DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first
inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the response filed on April 16, 2026.
Claim 1 was amended.
Claims 7-20 were canceled.
Claims 21-34 were added.
Claim(s) 1-6 and 21-34 are currently pending and have been examined.
This action is made Final.
Response to Arguments
Applicant argued that Examiner’s 101 rejection was improper because the claims are not directed to mental processes. Applicant further argued that the claims, as amended, are not capable of performance in the human mind, as they involve executing a machine-learning model to extract word embeddings from text descriptions. Examiner disagrees. The execution of the machine-learning model to extract word embeddings from text descriptions is not included as part of the recited abstract idea. It is considered part of the additional limitations. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the claims are not directed to certain methods of organizing human activity. Examiner disagrees. In response to Applicant’s amended claims, Examiner’s 101 rejection does not rely on a finding that the claims are directed to certain methods of organizing human activity, thereby making Applicant’s argument moot. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the claims recite a practical application of any alleged abstract idea by reciting specific technological implementations that provide concrete solutions to the technical problem of accurately categorizing transactions when transaction description are incomplete or ambiguous. Examiner disagrees. The concrete solution provided by Applicant’s claimed invention is to augment data for use by the machine learning model in order to improve the model’s accuracy. Determining that the data requires augmentation, augmenting the data, and feeding the data to the machine learning model, which is the technical solution offered by the claimed invention, is a part of the recited abstract idea. The machine learning model and the accompanying computer components are merely used as tools to implement the abstract idea of augmenting data to improve the model’s accuracy. Such a technological implementation, despite its specificity, is not indicative of patent eligible subject matter. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the combination of elements imposes meaningful limits on any alleged abstract idea. Examiner disagrees. Applicant’s claimed invention does not impose meaningful limits on the abstract idea because the additional limitations are merely used as tool to implement the abstract idea. Such an implementation does not impose any meaningful limits on the abstract idea. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the prior art did not teach or suggest “execute a machine-learning model using as input the transaction data to extract a first confidence vector comprising confidence scores for transaction categories, wherein the machine-learning model extracts first word embeddings from the description”, as claimed. Examiner disagrees. The Wang reference teaches or suggests limitations to “execute a machine-learning model using as input the transaction data to extract a first confidence vector comprising confidence scores for transaction categories, wherein the machine-learning model extracts first word embeddings from the description” (Wang: pgh 44). Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the prior art did not teach or suggest “in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment a text of the description of the transaction using additional data based on correlating the description with location-specific merchant data to generate an augmented description”. Examiner disagrees. The Dongare reference teaches “in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment a text of the description of the transaction using additional data based on correlating the description with location-specific merchant data to generate an augmented description” (Dongare: pgh 115). Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the prior art did not teach or suggest “execute the machine-learning model using as input the transaction data and the augmented description to extract a second confidence vector, wherein the machine-learning model extracts second word embeddings from the augmented description.” Examiner disagrees. The Wang reference teaches limitations to “execute the machine-learning model using as input the transaction data and the augmented description to extract a second confidence vector, wherein the machine-learning model extracts second word embeddings from the augmented description.” (Wang: pgh 20). Therefore, Examiner finds Applicant’s argument non-persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-6 and 21-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1-6 and 21-33 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent system claim 1 as the claim that represents the claimed invention for analysis and is similar to independent claim 25. Claim 1 recites the following limitations:
[a system comprising: one or more processors; and a computer-readable, non-transitory medium including instructions which, when executed by the one or more processors, cause at least one of the one or more processors to:]
obtain transaction data of a transaction, the transaction data including an amount and a description;
[execute a machine-learning model using as input the transaction data to] extract a first confidence vector comprising confidence scores for transaction categories, wherein the [machine-learning model] extracts first word embeddings from the description;
determine whether a first confidence score of a first transaction category of the first confidence vector satisfies a predetermined threshold;
in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment a text of the description of the transaction using additional data based on correlating the description with location-specific merchant data to generate an augmented description;
[execute the machine-learning model using as input the transaction data and the augmented description] to extract a second confidence vector, wherein the machine-learning model extracts second word embeddings from the augmented description; and
in response to determining that a second confidence score in the second confidence vector of the transaction category satisfies the predetermined threshold, assign the transaction category to the transaction.
Square brackets denote additional elements in the claims. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as a mental process because the limitations recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The one or more processors, computer-readable, non-transitory medium, and machine-learning model in Claim 1 are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of one or more processors, a computer-readable, non-transitory medium, and a machine-learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1 and 25 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claims 1 and 25 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-6, 21-24, and 26-33 further define the abstract idea that is present in independent claims 1 and 25, respectively, and thus correspond to a mental process and hence are abstract for the reasons presented above. Dependent claims 2-6, 21-24, and 26-33 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, dependent claims 2-6, 21-24, and 26-33 are directed to an abstract idea. Thus, claim(s) 1-6 and 21-33 are not patent-eligible.
