Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding the rejection of claims 17–20 under 35 U.S.C. 101, amendments to the claims have overcome the rejections, which are withdrawn.
Regarding the rejection of claims under 35 U.S.C. 103, Applicant’s arguments are directed towards amendments to the claims which have not been previously examined, and for which new grounds of rejection are given below.
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.
Claims 1, 5, 8–9, 13–17, and 2–21 rejected under 35 U.S.C. 103 over Paez Martinez, US Pre-Grant Publication No. 2020/0409920 (hereafter Martinez) in view of Wong et al., US Pre-Grant Publication No. 2023/0118240 (hereafter Wong).
Regarding claim 1 and analogous claims 9 and 17:
Martinez teaches:
“An apparatus comprising: a storage device comprising a feature store; and a processor configured to”: Martinez, paragraph 0006, “Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods”; Martinez, paragraph 0049, “The computing device 400 includes a processor [a processor] 402, a memory 404, a storage device [a storage device] 406, a high-speed interface 408 connecting to the memory 404 and multiple high-speed expansion ports 410, and a low-speed interface 412 connecting to a low-speed expansion port 414 and the storage device 406. Each of the processor 402, the memory 404, the storage device 406, the high-speed interface 408, the high-speed expansion ports 410, and the low-speed interface 412, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.”
“execute a machine learning model on the encoding to convert the data and the name into a vector that comprises a fixed dimensional structure defined by a hyperparameter, wherein the fixed dimensional structure standardizes representation of entities across entries in the feature store”: Martinez, paragraph 0021, “The vector determinator 110 may determine the vectors based on a training algorithm. The training algorithm used by the vector determinator 110 may use training data that includes a plurality of positive samples and a plurality of negative samples. For example, the vector determinator 110 may obtain positive samples from the log of actions performed by users in relation to entities, and negative samples that correspond to actions that the log indicates were not performed by users in relation to entities. The training algorithm used by the vector determinator 110 may randomly initialize vectors and then given a particular user and particular entity [name associated with the data], identify two consecutive actions, 'p' and 'i', performed by the user from the log and then identify a third action, 'j', that was not next performed by the user. In the algorithm, it is assumed that the probability of moving from 'p' to 'i' is given by ß=d(p+u,i) where d(x, y) is a distance function that gives the distance between x and y, and , is a bias term associated with item i. The vector determinator 110 adjusts the values of all the parameters so as to minimize the probability of j and maximize the probability of i using stochastic gradient descent [defined by a hyperparameter, wherein the fixed dimensional structure standardizes representation of entities across entries in the feature store]”; Martinez, paragraph 0028, “The vector combiner 160 may receive the entity vector selected by the entity selector 130, the action vector selected by the action selector 140, the user vector selected by the user selector 150, and, in response, determine a resultant vector. For example, the vector combiner 160 may receive the entity vector for Entity Z, the action vector for Action Y, the user vector for User X, and add the three vectors to obtain a resultant vector [execute a machine learning model on the encoding to convert the data and the name into a vector that comprises a fixed dimensional structure ]. Adding the vectors may include adding each component of each vector with the corresponding component of the other vectors.”
“and the vector represents aggregated characteristics of an entity derived from multiple actions”: Martinez, paragraph 0028, “The vector combiner 160 may receive the entity vector selected by the entity selector 130, the action vector selected by the action selector 140, the user vector selected by the user selector 150, and, in response, determine a resultant vector. For example, the vector combiner 160 may receive the entity vector for Entity Z, the action vector for Action Y, the user vector for User X, and add the three vectors to obtain a resultant vector [the vector represents aggregated characteristics of an entity derived from multiple actions]. Adding the vectors may include adding each component of each vector with the corresponding component of the other vectors.”
“retrieve the vector from the feature store and execute a second machine learning model on the vector to generate a predicted output associated with the entity”: Martinez, paragraph 0032, “In some implementations, the vectors stored by the vector database 120 may be provided to other machine learning systems [retrieve the vector from the feature store and execute a second machine learning model on the vector]”; Martinez, paragraph 0004, “The vector space can be used to predict a particular action on a particular entity that a particular user is most likely to perform next [generate a predicted output associated with the entity].”
