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 No therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding claim 1,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a process.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of extracting fields from the electronic transaction data; recites an evaluation of transaction data to determine individual fields, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of assigning a respective class of a set of classes to each respective extracted field of the extracted fields … recites a judgement of a classification for a data field, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generating candidate mappings for the transactions based on applying multiple sets of mapping rules to the extracted fields; recites an evaluation of candidate mappings and mapping rules, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of generating a score for each candidate mapping of the candidate mappings based on applying scoring rules to the candidate mappings; recites a judgement of a score for candidate mappings, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of selecting a set of mapping rules from the multiple sets of mapping rules for categorizing transactions involving the entity based on the generated score for a corresponding candidate mapping of the candidate mappings; recites a judgement of selecting a set of mapping rules based on a score, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of and creating mappings of transactions associated with a particular user based on applying the selected set of mapping rules to each transaction associated with the particular user and the entity recites an evaluation of transactions to create mappings, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of receiving electronic transaction data associated with transactions involving multiple users and an entity; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of assigning…using a machine learning model trained through a supervised learning process to assign classes to input fields; recites mere instructions to apply a trained machine learning to assign classes, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of receiving electronic transaction data associated with transactions involving multiple users and an entity; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of assigning…using a machine learning model trained through a supervised learning process to assign classes to input fields; recites mere instructions to apply a trained machine learning to assign classes, which does not integrate the exception into a practical application, MPEP 2106.05(f).
Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 2,
Claim 2 adds the additional limitations to claim 1:
further comprising using the scoring rules to score the mappings of the transactions associated with the particular user recites an evaluation of scoring rules to create scores for transaction mappings, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 3,
Claim 3 adds the additional limitations to claim 2:
determining that a threshold number of scores for respective mappings of respective transactions associated with the entity and a plurality of different users determined using the selected set of mapping rules do not exceed a score threshold; recites a judgement that a threshold number of scores do not exceed a threshold, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
selecting a different set of mapping rules from the multiple sets of mapping rules for categorizing transactions involving the entity based on the determining that the threshold number of scores for the respective mappings of the respective transactions associated with the entity and the plurality of different users determined using the selected set of mapping rules do not exceed the score threshold recites a judgement of selecting a set of mapping rules, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 3 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 4,
Claim 4 adds the additional limitations to claim 1:
wherein the set of classes includes one or more of a date class, an amount class, or a description class recites further detail on classes that can be assigned to fields in transaction data, without changing that assigning classes to fields in data is a judgement, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 4 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 5,
Claim 5 adds the additional limitation to claim 4:
wherein information within a field assigned to the amount class comprises an indication that a given transaction involved making a withdrawal from an account associated with a given user recites further detail on the information in a field that was extracted from transaction data, without changing that extracting fields from transaction data is an evaluation of the transaction data, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 5 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 6,
Claim 6 adds the additional limitation to claim 5:
wherein a mapping rule of the set of mapping rules involves negating a value associated with the field assigned to the amount class based on the indication recites a mathematical calculation of negating a value, which is a mathematical concept, which is an abstract idea.
Therefore, claim 6 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 7,
Claim 7 adds the additional limitation to claim 1:
wherein the scoring rules are chosen for use in generating the score for each candidate mapping of the candidate mappings based on a type corresponding to the entity recites a judgement of selecting scoring rules based on the type of an entity involved in a transaction, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 7 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 8,
Claim 8 adds the additional limitation to claim 1:
wherein the scoring rules involve scoring transactions associated with the entity based on how many transactions associated with the entity include an amount that is below a threshold amount recites a judgement of scoring transactions based on a number of transactions below a threshold, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 8 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claims 9-16,
Claims 9-16 recite a system with at least one processor and a memory that implements the function of the method of claims 1-8, respectively, with substantially the same limitations. Therefore the same analysis and rejection applied to claims 1-8 applies to claims 9-16.
Therefore, claims 9-16 are found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claims 17-20,
Claims 17-20 recite a non-transitory computer readable storage medium with instructions that implement the function of the method of claims 1-3 and 7, respectively, with substantially the same limitations. Therefore the same analysis and rejection applied to claims 1-3 and 7 applies to claims 17-20.
Therefore, claims 17-20 are found to be ineligible subject matter under 35 U.S.C. 101.
