0DETAILED ACTION
This communication is in response to the Applicant Arguments/Remarks dated 1/2/2026. Claims 1-7, 9-10, 12-19, 21-24 are pending in the application.
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 .
Response to Arguments
Applicant's arguments filed 1/2/2026 have been fully considered.
Regarding the arguments on pages 12-14 in relating to the amended independent claim 1, please see the new combination of references cited below.
Regarding the arguments on pages 14-15 in relating to the limitation “wherein
loading the communication data into the second container triggers the communication action”, “Nothing in KUMAR, [0120], discloses that computing and adding new attributes to the corporate client profile triggers a communication action. Accordingly, KUMAR, [0120], cannot disclose or suggest wherein the communication data is based on the communication action being triggered by loading the communication data into the second container, as recited in claim 1, as amended”, examiner respectfully disagrees.
Fidanza teaches at para. 56: once the pre-actions are performed, the state machine can make the transition for the next step. The transition may fire some actions that are managed by the backend component. Some of these actions may send a notification, update a table in the database, produce a repayment scheduler as, for example, to determine payment terms between others; para. 98: permit image and object uploads and updates to the customer transactions database by responding to inputs and provision backend services and custom http requests; para. 120: once the new corporate client is recorded in the Data Warehouse and the initial Maximum Credit score generated, the settlement card system and particularly the KEO first computing system may initiate the process of adding and computing new attributes to the corporate client profile using the loan activities and acquiring all transactional data.
Claim 1 do not contain the argued limitation “computing and adding new attributes to the corporate client profile triggers a communication action”. The combination of references does teach the argued limitations.
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) 1-7, 9-10, 14, 16, 18-19, 21, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fidanza et al. (US 20240169355) in view of Cherubini (US 20220060430) and further in view of Oberle (US 20240146570).
As per claims 1, 9, 16 Fidanza et al. (US 20240169355) teaches
system for communicating via a communication channel based on an extract, transform, and load (ETL) process, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories (para. 111: receive data from data sources that interoperate with ETL (extract, transform, load) jobs and machine learning components that in turn interoperate with a data store such as the Amazon simple cloud storage service; para. 31: computing system may include at least one processor and memory),
configured to: obtain, via a first gateway network device of the system, raw data including events data indicating a series of events involving interactions between an entity and an individual, and including identity data for the individual, wherein obtaining the raw data is triggered by a computer network request associated with a computer network protocol (fig. 2: computing system network, acquirer to payment gateway; para. 126: data may be gathered and copied from the web to a local repository and raw data is then cleansed, transformed, aggregate features constructed, and final features selected; para. 28: the second computing system identifies the card issuer and customer from the stored card data and forwards the authorization request to the first computing system, which determines if the transaction is within the monetary limit determined for the customer, and if no, reject the transaction, if yes, accept the transaction and determine customer payment terms for that transaction; para. 88: communicate an authentication request and verify corporate client (can be an individual who manage the account) identity and bank account data from a financial institution, and select and extract client data features from public data sources; para. 108: control over the virtual networking environment, including IP address ranges, subnets and configurations for route tables and network gateways; para. 126: data may be gathered and copied from the web to a local repository and raw data is then cleansed, transformed, aggregate features constructed, and final features selected);
load the raw data into a first container; initiate an ETL process based on loading the raw data into the first container; wherein loading the raw data into the first container triggers the ETL process (para. 31: the server network may be part of a cloud-based network and include one or more processors and memories at a specific location or multiple locations, such as a main business or enterprise location such as two data hosting or host server locations; para. 86-87: after gathering and identifying
a data scheme, the settlement card system and particularly the KEO first computing system may clean/transform data, e.g., using a glue program and create a task for data cleaning and transformation, such as changing data format and converting
numerical data via a job process. Provisioning and data management may be reduced with scaling of resources required to run an ETL job, which may be managed on a
scaled-out apache spark environment; para. 91, 111);
extract, in connection with an extract phase of the ETL process, a portion of the raw data from the first container that is related to a particular individual who has not received or interacted with a communication in more than a threshold time period (para. 5: a transaction card that is loaded with an amount of credit, which a user must repay over a pre-defined period. A credit card is subject to the constant transaction rules with defined payment terms and penalties incurred when payment is not made; para. 9-10, 27: KEO World determines a transaction customer payment terms monetary limit and customer payment terms that differ for each subsequently single transaction by the customer with the card specific merchant may be configured to pull past financial transaction data and associated business data for the card customer from public and private data sources and extract customer data features as decision values. Thus, when the customer’s payment has not been received, extract the relating data; para. 86-87: the settlement card system and its KEO first computing system may extract data from many different external public data sources and transform the data and load it into a relational transaction database, for example, as an ETL process);
process, in connection with a transform phase of the ETL process, the extracted raw data to determine, based on the events data and the identity data, a communication type, a communication channel and a content for a communication to the particular individual (para. 8, 87-88: different AWS tools may be used to create automatic processes for extracting, transforming, and loading the information; communicate an authentication request and verify corporate client identity and bank account data from a financial institution, and select and extract client data features from public data sources; use computing endpoints to obtain corporate
client transaction data. Different computing endpoints may track transactions of a corporate client over time, such as over 24-hour periods, and extract and update client data features and client transaction data, which can be processed and transformed into new and updated client data features and client transaction data; para. 98: permit image and object uploads and updates to the customer transactions database by responding to inputs and provision backend services and custom http requests; para. 103-104,132: track events across channels and align them in time and find correlation between multi-channel behavior).
