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 .
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.
Claim(s) 1-4, 7-11, 14-18 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method (claims 1-4 and 7), an apparatus (claims 8-11 and 14), or One or more non-transitory computer-readable storage media (claims 15-18 and 21) and thus fall into at least one statutory category enumerated in 35 U.S.C. § 101 (Eligibility Step 1:YES) (see MPEP §2106.03 II).
However, claim(s) 8 recite(s):
generate a personality profile for a user based on questionnaire data and historical transactional data;
track at a first time period, first financial activity of the user, the first financial activity including spending and cash flow of the user obtained from electronic transaction data
update an emotional state machine learning model based on a correlation between the first financial activity and user-reported feedback regarding an emotional state of the user at the first time period, the emotional state machine learning model having been previously trained on emotional data and financial activity of a group of users;
track, at a second time period, second financial activity of the user;
predict, via the updated emotional state machine learning model at a second time period, a current emotional state of the user based on user location data and the second financial activity;
predict, via a financial-risk machine learning model, one or more future financial- risk events corresponding to the user in accordance with the personality profile, the second financial activity, user data, and the current emotional state;
and transmit, , control instructions to modify one or more account parameters associated with one or more future financial accounts of the user l , the one or more account parameters corresponding to one or more of a savings rate, a contribution rate, an investment strategy, a geographic location for use of one or more financial instruments associated with the one or more accounts, or an amount that can be withdrawn or spent via the one or more financial instruments.
This is an abstract idea that falls into the grouping of abstract ideas of commercial or legal interactions (including sales activities or behaviors) at least because the claim recites modify[ing] one or more account parameters associated with one or more future financial accounts of the user , the one or more account parameters corresponding to one or more of a savings rate, a contribution rate, an investment strategy, a geographic location for use of one or more financial instruments associated with the one or more accounts, or an amount that can be withdrawn or spent via the one or more financial instruments (see MPEP 2106.04(a)(2) subsection II.B) and fundamental economic principles or practices include mitigating risks at least because the claim recites predicting “one or more future financial risk events”. (see MPEP § 2106.04(a)(2), subsections II.A)
This judicial exception is not integrated into a practical application because the additional elements include: An apparatus […], comprising: one or more processors; and one or more memories coupled with the one or more processors and storing processor-executable code that, when executed by the one or more processors, is configured to cause the apparatus to […] “autonomously transmit” ; a transaction server, one or more transaction servers remotely located from the transaction server; via a positioning system, transmit from the transaction server to the one or more transaction servers, and a positioning system.
The Applicant’s specification at ¶[0136] explains:
The processor may be a neural network processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array signal (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. The processor may be a microprocessor, controller, microcontroller, or state machine specially configured as described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or such other special configuration, as described herein.
and at ¶[0038] of the specification:
The memory 118 may include one or more different types of memory, such as random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), and/or another type of memory.
Thus, the additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer and computer networking components or amount to merely using a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements, individually and in combination, that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components or merely using a computer as a tool to perform the abstract ideas amount to no more than mere instructions to apply the exception using generic computer and computer network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claim is not patent-eligible. Independent claims 1 and 15 recite substantially the same limitations as independent claim 8 but are directed to a method reciting the same functionality (claim 1) or a computer readable medium storing instructions/”program code” for executing the same functionality, and thus does not integrate the abstract idea into a practical application or provide significantly more.
Regarding claim(s) 2, 9, and 16, the claims recite generating the personality profile in accordance with a questionnaire. This is part of the abstract idea. The user answers questions through a “web interface.” This additional element is recited at a high level of generality and does not integrate the abstract idea into a practical application or provide significantly more.
Regarding claim(s) 3, 10 and 17, recites updating the profile based on financial activity. This is part of the abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Regarding claim(s) 4, 11, and 18, the claims specify types of input data and is part of the abstract idea and does not add any additional elements to the abstract idea.
Regarding claim(s) 7, 14 and 21.
