805DETAILED 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 .
Election/Restrictions Acknowledgement
Applicant's election with traverse of Species A in the reply filed on 3/21/2025 is acknowledged. The traversal is on the ground(s) that :
(a) The claims are directed to a single inventive concept
(b) No serious search or examination burden exists
This is not found persuasive because:
In regards to traversal (a), for simplicity of argument, claim limitations in Species A and claim limitations in Species B are directed to two different embodiments represented in Fig 9 and Fig 10 of the specifications. Claim limitations in Species A are directed to generating tokens and inputting such tokens in a machine learning model to generate predictions, while claims limitations in Species B are directed to generating embeddings and inputting such embeddings in a machine learning model to generate predictions.
Further, Species A and B (independent claims and dependent claims in combination) contain overlapping claim elements and distinct claim elements. Therefore, the claimed inventions, despite overlapping claim elements, are still independent and distinct. Therefore, the requirement is still deemed proper and is therefore made FINAL.
In regards to traversal (b), based upon the mandatory electronic searches required for application allowance, including internal database searches (e.g. patents, pre-grant publications) and external non-patent literature database searches (e.g. IP.com), searching for additional patently distinct claim limitations would create an undue burden. Specifically, Species A and Species B would require searching different fields and CPC classifications. For example, Species A would require a search in classification such as G06Q 20/38 while Species B would require a search in classification such as G06F 18/21. Therefore, the requirement is still deemed proper and is made FINAL.
Conclusion - Applicant has received a requirement for restriction/election for the originally presented invention and has elected Species A (Claims 1-7 and 14-20) without prejudice, and withdrawn claims 8-13. Since this election has been made FINAL, examination is directed to claims 1-7 and 14-20.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on April 23, 2025 is being considered by the examiner.
Status of the Claims
This is a non-final rejection prepared in response to U.S. Patent Application 18/940,805 filed on
November 7, 2024.
Claims 1-7 and 14-20 are elected.
Claims 8-13 are withdrawn.
Claims 1-20 are pending.
Claim Objections
Claims 2 and 15 are objected to because of the following informalities:
Claims 2 and 15, the recited “a subset of transaction features…” on line 2 should be amended to “the subset of transaction features…” as “a subset of transaction features…” was previously recited on claim 1 line 6.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14-20 are rejected under 35 U.S.C. 101 because they fail to claim statutory subject matter.
Claim 14-20 are directed to "A computer storage medium …". The broadest reasonable interpretation (BRI) of machine-readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See MPEP 2106.03, subsection II; In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Therefore, claims 14-20 are rejected under 35 U.S.C. 101 as failing to claim statutory subject matter. However, in the interest of compact prosecution, examination will continue as if claims 14-20 were amended to include the recitation “non-transitory.”
Claims 1-7 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a system (i.e., machine, and manufacture). Claims 14-20 are
directed to a computer-storage media (i.e., manufacture). Therefore, these claims fall within the four statutory categories of invention, and thus must be further analyzed at Step 2A to determine if the claims are directed to a judicial exception (See MPEP 2106.03, subsection II).
Step 2A Prong One: Claim 1, recites (i.e., sets forth or describes) an abstract idea. More specifically, the following bolded claim elements recite abstract ideas while the non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to:
obtain a group of transactions associated with an account;
convert the group of transactions into a group of transaction strings using a subset of transaction features of the group of transactions;
for each transaction string in the group of transaction strings, generate a transaction token based on the transaction string using a token vocabulary of a tokenizer, wherein the token vocabulary associates transaction strings with transaction tokens;
provide the generated transaction tokens associated with the group of transaction strings to a machine learning (ML) model as input;
generate, by the ML model, an estimated transaction token based on the provided transaction tokens;
transform the generated estimated transaction token into an estimated transaction using a de-tokenizer associated with the tokenizer, wherein the estimated transaction is associated with the account with which the obtained group of transactions is associated; and
approve a new transaction associated with the account using the estimated transaction, wherein the estimated transaction is compared to the new transaction.
