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 action is in reply to the claims filed on 17 November 2025. Claims 1-4, 12-14, and 20 were amended. Claims 1-20 are currently 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 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-20 fall within one or more statutory categories. Claims 1-10 fall within the category of a machine. Claims 11-19 fall within the category of a process. Claim 20, if amended to recite “non-transitory,” falls within the category of a manufacture.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-20 recite an abstract idea. Representative claim 1 recites:
… identify anomalous transaction data of the person and identify a present need of the person based on the anomalous transaction data … using historical transaction data of the person and historical user transaction data for a plurality of users that is labeled with associated user need data, wherein the plurality of users are included within a group having similar demographics of the person;
receive person data associated with the person including payment transaction records representing payment transactions initiated by the person with a merchant, the payment transaction records including at least (i) an account identifier associated with a payment account of the person, (ii) a merchant identifier for identifying a merchant involved in the transaction, and (iii) a description of a good or service purchased;
(i) identifying anomalous transaction data of the person from the payment transaction records, (ii) identifying at least one present need of the person from the anomalous transaction data, and (iii) outputting a recommendation for addressing the at least one present need including identifying at least one service provider located within a predefined proximity to the person and qualified to address the at least one present need.
Therefore, the claim as a whole is directed to “identifying user needs,” which is an abstract idea because it is a method of organizing human activity. “Identifying user needs” is considered to be a method of organizing human activity because it is an example managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims include reviewing the activity of a person to find patterns. It is also an example of a fundamental economic principles or practices (including hedging, insurance, mitigating risk), that of targeted advertising.
Similarly, this is also an example of a mental process, concepts performed in the human mind (including an observation, evaluation, judgment, opinion) with the aid of pen and paper.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
at least one memory device;
an AI modeling component for storing at least one model …, the at least one model [is] trained; and
at least one processor in communication with the at least one memory device and the AI modeling component, the at least one processor programmed to: [perform the abstract idea discussed above].
transmit a notification message to a caregiver computer device associated with a caregiver of the person, the notification message including the identified at least one present need of the person and the outputted recommendation.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional elements merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, transmitting data between devices merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Accordingly, 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. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, transmitting data between devices merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) and is considered to recite well-understood, routine, and conventional activity (see MPEP § 2106.05(d)(II), “Receiving or transmitting data over a network”). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising a geographic location area;
if the tracking alert criteria is satisfied, input payment transaction records into the at least one AI model to generate one or more outputs including an indication of whether the person has initiated a transaction associated with the geographic location area; and
if the person has not initiated the transaction, transmit a notification message to the caregiver computer device.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 2 is ineligible.
Dependent claim 3 recites the method of claim 1, wherein:
the processor is further programmed to: build a first training dataset including a plurality of historical user records associated with a plurality of historical users, wherein each of the historical user records includes historical transaction data, historical user data, and at least one historical need of the historical user; and
train, in a first training session, the AI model using the first training dataset to generate the trained caregiving model.
This merely further limits the abstract idea (mathematical calculation) of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible.
Dependent claim 4 recites the method of claim 1, wherein:
the processor is further programmed to: build a second training dataset including a plurality of historical person records associated with the person, wherein each of the historical person record includes historical transaction data, historical person data, historical person data, and at least one historical need of the person as previously determined using the AI model; and
re-train, in a second training session, the AI model using the second training dataset to generate the trained AI model.
This merely further limits the abstract idea (mathematical calculation) of claim 1 discussed above and does not provide further additional elements. Therefore, claim 4 is considered to be ineligible.
Dependent claim 5 recites the method of claim 1, wherein:
the processor is further programmed to: build a first training dataset including a plurality of historical user records and group records, the historical user records are associated with a plurality of historical users, wherein each historical user records includes historical transaction data, historical user data, and at least one historical need of the historical user as previously determine using the AI model, the group records are associated with a group of historical users, wherein group records include an average transaction amount for a plurality of users within the group; and
re-train, in a first training session, the AI model using the first training dataset to generate the trained AI model.