The claims recite determining a current emotional state, predicting financial events, and performing an action. This is part of the abstract idea. The claims also recite using a generic machine learning model to carry out these abstract ideas. The machine learning model is recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer or amount to merely using a computer as a tool to perform the abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are to an abstract idea.
Accordingly, each of claims 1-4, 7-11, 14-18 and 21 are rejected under 35 U.S.C. § 101 as being patent-ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7-11, 14-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over PAVLETIC (US 20190295114 A1 to PAVLETIC, M. et al.) in view of ZARLENGO (US 20210374863 A1 to ZARLENGO, A. et al.) in further view of MOOLLA (US 10229442 B1 to Moolla, R.) and in further view of CHENG (US 20210390445 A1 to CHENG, L.N.L. et al.)
Regarding claim(s) 1, 8, and 15,
PAVLETIC discloses:
An apparatus for autonomous decision making, comprising: one or more processors; and one or more memories coupled with the one or more processors and storing processor-executable code that, when executed by the one or more processors, is configured to cause the apparatus to: [claim 8] (PAVLETIC: ¶[0022]: In another aspect, the processor is configured to allocate (e.g., determine contiguous memory locations, reserve memory addresses, etc.))
A non-transitory computer-readable medium having program code recorded thereon for autonomous decision making, the program code executed by one or more processors and comprising: program code [claim 15]: (PAVLETIC ¶[0073]: The primary server 108 includes one or more processors, computer memory, and non-transitory computer readable media; ¶[0240]: The technical solution of embodiments may be in the form of a software product. The software product may be stored in a non-volatile or non-transitory storage medium […]. The software product includes a number of instructions that enable a computer device (personal computer, server, or network device) to execute the methods provided by the embodiments.)
A method for autonomous decision making, comprising (PAVLETIC: ¶[0235]: The embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software; ¶[0196]: accuracy based on automatically generated estimations tracked from aggregated extracts of user behavior; fig. 24, item 2012: Output top actions):
generating, at a transaction server, a personality profile for a user based on questionnaire data and historical transactional data (PAVLETIC: ¶[0073]: The primary server 108 […] is configured to receive the transactional and non-transactional data sets […] and tracks, for each user, a profile storing at least data records that include a computer-approximation generated representing user behavior, in the form of multi-dimensional vectors; ¶[0072]: Transactional data sets may include transactional information provided synchronously, asynchronously, in bulk, or concurrent with transactions, and the transactional data sets may include purchase information, including price, retailer, point of sale location, and point of sale device. The data sets are received in the form of raw data or metadata payloads; ¶[0147]: The server pulls transactional data directly from the payment processor 1004 over a communication network. The information may include payment details such as price, date, location, point of sale terminal, etc.)
tracking, at the transaction server at a first time period, first financial activity of the user, the first financial activity including spending and cash flow of the user obtained from electronic transaction data received from one or more transaction servers remotely located from the transaction server (PAVLETIC: ¶[0147]: The server pulls transactional data directly from the payment processor 1004 over a communication network; ¶[0188]: The primary server 108 collects member payment records from transactions from the payment processor when members move money in and out of their account to make purchases, transfer money to others, or conduct other financial activities that the server may gather. Payment records include but are not limited to purchase records and transfer records. The server defines purchase records as a record of the address and time of the purchase, the name and type of the vendor, amount spent, and the category or type of purchase, such as electronics, groceries, or clothing; ¶[0189]: Transfer records include money being added to an account, or being withdrawn or moved out of the account. These records include the source of the funds and the destination. The server stores these records in a database of the member's spending history. On every transaction event, the primary server 108 may compare the pattern of spending behavior with the aggregate cohort; figure 13: server 108 receives transaction data from a remote payment processor 1004);
[…]