Claim 1, recites (i.e., sets forth or describes) a method for processing transactions utilizing analysis of historical data. The claim achieves this by obtaining a group of transactions associated with an account; converting the group of transactions into strings; generating tokens based on the strings; processing the generated token utilizing a mathematical model to generate an estimated transaction token, de-tokenizing the estimated transaction token to obtain an estimated transaction; and approving a new transaction data based on the comparison of the new transaction with the estimated transaction. Claim 14 is significantly similar to claim 1. As such claim 14 also recite an abstract idea. Specifically, but for the additional elements, the claim under its broadest reasonable interpretation recites limitations grouped within the “certain methods of organizing human activity” grouping of abstract ideas (i.e., commercial or legal interactions) and “mathematical concepts”.
Step 2A Prong Two: Because the claim recites abstract ideas, the analysis proceeds to
determine whether the claim recites additional elements that recite a practical application of the
abstract ideas. Here, the additional elements of a system comprising: a processor; and a memory, a tokenizer, a de-tokenizer and a machine learning (ML) model merely serve as a tools to perform the abstract idea (MPEP § 2106.05(f)). Therefore, the claim as a whole fail to recite a practical application of the abstract ideas.
Step 2B: Determines whether the claim as a whole amount to significantly more than the exception itself. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Here, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. As discussed previously with respect to Step 2A, the additional elements merely serve as a tool to perform an abstract idea. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Dependent Claims: Claims 2-7 and 15-20 have also been analyzed for subject matter
eligibility. However, claims 2-7 and 15-20 also fail to recite patent eligible subject matter for the
following reasons:
Claims 2 and 15 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
converting the group of transactions into the group of transaction strings using a subset of transaction features of the group of transactions includes: identifying a numeric transaction feature of the subset of transaction features;
determining a numeric value of the identified numeric transaction feature for a transaction of the group of transactions; and
converting the determined numeric value of the identified numeric transaction feature into a categorical feature value of a categorical feature that is associated with a numeric value range of the numeric transaction feature, wherein the transaction string of the transaction includes the categorical feature value to which the determined numeric value was converted.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas.
Claims 3 and 16 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
approving the new transaction associated with the account using the estimated transaction includes: generating an approval notification associated with the new transaction; and
causing the generated approval notification to be sent to one or more entities associated with the new transaction, wherein the one or more entities associated with the new transaction include a merchant, an acquirer, or an issuer
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” grouping of abstract ideas.
Claims 4 and 17 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
estimate future account behavior of the account using the estimated transaction;
determine that the estimated future account behavior indicates a reduction in user loyalty with respect to the account; and
recommend a promotional offer to be offered to a user of the account, whereby the reduction in user loyalty with respect to the account is less likely.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas.
Claims 5 and 18 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
generate, by the ML model, a second estimated transaction token based on the provided transaction tokens;
transform the second generated estimated transaction token into a second estimated transaction using a de-tokenizer associated with the tokenizer, wherein the second estimated transaction is associated with the account with which the obtained group of transactions is associated;
determine a difference between the second estimated transaction and a second new transaction associated with the account exceeds a threshold; and
generate a preemptive fraud indicator based on the determined difference exceeding the threshold.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas. The non-bolded additional elements of a ML model, a tokenizer and a de-tokenizer fail to recite a practical application or significantly more than the abstract idea because it merely serves as a tool to perform the abstract idea (MPEP §2106.05(f)). Further, the additional elements, taken individually and in combination, do not result in the claim as a whole, amounting to significantly more than the judicial exception. Thus, there is no inventive concept in the claim and thus the claim is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis.
Claims 6 and 19 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
generate an inventory management indicator associated with the estimated transaction, wherein the inventory management indicator indicates, to a receiving merchant, a need to order an item with which the estimated transaction is associated; and
cause the generated inventory management indicator to be sent to a merchant with which the estimated transaction is associated
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” and “mathematical concepts” grouping of abstract ideas.
Claims 7 and 20 recite the following bolded claim elements as abstract ideas while the
non-bolded claim elements recite additional elements according to MPEP 2106.04(a).
receive a natural language query associated with the account;
generate a natural language query response based on the estimated transaction; and
respond to the received natural language query with the generated natural language response.
The claim further recites an abstract idea. In other words, it recites limitations grouped within the “certain methods of organizing human activity” grouping of abstract ideas.