transform, in connection with the transform phase of the ETL process, the extracted raw data into communication data that includes the content, includes timing information for the communication data to be transmitted, and has a format used for the communication channel (para. 85-87: different attributes may be collected from public data sources corresponding to customer and corporate client features. They may be transformed into data as a user attribute string and stored with other user attribute strings and pre-approved loan amounts in the database system; extract data from many different external public data sources and transform the data and load it into a relational transaction database, for example, as an ETL; Different AWS tools may be used to create automatic processes for extracting, transforming, and loading the information process; para. 9-10: The customer may make payment to the first computing system for the transaction based upon payment terms determined by the first computing system for that specific, single transaction; update decision values and apply the machine learning model and a set of new rules to the updated decision values and determine a new customer monetary limit and payment terms for the subsequent, single transaction by the customer);
load, in connection with a load phase of the ETL process, the communication data into a second container to initiate a communication action (para. 85-87: extract data from many different external public data sources and transform the data and load it into a relational transaction database, for example, as an ETL process. After gathering and identifying a data scheme, the settlement card system and particularly the KEO first computing system may clean/transform data, such as changing data format and converting numerical data via a job process. Provisioning and data management may be reduced with scaling of resources required to run an ETL job, which may be managed on a scaled-out apache spark environment. Data may be stored in Parquet format in relational attribute databases, including S3 buckets; para. 111: a data warehouse may receive data from data sources that interoperate with ETL (extract, transform, load) jobs and machine learning components that in tum interoperate with a data store such as the Amazon simple cloud storage service (S3), and in a non-limiting example, Amazon Redshift as an internet data warehouse service);
wherein loading the communication data into the second container triggers the communication action (para. 56: once the pre-actions are performed, the state
machine can make the transition for the next step. The transition may fire some actions that are managed by the back end component; para. 60-61, 98: permit image and object uploads and updates to the customer transactions database by responding to inputs and provision backend services and custom http requests; para. 111, 120: once the new corporate client is recorded in the Data Warehouse and the initial Maximum Credit score generated, the settlement card system and particularly the KEO first computing system may initiate the process of adding and computing new attributes to the corporate client profile using the loan activities and acquiring all transactional data; para. 85-87: containers/databases);
transmit, via a second gateway network device of the system, based on the
communication action being triggered, and based on the transmission being queued or scheduled according to the timing information, in connection with the communication action (para. 56: produce a repayment scheduler as, for example,
to determine payment terms between others; para. 104: a due date for repayment of the amount may be established and the settlement card system may store data about repeated transactions with the customer that includes repayment data for each transaction; para. 123: bill payment transactions related to the corporate client's other transactions (type of bill, status of bill [ expired, early payment, on-time], amounts, date and time); the money transfer transactions (sent/received, sent by/received by, value, location, date and time)),
the communication data to an endpoint of the communication channel to cause a communication transmission service associated with the communication channel to transmit the communication that includes the content, using the communication type indicated by the communication data (fig. 2: payment gateway; para. 8: transfer a payment to the third computing system in the amount/content of the transaction; para. 87-88: communicate an authentication request and verify corporate client identity and bank account data from a financial institution, and select and extract client data features from public data sources, use computing endpoints to obtain corporate client transaction data. Different computing endpoints may track transactions of a corporate client over time; para. 121: the settlement card system matches relevant external attributes to a corporate client profile, generate a database of external data that are imported from a variety of public domain sources as the external data sources in an example. This external data may be continuously updated and correlated to corporate clients and linking to initial generic attributes, e.g., location linked attributes; purchase linked attributes over time; and other business related attributes of the corporate client; para. 106-107: the API operating with a cloud front but other types of network systems could be implemented and used besides the Amazon Web Services/AWS).