PAVLETIC discloses the […] machine learning model having been previously trained on […] financial activity of a group of users (PAVLETIC: ¶[0199]: The process flow includes training a multi-level neural network to recommend actions based on user goals. Given data sets representative of a history of prior user actions that led to achieving a goal (e.g., by way of the user feature vectors), the neural network is trained with a prior user's then ‘current state’ and next action; ¶[0018]: predictor neural network is configured for training over a period of time using user multi-dimensional vectors by generating predictions; ¶[0075]: the primary server 108) for maintaining, generating, tracking, and/or updating maintaining electronic representations of aggregate user behavior. The aggregate user behavior is represented and stored as a plurality of multi-dimensional vectors, each multi-dimensional vector corresponding to a user of a plurality of users and representing an approximation of the user's behavior as a point in n-dimensional space; ¶[0015]: The vectors are obtained by the device including a data receiver configured to receive, from point of sale devices, transaction information data sets representing purchase transactions of each user of the plurality of users,)
tracking, at the transaction server at a second time period, second financial activity of the user (PAVLETIC: ¶[0034]: : tracking historical activity by users into a series of actions separated by time, the historical activity captured by in the one or more multi-dimensional vectors; ¶[0034]: the subset of the one or more multi-dimensional vectors representing behavior of a user tracked for a period of time prior to a detected achievement of a financial goal; ¶[0194]: To learn user histories in time, the system of some embodiments periodically determines an action history for each user. For example a 3 month period divided into month long time-steps; ¶[0012]: an aggregate vector can be formed from each user's corpus of transactions, the aggregate vector being updated whenever a transaction or interaction is obtained; ¶[0191]: The primary server 108 adds each recorded payment to the database, and used it in future comparisons.);
predicting, via a financial-risk machine learning model, (PAVLETIC ¶[0197]: method involves training a neural network on the actions taken by users who achieved goals. The user's features and goal features are passed to the network in addition to publicly available information such as external economic factors such as inflation rate and GDP growth; ¶[0199]: , the neural network is trained with a prior user's then ‘current state’ and next action; ¶[0200]: The actions recommended by the system can, in some embodiments, be based off of the actual prior actions that led to achievement of goals by other users, and combinations thereof. Additional RELU layers are included for the network to be able to model complex interactions between features. More layers aid in machine learning.)
[(predicting based on)] user data (PAVLETIC: [0197] There are several embodiments of the method of providing financial insights to users based on goals: The first method involves training a neural network on the actions taken by users who achieved goals. The user's features and goal features are passed to the network in addition to publicly available information such as external economic factors such as inflation rate and GDP growth; ¶[0099]: FIG. 4 illustrates an example user feature vector 4000. Only a subset of dimensions are shown, and the partial subset illustrates demographics, sentiment, interactions, social data, social scores, social embeddings, product redemptions, goal data, referral data, cohort data, transaction data, and referral data, among others. As the user's activity is automatically tracked and monitored, the user feature vector is continuously or periodically maintained such that the feature vector is updated in accordance with data sets representative of the user's activity),
PAVLETIC does not expressly disclose the following limitations, which ZARLENGO however, teaches:
generating, at a transaction server, a personality profile for a user based on questionnaire data (ZARLENGO: ¶[0004]: A user may be assigned to one or more personality outcomes based upon, at least in part, the one or more interactive graphical psychometric tests provided in the user interface of the computing device; ¶[0048]: the assessment items of the graphical psychometric test (e.g., graphical psychometric test 300) may be based upon the Ten-Item Personality Inventory (TIPI), which was designed to assess the traits defined by the Big Five Personality Inventory. As will be described in greater detail below, the evaluation results derived from users interacting with the graphical psychometric test (e.g., graphical psychometric test 300) may correlate well with the evaluation results derived from the TIPI ten-item questionnaire, as shown in FIG. 9; Figures 3 and 9: questionnaires) and historical transactional data (ZARLENGO: ¶[0081]: process 10 may provide an interactive virtual assistant that is able to combine the user's psychological traits, bank and credit card account transactions, credit history, and life stage information to understand the user's complete financial picture and provide personalized options and/or suggestions for the user's benefit);
one or more future financial-risk events corresponding to the user in accordance with the personality profile (ZARLENGO: ¶[0058]: the assignment 208 of the user by interactive virtual assistant process 10 may provide valuable insights into the financial personality of a user. For example and referring also to FIG. 4, the one or more personality outcomes may be correlated or mapped to financial characteristics associated with the user. In some implementations, by defining the one or more personality outcomes for a user, interactive virtual assistant process 10 may prompt the user, via the interactive virtual assistant, with one or more options to help the user avoid financial difficulties.)