Martinez does not explicitly teach:
“query data read from a point of sale (POS) system and convert the data and a name associated with the data into an encoding”
“generate an entry comprising metadata of the entity, a geographic location associated with the POS system, and the generated vector”
“store the entry in the feature store”
Wong teaches:
“query data read from a point of sale (POS) system and convert the data and a name associated with the data into an encoding”: Wong, 0003, “Digital payments have exploded over the last twenty years, with more than three-quarters of global payments using some form of payment card or electronic wallet. Point of sale systems are progressively becoming digital rather than cash based [a point of sale (POS) system ]”; Wong, paragraph 0133, “At block 1302, the method 1300 comprises receiving transaction data for anomaly detection. This may comprise operations similar to block 522 in FIG. 5A. The transaction data may relate to a proposed transaction, e.g. as described with reference to blocks 512 to 518 in FIGS. 5A and 5B. The transaction data may be received as data packets accompanying an API request, e.g. with respect to an internal function, or external RESTful, interface. At block 1304, a first set of features are generated based on the received transaction data. This may be performed as part of one or blocks 522 or 524 in FIGS. 5A or 5B. It may comprise applying an observable feature generator 910 generate an observable feature vector as described with reference to FIG. 9 [query data read from a point of sale (POS) system and convert the data and a name associated with the data into an encoding]. The first set of features may be configured as a vector of numeric values that are computed based, at least, on the data packets accompanying an API request, i.e. data for a proposed transaction.”
“generate an entry comprising metadata of the data, a geographic location associated with the POS system, and the generated vector”: Wong, paragraph 0052, “Other fields present in the transaction data can include, but are not limited to, an account number (e.g., a credit card number), a location of where the transaction is occurring [a geographic location associated with the POS system], and a manner (e.g., in person, over the phone, on a website) in which the transaction is executed [metadata of the data].”
“store the entry in the feature store”: Wong, paragraph 0052, “FIG. 3B shows how transaction data 330 for a particular transaction may be stored in numeric form for processing [store the entry in the feature store] by one or more machine learning models.”
Wong and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the application of vectorization to transactional data of Wong with the teachings of Martinez to arrive at the present invention, in order to detect anomalous transactions, as stated in Wong, paragraph 0027, “The output of the machine learning system may be used to prevent a wide variety of fraudulent and criminal behaviour such as card fraud, application fraud, payment fraud, merchant fraud, gaming fraud and money laundering.”
Regarding claim 5 and analogous claims 13 and 20:
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Wong further teaches “wherein the processor is configured to identify a category type of the data from among a plurality of possible category types, and store the vector within a location in the feature store based on the identified category type”: Wong, paragraph 0050, “In FIG.2B, a secure logical storage layer 270 is provided using the physical data storage device 260. The secure logical storage layer 270 may be a virtualized system that appears as separate physical storage devices to the machine learning system 210 while actually being implemented independently upon the at least one data storage device 260. The logical storage layer 270 may provide separate encrypted partitions 280 for data relating to groups of entities (e.g., relating to different issuing banks etc.) [identify a category type of the data from among a plurality of possible category types, and store the vector within a location in the feature store based on the identified category type] and the different sets of historical transaction data 240-A to N and ancillary data 242-A to N may be stored in the corresponding partitions 280-A to N.”
Wong and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the partitioned storage to transactional data of Wong with the teachings of Martinez to arrive at the present invention, in order to improve data security, as stated in Wong, paragraph 0006, “Moreover, engineers need to contend with the problem of implementing machine learning models on data that is siloed or partitioned based on access security, and in situations where the velocity of data updates is extreme.”
Regarding claim 8 and analogous claim 16:
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Wong further teaches “wherein the processor is configured to identify a combination of attributes including a name, a type, and a code, and convert the combination of attributes into numerical values within the encoding”: Wong, paragraph 0052, “FIG. 3B shows how transaction data for a particular transaction may be stored in numeric form for processing [convert the combination of attributes into numerical values] by one or more machine learning models. For example, in FIG. 3B, transaction data has at least fields: transaction amount, timestamp (e.g., as a Unix epoch), transaction type [a type] (e.g., card payment or direct debit), product description or identifier (i.e., relating to items being purchased), merchant identifier [a name], issuing bank identifier, a set of characters (e.g., Unicode characters within a field of predefined character length), country identifier [a code] etc. It should be noted that a wide variety of data types and formats may be received and pre-processed into appropriate numerical representations”; Wong, paragraph 0074, “In certain cases, the output of the first multilayer perceptron 610, which may be considered a normalized transaction data feature vector [within the encoding], may be concatenated, or otherwise combined with output data of the recurrent neural network architecture 620. to provide an input to the one or more attention neural network architectures 660.”
Wong and Martinez are combinable for the rationale given under claim 1.