Even if Fidanza does not explicitly teach the limitation: a communication type, a communication channel, using the communication type indicated by the communication channel,
Cherubini et al. teaches said limitations at col. 3:35-43; col. 5:13-57: the notifications requests can be ordered based on the time at which corresponding notifications are to be generated and delivered, such as by transmission of the corresponding notification to a device that is associated with the user. The notification requests can be stored in the form of a queue or list that is constantly updated by the notification aggregator as new notifications are received; col. 6:44-45: a notification scheduled to be delivered by a pop-up message could instead be delivered by SMS or email/channel.
Cherubini also teaches
wherein the timing information is determined based on times at which the particular individual receiving the communication data has a probability of being available that is greater than a threshold probability (col. 5:24-25: predict, such as by calculating a probability, whether the user will be available to receive a notification; col. 8:29-30: if the probability is above the threshold, the notification is delivered according to the first notification strategy at operation; claim 1: determining, by the one or more computers, a probability that the user will be available to receive the notification from the device associated with the user at the first time based on the user information and the delivery strategy information; responsive to the probability that the user will be available at the first time exceeding a threshold, causing the notification to be output for display at the device associated with the user at the first time).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza and communication type, channel and user availability of Cherubini in order to effectively communicate to users and/or for displaying contents/messages for the user to better view and/or analyze/manipulate the available data.
Even if Fidanza, Cherubini et al. do not explicitly teach containers,
Oberle teaches at para. 43, 36: the cloud-native application runtime may also be associated with a framework to run distributed systems based on containers.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al., Cherubini and containers of Oberle in order to effectively provide storage for performing communication/actions between endpoint devices.
As per claims 2, 13, 19, Fidanza et al. teaches
wherein the one or more processors, to process the extracted raw data, are configured to: select, based on the events data and the identity data, the communication type from a set of communication type options and the communication channel from a set of communication channel options (para. 88, 108: the data center of the first computing system includes appropriate servers or processors, databases, and communications modules that communicate with a server corresponding to the KEO first computing system and the other second and/or third computing systems, which in a non-limiting example, could incorporate a corporate data center).
Even if Fidanza does not explicitly teach a communication type, a communication channel, and/or using the communication type, via the communication channel,
Matsuoka et al. teaches said limitations at para. 198: the member prefers task recommendation to be communicated via push notifications rather than SMS or email. The task generator may identify these details in the training data so as to generate task recommendations that are tailored to the member and in a manner preferable to the member; para. 201-202: tailor task recommendations to particular members, e.g., via selection of particular vendors that the member has used before or indicated a preference for, selection of particular travel accommodations the member has used before or indicated a preference for, selection of particular communication protocols and/or formats, selection of particular task recommendation formats, etc. e.g., through the chat interface, SMS, email, the task facilitation service, and/or the like; para. 53: the task facilitation service may maintain a web server that hosts one or more websites configured to present or otherwise make available an interface through which the member may access the task facilitation service and initiate the onboarding process; para. 274, 350.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al. and communication type and channel of Matsuoka in order to effectively communicate to users and/or for displaying contents/messages for the user to better view and/or analyze/manipulate the available data.
As per claim 3, Fidanza et al. teaches
wherein the first container and the second container (para. 87: data may be stored in Parquet format in relational attribute databases, including S3 buckets; para. 109: an “S3” bucket or database as a cloud storage in one example with one or more databases such as could be part of a data warehouse operative as the transaction database and provides visibility of the user activity since it records the API calls made on the account of the system) use a first cloud-based service (para. 26: a first computing system that may include at least one processor and memory, including a cache, shown at 112 and may operate as a server network or cloud network with a data hosting service), and the ETL process uses a second cloud-based service (para. 107-111: receive data from data sources that interoperate with ETL (extract, transform, load) jobs and machine learning components that in turn interoperate with a data store such as the Amazon simple cloud storage service (S3), and in a non-limiting example, Amazon Redshift as an internet data warehouse service).