This merely further limits the abstract idea (mathematical calculation) of claim 1 discussed above and does not provide further additional elements. Therefore, claim 5 is considered to be ineligible.
Dependent claim 6 recites the method of claim 1, wherein:
the processor is further programmed to: apply the person data to a trained AI model to generate one or more model outputs, wherein model outputs include an identified need of the person and a severity score associated with each identified need of the person; and
compare the severity score to a severity criteria, when the severity criteria are satisfied, transmit the notification message to the caregiver computer device, and if the severity criteria are not satisfied transmit a prompting message to a person computer device associated with the person.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
the processor is further programmed to: apply the person data to the trained AI model to generate one or more model outputs, wherein model outputs include the identified need of the person and a recurring payment of the person, the recurring payment including a recurring payment deadline and a recurring payment amount;
retrieve a balance in the payment account of the person; and
compare the retrieved balance to the recurring payment amount in advance of the recurring payment deadline, and when the balance is less than the recurring payment amount, transmit a request message to the caregiver computer device, wherein the request message includes an authorization to transfer funds from a payment account of the caregiver to the payment account of the person.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 7 is considered to be ineligible.
Dependent claim 8 recites the method of claim 1, wherein:
the processor is further programmed to: receive one or more response messages from a person computer device associated with the person; and
apply the response messages to the trained AI model to generate one or more model outputs, wherein model outputs include an identified updated need of the person and a severity score associated with each identified updated need of the person.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 8 is ineligible.
Dependent claim 9 recites the method of claim 1, wherein:
the processor is further programmed to: receive person data associated with the person being cared for by a caregiver, the person data including sensor data collected by a sensor of a person computer device associated with the person, wherein sensor data includes location data.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 9 is ineligible.
Dependent claim 10 recites the method of claim 1, wherein:
the processor is further programmed to: receive person data associated with a person being cared for by a caregiver, the person data including calendar data from a person computer device associated with the person, wherein calendar data includes an appointment time and an appointment location.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 10 is considered to be ineligible.
Claims 11-20 are parallel in nature to claims 1-9. Accordingly claims 11-20 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
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.
Claims 1-3, 6, 8-13, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (U.S. 2011/0125529), hereinafter “Miller,” in view of Kinsey, II et al. (U.S. 2014/0136443), hereinafter “Kinsey.”
Regarding Claim 1, Miller discloses a computer system for analyzing data using artificial intelligence (AI) modeling tools to detect anomalous transaction data associated with a person, the computer system comprising:
at least one memory device (See Miller [0018] the system includes memory used in connection with a processor.);
…identify a present need of the person based on the anomalous transaction data … using historical transaction data of the person (See Miller [0043] the system gathers information related to customer purchases related to health care products to predict a disease state of the customer. The purchases may include tags that associate them with the treatment of a particular condition. The information on all purchases may together point to different disease or condition that need be treated.);
at least one processor in communication with the at least one memory device … (See Miller [0021] the system includes memory used in connection with a processor and various modules, including a disease state progression marketing engine.), the at least one processor programmed to:
receive person data associated with the person including payment transaction records representing payment transactions initiated by the person with a merchant (See Miller [0030] the system can use purchase history with the disease state progression marketing system. The purchase history may include data related to purchases the customer routinely makes or has made at pharmacies.) the payment transaction records including at least (i) an account identifier associated with a payment account of the person (See Miller [0031] the system can store a customer profile that includes information such as credit card information or other payment information.) (ii) a merchant identifier for identifying a merchant involved in the transaction (See Miller [0032] the customer record can include a prescribing physician connected to the prescription made to the customer. A prescribing physician meets the broadest reasonable interpretation of “a merchant involved in the transaction.”), and (iii) a description of a good or service purchased (See Miller [0030] The purchase history data may include any product sold by the pharmacies and purchased by a customer, whether in person or online. This is understood to include a description of the product purchased at the pharmacy.);