Prior Art
The following references are used for prior art claim rejections:
Rusu et al. (U.S. Patent Application Publication No. 2022/0198581), hereinafter Rusu
McGlynn et al. (U.S. Patent Application Publication No. 2009/0222364), hereinafter McGlynn_1
Wu et al. “Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study”, hereinafter Wu
Medicherla et al. (U.S. Patent Application Publication No. 2023/0153278), hereinafter Medicherla
McGlynn et al. (U.S. Patent Application Publication No. 2009/0222365), hereinafter McGlynn_2
Grosset et al. (U.S. Patent Application Publication No. 2020/0074563), hereinafter Grosset
Omoseebi et al. “Rule-Based Systems in AML”, hereinafter Omoseebi
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, 2, 4, 9, 10, 12, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rusu, in view of McGlynn_1, further in view of Wu, further in view of Medicherla.
Regarding claim 1,
Rusu teaches A method of automated transaction categorization, ((Rusu [0063]) “By associating financial records of transactions with accounting codes, transactions can be categorised or labelled”) comprising:
receiving electronic transaction data associated with transactions involving [multiple] user[s] and an entity; ((Rusu [0059]) “candidate financial record (such as a bank statement or a line item of a bank feed) associated with a transaction between a first entity (an accounting entity) and a second entity is received at an accounting system”, Rusu does not explicitly teach multiple users)
extracting fields from the electronic transaction data; ((Rusu [0097]) “Where multiple financial records are received as financial data, the transaction data determination module 211 may process the financial data to determine or extract individual financial records. Each determined financial record may include one or more attributes such as transaction data, payee details, a reference, a description, a transaction amount, transaction currency, and/or transaction type detail”)
assigning a respective class of a set of classes to each respective extracted field of the extracted fields ((Rusu [0073]) “During reconciliation 118, for each transaction 120 the payer 102 has to identify the transaction description and amount to identify the accounting data 122, which may include the corresponding payee, the account or account code in the accounting system, the amount in the accounting system, and/or other attributes, such as tax rate, tax amount, and the like”) using a machine learning model trained through a supervised learning process to assign classes to input fields; ((Rusu [0070]) “Some embodiments relate to an automatic approach for generating a training dataset for training a transaction attribute prediction model, such as entity prediction model, to determine attribute(s) (such as entity identifiers) of or associated with financial transactions…embodiments relate to generating a training data set including examples comprising…a label entity identifier”, training using a dataset with labels is a supervised learning process)
McGlynn_1 teaches the following further limitations that Rusu does not teach:
receiving electronic transaction data associated with transactions involving multiple users and an entity; ((McGlynn_1 [0033]) “one user 202 may shop SEARS primarily for clothing, while another user 202 shops SEARS for power tools. In this case, the designation engine 210 will suggest both designations to the user 202 and learn which designation to use on future SEARS transactions”)
generating candidate mappings for the transactions based on applying multiple [sets of] mapping rules to the extracted fields; ((McGlynn_1 [0019]) “The transaction categorization system 100 can compute match scores for each combination of transaction profile 123 and designation rule 119 based on the number of transaction attributes that match”, McGlynn_1 does not teach applying multiple sets of mapping rules to create candidate mappings)
generating a score for each candidate mapping of the candidate mappings based on applying scoring rules to the candidate mappings; ((McGlynn_1 [0019]) “The designation rules 119 are associated with designations and contain one or more transaction attributes that are compared with one or more transaction attributes in the transaction profiles 123. Each designations rule 119 is associated with one designation. The transaction categorization system 100 can compute match scores for each combination of transaction profile 123 and designation rule 119 based on the number of transaction attributes that match”)
and creating mappings of transactions associated with a particular user based on applying the selected set of mapping rules to each transaction associated with the particular user ((McGlynn_1 [0020]) “The designation rule that yields the highest match score or a match score that meets a match criteria (e.g., exceeds a threshold) will be applied to the transaction and the transaction will be designated according to the designation rule. The transaction categorization server 101 repeats this process for all available transactions associated with the user 102”) and the entity ((McGlynn_1 [0017]]) “A commercial entity 122 (e.g., banks, stores, restaurants, etc.) is in communication with and submits transaction profiles 123 associated with a user 102 to a transaction categorization server 101”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu and McGlynn_1 by taking the method for categorizing transactions by extracting fields from received transaction data by assigning classes to each extracted field using a trained machine learning model, taught by Rusu, and including transactions from multiple users, mapping rules to create candidate mappings for the transactions, scoring the candidate mappings with scoring rules, and creating mappings with selected mapping rules, taught by McGlynn_1, as using rules to accomplish the categorization of financial data allows both for a user to view an interpretable rationale for the categorization, increasing user confidence in the correctness of the categorization, and allows for the rationale of the categorization to be audited, which could be necessary to fulfill regulatory requirements related to handling of financial data. Such a combination would be obvious.
