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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination (“RCE”) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 30, 2026 has been entered.
Acknowledgements
Claims 1, 2, 4-6, 8, 9, and 11-13 are pending in the application.
Claims 1, 2, 4-6, 8, 9, and 11-13 are examined below.
Examiner Request
Applicant is requested to indicate where in the specification there is support for amendments to claims should applicant amend. The purpose of this is to reduce potential 35 USC 112(a) or 35 USC 112, 1st paragraph issues that can arise when claims are amended without support in the specification. Examiner thanks applicant in advance. See also relevant portions of MPEP 2163.II.A:
With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a "simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported."); see also MPEP § 714.02 and § 2163.06 ("Applicant should ... specifically point out the support for any amendments made to the disclosure."); and MPEP § 2163.04 ("If applicant amends the claims and points out where and/or how the originally filed disclosure supports the amendment(s), and the examiner finds that the disclosure does not reasonably convey that the inventor had possession of the subject matter of the amendment at the time of the filing of the application, the examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.").
Priority
Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the prior-filed application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. § 112. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, application no. 63/537,096 (including any material incorporated by reference), fails to provide adequate support in the manner required by 35 U.S.C. § 112 for one or more claims of this application. The above-listed application fails to provide support for the independent claims. In particular, the limitation of “retrieving payment transaction data from a banking transaction database” (or similar) does not appear to be disclosed. Additionally, dependent claim limitations of “receiving banking access credentials and logging into the banking transaction database” does not appear to be disclosed.
For the above reasons, the claims affected and their dependents are being compared to the prior art based on a filing date of November 30, 2023 (the filing date of this non-provisional application).
Claim Objections
Claims 1 and 8 are objected to because of the following informalities: where applicant recites “the payment transaction data comprises a plurality of payment transactions”, it appears that applicant intends to recite “the payment transaction data comprises data from a plurality of payment transactions” or similar. For the purpose of comparison with the prior art, the examiner is taking it as such. 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 1, 2, 4-6, 8, 9, and 11-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claims 1, 2, 4-6, 8, 9, and 11-13, the claimed invention is directed to an abstract idea without significantly more. Representative claim 1 recites retrieving payment transactions, categorizing the payment transactions, assigning a confidence level to the categorizations, and validating the categorization(s), which constitutes a certain method of organizing human activity (specifically, a commercial or legal interaction) or mental process. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application because the additional elements beyond the abstract idea simply link the abstract idea to a particular technological environment (computers and neural networks) or amount to extra-solution activity. Because the abstract idea is not integrated into a practical application, claim 1 is “directed to” an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements identified above represent only well-understood, routine, and conventional technology (or placeholders for such) when compared with the court decisions listed in MPEP § 2106.05(d). Viewing the additional elements as a combination does not add anything further than the individual elements. Therefore, the additional elements in the claim are not sufficient to amount to an inventive concept. Because claim 1 is directed to an abstract idea and fails to recite an inventive concept, it is patent ineligible.
Independent claim 8 contains limitations similar to claim 1 and is therefore rejected using the same rationale.
The dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101. The additional limitations added by these claims fail to either integrate the claims into a practical application or add an inventive concept, because they serve to further narrow the abstract idea without integrating it into a practical application (claims 2, 4-6, 9, and 11-13), add additional elements that simply further link to a particular technological environment (claim 6), and/or amount to additional extrasolution activity (claims 2, 5, 9, and 12). Viewing the additional elements of the dependent claims as a combination does not add anything further than the individual elements. Therefore, the dependent claims neither practically integrate the abstract idea nor constitute an inventive concept, and these claims are also rejected as patent ineligible.
Claim Rejections - 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd Paragraph
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 4-6, 8, 9, and 11-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter that the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 1, applicant’s use of the term “AI processor” (line 9) would have been unclear to a person having ordinary skill in the art at the time of the invention. Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “AI processor” is used to encompass such things as an “AI model," while the accepted meaning does not include an AI model but rather the physical processor, like a GPU. Although applicant makes it clear in the spec at [0054] that “AI processor” does not mean a physical processor, this discussion of “AI processor” lacks the clarity, deliberateness, and precision necessary for a lexicographic definition. Therefore, one of ordinary skill would not have understood the meaning of the term “AI processor” in claim 1, because the specification does not clearly redefine the term. For the purposes of comparison with the prior art and determination of statutory eligibility, the examiner is interpreting the “AI processor” to be a neural network, consistent with the claim language.
Regarding claim 1, it is unclear whether there are one or two AI processors. Applicant introduces “a plurality of tax classification categories” in lines 6-7 and “a plurality of tax classification categories” in the third from the last line. “Where a claim lists elements separately, ‘the clear implication of the claim language’ is that those elements are ‘distinct component[s]’ of the patented invention.” Becton, Dickinson and Co. v. Tyco Healthcare Group LP, 95 USPQ2d 1752, 1757 (Fed. Cir. 2010) (citation omitted). However, both of these pluralities appear to have the same function in the claim. Further, an interpretation that there are two separate pluralities does not appear to be supported by the specification. Therefore, it would be unclear to a person having ordinary skill in the art at the time the invention was made as to whether there are one or two pluralities of tax classification categories.