input the person data into the [the model] to generate one or more outputs including (i) identifying anomalous transaction data of the person from the payment transaction records (See Miller [0043] the system gathers information related to customer purchases related to health care products to predict a disease state of the customer. The purchases may include tags that associate them with the treatment of a particular condition. The information on all purchases may together point to different disease or condition that need be treated. This meets the broadest reasonable interpretation of “anomalous transaction data.”), (ii) identifying at least one present need of the person from the anomalous transaction data, and (iii) outputting a recommendation for addressing the at least one present need (See Miller [0045] the system can determine certain health care products associated with any combination of progression states, conditions or symptoms, and time period data. The system can anticipate the customer's disease state progression and send marketing data corresponding to products known to treat any other conditions that are likely to occur during clinical stage in combination with the conditions the customer currently exhibits (i.e. a recommendation and a present need).)…;
transmit a notification message to a caregiver computer device associated with a caregiver of the person, the notification message including the identified at least one present need of the person and the outputted recommendation (See Miller [0045] the system can determine certain health care products associated with any combination of progression states, conditions or symptoms, and time period data may be marketed to the customer by sending marketing information to that customer. [0025] the term “customer” can include a caregiver taking care of a patient.).
Miller does not disclose:
an AI modeling component for storing at least one model configured to identify anomalous transaction data of the person and identify a present need of the person based on the anomalous transaction data, the at least one model trained … historical user transaction data for a plurality of users that is labeled with associated user need data, wherein the plurality of users are included within a group having similar demographics of the person;
including identifying at least one service provider located within a predefined proximity to the person and qualified to address the at least one present need.
Kinsey teaches:
an AI modeling component for storing at least one model configured to identify anomalous transaction data of the person and identify a present need of the person based on the anomalous transaction data (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations.), the at least one model trained using … historical user transaction data for a plurality of users that is labeled with associated user need data (See Kinsey [0217] the historical data used to train the model can include customer attributes, merchant attributes, service purchases by customers, services offered by merchants, service prices charged by merchants and costs incurred by customers, service completion times, customer and merchant timeliness, ratings, issue resolutions, and other data related to a customer, merchant, or service.), wherein the plurality of users are included within a group having similar demographics of the person (See Kinsey [0308] the system provides a reasonably accurate prediction, a classifier of the system will have been trained to predict whether a given customer (having certain customer attributes) would request the service using the historical data. These customer attributes can include identification attributes including gender, which is a demographic.);
including identifying at least one service provider located within a predefined proximity to the person and qualified to address the at least one present need (See Kinsey [0218] system can include attributes related to location preferences. [0214] this paragraph gives an example of automatically matching consumers to merchants within a specific radius that offers the good or service.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 2, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller further discloses a method, comprising:
if the person has not initiated the transaction, transmit a notification message to the caregiver computer device (See Miller [0045] the system can determine certain health care products associated with any combination of progression states, conditions or symptoms, and time period data may be marketed to the customer by sending marketing information to that customer. [0025] the term “customer” can include a caregiver taking care of a patient.).
Miller does not disclose:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising a geographic location area;
if the tracking alert criteria is satisfied, input payment transaction records into the at least one AI model to generate one or more outputs including an indication of whether the person has initiated a transaction associated with the geographic location area.
Kinsey teaches:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising a geographic location area (See Kinsey [0250] system can set a reminder for “no eating or drinking” when a scheduled appointment relates to blood test or surgery, and monitor location for when the user enters a restaurant.);
if the tracking alert criteria is satisfied, input payment transaction records into the at least one AI model to generate one or more outputs including an indication of whether the person has initiated a transaction associated with the geographic location area` (See Kinsey [0217] the training data for a classifier can include geographic location of a customer. Therefore, it is understood that location would be an input for the trained model. [0250] the system can be adapted to monitor the conditions of a person's calendar or geographical location to remind them of the "no eating or drinking" requirement when, for example, they have a dinner appointment on their calendar or their mobile device indicates that they have just entered a fast food restaurant location.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 3, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a method, comprising:
the processor is further programmed to: build a first training dataset including a plurality of historical user records associated with a plurality of historical users, wherein each of the historical user records includes historical transaction data, historical user data, and at least one historical need of the historical user; and
train, in a first training session, the AI model using the first training dataset to generate the trained caregiving model.