Wu teaches the following further limitations that Rusu does not teach and more explicitly than McGlynn_1 teaches:
generating candidate mappings [for the transactions] ((Wu Pg. 3) “We use a rule-based method to predict the in-hospital death events of sepsis patients in our study”, McGlynn_1 but not Wu teaches candidate mappings for transactions) based on applying multiple sets of mapping rules ((Wu Pg. 5) “In the rule generation step, random forest is used to discover possible rules from the data set. Random forest is an ensemble learning method for prediction tasks…on each data set, a decision tree is established by a subset of randomly selected risk factors and each tree could be viewed as a set of rules characterizing a sub-population”) to the extracted fields; ((Wu Pg. 3) “In order to find important risk factors and informative rules for the prediction of in-hospital death of sepsis patients, we extract the sepsis data set from MIMIC-III for the purpose of our study”, (Wu Pg. 5) “In this way, random forest is able to capture traits of the whole population and search for potential rules over all risk factors”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, and Wu by taking the method for categorizing transactions by extracting fields from received transaction data by assigning classes to each extracted field using a trained machine learning model, creating candidate mappings for each field of the transactions by applying a set of mapping rules, scoring the candidate mappings with scoring rules, and creating mappings with selected mapping rules, jointly taught by Rusu and McGlynn_1, and including applying multiple sets of mapping rules to the extracted fields, taught by Wu, as Wu teaches: (Wu Pg. 5) “on each data set, a decision tree is established by a subset of randomly selected risk factors and each tree could be viewed as a set of rules characterizing a sub-population, as shown in the example of Fig. 2. In this way, random forest is able to capture traits of the whole population and search for potential rules over all risk factors”. Such a combination would be obvious.
Medicherla teaches the following further limitations that neither Rusu, nor McGlynn_1, nor Wu teach:
selecting a set of mapping rules from the multiple sets of mapping rules [for categorizing transactions involving the entity] ((Medicherla [0050]) “Finally, at step 732, a set of candidate data transformation rules is picked out of the plurality of candidate data transformation rules based on a top user-defined number of rankings”, Rusu and McGlynn_1 but not Medicherla teaches categorization of transactions) based on the generated score for a corresponding candidate mapping of the candidate mappings; ((Medicherla [0050]) “In the next step 730, each program is ranked using a score assigned to each DSL operator in the DSL, wherein a higher score is assigned to operators which have a higher frequency of use in the historical data mapping as compared to operators which have lower frequency of use”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, Wu, and Medicherla by taking the method for categorizing transactions by extracting fields from received transaction data by assigning classes to each extracted field using a trained machine learning model, creating candidate mappings for each field of the transactions by applying multiple sets of mapping rules, scoring the candidate mappings with scoring rules, and creating mappings with selected mapping rules, jointly taught by Rusu, McGlynn_1, and Wu, and including selecting a set of mapping rules from multiple based on generated scores for candidates, taught by Medicherla, as Medicherla teaches: (Medicherla [0052]) “The generated rule is precise when it is logically equivalent to the rule in the gold standard” and (Medicherla [0054]) “Out of the 236 fields, the rule generator generated precise rules for 191 target fields in E1, whereas with the gold standard matches (E2), the system 100 has generated 227 precise rules. This efficiency is highly promising”, that is, that the method of Medicherla empirically mostly selects rules that are equivalent to a gold standard. Such a combination would be obvious.