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, 3, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi (US 20230351382 A1) in view of Solis (US 10410210 B1), in further view of Steele (US 20240378063 A1) in further view of Faith (US 9773246 B2).
Regarding claims 1 and 14, Zehtabi discloses:
obtain a group of transactions associated with an account; (¶0015, receive, from a user via the communication interface, first information that relates to a plurality of transactions;
convert the group of transactions into a group of transaction strings using a subset of transaction features of the group of transactions; (¶0015, extract, from the first information, a set of transaction-specific features and a set of transaction-specific textual information; generate at least one meta-transaction by clustering a subset of the plurality of transactions based on the extracted set of transaction-specific features and the extracted set of transaction-specific textual information; ¶0018, The processor may be further configured to generate the at least one meta-transaction by: converting the set of transaction-specific features into transaction-specific words. ¶0073, At step S406, the transactions reconciliation by integrating clustering and optimization module 302 tokenizes the features extracted in step S404 by converting the raw information into words and/or tokens.)
Zehtabi further discloses:
a processor; and (¶0037, As illustrated in FIG. 1, the computer system 102 may include at least one processor 104. The processor 104 is tangible and non-transitory.)
a memory comprising computer program code (¶0038, The computer system 102 may also include a computer memory 106. The computer memory 106 may include a static memory, a dynamic memory, or both in communication.)
A computer storage medium (¶0023, According to yet another aspect of the present disclosure, a non-transitory computer readable storage medium storing instructions for reconciling transactions is provided. The storage medium includes executable code which, when executed by a processor, causes the processor to…)
Zehtabi do not disclose, however Solis teaches:
for each transaction string in the group of transaction strings, generate a transaction token based on the transaction string using a token vocabulary of a tokenizer, wherein the token vocabulary associates transaction strings with transaction tokens; (col 2 lines 4-13, the secure execution environment includes a tokenization system, wherein the tokenization system is configured to receive data that includes an encrypted tokenization function and a character string. The tokenization system, responsive to receiving the encrypted tokenization function, can decrypt the tokenization function utilizing a decryption algorithm that is securely retained in the secure execution environment. The tokenization system can then execute the tokenization function over the character string to generate a token that is representative of the character string.)
transform the generated estimated transaction token into an estimated transaction using a de-tokenizer associated with the tokenizer, wherein the estimated transaction is associated with the account with which the obtained group of transactions is associated; and (col 2 lines 17-25, the tokenization system can receive data that includes the encrypted tokenization function and a token from the token database. The tokenization system, responsive to receiving the encrypted tokenization function, can decrypt the tokenization function utilizing the decryption algorithm. The tokenization system can invert the tokenization function, and can execute the inverted function over the received token, resulting in a character string that is represented by the token.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the disclosure of Zehtabi with the teaching of Solis. One of ordinary skills in the art would have been motivated to combine these common elements in order to enhance the security of the data and limit the exposure of sensitive information.
The combination of Zehtabi and Solis do not disclose, however Steele teaches:
provide the generated transaction tokens associated with the group of transaction strings to a machine learning (ML) model as input; generate, by the ML model, an estimated transaction token based on the provided transaction tokens; (¶0066, Still referring to FIG. 1, generating modified token 128 may include utilizing a token machine learning model 132… In some embodiments, token 124 may be input into token machine learning model 132 to output modified token 128.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi and Solis with the teaching of Steele. One of ordinary skills in the art would have been motivated to combine these common elements in order to perform predictive analysis of transactions utilizing the generated token instead of the original data.
The combination of Zehtabi, Solis and Steele do not disclose, however Faith teaches:
approve a new transaction associated with the account using the estimated transaction, wherein the estimated transaction is compared to the new transaction. (col 11 lines 4-8, For example, if an authorization request for a current transaction is received from a merchant, the current transaction can be matched to the predicted transaction of the generated authorization. Once the current transaction is matched, an approval can be sent to the merchant. col 11 lines 12-13, In one implementation, keys can be used to match the current transaction with the predicted transaction. col 11 lines 25-36, In one embodiment, a token (e.g. an authorization code) associated with the authorization can be sent to the consumer, who can use it for a transaction with a merchant. A token can be any electronically identifiable object, which can include characters that can be entered for electronic transmission. A token could include the account number of a consumer. The merchant can then send the token to an authorization server (e.g. of a payment processing network), which can use the token to cross-reference a list of generated authorizations to determine if a corresponding authorization exists and if the authorization is valid (e.g. checking whether the transaction is within a time window))
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis and Steele with the teaching of Faith. One of ordinary skills in the art would have been motivated to combine these common elements in order to ensure that only new transactions that are consistent with the estimated transactions are approved.