Regarding claim 14:
Martinez as modified by Wong teaches “The method of claim 9.”
Wong further teaches “wherein the method further comprises executing the machine learning model on additional data to generate an additional vector that comprises vectorized values corresponding to latent features of the additional data embedded within slots of the additional vector, respectively, and store the additional vector within the feature store”: Wong, paragraph 0111, “In FIG. 9, the observable feature generator 910 receives transaction data and uses this to generate an observable feature vector 916. The context feature generator 930 receives ancillary data and uses this to generate a context feature vector 926 [generate an additional vector that comprises vectorized values corresponding to latent features of the additional data]. The observable feature vector 916 and the context feature vector 926 are then combined to generate the overall feature vector 930. In one case, the observable feature vector 916 and the context feature vector 926 may be combined by concatenating the two feature vectors 916 and 926 to generate a longer vector”; Wong, paragraph 0044, “The payment processor server 140 is communicatively coupled to a first data storage device 142 storing transaction data 146 and a second data storage device 144 storing ancillary data 148 [store the additional vector within the feature store].”
Wong and Martinez are combinable for the rationale given under claim 9.
Regarding claim 15:
Martinez as modified by Wong teaches “The apparatus of claim 14.”
Wong further teaches “wherein the execution comprises inputting the vector into the machine learning model when generating the additional vector”: Wong, paragraph 0111, “In FIG. 9, the observable feature generator 910 receives transaction data and uses this to generate an observable feature vector 916. The context feature generator 930 receives ancillary data and uses this to generate a context feature vector 926 [inputting the vector into the machine learning model when generating the additional vector]. The observable feature vector 916 and the context feature vector 926 are then combined to generate the overall feature vector 930. In one case, the observable feature vector 916 and the context feature vector 926 may be combined by concatenating the two feature vectors 916 and 926 to generate a longer vector.”
Wong and Martinez are combinable for the rationale given under claim 9.
Regarding claim 21:
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Martinez further teaches “wherein the fixed dimensional structure defined by the hyperparameter enforces a consistent number of vector slots across entries stored in the feature store”: Martinez, paragraphs 0020-0021, “The vector determinator 110 may determine the vectors to have the same number of dimensions in a same vector space of the same number of dimensions, where the values for the dimensions vary between the vectors. For example, the vector determinator 110 may determine the vectors for actions, vectors for users, and vectors for entities to each have two hundred dimensions with varying values for the dimensions [the fixed dimensional structure … enforces a consistent number of vector slots across entries stored in the feature store]. The vector determinator 110 may determine the vectors based on a training algorithm. The training algorithm used by the vector determinator 110 may use training data that includes a plurality of positive samples and a plurality of negative samples. For example, the vector determinator 110 may obtain positive samples from the log of actions performed by users in relation to entities, and negative samples that correspond to actions that the log indicates were not performed by users in relation to entities. The training algorithm used by the vector determinator 110 may randomly initialize vectors and then given a particular user and particular entity, identify two consecutive actions, 'p' and 'i', performed by the user from the log and then identify a third action, 'j', that was not next performed by the user. In the algorithm, it is assumed that the probability of moving from 'p' to 'i' is given by ß=d(p+u,i) where d(x, y) is a distance function that gives the distance between x and y, and , is a bias term associated with item i. The vector determinator 110 adjusts the values of all the parameters so as to minimize the probability of j and maximize the probability of i using stochastic gradient descent [defined by the hyperparameter].”
Claims 2–3, 10–11, and 18 rejected under 35 U.S.C. 103 over Martinez as modified by Wong in view of Shmueli, US Pre-Grant Publication No. 2017/0109344 (hereafter Shmueli).
Regarding claim 2 and analogous claims 10 and 18:
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Martinez as modified by Wong does not explicitly teach “wherein the data comprises a plurality of different variations of the name, and the processor is further configured to normalize the different variations of the name into a single name and generate the encoding based on the single name.”
Shmueli teaches “wherein the data comprises a plurality of different variations of the name, and the processor is further configured to normalize the different variations of the name into a single name and generate the encoding based on the single name”: Shmueli, paragraph 0046, “Step 204 produces a new sequence by replacing each word w occurrence oc in the input sequence by w_j where 1<=j<=k is the index of the class, among the k closest classes whose c' vector is cosine closest to the average context vector for the words in a window around oc as shown in FIG. 3. More specifically, step 204 replaces in the text word occurrence tm with a ‘variation’ wi_j. For example, if word occurrence tm=car and ‘car’ has 3 ‘strong’ classes and the current context (of tm) average vector is closest to the vector c'i2 representing the second strong class, then tm=car is replaced by car_2. So, each occurrence is replaced with the appropriate ‘sense’ of the word in the current window context usage [normalize the different variations of the name into a single name and generate the encoding based on the single name].”