Even if Fidanza does not explicitly teach the limitation container,
Matsuoka et al. teaches said limitations at para. 381: services provided by a computing resources provider include data analytics, containers etc.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al. and containers of Matsuoka in order to effectively facilitate the storing of users and/or relating data for processing.
As per claims 4, 12, Fidanza et al. teaches
wherein the one or more processors, to process the extracted raw data, are configured to: determine, based on the events data and the identity data, one or more of: an object or service of interest to the particular individual, or a level of interest that the particular individual has for the object or service, wherein the content is based on at least one of the object or service, or the level of interest (para. 84: the customer as a corporate client may have data features that may include behavior variables having information related to websites visited by the customer, product categories purchased by the customer, stores visited by the customer, ratings on e-commerce websites, and the consumer segment to which the customer belongs; para. 102-103: a behavioral profile for a customer may be generated using a customer conversation modeling or a multi-threaded analysis or any combination thereof. The behavioral profile may be based on segmentation with corporate client information provided via the contents of each transaction and using affinity and purchase path analysis to identify products that sell in conjunction with each other depending on promotional and seasonal basis and linking between purchases over time – thus, reflect the level of user interest; para. 110; permit the settlement card system to control
individual and group access in a secure manner and create and manage user identities and grant permissions for those users to access the different resources).
Even if Fidanza does not explicitly teach the limitation: “particular individual”,
Dayanandan teaches at para. 425: cloud infrastructure system provides the cloud services via different deployment models. In a public cloud model, cloud infrastructure system may be owned by a third party cloud services provider and the cloud services are offered to any general public customer, where the customer can be an individual or an enterprise.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al. and the customer can be an individual or an enterprise of Dayanandan in order to effectively provide services to a larger set of entities.
Matsuoka also teaches at para. 44: the task facilitation service may use the features to derive an interest of the member such as an interest in a particular musician, film, etc. The task facilitation service may then recommend tasks associated with that interest such as concert tickets, movies tickets, etc.; para. 56-57.
As per claim 5, Fidanza et al. teaches
wherein the one or more processors, to process the extracted raw data, are configured to: determine, based on the events data and the identity data, a communication-type preference for the particular individual, wherein the communication type and the communication channel are based on the communication-type preference (para. 88, 108: the data center of the first computing system includes appropriate servers or processors, databases, and communications modules that communicate with a server corresponding to the KEO first computing system and the other second and/or third computing systems, which in a non-limiting example, could incorporate a corporate data center).
Even if Fidanza does not explicitly teach the limitation: “particular individual”,
Dayanandan teaches at para. 425: cloud infrastructure system provides the cloud services via different deployment models. In a public cloud model, cloud infrastructure system may be owned by a third party cloud services provider and the cloud services are offered to any general public customer, where the customer can be an individual or an enterprise.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al. and the customer can be an individual or an enterprise of Dayanandan in order to effectively provide services to a larger set of entities.
Even if Fidanza, Dayanandan do not explicitly teach a communication type, a communication channel, and/or using the communication type, via the communication channel,
Matsuoka et al. teaches said limitations at para. 198: the member prefers task recommendation to be communicated via push notifications rather than SMS or email. The task generator may identify these details in the training data so as to generate task recommendations that are tailored to the member and in a manner preferable to the member; para. 201-202: tailor task recommendations to particular members, e.g., via selection of particular vendors that the member has used before or indicated a preference for, selection of particular travel accommodations the member has used before or indicated a preference for, selection of particular communication protocols and/or formats, selection of particular task recommendation formats, etc. e.g., through the chat interface, SMS, email, the task facilitation service, and/or the like; para. 53: the task facilitation service may maintain a web server that hosts one or more websites configured to present or otherwise make available an interface through which the member may access the task facilitation service and initiate the onboarding process; para. 274, 350.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al., Dayanandan and communication type and channel of Matsuoka in order to effectively communicate to users and/or for displaying contents/messages for the user to better view and/or analyze/manipulate the available data.