Further, the claimed limitation “wherein the token vocabulary associates transaction strings with transaction tokens.”, “wherein the estimated transaction is associated with the account with which the obtained group of transactions is associated” and “wherein the estimated transaction is compared to the new transaction” only describe characteristics of the token vocabulary and the estimated transaction which are non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Regarding claims 3 and 16, the combination of Zehtabi, Solis, Steele and Faith further disclose:
approving the new transaction associated with the account using the estimated transaction includes: generating an approval notification associated with the new transaction; and causing the generated approval notification to be sent to one or more entities associated with the new transaction, wherein the one or more entities associated with the new transaction include a merchant, an acquirer, or an issuer. (Faith col 11 lines7-8, Once the current transaction is matched, an approval can be sent to the merchant.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Faith’s additional teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to communicate to the entity the approval of the transaction to ensure transaction completeness.
Further, the claimed limitation “wherein the one or more entities associated with the new transaction include a merchant, an acquirer, or an issuer” only describe characteristics of the one or more entities which is non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Furthermore, the claimed limitation “causing…” in “causing the generated approval notification to be sent to one or more entities associated with the new transaction, wherein the one or more entities associated with the new transaction include a merchant, an acquirer, or an issuer” consists of language disclosing an intended use or intended result, so it is considered but given no patentable weight. (see MPEP 2111.05, MPEP 2114 and authorities cited therein). The reference is provided for the purpose of compact prosecution.
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi, Solis, Steele and Faith as applied to claims 1 and 14 above, in further view of Fly (US 20200082296 A1).
Regarding claims 2 and 15, the combination of Zehtabi, Solis, Steele and Faith do not disclose, however Fly teaches:
converting the group of transactions into the group of transaction strings using a subset of transaction features of the group of transactions includes: identifying a numeric transaction feature of the subset of transaction features; determining a numeric value of the identified numeric transaction feature for a transaction of the group of transactions; and (¶0027, In one embodiment, flagging certain features as having low relevance to scores provided by the machine learning model is further comprised of identifying feature values that are unique throughout a feature within the scored dataset. Identifying unique feature values is further comprised of identifying feature values that have been partially redacted for privacy or security purposes. ¶0092, The specific determination of which features and/or feature values to prepare varies. Typically, the determination is based on an identification of features and/or feature values that are likely to receive the same prediction score from a machine learned model. The methodologies for identifying features and/or feature values to prepare would be understood by a person of ordinary skill in the art, and are not described in greater detail herein.)
converting the determined numeric value of the identified numeric transaction feature into a categorical feature value of a categorical feature that is associated with a numeric value range of the numeric transaction feature, wherein the transaction string of the transaction includes the categorical feature value to which the determined numeric value was converted. (¶0026, converting certain feature values within the scored dataset, the converted feature values identified as feature values that have a high likelihood of receiving similar scores from the machine learning model… ¶0027, The converting step may be further comprised of converting numerical feature values into categorical feature values. A binning, scaling, and/or imputation methodology may be used to covert feature values that have a high likelihood of receiving similar scores from the machine learning model.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Fly’s teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to improve the system’s ability to identify patterns associated different categories.
Further, the claimed limitation “wherein the transaction string of the transaction includes the categorical feature value to which the determined numeric value was converted” only describe characteristics of the transaction string which is non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi, Solis, Steele and Faith as applied to claims 1 and 14 above, in further view of Kim (KR 102162765 B1).