Kinsey teaches:
the processor is further programmed to: build a first training dataset including a plurality of historical user records associated with a plurality of historical users, wherein each of the historical user records includes historical transaction data, historical user data, and at least one historical need of the historical user (See Kinsey [0217] the historical data used to train the model can include customer attributes, merchant attributes, service purchases by customers, services offered by merchants, service prices charged by merchants and costs incurred by customers, service completion times, customer and merchant timeliness, ratings, issue resolutions, and other data related to a customer, merchant, or service. [0308] this includes data from different customers that have previously requested that service.); and
train, in a first training session, the AI model using the first training dataset to generate the trained caregiving model (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 6, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a method, comprising:
the processor is further programmed to: apply the person data to a trained AI model to generate one or more model outputs, wherein model outputs include an identified need of the person and a severity score associated with each identified need of the person; and
compare the severity score to a severity criteria, when the severity criteria are satisfied, transmit the notification message to the caregiver computer device, and if the severity criteria are not satisfied transmit a prompting message to a person computer device associated with the person.
Kinsey teaches:
the processor is further programmed to: apply the person data to a trained AI model to generate one or more model outputs (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations.), wherein model outputs include an identified need of the person and a severity score associated with each identified need of the person (See Kinsey [0213] the user can provide preferences which can be used for matching with service providers, and can be forced to meet a specific preference matching threshold. The use of matching thresholds indicates the calculation of a score to compare to that threshold.); and
compare the severity score to a severity criteria (See Kinsey [0213] the user can provide preferences which can be used for matching with service providers, and can be forced to meet a specific preference matching threshold. The use of matching thresholds indicates the calculation of a score to compare to that threshold. The matching threshold meets the broadest reasonable interpretation of “a severity criteria”.), when the severity criteria are satisfied, transmit the notification message to the caregiver computer device, and if the severity criteria are not satisfied transmit a prompting message to a person computer device associated with the person (See Kinsey [0393] the user can be prompted to set up classifying details for classifying tasks to be automatically addressed when conditions are met or send further prompt when conditions are not met.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 8, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a method, comprising:
the processor is further programmed to: receive one or more response messages from a person computer device associated with the person; and
apply the response messages to the trained AI model to generate one or more model outputs, wherein model outputs include an identified updated need of the person and a severity score associated with each identified updated need of the person.
Kinsey teaches:
the processor is further programmed to: receive one or more response messages from a person computer device associated with the person (See Kinsey [0213] the user can provide preferences which can be used for matching with service providers.); and
apply the response messages to the trained AI model to generate one or more model outputs (See Kinsey [0217] the system can be trained using customer attributes. [0218] the customer attributes can include the customer preferences from [0213].), wherein model outputs include an identified updated need of the person and a severity score associated with each identified updated need of the person (See Kinsey [0213] the user can provide preferences which can be used for matching with service providers, and can be forced to meet a specific preference matching threshold. The use of matching thresholds indicates the calculation of a score to compare to that threshold.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 9, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a method, comprising:
the processor is further programmed to: receive person data associated with the person being cared for by a caregiver, the person data including sensor data collected by a sensor of a person computer device associated with the person, wherein sensor data includes location data.