Shmueli and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the name normalization of Shmueli with the teachings of Martinez to arrive at the present invention, in order to better represent inputs in context, as stated in Shmueli, paragraph 0046, “So, each occurrence is replaced with the appropriate ‘sense’ of the word in the current window context usage.”
Regarding claim 3 and analogous claim 11:
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Martinez as modified by Wong teaches does not explicitly teach “wherein the machine learning model comprises a word to vector (Word2Vec) model.”
Shmueli teaches “wherein the machine learning model comprises a word to vector (Word2Vec) model”: Shmueli, paragraph 0025, “The first distribution representation device 101 receives the sequence of items 150 and produces a distributed representation for each item ‘w’ as a word vector and a context vector. That is, the distribution representation device 101 uses a vector producing algorithm such as word2vec (w2v) or Glove, and produces output vectors of two types per each item: word (Syn0 type in w2v) and context (Synlneg in w2v)). More specifically, given a sequence of items (words, concepts, nodes in a graph, etc. or a combination thereof), the first distribution representation device 101 learns a distributed representation (i.e. vectors) of dimension n (a user specified parameter), using a tool such as word2vec [wherein the machine learning model comprises a word to vector (Word2Vec) model] or Glove.”
Shmueli and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the word vectorization of Shmueli with the teachings of Martinez to arrive at the present invention, in order to include textual input, as stated in Shmueli, paragraph 0026, “More specifically, given a sequence of items (words, concepts, nodes in a graph, etc. or a combination thereof), the first distribution representation device 101 learns a distributed representation (i.e. vectors) of dimension n (a user specified parameter), using a tool such as word2vec.”
Claims 4, 12, and 19 rejected under 35 U.S.C. 103 over Martinez as modified by Wong in view of Yoon et al., US Pre-Grant Publication No. 2021/0081768 (hereafter Yoon).
Regarding claim 4 and analogous claim 12:
Martinez as modified by Wong teaches “The apparatus of claim 3.”
Martinez as modified by Wong does not explicitly teach “wherein the processor is further configured to execute the Word2Vec model on training data to generate a weight matrix for the Word2Vec model, prior to generating the vector, and determine the vector based on the weight matrix for the Word2Vec model.”
Yoon teaches “wherein the processor is further configured to execute the Word2Vec model on training data to generate a weight matrix for the Word2Vec model, prior to generating the vector, and determine the vector based on the weight matrix for the Word2Vec model”: Yoon, paragraph 0070, “In Equation 8, Ewi is a vector corresponding to the weight matrix W matched with the input word wi in FIG. 2, and E'wj is the vector for the output word w obtained using the weight matrix W', When a corpus T is given, the Word2Vec algorithm learns [execute the Word2Vec model on training data to generate a weight matrix for the Word2Vec model, prior to generating the vector] to maximize the log probability of Equation 7 and obtains a weight matrix to be used as the latent vector for the word [determine the vector based on the weight matrix for the Word2Vec model], to efficiently calculate the denominator of Equation 8, a negative sampling loss function may be used.”
Yoon and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the Word2Vec matrix training of Yoon with the teachings of Martinez to arrive at the present invention, in order to use the well-known Word2Vec algorithm for producing context-based data embeddings, as stated in Yoon, paragraph 0069, “As shown in FIG, 1, the Word2Vec algorithm learns to predict the context words wj, wj+1, … , wj+n of the input word w,, which belongs to the word set w consisting of V words by using a neural-network-based model that comprises an input layer, a projection layer, and an output layer.”
Regarding claim 19:
Martinez as modified by Wong teaches “The computer-readable storage medium of claim 17.”
Martinez as modified by Wong does not explicitly teach “wherein the method further comprises executing the trained machine learning model on training data to generate a weight matrix for the trained machine learning model, prior to generating the vector, and the executing comprises determining the vector based on the weight matrix.”