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of PAVLETIC, which discloses systems and methods of conducting machine learning on multi-dimensional vectors in order to predict financial behavior of users (see PAVLETIC ¶[0005] and ¶[0012]) and recommend actions for users based on user goals (see PAVLETIC ¶[0199]) with the technique of ZARLENGO in order to understand the user's complete financial picture and provide personalized options and/or suggestions for the user's benefit (ZARLENGO ¶[0104]) and to help the user avoid financial difficulties (see ZARLENGO ¶[0058]).
PAVLETIC does not expressly disclose the following limitations, which MOOLLA however, teaches:
updating an emotional state machine learning model based on a correlation between the first financial activity and user-reported feedback regarding an emotional state of the user at the first time period (MOOLLA: col. 3, ll. 67 to col. 4, ll. 1-3: Customer A may post “Customer A is feeling angry” where “angry” is selected from suggested emotions for Customer A to explicitly convey in the social media datapoint; col. 3, ll. 40-43: In one embodiment, the emotional state component 120 utilizes machine learning of historical data to facilitate emotional state determination; col. 6, ll. 19-23: a database can be built with financial transactions matched to social media posts and customer emotional states; col. 4, ll. 21-27: The social media datapoint is matched temporally to a financial transaction; col. 6, ll. 20-26: , a database can be built with financial transactions matched to social media posts and customer emotional states. In the example, timestamps of the Facebook posts are matched to timestamps of financial transactions of the customer, e.g. credit card transaction, online purchases, or the like; col. 6, ll. 30-34: The database also associates the emotional states of each Facebook post such that the emotional states are associated to a financial transaction; col. 6, ll. 34-37: The determined emotional state for the post, as stated above, is “stress.” The database records an association between ice cream and stress for future use.),
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of PAVLETIC, which discloses systems and methods of conducting machine learning on multi-dimensional vectors in order to predict financial behavior of users (see PAVLETIC ¶[0005] and ¶[0012]) and recommend actions for users based on user goals (see PAVLETIC ¶[0199]) with the technique of MOOLLA in order to match personalized offers of related financial transactions associated with determined emotional states (MOOLLA col. 1, ll. 46-47 and MOOLLA col. 6, ll. 45-60) and increase likelihood of user response (MOOLLA col. 1, ll. 46-49).
PAVLETIC discloses using sentiment analysis using trained neural networks to measure how a user feels towards an entity (PAVLETIC ¶[0144]) and training the machine learning model on financial activity of a group of users but does not explicitly disclose the following which CHENG, however teaches:
the emotional state machine learning model having been previously trained on emotional data and financial activity of a group of users (CHENG: ¶[0008]: generating, by a plurality of devices, a plurality of data […], using the plurality data, a sentiment analysis machine learning model and a behavior analysis machine learning model; […], using the sentiment analysis machine learning model, a sentiment analysis on the at least one data input to generate sentiment information indicative of an emotional state of the user; performing by the server, using the behavior analysis machine learning model, a behavior analysis on the at least […]; ¶[0097]: the sentiment analysis ML model may be trained using labeled or unlabeled data collected from the plurality of data devices. For example, the data can be labeled to indicate at which point the user is stressed, at which point the user is not stressed; ¶[0007]: receive at least one data input from each of a plurality of devices; ¶[0008]: training, using the plurality data, a sentiment analysis machine learning model and a behavior analysis machine learning model; ¶[0058]: . Based on the training, voice anomalies of the user can be detected; [0106]: The behavior analysis ML model may be trained using labeled or unlabeled data collected from the plurality of data devices. The behavior analysis ML model is then used to perform behavior analysis on the multi-channel data inputs.);
[…]
predicting, via the updated emotional state machine learning model at a second time period, a current emotional state of the user based on user location data tracked via a positioning system and the second financial activity (CHENG: ¶[0101]: In step 715, the sentiment analysis ML model receives geolocation data of the user. The geolocation data may be received from the plurality of data devices. The geolocation data may include, but not limited to, GPS data, user-provided location data, location image, and the like.; ¶[0104]: In step 730, the sentiment analysis ML model performs a sentiment analysis on the […], geolocation data, […], and the history transactional data to produce a result of sentiment analysis. The sentiment analysis may include evaluating […] whether the user is happy, sad, angry, frustrated, nervous, or frightened, how stressful of the user, what the voice tone/intonation of the user is, what the speaking speed of the user is, whether the user is disoriented, whether the user is making a large shopping, and the like. The result comprises an emotional state of the user that may comprise multiple components […] along with time stamps indicative of when a component occurs.; ¶[0103]: In step 725, the sentiment analysis ML model receives history transactional data of the user. The history transactional data may be received from the plurality of data devices. The history transactional data may include, but not limited to, transaction charges, transaction date and time, transaction locations, merchants, warranty information, and the like);