Kinsey teaches:
the processor is further programmed to: receive person data associated with the person being cared for by a caregiver (See Kinsey [0245] system can track, monitor, and notify of medical conditions using the multitude of devices that exist to track such things as blood pressure and notify the person’s doctor.), the person data including sensor data collected by a sensor of a person computer device associated with the person, wherein sensor data includes location data (See Kinsey [0318] the system can use a customer's device (e.g., a smart phone, a smart watch, smart glasses, a portable computer, a tablet computer, etc.) to monitor their location.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claim 10, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a method, comprising:
the processor is further programmed to: receive person data associated with a person being cared for by a caregiver, the person data including calendar data from a person computer device associated with the person, wherein calendar data includes an appointment time and an appointment location.
Kinsey teaches:
the processor is further programmed to: receive person data associated with a person being cared for by a caregiver (See Kinsey [0245] system can track, monitor, and notify of medical conditions using the multitude of devices that exist to track such things as blood pressure and notify the person’s doctor.), the person data including calendar data from a person computer device associated with the person, wherein calendar data includes an appointment time and an appointment location (See Kinsey [0250] the system can use the customer’s calendar to monitor appointment times. [0251] the system can monitor the customer location as related to the appointment location. Therefore, the appointment location is also monitored.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Regarding claims 11-13, 16, and 18-19, Miller in view of Kinsey discloses the system of claims 1-3, 6, and 8-9 as discussed above. Claims 11-13, 16, and 18-19 recite a method that is substantially similar to the method performed by the system of claims 1-3, 6, and 8-9. Accordingly, claim 11-13, 16, and 18-19 is rejected based on the same analysis.
Regarding claim 20, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Claim 20 recites a storage medium storing a method that is substantially similar to the method performed by the system of claim 1. Accordingly, claim 20 is rejected based on the same analysis.
Claim(s) 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (U.S. 2011/0125529), hereinafter “Miller,” in view of Kinsey, II et al. (U.S. 2014/0136443), hereinafter “Kinsey,” and further in view of Hsiao et al. (U.S. 2016/0110794), hereinafter “Hsiao.”
Regarding Claim 4, Miller in view of Kinsey discloses the system of claim 3 as discussed above. Miller does not further disclose a system, wherein:
the processor is further programmed to: build a second training dataset including a plurality of historical person records associated with the person, wherein each of the historical person record includes historical transaction data [and] historical person data, and
[the historical person record includes]at least one historical need of the person as previously determined using the AI model; and
re-train, in a second training session, the AI model using the second training dataset to generate the trained AI model.
Kinsey teaches:
the processor is further programmed to: build a second training dataset including a plurality of historical person records associated with the person (See Kinsey [0106] the classifier can be trained using historical data. [0105] this historical data includes attributes about the first customer, not just the different customers discussed elsewhere in this disclosure.), wherein each of the historical person record includes historical transaction data [and] historical person data … (See Kinsey [0217] the historical data used to train the model can include customer attributes, merchant attributes, service purchases by customers, services offered by merchants, service prices charged by merchants and costs incurred by customers, service completion times, customer and merchant timeliness, ratings, issue resolutions, and other data related to a customer, merchant, or service.); and
re-train, in a second training session, the AI model using the second training dataset to generate the trained AI model (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Hsiao teaches:
[the historical person record includes] at least one historical need of the person as previously determined using the AI model (See Hsiao [0031] the system can use iterative learning and prediction process may run continuously to provide a never-ending learning of model. The system can constantly and continuously update itself using user feedback, which provides an assurance in a level of quality and user satisfaction, which in turn yields more abundant and informative training events for refreshing the model. See also [0032].).
The system of Hsiao is applicable to the disclosure of Miller in view of Kinsey as they both share characteristics and capabilities, namely, they are directed to recommending products/services to a user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include feedback training as taught by Hsiao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to provide an assurance in a level of quality and user satisfaction, which in turn yields more abundant and informative training events for refreshing the model (see Hsiao [0031]).