Regarding claims 4 and 17, the combination of Zehtabi, Solis, Steele and Faith do not disclose, however Kim discloses:
estimate future account behavior of the account using the estimated transaction;
determine that the estimated future account behavior indicates a reduction in user loyalty with respect to the account; and (P.7 ¶2, Similar to the above-described churn prediction engine 230, the churn cause estimation unit 24 may include a churn cause estimation engine 240 that estimates the churn cause of a potential churn customer by applying a machine learning model. The reason of departure estimation engine 240 uses customer information and operating system information whose reason of departure is known as a learning dataset, and uses machine learning about what causes of departure of customers with patterns of specific personal information, transaction information, and/or behavioral information. )
recommend a promotional offer to be offered to a user of the account, whereby the reduction in user loyalty with respect to the account is less likely. (P.7 ¶3, Next, the personalized recommendation unit 25 may execute personalized recommendations effective to prevent churn for potential churn customers. In this specification, the execution of a personalized recommendation refers to sending an offer for a specific product or service to a potential churning customer, encouraging a potential churning customer to participate in a specific event and/or event, or encouraging a potential churning customer to a specific event and/or Selecting as a beneficiary for the event (e.g., discounting transaction amount, giving points or coupons, etc.), or conducting communication for customer relationship management to potential churn customers (e.g., customer contact via phone, text, SNS, etc.) Etc.) and the like. Claim 4, Including a personalized recommendation unit configured to execute a personalized recommendation for preventing churn to a potential churning customer determined based on the churn risk)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Kim’s teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to mitigate the loss of customers.
Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi, Solis, Steele and Faith as applied to claims 1 and 14 above, in further view of Rephlo (US 20200302552 A1).
Regarding claims 5 and 18, the combination of Zehtabi, Solis, Steele and Faith further disclose:
generate, by the ML model, a second estimated transaction token based on the provided transaction tokens; (Solis col 2 lines 4-13, the secure execution environment includes a tokenization system, wherein the tokenization system is configured to receive data that includes an encrypted tokenization function and a character string. The tokenization system, responsive to receiving the encrypted tokenization function, can decrypt the tokenization function utilizing a decryption algorithm that is securely retained in the secure execution environment. The tokenization system can then execute the tokenization function over the character string to generate a token that is representative of the character string.)
transform the second generated estimated transaction token into a second estimated transaction using a de-tokenizer associated with the tokenizer, wherein the second estimated transaction is associated with the account with which the obtained group of transactions is associated; (Solis col 2 lines 17-25, the tokenization system can receive data that includes the encrypted tokenization function and a token from the token database. The tokenization system, responsive to receiving the encrypted tokenization function, can decrypt the tokenization function utilizing the decryption algorithm. The tokenization system can invert the tokenization function, and can execute the inverted function over the received token, resulting in a character string that is represented by the token.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Solis’ additional teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to enhance the security of the data and limit the exposure of sensitive information.
The combination of Zehtabi, Solis, Steele and Faith do not disclose, however Rephlo teaches:
determine a difference between the second estimated transaction and a second new transaction associated with the account exceeds a threshold; and (claim 13, determine that a difference between the one or more actual expense transactions and the one or more predicted expense transactions is greater than a predefined threshold)
generate a preemptive fraud indicator based on the determined difference exceeding the threshold. (claim 13, transmit, to a mobile device associated with the account holder, a first alert indicating that the difference between the one or more actual expense transactions and the one or more predicted expense transactions exceeds the predefined threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Rephlo’s teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to reduce processing of fraudulent transactions.
Further, the claimed limitation “wherein the second estimated transaction is associated with the account with which the obtained group of transactions is associated” only describe characteristics of the second estimated transaction which is non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Claims 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi, Solis, Steele and Faith as applied to claims 1 and 14 above, in further view of Lee (KR 102523860 B1).