As per claims 6-7, Fidanza et al. teaches
wherein the extracted raw data identifies the entity, and wherein the one or more processors, to process the extracted raw data, are configured to: determine the communication channel based on at least one of a communication-type preference of the entity or a communication transmission service preference of the entity; wherein the communication type is a text message or an email message (para. 84-85, 102-103: a behavioral profile for a customer may be generated using a customer conversation modeling or a multi-threaded analysis or any combination thereof. The behavioral profile may be based on segmentation with corporate client information provided via the contents of each transaction and using affinity and purchase path analysis to identify products that sell in conjunction with each other depending on promotional and seasonal basis and linking between purchases over time; para. 108: appropriate servers or processors, databases, and communications modules that communicate with a server corresponding to the KEO first computing system and the other second and/or third computing systems).
Even if Fidanza does not explicitly teach a communication type preference and the communication channel,
Matsuoka et al. teaches said limitations at para. 198: the training data includes data that indicates the member's preferences, interests, and/or the like. For example, the training data indicates events that the member is likely to find of interest, particular vendors or third-party services for use in performing tasks, etc., the member prefers task recommendation to be communicated via push notifications rather than SMS or email. The task generator may identify these details in the training data so as to generate task recommendations that are tailored to the member and in a manner preferable to the member; para. 201-202: tailor task recommendations to particular members, e.g., via selection of particular vendors that the member has used before or indicated a preference for, selection of particular travel accommodations the member has used before or indicated a preference for, selection of particular communication protocols and/or formats, selection of particular task recommendation formats, etc. e.g., through the chat interface, SMS, email, the task facilitation service, and/or the like; para. 53: the task facilitation service may maintain a web server that hosts one or more websites configured to present or otherwise make available an interface through which the member may access the task facilitation service and initiate the onboarding process; para. 274, 350.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza et al. and communication type and channel of Matsuoka in order to effectively communicate to users and/or for displaying contents/messages for the user to better view and/or analyze/manipulate the available data.
As per claim 10, Fidanza et al. teaches
wherein the raw data is obtained using a first cloud-based serverless compute function, and wherein the communication data is transmitted using a second cloud-based serverless compute function (para. 29, 91: a database network may operate in a cloud or serverless environment and interoperate with the cache system as an in-memory data storage and may be operable to retrieve all required data with a low latency; para. 95: the database network may include an object database and store the data and objects to replicate or modify existing objects to make new objects, such as data relating to the decision values, and if accomplished, corporate client predictions. Different serverless databases may be used, and in an example, a Dynamo database (DynamoDB) may operate as a managed NoSQL database service as part of the database network).
As per claim 14, Fidanza et al. teaches
wherein the format is used by an application programming interface (API) for the communication channel (para. 8, 87-88: as an operational example, the data may enter the first computing system from flat text files and internal or external databases. Different AWS tools may be used to create automatic processes for extracting, transforming, and loading the information. When the source of data is an external database, the data may be consulted through an API; para. 107-109).
As per claim 18, Fidanza et al. teaches
transmit, via an application programming interface (API) endpoint, a request for the extracted raw data; receive, via the API endpoint, a response indicating the extracted raw data (para. 88: as an operational example, the data may enter the first computing system from flat text files and internal or external databases. Different AWS tools may be used to create automatic processes for extracting, transforming, and loading the information. When the source of data is an external database, the data may be consulted through an API; para. 107-111: receive data from data sources that interoperate with ETL (extract, transform, load) jobs and machine learning components that in turn interoperate with a data store such as the Amazon simple cloud storage service (S3), and in a non-limiting example, Amazon Redshift as an internet data warehouse service).
As per claim 21, Fidanza et al. teaches
wherein the first gateway network device and the second gateway network device may be the same gateway network device (para. 108: control over the virtual
networking environment, including IP Address ranges, subnets and configurations for route tables and network gateways; fig. 2: payment gateway/same gateway network device). Even if Fidanza, Dayanandan and Matsuoka et al. do not explicitly teach gateway devices,
Oberle teaches at para. 54: home automation system typically connects controlled devices to a central smart home hub (sometimes called a “gateway”); para. 65: routers between smart devices and the analytics cloud.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza, Dayanandan, Matsuoka and a central gateway of Oberle in order to effectively communicate between controlled devices.
As per claim 23, Fidanza teaches
wherein the endpoint is identified based on the communication channel by using a table or a mapping of endpoints to communication channels (para. 88: use computing endpoints to obtain corporate client transaction data. Different computing endpoints may track transactions of a corporate client over time, such as over 24 hour periods, and extract and update client data features and client transaction data, which can be processed and transformed into new and updated client data features and client transaction data).