Regarding claim 5, Miller in view of Kinsey discloses the system of claim 3 as discussed above. Miller does not further disclose a system, wherein:
the processor is further programmed to: build a first training dataset including a plurality of historical user records and group records, the historical user records are associated with a plurality of historical users, wherein each historical user records includes historical transaction data [and] historical user data;
[the historical user records include] at least one historical need of the historical user as previously determine using the AI model,
the group records are associated with a group of historical users, wherein group records include an average transaction amount for a plurality of users within the group; and
re-train, in a first training session, the AI model using the first training dataset to generate the trained AI model.
Kinsey teaches:
the processor is further programmed to: build a first training dataset including a plurality of historical user records and group records, the historical user records are associated with a plurality of historical users, wherein each historical user records includes historical transaction data [and] historical user data (See Kinsey [0217] the historical data used to train the model can include customer attributes, merchant attributes, service purchases by customers, services offered by merchants, service prices charged by merchants and costs incurred by customers, service completion times, customer and merchant timeliness, ratings, issue resolutions, and other data related to a customer, merchant, or service. [0308] this includes data from different customers that have previously requested that service.);
the group records are associated with a group of historical users, wherein group records include an average transaction amount for a plurality of users within the group (See Kinsey [0352] the system can have records average amounts paid. [0260] the system can have records for average amounts of time to complete transactions.); and
re-train, in a first training session, the AI model using the first training dataset to generate the trained AI model (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Hsiao teaches:
[the historical user records include] at least one historical need of the historical user as previously determine using the AI model (See Hsiao [0031] the system can use iterative learning and prediction process may run continuously to provide a never-ending learning of model. The system can constantly and continuously update itself using user feedback, which provides an assurance in a level of quality and user satisfaction, which in turn yields more abundant and informative training events for refreshing the model. See also [0032].).
The system of Hsiao is applicable to the disclosure of Miller in view of Kinsey as they both share characteristics and capabilities, namely, they are directed to recommending products/services to a user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include feedback training as taught by Hsiao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to provide an assurance in a level of quality and user satisfaction, which in turn yields more abundant and informative training events for refreshing the model (see Hsiao [0031]).
Regarding claims 14-15, Miller in view of Kinsey and Hsiao discloses the system of claims 14-15 as discussed above. Claims 14-15 recite a method that is substantially similar to the method performed by the system of claims 4-5. Accordingly, claims 14-15 are rejected based on the same analysis.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (U.S. 2011/0125529), hereinafter “Miller,” in view of Kinsey, II et al. (U.S. 2014/0136443), hereinafter “Kinsey,” and further in view of Connors et al. (U.S. 2015/0073959), hereinafter “Connors.”
Regarding Claim 7, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller does not further disclose a system, wherein:
the processor is further programmed to: apply the person data to the trained AI model to generate one or more model outputs, wherein model outputs include the identified need of the person and a recurring payment of the person,
the recurring payment including a recurring payment deadline and a recurring payment amount;
retrieve a balance in the payment account of the person; and
compare the retrieved balance to the recurring payment amount in advance of the recurring payment deadline, and when the balance is less than the recurring payment amount, transmit a request message to the caregiver computer device, wherein the request message includes an authorization to transfer funds from a payment account of the caregiver to the payment account of the person.
Kinsey teaches:
the processor is further programmed to: apply the person data to the trained AI model to generate one or more model outputs, wherein model outputs include the identified need of the person and a recurring payment of the person (See Kinsey [0217] system can use machine learning techniques that include training using various historical data. This can be used to make predictions about customer or merchant behaviors, merchant and service recommendations. [0428] the system can include functionality to administer various recurring appointments with merchants, which are understood to be connected to recurring payments.).
The system of Kinsey is applicable to the disclosure of Miller as they both share characteristics and capabilities, namely, they are directed to monitoring transactions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include artificial intelligence and tracking elements as taught by Kinsey. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to Combine in order address a need for automation and innovation in the way that consumers purchase and consume locally-delivered consumer services (see Kinsley [0003]).