and the current emotional state (CHENG: ¶[0025]: The emotional state and/or behavioral state of the user can drive a variety of decisions that the user makes. For example, the user may have various compulsive behaviors that the user is more likely to engage in unrestricted shopping based on their emotional state; ¶[0026]: The present disclosure can correct such compulsive and destructive financial behaviors and prevent a user's action when they are under poor emotional and/or behavioral influence. For example, transactions can be limited or controlled based on emotional state of the user, such as blocking anxiety/stress shopping, limiting specific transactions, such as items costing more than a threshold amount, financial transactions (e.g., withdrawals or funds transfers), purchasing dangerous items, and purchasing age-restricted items, and the like; ¶[0006]: The server comprises a decision engine, a sentiment analysis machine learning model, and a behavior analysis machine learning model; ¶[0056]: The sentiment analysis ML model 1231 of the server 120 may process and analyze the plurality of data inputs to generate a result comprising sentiment information indicative of a sentimental state of the user. The result (an emotional state) may be represented as a multi-component vector that comprises, for example, a stress level component, a blood pressure level component, a heat beat rate component, a facial expression level component, a voice tension level, and the like. That is, the emotional state is determined based on a combination of data inputs,);
and autonomously transmitting, from the transaction server to the one or more transaction servers, control instructions (CHENG: ¶[0006]: determine, using the decision engine, a responsive action based on the sentiment information and the behavior information; and perform the responsive action.; ¶[0069]: The server can transmit an action command based on the decision to the customer financial account server associated with the third party financial institution. The customer financial account server associated with the third party financial institution can perform the action according to the action command. Alternatively, the server may also transmit an emotional state determined from the sentiment analysis ML model and/or a behavior state derived from the behavior analysis ML to the customer financial account server associated the third party financial institution.)
to modify one or more account parameters associated with one or more future financial accounts of the user (CHENG: ¶[0021]: freezing a credit card of the user when the user is evaluated to act abnormally while shopping; ¶[0026]: The present disclosure can correct such compulsive and destructive financial behaviors and prevent a user's action when they are under poor emotional and/or behavioral influence. For example, transactions can be limited or controlled based on emotional state of the user, such as blocking anxiety/stress shopping, limiting specific transactions, such as items costing more than a threshold amount, financial transactions (e.g., withdrawals or funds transfers) ,
the one or more account parameters corresponding to one or more of a savings rate, a contribution rate, an investment strategy, a geographic location for use of one or more financial instruments associated with the one or more accounts (CHENG: ¶[0024]: There may be a number of different ways that a user can set up their own rules around, for example, what can be spent, where, and when; ¶[0021]: action can include taking out money from a saving account; ¶[0054]: taking out money from a saving account; ¶[0064]: responsive action may be an action that takes out $1 from a first bank account of the user and moves to a second bank account of the user),
or an amount that can be withdrawn or spent via the one or more financial instruments (CHENG: ¶[0022]: “don't allow me to spend more than $50 if I'm stressed”; ¶[0026]: For example, transactions can be limited or controlled based on emotional state of the user, such as blocking anxiety/stress shopping, limiting specific transactions, such as items costing more than a threshold amount, financial transactions (e.g., withdrawals or funds transfers); ¶[0021]: freezing a credit card of the user when the user is evaluated to act abnormally while shopping,)
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of PAVLETIC, which discloses systems and methods of conducting machine learning on multi-dimensional vectors in order to predict financial behavior of users (see PAVLETIC ¶[0005] and ¶[0012]) and recommend actions for users based on user goals (see PAVLETIC ¶[0199]) with the technique of CHENG in order to protect uses from making poor purchasing decisions and other compulsive and destructive financial behaviors when under poor emotional or behavioral influence (CHENG ¶[0026]).