Claim(s) 34 is directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
Claim 34 recites the following limitations:
[A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:] obtain transaction data of a transaction, the transaction data including an amount and a description, wherein the description is a truncated description comprising fewer characters than a full merchant name;
[execute a machine-learning model using as input the transaction data to] extract a first confidence vector comprising confidence scores for transaction categories, wherein [the machine-learning model] extracts first word embeddings from the truncated description, and wherein [the machine-learning model is trained to] categorize transactions using a training set comprising a set of categorized transactions having standardized transaction categories and augmented descriptions;
determine whether a first confidence score of a first transaction category of the first confidence vector satisfies a predetermined threshold;
in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment a text of the truncated description of the transaction by adding additional words to the truncated description based on correlating the truncated description with location-specific merchant data to generate an augmented description;
[execute the machine-learning model using as input the transaction data and the augmented description to] extract a second confidence vector, wherein [the machine-learning model] extracts second word embeddings from the augmented description, and wherein the second word embeddings differ from the first word embeddings based on the additional words in the augmented description, resulting in the second confidence vector having different confidence scores than the first confidence vector; and
in response to determining that a second confidence score in the second confidence vector of the transaction category satisfies the predetermined threshold, assign the transaction category to the transaction.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes because the limitations recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as concepts performed in the human mind, including observations, evaluations, judgments, and opinions, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The non-transitory computer-readable medium, processors, and machine learning model in Claim 34 are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a non-transitory computer-readable medium, processors, and a machine learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claim 34 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim 34 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-6, 21-27, 29-34 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2024/0144050) in view of Dongare (US 2023/0385825).
Regarding claim(s) 1 and 25:
Wang teaches:
a system comprising: one or more processors; and (Wang: Fig. 1, item 130. Discloses one or more processors.)
a computer-readable, non-transitory medium including instructions which, when executed by the one or more processors, cause at least one of the one or more processors to: (Wang: pgh 26, “…the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium.”)
obtain transaction data of a transaction, the transaction data including an amount and a description; (Wang: Fig. 7, item 702 discloses obtaining a dataset comprising transaction data; pgh 17, “The transaction attributes may include, for example, user, transaction description, payee, time, amount, and other metadata from the institution.”)
execute a machine-learning model using as input the transaction data to extract a first confidence vector comprising confidence scores for transaction categories; (Wang: pgh 44, “The multilayer perceptron learner model…may be trained with a final softmax layer that produces a vector output of predicted category probability distribution, which may also be used as the confidence measure vector.”)
execute the machine-learning model using as input the transaction data and the augmented description to extract a second confidence vector; and (Wang: pgh 20, “A second stage (meta learning) includes a meta machine learning model that is trained to receive the meta dataset as a model input, and to generate a final prediction, along with a final confidence score…”)
in response to determining that a second confidence score in the second confidence vector of the transaction category satisfies the predetermined threshold, assign the transaction category to the transaction. (Wang: pgh 6, “The system may further be caused to generate final inference results comprising categorized transactions from a trained meta machine learning model based on the meta dataset…”)
Wang does not teach, however, Dongare teaches:
determine whether a first confidence score of a first transaction category of the first confidence vector satisfies a predetermined threshold; in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment the description of the transaction using additional data; (Dongare: pgh 115, “If the output from the model results in an entity name with an entity confidence score at or above a predetermined threshold value, the standardized entity name and parent entity name are extracted from the lookup table and used to label the respective transaction…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang to include the teachings of Dongare to improve upon model outputs (Dongare: pgh 39).
Regarding claim(s) 2 and 26:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 1. Dongare further teaches:
wherein the machine-learning model is trained to categorize transactions using a training set comprising a set of categorized transactions. (Dongare: pgh 75, “Fig. 6 is a flowchart illustrating an exemplary computer-implemented method of model training for improving the categorization and classification of open banking transactions…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang to include the teachings of Dongare to improve upon model outputs (Dongare: pgh 39).
Regarding claim(s) 3 and 27:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 2. Dongare further teaches:
wherein the categorized transactions comprise standardized transaction categories and augmented descriptions. (Dongare: pgh 42, “…the data mining goal may be defined as the following: i) categorize the transaction data based on the text at a granular level, and ii) extract and standardize the transaction entity from the given transaction dataset…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang to include the teachings of Dongare to improve upon model outputs (Dongare: pgh 39).
Regarding claim(s) 5 and 29:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 4. Wang further teaches:
wherein the additional information comprises at least one of an amount of the transaction, information from a website associated with a merchant associated with the transaction, historical user input, and location-specific information. (Wang: pgh 17, “The transaction attributes may include…amount…”)
Regarding claim(s) 6 and 30:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 1. Wang further teaches:
wherein the second confidence score is a highest confidence score of the second confidence vector. (Wang: pgh 32, “Once trained, the meta learner model and confidence score model produce the final predictions…”; Fig. 2, item 226.)