Regarding claim 2,
Rusu, McGlynn_1, Wu, and Medicherla jointly teach The method of Claim 1,
McGlynn_1 further teaches:
further comprising using the scoring rules to score the mappings of the transactions associated with the particular user ((McGlynn_1 [0050]) “After the transaction has been evaluated against all designation rules, the designation rule that generates the best match score is utilized to associate a transaction designation to the transaction. In one implementation, the best match score must satisfy a match criterion (e.g. exceed a confidence threshold) to be considered applicable. If the best match score satisfies the match criterion, then the transaction will be designated according to the designation rule”, (McGlynn_1 [0051]) “The scoring of a designation rule against the transaction is performed by combining (e.g. summing, averaging) individual scores on transaction attributes (e.g. textual, non-textual, and non-transactional) with a configurable weight applied to each attribute. The weighting enables specific attributes to contribute more or less to the match score”, scoring transaction mappings with configurable weights, summing or averaging individual scores, and comparison with a confidence threshold correspond to using scoring rules to score transactions)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Rusu, McGlynn_1, Wu, and Medicherla for the parent claim of claim 2, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 4,
Rusu, McGlynn_1, Wu, and Medicherla jointly teach The method of Claim 1,
Rusu further teaches:
wherein the set of classes includes one or more of a date class, an amount class, or a description class ((Rusu [0097]) “Where multiple financial records are received as financial data, the transaction data determination module 211 may process the financial data to determine or extract individual financial records. Each determined financial record may include one or more attributes such as transaction data, payee details, a reference, a description, a transaction amount, transaction currency, and/or transaction type detail”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Rusu, McGlynn_1, Wu, and Medicherla for the parent claim of claim 4, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claims 9, 10 and 12,
Claims 9, 10, and 12 recite a system comprising at least one processor and a memory for performing the function of the method of claims 1, 2, and 4, respectively. Specifically, claim 9 recites A system for automated transaction categorization, comprising: one or more processors; and a memory comprising instructions that, when executed by the one or more processors, cause the system to: [perform the method of claim 1]. Rusu recites: (Rusu [0027]) “Some embodiments relate to a system comprising: at-least one processor configured to communicate with a memory, wherein the memory comprises program code executable by the at-least one processor to:…determine, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record”.
All other limitations in claims 9, 10, and 12 are substantially the same as those in claims 1, 2, and 4, respectively, therefore the same rationale for rejection applies.
Regarding claims 17 and 18,
Claims 17 and 18 recite a non-transitory computer readable storage medium comprising instructions for performing the function of the method of claims 1 and 2, respectively. Specifically, claim 17 recites A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to: [perform the method of claim 1]. Rusu recites (Rusu [0046]) “Some embodiments relate to a computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform any one of the described methods”.
All other limitations in claims 17 and 18 are substantially the same as those in claims 1 and 2, respectively, therefore the same rationale for rejection applies.
Claims 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rusu, in view of McGlynn_1, further in view of Wu, further in view of Medicherla, further in view of McGlynn_2.
Regarding claim 3,
Rusu, McGlynn_1, Wu, and Medicherla jointly teach The method of Claim 2, further comprising:
McGlynn_2 teaches the following further limitations that neither Rusu, nor McGlynn_1, nor Wu, nor Medicherla teach:
determining that a threshold number of scores ((McGlynn_2 [0024]) “If a designation rule contains multiple transaction attributes, application of the designation rule may yield multiple attribute scores. The multiple attribute scores may be summed or averaged to yield an overall match score for the transaction”, a summing or average score corresponds to a number of scores) for respective mappings of respective transactions associated with the entity and a plurality of different users determined using the selected set of mapping rules do not exceed a score threshold; ((McGlynn_2 [0025]) “The individual rules 119 are first applied to the transaction profiles 123 of the specific user 102. If one or more of the individual rules 119 yields a match score that meets a match criterion (e.g., exceeds a threshold) for a specific transaction, the individual rule 119 with the highest match score will be applied and the transaction will be designated according to the individual rule 1 19. If none of the individual rules 119 yields a match score that meets the match criterion for the transaction, the community rules 120 are applied”)
and selecting a different set of mapping rules from the multiple sets of mapping rules for categorizing transactions involving the entity based on the determining that the threshold number of scores for the respective mappings of the respective transactions associated with the entity and the plurality of different users determined using the selected set of mapping rules do not exceed the score threshold ((McGlynn_2 [0025]) “If none of the individual rules 119 yields a match score that meets the match criterion for the transaction, the community rules 120 are applied”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, Wu, Medicherla, and McGlynn_2 by taking the method for categorizing transactions involving mapping rules and scoring mappings of claim 2, jointly taught by Rusu, McGlynn_1, Wu, and Medicherla, and including selecting an alternate set of mapping rules when the scores of a mapping do not exceed a threshold, taught by McGlynn_2, as McGlynn_2 teaches: (McGlynn_2 [0054]) “An individual rule that is promotable to a community rule meets certain criteria designed to exclude individual rules from becoming community rules if their inclusion is not likely to enhance the community rule base. In one implementation, individual rules that relate to income are not promotable because income is highly personalized”, that is, that some rules are more personalized while others are more general, and that different transactions, based on their unique characteristics, are best categorized by different rule sets. Such a combination would be obvious.