Regarding claim(s) 2, 9, and 16,
PAVLETIC, ZARLENGO, MOOLLA and CHENG teach the limitations of claims 1, 8, and 15.
PAVLETIC teaches information gathering though an interface (see figure 7 and ¶[0137]) and using information at registration to generate a user's multi-dimensional feature vector.
However, PAVLETIC does not disclose the following, which ZARLENGO teaches:
wherein the personality profile is a financial personality profile that is generated in accordance with the questionnaire data via a web interface (ZARLENGO:¶[0056]: interactive virtual assistant process 10 may include assigning 208 a user to one or more personality outcomes based upon, at least in part, the one or more interactive graphical psychometric tests provided in the user interface of the computing device. […] `In some implementations, the responses to at least a portion (e.g., one or more of) the one or more interactive graphical psychometric tests may be used to assign 208 the user to, e.g., one of ten, possible personality outcomes.; figure 9 and [0048]: the assessment items of the graphical psychometric test (e.g., graphical psychometric test 300) may be based upon the Ten-Item Personality Inventory (TIPI), which was designed to assess the traits defined by the Big Five Personality Inventory. As will be described in greater detail below, the evaluation results derived from users interacting with the graphical psychometric test (e.g., graphical psychometric test 300) may correlate well with the evaluation results derived from the TIPI ten-item questionnaire, as shown in FIG. 9.; figure 4 and ¶[0058]: the assignment 208 of the user by interactive virtual assistant process 10 may provide valuable insights into the financial personality of a user. For example and referring also to FIG. 4, the one or more personality outcomes may be correlated or mapped to financial characteristics associated with the user; ).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of PAVLETIC, which discloses systems and methods of conducting machine learning on multi-dimensional vectors in order to predict financial behavior of users (see PAVLETIC ¶[0005] and ¶[0012]) and recommend actions for users based on user goals (see PAVLETIC ¶[0199]) with the technique of ZARLENGO in order to understand the user's complete financial picture and provide personalized options and/or suggestions for the user's benefit (ZARLENGO ¶[0104]) and to help the user avoid financial difficulties (see ZARLENGO ¶[0058]).
Regarding claim(s) 3, 10 and 17,
PAVLETIC, ZARLENGO, MOOLLA and CHENG teach the limitations of claims 1, 8, and 15.
PAVLETIC further discloses:
further comprising updating the personality profile based on one or both of the first financial activity or the second financial activity , wherein the personality profile used for predicting the one or more financial events is the updated personality profile (PAVLETIC: [0012]: If each vector represents a point in n-dimensional space, an aggregate vector can be formed from each user's corpus of transactions, the aggregate vector being updated whenever a transaction or interaction is obtained; [0017]: The temporary multi-dimensional vector is used to update an aggregated multi-dimensional vector generated from a corpus of purchase transactions associated for the user by incorporating the temporary multi-dimensional vector into the aggregated multi-dimensional vector; [0077]: As transactions and non-transactional data is received from client applications 110, 112, and 114, vectors are continually updated).
Regarding claim(s) 4, 11, and 18,
PAVLETIC, ZARLENGO, MOOLLA and CHENG teach the limitations of claims 1, 8, and 15.