Regarding claim(s) 21 and 31:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claims 1 and 25, respectively. Wang further teaches:
wherein the first word embeddings and the second word embeddings are extracted using a neural network layer of the machine-learning model. (Wang: pgh 44, “The multilayer perceptron learner model…may be one or more neural network based classification models trained to infer the prediction results and measure of the prediction confidence based on one-hot encoding or embedding encoded categorical and text fields together with numerical fields…”)
Regarding claim(s) 22 and 32:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claims 1 and 25, respectively. Wang further teaches:
wherein the location-specific merchant data comprises merchant category information associated with a geographic location of the transaction. (Wang: pgh 18, “The transaction information may be obtained from one or more sources and may include a plurality of attributes, including entity specific data, third party specific data…and transaction related data, such as time, amount, location, etc…”)
Regarding claim(s) 23 and 33:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claims 1 and 25, respectively. Wang further teaches:
wherein the second confidence vector comprises higher confidence scores for the transaction categories than the first confidence vector based on the augmented description. (Wang: pgh 32, “The confidence score model, for example, may be a binary classification model trained to generate confidence scores by combining heterogeneous measures of prediction confidences stored in the stacked dataset from the plurality of base learner models.”)
Regarding claim(s) 24:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 4. Dongare further teaches:
The system of claim 4, wherein the additional words added to the description comprise merchant name information derived from the location-specific merchant data. (Dongare: pgh 114, “…raw customer data (e.g., financial transaction data) is input to the trained entity resolution model for entity determination and standardization…If the entity name is included in the lookup table, at step 1106 the standardized entity name and parent entity name are extracted from the lookup table and used to label the respective transaction.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang to include the teachings of Dongare to improve upon model outputs (Dongare: pgh 39).
Regarding claim(s) 34:
Wang teaches:
a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: (Wang: pgh 26, “…the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium.”)
obtain transaction data of a transaction, the transaction data including an amount and a description, wherein the description is a truncated description comprising fewer characters than a full merchant name; (Wang: Fig. 7, item 702 discloses obtaining a dataset comprising transaction data; pgh 17, “The transaction attributes may include, for example, user, transaction description, payee, time, amount, and other metadata from the institution.”)
execute a machine-learning model using as input the transaction data to extract a first confidence vector comprising confidence scores for transaction categories, wherein the machine-learning model extracts first word embeddings from the truncated description, and wherein the machine-learning model is trained to categorize transactions using a training set comprising a set of categorized transactions having standardized transaction categories and augmented descriptions; (Wang: pgh 44, “The multilayer perceptron learner model…may be trained with a final softmax layer that produces a vector output of predicted category probability distribution, which may also be used as the confidence measure vector.”)
execute the machine-learning model using as input the transaction data and the augmented description to extract a second confidence vector, wherein the machine-learning model extracts second word embeddings from the augmented description, and wherein the second word embeddings differ from the first word embeddings based on the additional words in the augmented description, resulting in the second confidence vector having different confidence scores than the first confidence vector; and (Wang: pgh 20, “A second stage (meta learning) includes a meta machine learning model that is trained to receive the meta dataset as a model input, and to generate a final prediction, along with a final confidence score…”)
in response to determining that a second confidence score in the second confidence vector of the transaction category satisfies the predetermined threshold, assign the transaction category to the transaction. (Wang: pgh 6, “The system may further be caused to generate final inference results comprising categorized transactions from a trained meta machine learning model based on the meta dataset…”)
Wang does not teach, however, Dongare teaches:
determine whether a first confidence score of a first transaction category of the first confidence vector satisfies a predetermined threshold; in response to determining that the first confidence score of the transaction category does not satisfy the predetermined threshold, augment a text of the truncated description of the transaction by adding additional words to the truncated description based on correlating the truncated description with location-specific merchant data to generate an augmented description; (Dongare: pgh 115, “If the output from the model results in an entity name with an entity confidence score at or above a predetermined threshold value, the standardized entity name and parent entity name are extracted from the lookup table and used to label the respective transaction…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang to include the teachings of Dongare to improve upon model outputs (Dongare: pgh 39).
Claims 4 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Dongare in view of Bireley (US 2023/0360121).
Regarding claim(s) 4 and 28:
The combination of Wang/Dongare, as shown in the rejection above, discloses the limitations of claim 1. Bireley further teaches:
wherein the at least one of the one or more processors augments the description of the transaction by adding additional words to the description based on the description and additional information. (Bireley: pgh 36, “The income identification function is a useful example that can be implemented by the income/debt identification function to identify and add descriptive metadata to income transactions…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Wang/Dongare to include the teachings of Bireley to improve the data categorization process (Bireley: pgh 6).
Conclusion
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Sakai (US 2024/0062048) discloses a learning device, learning method, and storage medium.
THIS ACTION IS MADE FINAL. 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 of 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN O PRESTON whose telephone number is (571)270-3918. The examiner can normally be reached 12:00 pm - 8:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached on 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN O PRESTON/Examiner, Art Unit 3693
June 26, 2026
/Mike Anderson/ Supervisory Patent Examiner, Art Unit 3693
Prosecution Insights