Regarding claim 11,
Claim 11 recites a system for performing the function of the method of claim 3. All other limitations in claim 11 are substantially the same as those in claim 3, therefore the same rationale for rejection applies.
Regarding claim 19,
Claim 19 recites a non-transitory computer readable storage medium for performing the function of the method of claim 3. All other limitations in claim 19 are substantially the same as those in claim 3, therefore the same rationale for rejection applies.
Claims 5, 6, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rusu, in view of McGlynn_1, further in view of Wu, further in view of Medicherla, further in view of Grosset.
Regarding claim 5,
Rusu, McGlynn_1, Wu, and Medicherla jointly teach The method of Claim 4,
Grosset teaches the following further limitations that neither Rusu, nor McGlynn_1, nor Wu, nor Medicherla teach:
wherein information within a field assigned to the amount class ((Grosset [0051]) “a value for an amount criterion may be required with separate values for credit and debit criterions for each data entry”) comprises an indication that a given transaction involved making a withdrawal from an account [associated with a given user] (Grosset Pg. 24, Table 2 shows transactions with debits from accounts, debits are withdrawals that are shown to reduce a balance amount, McGlynn_1 but not Grosset explicitly teaches accounts associated with users)
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At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, Wu, Medicherla, and Grosset by taking the method for categorizing transactions involving extracting fields from transactions, including a field of the amount class, of claim 4, jointly taught by Rusu, McGlynn_1, Wu, and Medicherla, and including transactions with amounts indicating withdrawals from an account, taught by Grosset, as Grosset teaches: (Grosset [0106]) “In one implementation, the data analysis tool 116B may require the amount criterion to have sets of values for debit and credit to be separated into two sets of values. In this case, all numeric amounts in the values would be positive, as numeric amounts that are negative would be converted to a positive debit amount. In another implementation, the data analysis tool 116B may require the amount criterion to have sets of values for debit and credit to be combined into a single set of values. In this case, each of the values linked to a debit may need a negative signifier to be added to its number signifiers and resultant value added to the set of values linked to credits for their data entries”, that is, that determining if an amount in a transaction is a withdrawal is vital for correctly recognizing the format when dealing with heterogenous transaction data and working with downstream tools. Such a combination would be obvious.
Regarding claim 6,
Rusu, McGlynn_1, Wu, Medicherla, and Grosset jointly teach The method of Claim 5,
Grosset further teaches:
wherein a mapping rule of the set of mapping rules involves negating a value associated with the field assigned to the amount class based on the indication ((Grosset [0106]) “the data analysis tool 116B may require the amount criterion to have sets of values for debit and credit to be combined into a single set of values. In this case, each of the values linked to a debit may need a negative signifier to be added to its number signifiers”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Rusu, McGlynn_1, Wu, Medicherla, and Grosset for the parent claim of claim 6, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claims 13 and 14,
Claims 13 and 14 recite a system for performing the function of the method of claims 5 and 6, respectively. All other limitations in claims 13 and 14 are substantially the same as those in claims 5 and 6, respectively, therefore the same rationale for rejection applies.
Claims 7, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rusu, in view of McGlynn_1, further in view of Wu, further in view of Medicherla, further in view of Covington.