Regarding claim 1, applicant’s recitation "utilizing a validation user interface for a user to validate the single category of a plurality of tax classification categories" would have been unclear to a person having ordinary skill in the art. It is unclear whether there is a single category being validated or multiple categories. On one hand, read in isolation, this clearly indicates a single category. However, read in context of the claim, there are a plurality of categories (assigned to the plurality of transactions) for validation. Therefore, it would have been unclear whether all of the transactions are required to be validated, or only one.
Claims 8 contains language similar to the recitations in claim 1 discussed in the three immediately preceding paragraphs, and claim 8 is rejected for reasons similar to those discussed above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 statute.
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 of this title, 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.
The following is a quotation of 35 U.S.C. 103(a) (pre-AIA ) which forms the basis for all obviousness rejections set forth in this office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1, 2, 4-6, 8, 9, and 11-13, as understood by the examiner, are rejected under 35 U.S.C. 103 as being unpatentable over Kottoor (US 12,223,552 B1) in view of Kulkarni (US 2025/0045303 A1) and further in view of Weese (US 2022/0019860 A1).
Kottoor discloses as follows:
Claim
Limitation
Kottoor
1,8,15
retrieving payment transaction data from a banking transaction database, wherein the payment transaction data comprises a plurality of payment transactions
"a user may provide credentials for an external account and records process 344 can use the credentials to access a web interface for that account. Records process 344 can then scrape account information from the web interface, e.g., having been programmed to traverse a document object model (DOM) for the web interface to retrieve relevant account transactions and details"
1,8,15
utilizing an Al processor to categorize each payment transaction of the plurality of payment transactions into a category
"a machine learning model can be trained to predict categories for transactions and/or entities (e.g., parent entities) for transactions (as discussed in relation to FIG. 4)"
1,8,15
utilizing an Al processor to assign a confidence level to each payment transaction, wherein the confidence level is at or between zero percent and one hundred percent
"produce a confidence value with the prediction," and it would have been obvious to express this from 0-100%, as this is one of the most popular ways amongst a limited number of ways to express such a value.
1,8,15
wherein the confidence level is the level of confidence that the payment transaction has been placed in a correct category of the plurality of categories
This is meaning assigned to a number. It does not limit how the number (confidence level) is created. It represents an intended use or result of the number, which fails to further limit the claim.
2,9,16
requesting a validation from a user to validate the category of at least one transaction
"a user can be prompted to provide feedback about transactions with a predicted entity or category and a low confidence interval for the prediction"
3,10,17
utilizing validation data to update a confidence level algorithm utilized by the Al processor
"At block 704, process 700 aggregates the user feedback and generates training data. For example, the user feedback can be used to produce instances of transactions with corresponding transaction features (e.g., pieces of information relevant to the transaction) and labeled entities and/or categories. These instances can be aggregated into training data for a supervised machine learning model. At block 706, process 700 can train the machine learning model with the training data. For example, the training data can train one or more machine learning models to predict a category for a transactions and/or an entity for a transaction. Once trained, the model can be used, e.g., by discover entity 605 and/or by metadata generator 346 to generate metadata for received transactions."
4,11,18
requesting a validation from a user to validate the category of each transaction with a confidence level below a predetermined threshold
"a user can be prompted to provide feedback about transactions with a predicted entity or category and a low confidence interval for the prediction"
5,12,19
outputting payment transactions segregated into categories in an output document based on the validation from the user
See Fig. 5B
6,13,20
receiving banking access credentials and logging into the banking transaction database
"a user may provide credentials for an external account and records process 344 can use the credentials to access a web interface for that account. Records process 344 can then scrape account information from the web interface, e.g., having been programmed to traverse a document object model (DOM) for the web interface to retrieve relevant account transactions and details"
7,14
utilizing a categorization algorithm to categorize the transaction, wherein the categorization algorithm utilizes one or more inputs including description of the transaction, time of the transaction, monetary amount of the transaction, location of the transaction, or vendor of the transaction
"Machine learning component 402 of FIG. 4 can be implemented by records processor 164 of FIG. 1 and/or machine learning models 354 of FIG. 3. For example, a trained machine learning model may be configured to predict a category for a transaction and/or an entity party to the transaction. Predictions 408 can be generated using input 406, which includes features of a transaction (e.g., date and time, description, entity, sum, metadata, another suitable features). In an example, training data 404 can include instances of historic data including: previous transactions (e.g., descriptions, date and time, sum and other transaction data), derived entities, derived categories, labeled entities, and/or labeled categories. Accordingly, prediction 408 generated by machine learning component 402 trained by versions of training data 404 can be predicted entities and/or categories for a transaction"
Kottoor fails to explicitly disclose but Kulkarni teaches:
Claim
Limitation
Kulkarni
1,8,15
of a plurality of categories, wherein the plurality of categories are based on tax classification related to tax documentation, and wherein the AI processor is a neural network
"the AI model(s) 108 may be trained to identify a tax classification for a transaction" [0023]"the technical solution includes automatic analysis and categorization of transactions using one or more of manual labeling, rules-based processing, and artificial intelligence (AI), such as machine learning (ML) or a neural network formed generative AI" [0016]
