may be priority insights) than other types of intent insights or chronological rankings.”
Per claim 9, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches the details that describe the consumer computing device consist of at least one of: a phone number that corresponds to the consumer computing device; or a unique consumer identifier that corresponds to the consumer computing device in col 9 ln 36-55 “and matching the customer profile identifier with a saved identifier. In various embodiments, the customer profile identifier may be a telephone number, in which case, internal system 101 may automatically match the telephone number from which the telephone call is received with a stored telephone number associated with the customer profile, thereby identifying the customer profile and authenticating the identity of the customer.”
Per claim 10, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches based on determining the consumer concern does not correspond to transactions within the transaction history, requesting, from the consumer computing device, details that describe at least one transactional issue that consumer computing device experienced in col 12 ln 20-58: “ In various embodiments, the recommended action may be actions available to the customer, unrelated to the intent insight ranking. For example, predictive communication system 120 may recognize that a balance payment due data for a transaction account is on a day which the customer has travel plans (e.g., through internal system's 101 integration with an external channel 86 such as an airline). Therefore, arbitration engine 184 may determine that the recommended action may be payment of the balance. In response to determining a recommended action, presentation layer 180 may present the recommended action to the customer (step 306). The presentation may comprise routing the customer communication to the appropriate service system 188, such as, following the example above, a service system which may allow balance payment. Presentation layer 180, similar to method 200, may present an accuracy inquiry to see if the customer would like to take the recommended action, and the customer may input an accuracy response. “
Per claim 11, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches based on determining the transaction is not similar to the at least one previously received transaction, generating a solution to the consumer concern in col 12 ln 20-58: " In various embodiments, the recommended action may be actions available to the customer, unrelated to the intent insight ranking. For example, predictive communication system 120 may recognize that a balance payment due data for a transaction account is on a day which the customer has travel plans (e.g., through internal system's 101 integration with an external channel 86 such as an airline). Therefore, arbitration engine 184 may determine that the recommended action may be payment of the balance.
In response to determining a recommended action, presentation layer 180 may present the recommended action to the customer (step 306). The presentation may comprise routing the customer communication to the appropriate service system 188, such as, following the example above, a service system which may allow balance payment."
Per claim 12, Shimpi teaches the limitations of claim 11, above. Shimpi further teaches the generating the solution to the consumer concern comprises identifying, from a plurality of enterprise organization programs and services, at least one enterprise organization program or at least one enterprise organization service that addresses at least one transactional issue indicated in the consumer concern in col 12 ln 45-58: “In response to determining a recommended action, presentation layer 180 may present the recommended action to the customer (step 306). The presentation may comprise routing the customer communication to the appropriate service system 188, such as, following the example above, a service system which may allow balance payment.”
Per claim 15, Shimpi teaches the limitations of claim 1, above. Shimpi further teaches based on determining the transaction is similar to the at least one previously received transaction: identifying at least one solution that corresponds to the at least one previously received transaction; analyzing the at least one previously received transaction and the at least one solution that corresponds to the at least one previously received transaction; and transmitting the at least one solution that corresponds to the at least one previously received transaction to the consumer computing device in col 12 ln 20-58: “Predictive communication system 120 may determine a recommended action (step 304), or multiple recommended actions. Arbitration engine 184 may analyze the ranked intent insights received from context system 130, and determine a recommended action based on the business or analysis rules, similar to the analysis comprised in step 208 of method 200 (FIG. 2). The recommended action may be another action which the customer may wish to take during the customer communication. In various embodiments, the recommended action may be the second highest priority of the ranked invent insights. For example, a priority insight may have ranked highest (e.g., relating to fraud), but the closest intent insight temporally may have been a notice that an account balance payment is due (which was ranked second). Therefore, arbitration engine 184 may determine the recommended action may be paying the account balance. In various embodiments, the recommended action may be actions available to the customer, unrelated to the intent insight ranking. For example, predictive communication system 120 may recognize that a balance payment due data for a transaction account is on a day which the customer has travel plans (e.g., through internal system's 101 integration with an external channel 86 such as an airline). Therefore, arbitration engine 184 may determine that the recommended action may be payment of the balance.
In response to determining a recommended action, presentation layer 180 may present the recommended action to the customer (step 306). The presentation may comprise routing the customer communication to the appropriate service system 188, such as, following the example above, a service system which may allow balance payment. Presentation layer 180, similar to method 200, may present an accuracy inquiry to see if the customer would like to take the recommended action, and the customer may input an accuracy response. Based on the accuracy response, communication decision engine 186 may route the customer communication to the appropriate service system 188 (or allow the customer communication to remain at the current service system 188).”