Regarding claim 7,
Rusu, McGlynn_1, Wu, and Medicherla jointly teach The method of Claim 1,
Covington teaches the following further limitations that neither Rusu, nor McGlynn_1, nor Wu, nor Medicherla teach:
wherein the scoring rules are chosen for use in generating the score for each candidate mapping of the candidate mappings based on a type corresponding to the entity ((Covington [0065]) “The next step is to search the description of each transaction (e.g., "KROGER FUEL 2350 CINCINNATI Ohio 555-2345") for words, phrases, and other character strings that indicate the nature of the transactions. This is done with the aid of a set of dictionaries. In this example, KROGER matches the Groceries dictionary, which assigns it a specific initial goodness-of-fit of 0.8 (because the dictionary reflects the fact that Kroger can also be other things), and KROGER FUEL matches the AutomobileFuel dictionary with a category score of 1.0 because the dictionary encodes the knowledge that it cannot be anything else. Through such a process, each transaction is assigned a category score to each category, which is 0 if nothing in the appropriate dictionary matches it, or if, as sometimes happens, a dictionary encodes that a business is definitely not of a particular type. For instance, a tavern named TOLEDO BOWLING ALLEY, known to go by that name, might be in the sports dictionary with a category score of 0.0 so that the phrase BOWLING ALLEY does not lead to its being tagged as a sports venue. Dictionaries use exact matching of substrings and also matching to regular expressions”, choosing category score rules based on the type of business in a transaction description corresponds to choosing scoring rules based on types corresponding to entities)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, Wu, Medicherla, and Covington by taking the method for categorizing transactions involving using scoring rules to score candidate mappings of claim 1, jointly taught by Rusu, McGlynn_1, Wu, and Medicherla, and including choosing scoring rules based on the type of an entity involved in a transaction, taught by Covington, as doing so allows for specialized scoring rules for entity types to be developed, that can provide better results for known entity types than general scoring rules for all entities could. Such a combination would be obvious.
Regarding claim 15,
Claim 15 recites a system for performing the function of the method of claim 7. All other limitations in claim 15 are substantially the same as those in claim 7, therefore the same rationale for rejection applies.
Regarding claim 20,
Claim 20 recites a non-transitory computer readable storage medium for performing the function of the method of claim 7. All other limitations in claim 20 are substantially the same as those in claim 7, therefore the same rationale for rejection applies.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rusu, in view of McGlynn_1, further in view of Wu, further in view of Medicherla, further in view of Covington, further in view of Omoseebi.
Regarding claim 8,
Rusu, McGlynn_1, Wu, Medicherla, and Covington jointly teach The method of Claim 7,
Covington teaches the following further limitations that neither Rusu, nor McGlynn_1, nor Wu, nor Medicherla teach:
wherein the [scoring] rules involve [scoring] transactions associated with the entity based on how many transactions associated with the entity include an amount that is below a threshold amount ((Omoseebi Pg. 10) “These rules focus on detecting unusual or high-risk transactions that may indicate money laundering or other illicit activities…Rule: Flag accounts with repeated cash deposits or withdrawals just below reporting thresholds”, McGlynn_1 but not Omoseebi explicitly teaches scoring transactions)
At the time of filing, one of ordinary skill in the art would have motivation to combine Rusu, McGlynn_1, Wu, Medicherla, Covington, and Omoseebi by taking the method for categorizing transactions involving using scoring rules to score candidate mappings, based on entity type, of claim 7, jointly taught by Rusu, McGlynn_1, Wu, Medicherla, and Covington, and including rules for transactions based on how many transactions include amounts below a threshold, taught by Omoseebi, as doing so is a well-known way to categorize transactions as fraudulent, that compensates for an exploitable weakness in the use of static thresholds. Such a combination would be obvious.
Regarding claim 16,
Claim 16 recites a system for performing the function of the method of claim 8. All other limitations in claim 16 are substantially the same as those in claim 8, therefore the same rationale for rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Avagyan et al. (U.S. Patent No. 9,898,515) discloses systems and methods for processing transaction data to pair transactions in different formats based on information in selected fields.
Sanders et al. (U.S. Patent No. 10,803,064) discloses systems and methods to dynamically change rules used to match entities at a runtime.
Rodriguez et al. (U.S. Patent Application Publication No. 2019/0205993) discloses a method for processing transaction data to add metadata tags, such as user or vendor.
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/V.A.N./Examiner, Art Unit 2124
/MIRANDA M HUANG/ Supervisory Patent Examiner, Art Unit 2124
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