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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 23 April 2026 has been entered.
Status of Claims
This action is in reply to the RCE filed on 23 April 2026. Claims 1-2, 4, 6-12, 14, and 16-20 were amended. Claims 21-22 were newly added. Claims 1-22 are currently pending and have been examined.
Claim Objections
Claim 22 is objected to because of the following informalities: the element “a appointment” in line 5 appears to be a typographical error of “an appointment.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 and 21-22 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "person" in line 17. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination, this element will be considered to state “the purchaser.” Appropriate correction is required. Claims 2-10 and 21-22 inherit this deficiency.
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 1-22 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 and 21-22 fall within the category of a machine. Claims 11-19 fall within the category of a process. Claim 20 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 1-22 recite an abstract idea. Representative claim 1 recites:
(i) … identify anomalous transaction data associated with historical payment transactions of a purchaser and (ii) identify a present need of the purchaser based on the anomalous transaction data … using historical transaction data of the purchaser 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 purchaser;
receive purchaser data associated with the purchaser including payment transaction records representing payment transactions initiated by the purchaser with a merchant, the payment transaction records including at least (i) an account identifier associated with a payment account of the purchaser, (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 purchaser from the payment transaction records, (ii) identifying at least one present need of the purchaser 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 purchaser and qualified to address the at least one present need;
compare the one or more outputs … to payment transaction records of the purchaser to determine whether the purchaser has initiated a transaction associated with the identified at least one present need of the purchaser.
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 AI 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 that is different than the purchaser, the notification message including the identified at least one present need of the purchaser 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 system 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 purchaser has initiated a transaction associated with the geographic location area; and
if the purchaser 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 system 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 system of claim 3, wherein:
the processor is further programmed to: build a second training dataset including a plurality of historical purchaser records associated with the purchaser, wherein each of the historical purchaser record includes historical transaction data, historical purchaser data, and at least one historical need of the purchaser 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 system 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 system of claim 1, wherein:
the processor is further programmed to: apply the purchaser data to a trained AI model to generate one or more model outputs, wherein model outputs include an identified need of the purchaser and a severity score associated with each identified need of the purchaser; 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 purchaser computer device associated with the purchaser.
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 system of claim 1, wherein:
the processor is further programmed to: apply the purchaser data to the AI model to generate one or more model outputs, wherein model outputs include the identified need of the purchaser and a recurring payment of the purchaser, the recurring payment including a recurring payment deadline and a recurring payment amount;
retrieve a balance in the payment account of the purchaser; 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 purchaser.
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 system of claim 1, wherein:
the processor is further programmed to: receive one or more response messages from a purchaser computer device associated with the purchaser; 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 purchaser and a severity score associated with each identified updated need of the purchaser.
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 system of claim 1, wherein:
the processor is further programmed to: receive purchaser data associated with the purchaser being cared for by a caregiver, the purchaser data including sensor data collected by a sensor of a purchaser computer device associated with the purchaser, 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 system of claim 1, wherein:
the processor is further programmed to: receive purchaser data associated with a purchaser being cared for by a caregiver, the purchaser data including calendar data from a purchaser computer device associated with the purchaser, wherein calendar data includes an appointment time, appointment data, 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.
Dependent claim 21 recites the system 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 response time period associated with a threshold period of time over which the purchaser has not initiated a transaction associated with the identified at least one present need of the purchaser; and
if the tracking alert criteria is satisfied, 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 21 is ineligible.
Dependent claim 22 recites the system of claim 10, wherein:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising confirming that the purchaser has initiated a transaction associated with an appointment scheduled on the calendar on the same date as the appointment; and
if the tracking alert criteria is satisfied, 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 22 is ineligible.
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 18-22 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.);
[a component] configured to (i) identify anomalous transaction data associated with historical payment transactions of a purchaser and (ii) identify a present need of the purchaser based on the anomalous transaction data… (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 and the [component] (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 purchaser data associated with the purchaser including payment transaction records representing payment transactions initiated by the purchaser 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 purchaser data into the at least one AI model to generate one or more outputs including (i) identifying anomalous transaction data of the purchaser 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 purchaser 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 that is different than the purchaser, the notification message including the identified at least one present need of the purchaser 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. See also [0044] for sending information specifically to the healthcare provider instead of the patient.).
Miller does not disclose:
an AI modeling component for storing at least one AI model configured to [perform the identification], the at least one AI model trained using historical transaction data of the purchaser 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 purchaser; and
[the recommendation] including identifying at least one service provider located within a predefined proximity to the purchaser and qualified to address the at least one present need;
compare the one or more outputs of the AI model to payment transaction records of the purchaser to determine whether the purchaser has initiated a transaction associated with the identified at least one present need of the purchaser.
Kinsey teaches:
[the component is] an AI modeling component for storing at least one AI model configured to [perform the identification] (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, and merchant and service recommendations. This meets the broadest reasonable interpretation of identifying a present need. [0247] this includes transaction histories of customers.), the at least one AI model trained using historical transaction data of the purchaser and 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 purchaser (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.); and
[the recommendation] including identifying at least one service provider located within a predefined proximity to the purchaser 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.);
compare the one or more outputs of the AI model to payment transaction records of the purchaser to determine whether the purchaser has initiated a transaction associated with the identified at least one present need of the purchaser (See Kinsey [0313] customer can accept the merchant recommendation as suggested by the Marketplace Server. The server checks that the customer has accepted or rejected the recommendation. This input falls under the broadest reasonable interpretation of “payment transaction records” because scheduling appointments is directly linked to paying for those appointments. See also [0212]-[0214].).