Regarding claims 6 and 19, the combination of Zehtabi, Solis, Steele and Faith do not disclose, however Lee discloses:
generate an inventory management indicator associated with the estimated transaction, wherein the inventory management indicator indicates, to a receiving merchant, a need to order an item with which the estimated transaction is associated; and cause the generated inventory management indicator to be sent to a merchant with which the estimated transaction is associated. (abstract, According to an embodiment of the present invention, in a method for providing a customized smart ordering service for food materials requiring an order through consumption prediction in a service providing server, food material order information of a first restaurant company, which is one of a plurality of food service companies, is provided. Based on the latest order details, confirming the latest order date and order quantity for each food material; acquiring sales information generated by the first restaurant after the latest order date, and predicting consumption for each food material based on the sales information; Calculating a remaining amount for each food material by subtracting the amount of consumption from the amount of order, and determining whether or not an order for food material is required from the first food service company based on the remaining amount; And as a result of the determination, when it is determined that the remaining amount of the first food ingredient is less than the reference value and it is determined that the order of the first food ingredient is necessary, transmitting an order notification message for the first food ingredient to the terminal of the first food service company. P.6 ¶1 &2, The service providing server 300 predicts the consumption amount for each food ingredient based on the sales information generated by the food service company since the date of the latest order of the food service company, calculates the remaining amount for each food material by subtracting the consumption amount from the recent order amount, and informs the food service company through the remaining amount. You can determine whether you need to order food materials. When it is determined that food ingredients need to be ordered from the first food service provider, the service providing server 300 may transmit an order notification message for food ingredients to the first food service provider terminal 110. P.7 ¶8-13, In step S205, the service providing server 300 may predict the amount of consumption for each food material based on the sales information obtained in step S204. The service providing server 300 can predict the amount of consumption for each food material by using past cumulative sales information and past cumulative order information, and can predict the consumption amount for each food material using the food price of the food service provider and the amount of food material used in the food. For example, if the price of food for one person sold by a first food service company is 10,000 won, and 10g of first food ingredient and 20g of second food ingredient are used for one person's food, the service providing server 300 provides the latest As a result of checking the sales information from the order date to the present, if it is confirmed that 200,000 won, the consumption of the first food material can be predicted as 200 g, and the consumption of the second food material can be predicted as 400 g. In step S206, the service providing server 300 may calculate the remaining amount for each food material based on the latest order amount confirmed in step S203 and the predicted consumption amount in step S205. For example, the service providing server 300 may calculate the remaining amount of the first food ingredient as 2 kg when the latest order amount of the first food ingredient is 10 kg and the predicted consumption amount of the first food ingredient is 8 kg. In step S207, the service providing server 300 may determine whether it is necessary to order food ingredients from the first restaurant through the remaining amount calculated in step S206. P.8 ¶3, In step S207, if it is determined that an order for food materials is necessary and it is determined that an order for the first food material is required among the food materials, in step S208, the service providing server 300 sends the first food service company terminal 110 to the first food material. You can send a notification message notifying you that you need to place an order for.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Lee’s teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to improve the accuracy and efficiency of the inventory management.
Further, the claimed limitation “wherein the inventory management indicator indicates, to a receiving merchant, a need to order an item with which the estimated transaction is associated” only describe characteristics of the inventory management indicator which is non-functional descriptive material and these characteristics are not processed or used to carry out any functionality that specifically relies on these particular characteristics.
Furthermore, the claimed limitation “cause…” in “cause the generated inventory management indicator to be sent to a merchant with which the estimated transaction is associated” consists of language disclosing an intended use or intended result, so it is considered but given no patentable weight. (see MPEP 2111.05, MPEP 2114 and authorities cited therein). The reference is provided for the purpose of compact prosecution.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zehtabi, Solis, Steele and Faith as applied to claims 1 and 14 above, in further view of Fernandez (WO 2023096833 A1).
Regarding claims 7 and 20, the combination of Zehtabi, Solis, Steele and Faith do not disclose, however Fernandez teaches:
receive a natural language query associated with the account; (¶0004, receiving, by a query processing system, a query that is at least in part a natural language query received based on user input at a computing device. The query may further be at least in part defined by predefined query parameters. ¶0049, One illustrative embodiment of the present disclosure is directed to a method that includes receiving, by a query processing system, a query. The query is at least in part a natural language query that is received based on user input at a computing device. ¶0051, In the illustrated embodiment, computing environment 100 receives and processes a query 105 using a query processing system 110. The query 105 can be a natural language query that is based on user input a computing device in communication with the query processing system 110.