Connors teaches:
the recurring payment including a recurring payment deadline and a recurring payment amount (See Connors [0035] a user can share particular financial transactions, expenses incurred, and upcoming bills. These upcoming bills meet the broadest reasonable interpretation of recurring payment deadline and amount.);
retrieve a balance in the payment account of the person (See Connors [0049] user's financial information can be provided for presentation on the user device. The system can provide the most recent financial information, e.g., this month's transactions, account balances, debits, credits, and so on.); and
compare the retrieved balance to the recurring payment amount in advance of the recurring payment deadline, and when the balance is less than the recurring payment amount, transmit a request message to the caregiver computer device (See Connors [0095] the system can allow for notifications about shared accounts, including low account balance and bill reminders. [0111] reminders can be for low balances on financial accounts used for funding monthly bills.), wherein the request message includes an authorization to transfer funds from a payment account of the caregiver to the payment account of the person (See Connors [0063] Sharing a financial account means that a user can perform operations on the user's financial accounts. Some examples of operations that can be performed on another user's financial accounts include viewing financial transactions of the other user's shared financial account, providing comments on particular financial transactions of the shared financial account, identifying financial transactions and providing money to the user to reimburse the user for the financial transaction, and transferring money from the user's shared financial account to a financial account of the user to pay a bill.).
The system of Connors is applicable to the disclosure of Miller in view of Kinsey as they both share characteristics and capabilities, namely, they are directed to monitoring finances. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Miller to include bill payment monitoring elements as taught by Connors. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Miller in order to allow a child and an aging parent to establish a financial collaboration group that permits the child to monitor financial accounts of the parent (see Connors [0009]).
Regarding claim 17, Miller in view of Kinsey and Connors discloses the system of claim 7 as discussed above. Claim 17 recites a method that is substantially similar to the method performed by the system of claim 7. Accordingly, claim 17 is rejected based on the same analysis.
Response to Arguments
Applicant's arguments filed 17 November 2025, with respect to the 35 U.S.C. §101 rejection of the claims, have been fully considered but they are not persuasive. First, Applicant argues that the claims are not directed to an abstract idea because the claim as a whole integrates the judicial exception into a practical application under Step 2A Prong Two (see Applicant Remarks pages 11-19). This is not persuasive. The additional elements in the claims do not provide an improvement to technology or other technological field, as specified in the MPEP, but instead amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, transmitting data between devices merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). This is not enough to integrate the abstract idea into a practical application.
Finally, Applicant argues that the claims ae directed to significantly more under Step 2B (see Applicant Remarks pages 19-20). This is not persuasive. Similar to the discussion above, the additional elements in the claims amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, transmitting data between devices merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). This is also considered to recite well-understood, routine, and conventional activity (see MPEP § 2106.05(d)(II), “Receiving or transmitting data over a network”). Therefore, the claims do not include significantly more than the judicial exception. Accordingly, the claims remain rejected as being directed to ineligible subject matter.
Applicant's arguments filed 17 November 2025, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered but they are not persuasive. Applicant argues that the combination of the Miller and Kinsey references does not teach the use of historical data of the user as well as historical data of other users (see Applicant Remarks pages 20-22). This is not persuasive. As part of this argument, Applicant points to the use of the phrase “different customers” in Kinsey to show that the model is not trained using data from the current user. However, this use of the word “different” does not exclude the present user, but can reasonably be read to mean that the data used by the system is made up of data from separate customers, or in other words, more than one customer’s purchase history. In contrast, Kinsey states that the historical records include services purchased by customers (see Kinsey [0217]). While it is true that this does not explicitly recite the use of the current customers purchase history, it is not reasonable to say that it specifically excludes their purchase history. With that in mind, Miller explicitly recites the use of the current users purchase history to determine products to recommend (see Miller [0043]). It is the combination of both references together that teach the use of the current user’s data and other customer’s data to train the a model and make the recommendation. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Accordingly, the claims remain rejected as being obvious over the cited prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Merz et al. (U.S. 20140279185) discloses a system and method for recommending merchants/services based on user transactions.
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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/B.L.H./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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