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 system, comprising:
if the purchaser 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 purchaser 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 purchaser 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 system, 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 system, comprising:
the processor is further programmed to: apply the purchaser data to a trained AI model to generate one or more model outputs, wherein model outputs include an identified need of the purchaser and a severity score associated with each identified need of the purchaser; 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 purchaser computer device associated with the purchaser.
Kinsey teaches:
the processor is further programmed to: apply the purchaser 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 purchaser and a severity score associated with each identified need of the purchaser (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 purchaser computer device associated with the purchaser (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 system, comprising:
the processor is further programmed to: receive one or more response messages from a purchaser computer device associated with the purchaser; 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 purchaser and a severity score associated with each identified updated need of the purchaser.
Kinsey teaches:
the processor is further programmed to: receive one or more response messages from a purchaser computer device associated with the purchaser (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 purchaser and a severity score associated with each identified updated need of the purchaser (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 system, comprising:
the processor is further programmed to: receive purchaser data associated with the purchaser being cared for by a caregiver, the purchaser data including sensor data collected by a sensor of a purchaser computer device associated with the purchaser, wherein sensor data includes location data.
Kinsey teaches:
the processor is further programmed to: receive purchaser data associated with the purchaser 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 purchaser data including sensor data collected by a sensor of a purchaser computer device associated with the purchaser, 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 system, comprising:
the processor is further programmed to: receive purchaser data associated with a purchaser being cared for by a caregiver, the purchaser data including calendar data from a purchaser computer device associated with the purchaser, wherein calendar data includes an appointment time, an appointment data, and an appointment location.
Kinsey teaches:
the processor is further programmed to: receive purchaser data associated with a purchaser 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 purchaser data including calendar data from a purchaser computer device associated with the purchaser, wherein calendar data includes an appointment time, an appointment data, 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.
Regarding claim 21, Miller in view of Kinsey discloses the system of claim 1 as discussed above. Miller further discloses a system, comprising:
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. See also [0044] for sending information specifically to the healthcare provider instead of the 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 response time period associated with a threshold period of time over which the purchaser has not initiated a transaction associated with the identified at least one present need of the purchaser; and
if the tracking alert criteria is satisfied, [perform some action].
Kinsey teaches:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising a response time period associated with a threshold period of time over which the purchaser has not initiated a transaction associated with the identified at least one present need of the purchaser (See Kinsey [0269] system can be used to verify the appointment and the status of the transaction. [0318] system can determine if a person will be late for an appointment. This can be based on preferences for specific times (how long to monitor and how late they will accept the customer). [0320] system can be used to cancel or reschedule the appointment. [0349] system can determine late arrival for an appointment. See also [0290].); and
if the tracking alert criteria is satisfied, [perform some action] (See Kinsey [0318] system can determine if a person will be late for an appointment. This can be based on preferences for specific times (how long to monitor and how late they will accept the customer). [0320] system can be used to cancel or reschedule the appointment.).
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 22, Miller in view of Kinsey discloses the system of claim 10 as discussed above. Miller further discloses a system, comprising:
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. See also [0044] for sending information specifically to the healthcare provider instead of the 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 confirming that the purchaser has initiated a transaction associated with an appointment scheduled on the calendar on the same date as the appointment; and
if the tracking alert criteria is satisfied, [perform some action].
Kinsey teaches:
the processor is further programmed to: receive, from the caregiver computer device, at least one of a tracking alert criteria comprising confirming that the purchaser has initiated a transaction associated with an appointment scheduled on the calendar on the same date as the appointment (See Kinsey [0269] system can be used to verify the appointment and the status of the transaction. [0390] system can check for payment at time of appointment. [0412] system includes a service delivery verification, to verify that the patient actually received the service.); and
if the tracking alert criteria is satisfied, [perform some action] (See Kinsey [0318] system can determine if a person will be late for an appointment. This can be based on preferences for specific times (how long to monitor and how late they will accept the customer). [0320] system can be used to cancel or reschedule the appointment.).
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]).
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 purchaser records associated with the purchaser, wherein each of the historical purchaser record includes historical transaction data [and] historical purchaser data, and
[the historical purchaser record includes]at least one historical need of the purchaser 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 purchaser records associated with the purchaser (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 purchaser record includes historical transaction data [and] historical purchaser 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 purchaser record includes] at least one historical need of the purchaser 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 purchaser data to the AI model to generate one or more model outputs, wherein model outputs include the identified need of the purchaser and a recurring payment of the purchaser,
the recurring payment including a recurring payment deadline and a recurring payment amount;
retrieve a balance in the payment account of the purchaser; 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 purchaser.
Kinsey teaches:
the processor is further programmed to: apply the purchaser data to the AI model to generate one or more model outputs, wherein model outputs include the identified need of the purchaser and a recurring payment of the purchaser (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 purchaser (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 purchaser (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 27 March 2026, 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 (an improvement to technology or a technological field) under Step 2A Prong Two (see Applicant Remarks pages 12-24). This is not persuasive. The additional elements in the claims, including the broadly recited artificial intelligence model, 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 27 March 2026, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of previously uncited portions of the Kinsey reference.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN L HANKS whose telephone number is (571)270-5080. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.L.H./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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