As per claim 24, Fidanza teaches
wherein the endpoint is an application programming interface (API) endpoint of the communication channel, wherein the endpoint is identified based on the communication channel (para. 87-88: different AWS tools may be used to create automatic processes for extracting, transforming, and loading the information. When the source of data is an external database, the data may be consulted through an API; use computing endpoints to obtain corporate client transaction data. Different computing endpoints may track transactions of a corporate client over time, such as over 24 hour periods, and extract and update client data features and client transaction data, which can be processed and transformed into new and updated client data features and client transaction data).
Claim(s) 15, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fidanza et al. (US 20240169355) in view of Cherubini (US 9531651) and further in view of Oberle (US 20240146570) and Cella (US 20240118702).
As per claim 15, Fidanza et al. teaches at para. 49-50: the KYC/know-your-client process is also seen as an opportunity to understand the new customer as a customer, identify their needs and behaviors, create customized products, improve behaviors, and improve customer relationships.
Fidanza, Cherubini, Oberle do not teach CRM.
Cella teaches
wherein the extracted raw data is from a customer relationship management (CRM) system of the entity (para. 270: Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System; para. 282).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza, Cherubini, Oberle with the CRM teaching of Cella in order to effectively manage user needs and behaviors and thus, provide better services and/or goods to the users.
As per claim 17, Fidanza et al. teaches at para. 88-90: generate private access tokens and use computing endpoints to obtain corporate client transaction data. Different computing endpoints may track transactions of a corporate client over time, such as over 24 hour periods, and extract and update client data features and client transaction data, which can be processed and transformed into new and updated client data features and client transaction data.
Fidanza, Cherubini, Oberle do not teach a webhook endpoint.
Cella teaches wherein the one or more instructions, that cause the device to obtain the raw data, cause the device to: receive the extracted raw data via a webhook endpoint (para. 581: determine one or more data sources and types of data that feed or otherwise support each object, entity, or state that is depicted in the respective type of digital twin and may determine any internal or external software requests (e.g., API calls) that obtain the identified data types or other suitable data acquisitions mechanisms, such as webhooks, that configured to automatically receive data from an internal or external data source).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza, Cherubini, Oberle with client/user data extraction of Cella in order to effectively manage user interactions and thus, provide better/tailored services and/or goods to the users.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fidanza et al. (US 20240169355) in view of Cherubini (US 9531651) and further in view of Oberle (US 20240146570) and Kumar et al. (US 20240113904).
As per claim 22, Fidanza, Cherubini, Oberle do not teach wherein the determined timing for the communication data is determined based on times at which the particular individual receiving the communication data has a probability of being available, wherein the probability is greater than a threshold probability.
Kumar teaches said limitations at para. 115: automatically removing the particular meeting from a schedule of the particular user for a case in which the likelihood measure indicates that the likelihood measure fails to satisfy a prescribed threshold; fig. 5: individuals where communications are likely will happen; fig. 8; para. 39: collect facts about the scheduled meeting and the expected availability of the attendees.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fidanza, Cherubini, Oberle with client/user availability of Cella in order to effectively manage user interactions and thus, provide better/tailored services and/or needed communications to the users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li (US 11188393) teaches at para. 41: a system may satisfy a standard for high availability by ensuring an agreed level of operational performance, such as uptime, higher than a predefined threshold. High availability may be based on one or more of the following features: elimination of single points of failure, reliable crossover, and/or detection of failures as they occur. High availability may be defined as satisfying a threshold percentage of uptime over a period of time, such as a year.
Beaurepaire (20200098271) teaches at para. 91: the probability for the driver to arrive at the destination (3-minute walk from the user) in 10 minutes is 76%. Since 76% is higher than the next user required vehicle availability probability threshold (e.g., 75%), the vehicle sharing platform 105 proceeds to step 305. However, when the computed probability is lower than the next user required vehicle availability probability threshold.
Strong et al. (US 20190043201) teaches at para. 79-82: edge gateways or routers, cloud resources.
Murthy (US 20200311096) teaches at para. 46-47: an extract, transform, load (ETL) system, a data storage and query engine, and job scripts. The ETL system is typically an enterprise system (i.e., controlled by a separate enterprise or entity); para. 254: the computing device comprises a processor, memory, storage, an input device, a communication network interface, and an output device communicatively coupled to a communication channel. Bansod et al. (US 20200351345) teaches at para. 224: a text message or an email message.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINH BLACK/Examiner, Art Unit 2163 5/14/2026
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163
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