generate a natural language query response based on the estimated transaction; and (¶0004, The method further involves executing, by the query processing system, the pipeline, which involves inputting the query and the protocol documents into the pipeline, executing a set of rules and/or model associations of the pipeline on the protocol documents using data from the query, and obtaining search results based on executing the sets of rules and/or the model associations. The search results are provided as an answer to the query for presentation at the computing device. ¶0017, retrieving, using a semantic search, the search results from the protocol documents based on the embedding for the query, and generating a natural language answer to the query based on the search results and the embedding for the query. Generating the natural language answer to the query involves including the scope data, from the query, in the natural language answer and including one or more relevant terms associated with the scope data that occur in the search results or are derived from the search results, in the natural language answer.
respond to the received natural language query with the generated natural language response. (¶0018, In some embodiments, providing the search results comprises displaying, by the query processing system on the computing device, the natural language answer and sub portions of each protocol document within the search results that support the natural language answer. ¶0019, In some embodiments, each search result includes a hyperlink having a uniform resource identifier to each protocol document, and the method further involves receiving, by the query processing system, input from the user regarding selection of a hyperlink for a search result, and displaying, by the query processing system on the computing device, an entire protocol document associated with the search result to provide context and support for the natural language answer. ¶0023, In some embodiments, providing the search results to the query involves displaying, by the query processing system on the computing device, the natural language answer and sub portions of the tables in each protocol document within the search results that support the natural language answer.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to have modify the combination of Zehtabi, Solis, Steele and Faith with Fernandez’s teaching. One of ordinary skills in the art would have been motivated to combine these common elements in order to enable users to request an receive transaction information utilizing conventional natural language.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's
disclosure:
US 20250156875 A1 to Jones discloses: Provided is a system, method, and computer program product for graph-based authorization. The system includes at least one processor programmed or configured to process a plurality of electronic payment transactions for a plurality of merchant systems arranged in an electronic payment processing network, generate a graph data structure including a plurality of nodes and a plurality of edges based on transaction data and external data, the transaction data including transaction parameters from each electronic payment transaction of the plurality of electronic payment transactions, generate a node embedding for each node of the graph data structure by: converting the transaction data associated with the node to first text, converting the external data associated with the node to second text, and generating the node embedding based on the first text and the second text.
US 20220229980 A1 to Jin discloses: Systems and methods for data parsing are disclosed. In one aspect, a method of parsing raw data associated with one or more transactions involves receiving a text string including raw data for a transaction, matching the text string to a plurality of locations within a location corpus to extract location information from the text string, and identifying a candidate entity from the text string based on a similarity score with respect to a plurality of entities within an entity corpus. The method further involves in response to the similarity score of the identified candidate entity being less than a threshold score, generating entity information using the tokens indicative of entity information, and generating normalized transaction data including the extracted location information and one of the identified candidate entity or the generated entity information.
US 20240289893 A1 to Nicolson discloses: Disclosed herein are system, method, and computer program product embodiments for providing predictive revenue distribution using machine learning and a Real-Time Payment (RTP) network. A revenue distribution system may train and apply a machine learning model to merchant transaction records to generate a predicted revenue amount. The revenue distribution system may deposit predicted revenue amounts to provide predictive funding for merchant accounts. For example, this may provide same-day and/or next-day funding. The revenue distribution system may account for differences in predicted revenue amounts and actual transaction amounts by adjusting the deposits of subsequent days. The revenue distribution system may also account for different periods of time, such as several days, a week, or longer. The revenue distribution system may also detect potentially fraudulent transaction amounts and/or may aid in monitoring business performance based on a detected difference between a predicted revenue amount and a provided transaction amount.
US 20230196370 A1 to Levine discloses: An Artificial Intelligence (AI) based transaction data processing and reconciliation system analyzes transaction data of different accounts to determine anomalous transactions, tagged transactions with Required Adjustments tag (R-tag), or aging transactions. Different Artificial intelligence (AI) based models are trained to produce corresponding risk scores that enable the determinations. Those transactions having low-risk scores are automatically reconciled whereas transactions having higher risk scores can be flagged for further review. Furthermore, the accounts corresponding to the transactions are also analyzed via different AI-based account-level models to identify accounts that can be R-tagged and/or accounts that are at the risk of being de-certified. Those accounts with higher risk scores can be flagged for further review while accounts with lower risk scores can be automatically certified.
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/J.L./Examiner, Art Unit 3698
/STEVEN S KIM/Primary Examiner, Art Unit 3698
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