Yoon teaches “wherein the method further comprises executing the trained machine learning model on training data to generate a weight matrix for the trained machine learning model, prior to generating the vector, and the executing comprises determining the vector based on the weight matrix”: Yoon, paragraph 0070, “In Equation 8, Ewi is a vector corresponding to the weight matrix W matched with the input word wi in FIG. 2, and E'wj is the vector for the output word w obtained using the weight matrix W', When a corpus T is given, the Word2Vec algorithm learns [executing the trained machine learning model on training data to generate a weight matrix for the trained machine learning model, prior to generating the vector] to maximize the log probability of Equation 7 and obtains a weight matrix to be used as the latent vector for the word [determining the vector based on the weight matrix], to efficiently calculate the denominator of Equation 8, a negative sampling loss function may be used.”
Yoon and Martinez are analogous arts as they are both related to vectorizing data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the matrix training of Yoon with the teachings of Martinez to arrive at the present invention, in order to use the well-known Word2Vec algorithm for producing context-based data embeddings, as stated in Yoon, paragraph 0069, “As shown in FIG, 1, the Word2Vec algorithm learns to predict the context words wj, wj+1, … , wj+n of the input word w,, which belongs to the word set w consisting of V words by using a neural-network-based model that comprises an input layer, a projection layer, and an output layer.”
Claim 22 rejected under 35 U.S.C. 103 over Martinez as modified by Wong in view of Li et al., US Pre-Grant Publication No. 2021/0027145 (hereafter Li).
Martinez as modified by Wong teaches “The apparatus of claim 1.”
Wong further teaches (bold only) “wherein the processor is further configured to aggregate a sequence of actions associated with the entity and temporal attributes of the sequence of transaction into the encoding”: Wong, paragraph 0018, “The vector determinator [into the encoding] 110 may obtain a log of actions performed by users in relation to entities. For example, the vector determinator 110 may obtain a log often billion actions performed by a billion users in relation to one hundred million entities, where the ten billion actions include twenty thousand different types of actions. The log of actions [a sequence of actions] may include multiple entries, where each entry corresponds to a particular action performed by a particular user in relation to a particular entity [associated with the entity]. For example, the log of actions may include an entry that indicates that User X viewed a trailer for Movie U and a next entry that indicates that the next action that User X performed was viewing showtimes for Movie U.”
Wong and Martinez are combinable for the rationale given under claim 1.
Martinez as modified by Wong does not explicitly teach (bold only) “wherein the processor is further configured to aggregate a sequence of actions associated with the entity and temporal attributes of the sequence of transaction into the encoding.”
Li teaches (bold only) “wherein the processor is further configured to aggregate a sequence of actions associated with the entity and temporal attributes of the sequence of transaction into the encoding”: Li, paragraph 0007, “a fraudulent transaction detection method based on sequence wide and deep learning, comprising: a) performing feature mapping processing on each of a plurality of transaction data to generate corresponding feature vectors; b) converting the feature vectors of a transaction to be detected into integrated feature vectors based on a first self-learning model; c) respectively converting the feature vectors respectively of at least one time sequence transaction [sequence of transaction] into time sequence/time sequence feature vectors based on a second self-learning model; wherein the time sequence transaction represents a transaction conducted within a first time period”; Li, paragraph 0017, “Specifically, feature mapping processing is performed on raw transaction data, feature of interest may include consumption type, transaction time [temporal attributes of the sequence of transaction] and location, transaction amount, transaction frequency, transaction account type, registration time, and the like. In accordance with embodiments, it is also required to combine them for further statistical processing. As an example, statistical engineering may be performed to gather statistics on, for example, correlated variables of a current transaction, correlated variables of a last transaction, time length statistics, and trusted feature variables, and the like. All of the data corresponding to these variables require a series of preprocessing, such as mapping encoding, to transition to a numerical variable. Eventually, each transaction is mapped to a multi-dimensional feature vector.”
Li and Martinez are analogous arts as they are both related to data vectorization. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the time sequence data vectorization of Li with the teachings of Martinez to arrive at the present invention, in order to improve anomaly detection, as stated in Li, paragraph 0009, “The fraudulent transaction detection method and system disclosed by the embodiments carry out machine learning in terms of not only horizontal dimension features, but also longitudinal dimension features, which takes into consideration various factors, and in tum achieves balance between the two dimensions, enabling effective improvement of the detection precision of fraudulent transactions through the fraudulent transaction detection model.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Laserson et al., US Pre-Grant Publication No. 2021/0233101, discloses a method for transforming transaction data into vectors which are then combined into per-customer vectors (see paragraph 0006); the method uses a word2vec algorithm (paragraph 0015) and includes a manager component which determines dimensions based on historical data (paragraph 0053).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VAS/ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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