Per claims 16, 18, and 20, which are similar in scope, Shimpi teaches the limitations of claims 1, 17, and 19, above. Shimpi further teaches the receiving the consumer concern further causes the computing platform to receive, by the enterprise organization computing device, a phone call from the consumer computing device in col 7 ln 35- col 8 ln 6: “In various embodiments, arbitration engine 184 may implement business rules to determine which intent insights may be the highest ranking. For example, arbitration engine 184 may determine that the closest intent insight in time to the communication by the customer may be the highest ranking intent insight. As an example, a customer may access the associated customer profile online and an attempt to order a replacement transaction instrument, which may not be completed. In response, the customer may then call internal system 101 soon after attempting the online replacement to complete the replacement order. Arbitration engine 184 may recognize the temporal proximity of the intent insight reflecting the online attempted replacement (which may have been received by predictive communication system 120 from systems of record 82 and/or internal channels 84), and the customer call. Therefore, arbitration engine 184 may determine that the highest ranking intent insight is the most recent intent insight regarding the attempted online transaction instrument replacement. Arbitration engine 184 may also determine that an intent insight of the plurality of intent insights received by communication response system 170 may be a priority insight. As examples of priority insights, the business rules in arbitration engine 184 for prioritizing intent insights may cause intent insights related to fraud to rank highest, or certain correlations may cause an intent insight to be ranked higher than another that is temporally closer to the customer's communication. As an example, arbitration engine 184 may recognize that an intent insight reflecting an account payment is always conducted on a certain day of the month by the customer calling into internal system 101. Therefore, if the customer calls internal system 101 on that day of the month, the intent insight reflecting account payment may be ranked higher than other intent insights having different characteristics. By analyzing the ranking of the intent contexts, arbitration engine 184 may determine an intent prediction (i.e., the most likely reason for a customer's communication (e.g., telephone call)). Arbitration engine 184 may transmit the intent prediction to communication decision engine 186.” See also col 10 ln 48 – col 11 ln 14.
Therefore, claims 1-4, 6, 8-12, and 15-20 are rejected under 35 USC 102.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shimpi et al., US 12058219 B1 (“Shimpi”) in view of Kirillin et al., US PGPUB 20130297513 A1 (“Kirillin”).
Per claim 5, Shimpi teaches the limitations of claim 4, above. Shimpi does not teach based on determining the consumer computing device does not correspond to the unique consumer identifier, requesting personal identifiable information from the consumer computing device, wherein the personal identifiable information comprises at least one of: a phone number that is registered with the enterprise organization and is associated with the consumer computing device; the unique consumer identifier that is registered with the enterprise organization and is associated with the consumer computing device; or a unique party identification number that is registered with the enterprise organization and is associated with the consumer computing device.
Kirillin teaches multi factor security. See abstract.
Kirillin teaches based on determining the consumer computing device does not correspond to the unique consumer identifier, requesting personal identifiable information from the consumer computing device, wherein the personal identifiable information comprises at least one of: a phone number that is registered with the enterprise organization and is associated with the consumer computing device; the unique consumer identifier that is registered with the enterprise organization and is associated with the consumer computing device; or a unique party identification number that is registered with the enterprise organization and is associated with the consumer computing device in par 048: “At 410, SPOC server 307 can authorize customer 301 as a registered bank customer based on the login information, and find their user id of customer 301. If the login information does not match a known bank customer, SPOC server 307 can inform bank server (UD) 403 which can inform unauthorized client device 401 that the user is not authenticated and the session can be terminated. If the login information does match a known bank customer, SPOC server 307 can send the user id to bank server (UD) 403 at 412. At 414, bank server (UD) 403 can establish a user session with a session id, generate a RAND to prevent a replay of the session as a client accessing the session would need to know RAND. RAND can then be assigned to the session id. At 416, the session id and RAND can be sent to unauthorized client device 401. At 418, customer 301 is logged in and can see the balance of any account he or she holds, but cannot perform banking operations.” See also pars 47 and 49.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the transaction authentication and issue resolution teaching of Shimpi with the authentication teaching of Kirillin because Kirillin teaches “With customer expectations demanding multiple device connectivity, detecting fraudulent access to a customer’s accounts becomes more complex. Creating an online hub for customers to access their accounts necessarily involves creating a public portal capable of granting a plurality of customers’ access to their accounts. This public portal is also capable of being accessed by those without accounts such as those seeking to fraudulently access a customer’s account. One way of preventing such fraudulent access is through restricting account access to authenticated users and restricting the performance of banking operations to those who have passed a multi factor authentication process. However, protocols must be established to prevent improper access to bank accounts and improper performance of banking operations.” Par 003. Kirillin’s taught advantage, which would motivate one to combine, is that customers are able to interact with the bank using multiple forms but are also protected from fraud or other bad actors. One would therefore be motivated to combine Shimpi and Kirillin because one would be able to have more flexibility in connecting to a bank, for example, but the security would be preserved. Therefore, for these reasons, one would be motivated to modify Shimpi with Kirillin.
Per claim 7, Shimpi teaches the limitations of claim 6, above. Shimpi does not teach based on determining the code fails to correspond to the unique consumer identifier, transmitting, to the consumer computing device, a notifi