1,8,15
utilizing an Al processor to assign a confidence level to each payment transaction, wherein the confidence level is at or between zero percent and one hundred percent
"When the user selects an AI model to perform the analysis, the analysis results presented to the user via application 114 indicate how the transaction data has been pre-classified based on the AI model chosen and includes a level of confidence in the result" [0047]
1,8,15
wherein the confidence level is determined at least partially by one of a date range, a business or vendor type, a classification code, or a location of business operations
"the AI model(s) 108 may be trained to identify a tax classification for a transaction. The AI model(s) 108 may be trained using data and/or information stored in any suitable manner, such as a data lake, local attached storage, network attached storage, or data feed via API. For example, training the AI model(s) 108 may comprise determining expense categories for transactions, for example, by analyzing transactional data. The transactional data may comprise information relevant for determining an expense category for the transaction, such as, the type of data being analyzed. Data types that can be analyzed using embodiments of the invention include income tax data, transfer pricing data, indirect tax data, fixed asset data, trade and customs data, and employment tax data. Each of these data types may have a distinct use case. For example, income tax data can be used to determine the nature of expenses in order to calculate book-to-tax adjustments, i.e., to determine whether a given transaction is deductible or non-deductible. Transfer pricing data can be used to segment revenues between business entities based on one or more characteristic to determine tax treatment, e.g., onshore or offshore. Indirect tax data can be used to determine the category of goods and services involved in the transaction to determine its tax eligibility and the appropriate tax rate (e.g., VAT). Fixed asset data can be used to determine the type of asset involved in the transaction to apply the appropriate tax treatment. Trade and customs data can be used to assign HTS codes to products involved in the transaction to determine the appropriate customs charges. Employment tax data can be used to determine the total tax liability for the fringe benefits tax. The client device 104 and/or the server 102 may also create calculated data based on other, previously stored data. For example, a variance type of data can be created to track a difference between the date an order is created and the date the order is shipped. In addition, aggregated data fields can be generated by server 102 wherein a value across an imported file can be tracked. For example, the total amount of a value in every row of a file; effectively this adds a new row to the file for storage in the database system 106. As more and more transactions are subsequently included in the training set, the accuracy of the AI model's transaction classification increases" [0023]
It would have been obvious to one having ordinary skill in the art at the time of the invention to modify Kottoor to include the neural network and data inputs of Kulkarni in order to achieve the predictable result of improved prediction of tax classification.
Kottoor/Kulkarni fails to explicitly disclose but Weese teaches: utilizing a validation user interface for a user to validate the category of a plurality of categories, wherein the validation of the category updates the neural network such that the neural network includes back propagation via reinforced learning with human feedback (claims 1 and 8) at [0032] and [0033].
It would have been obvious to one having ordinary skill in the art at the time of the invention to modify Kottoor/Kulkarni to include the user interface and back propagation of Weese in order to achieve the predictable result of further improvements to the classification/categorization prediction.
Citation of Relevant Prior Art
All references listed on form PTO-892 are cited in their entirety. The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gopalakrishnan Nair (US 2022/0012707 A1) discloses a system for categorizing transactions to improve servicing of peer-to-peer transactions. See the WOISA in PCT/US2024/044924 for a mapping to claims similar to applicant's.
Cella (US 2023/0201722 A1) discloses a gaming system that includes categorizing transactions ([2275]), including using AI ([2374]).
Jayaraman (US 2024/0265437 A1) discloses a system for establishing display preferences for user data including segregating transactions by category (see Figs. 6, 8, 9).
Rathi (US 2023/0019194 A1) discloses a deep learning system in a virtual reality environment, including capturing user feedback to update a neural network ([0137]).
Response to Amendments and Arguments
Regarding the 101 rejection, applicant argues that the claim amendment addresses the 101 rejection. The examiner respectfully disagrees. The 101 rejection has been modified in response to applicant’s amendment but is otherwise maintained. Applicant further argues that the claims are statutory due to one of the limitations not being taught by the prior art. This argument fails at least for the reason that the limitation is taught by the prior art (see the above prior art rejection).
The 112(a) rejections of the claims are withdrawn in response to applicant’s cancellation of the claims in question.
The 112(b) rejections have been withdrawn, modified, or maintained in response to applicant’s amendment. Note, also, the new rejections above.
The previous prior art rejections are withdrawn in response to applicant’s argument. Note, however, the new rejections above. Applicant’s arguments are moot in light of the newly applied prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE KUCAB whose telephone number is (571)270-3025. The examiner can normally be reached Monday through Friday, 9 a.m. to 4:30 p.m. ET. The examiner’s email address is Jamie.Kucab@USPTO.gov. See MPEP 502.03 regarding email communications. Following is the sample authorization for electronic communication provided in MPEP 502.03.II: “Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.” Without such an authorization in place, an examiner is unable to respond via email.
If attempts to reach the examiner are unsuccessful, the examiner’s supervisor, Neha Patel, can be reached at telephone number (571) 270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMIE R KUCAB/Primary Examiner, Art Unit 3699
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