PAVLETIC further discloses:
wherein the user data comprises user content consumption habits, and/or user demographic information (PAVLETIC: [0099] FIG. 4 illustrates an example user feature vector 4000. Only a subset of dimensions are shown, and the partial subset illustrates demographics, sentiment, interactions, social data, social scores, social embeddings, product redemptions, goal data, referral data, cohort data, transaction data, and referral data, among others. As the user's activity is automatically tracked and monitored, the user feature vector is continuously or periodically maintained such that the feature vector is updated in accordance with data sets representative of the user's activity; [0102] The multi-dimensional user vector is updated with from the interactions, such as (but not limited to): demographic information such as age, income, sentiment analysis of posts, stories and texts, other social data such as number of comments and posts, a social score, aggregate embeddings of the user's social interaction, transaction data and related embeddings, crypto-currency transaction information and related embeddings, and cohort-information of the user; ¶[0017]: if a user A pursues coupons, typically purchases off-brand generic items, from stores generally located in rural Michigan, such features will be present in the aggregated multi-dimensional vector; ¶[0029]: In another aspect, a computer-implemented method for maintaining electronic representations of aggregate user behavior stored as a plurality of multi-dimensional vectors, each multi-dimensional vector corresponding to a user of a plurality of users and representing an approximation of the user's behavior in n-dimensional space, method comprising: receiving, from one or more point of sale devices, transaction information data sets representing purchase transactions of each user of the plurality of users, the transaction information including, for each purchase transaction, at least a user identifier, an approximate location of purchase, a time-stamp, a retailer, and a price; ¶[0104]: Vectors are extracted from various inputs, such as text, video, images, and audio from non-transactional data (e.g., not data received from a point of sale). […]. The inputs are pre-processed to extract features and determine relevancy (e.g., did the user take a picture of a coffee he/she just purchased) such that associations (e.g., picture of a coffee related to tracked transactional data) and weights (e.g., confidence scores) can be determined; [0106] In addition to contextual data, the user feature vector may be augmented by application usage data which may be but not limited to the number of times a user a viewed an offer, the number of page views a user has, or any interaction such a clicking a button that the user has with the application.).
Regarding claim(s) 7, 14 and 21,
PAVLETIC, ZARLENGO, MOOLLA and CHENG teach the limitations of claims 1, 8, and 15.
However, PAVLETIC does not disclose the following, which CHENG teaches:
Wherein the control instructions are autonomously transmitted via an action machine learning model (CHENG: figure 4: step 435 “server transmits a command of performing the action to an action performing device”; ¶[0085]: [after discussion of automatic actions such as dialing a number or launching an app] “Similarly the customer financial account server 360 may receive a command from the server 320 to perform an action. For example, if the action is to decline a transaction of the user, the customer financial account server 360 may perform the action to block the transaction or freeze a credit card of the user involved in the transaction.”).
Response to Arguments
Drawings
Applicant argues that the objection to figure 3 should be withdrawn in view of Applicant’s amendments. This argument has been considered and is persuasive. The objection to the drawings has been withdrawn.
Specification
Applicant requests withdrawal of the objections to the specification in view of the amendments filed 12/30/2025. This argument has been considered and is persuasive. The objection to the specification has been withdrawn.
Claim Objections
Applicant argue that the claims have been amended and the claim objections should be withdrawn at page 12. This argument has been considered and is persuasive. The objection to the claims has been withdrawn.
35 U.S.C. § 101
Applicant argues at page 13-16 that amended claim 1 is not directed to an abstract idea because it is directed to a technical machine learning based process for emotional sate modeling and prediction using multisource data over time. Claim one predicts financial risk events and modifies parameters for commercial activity. Receiving data is an extra-solution activity and the additional elements are recited at high level of generality such that the claims merely use a computer as tool to carry out the abstract idea. The claims do not improve technology and so do not integrate the abstract idea into a practical application nor do they provide significantly more than the abstract idea.
35 U.S.C. § 103
Applicant’s arguments with respect to the rejection of claim(s) 1-20 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOLKO HAMERSKI whose telephone number is (571)270-7621. The examiner can normally be reached Monday-Friday 10:00 AM to 6:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BENNETT SIGMOND can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BOLKO HAMERSKI
Examiner
Art Unit 3694
/BOLKO M HAMERSKI/Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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