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 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 17, 2026 has been entered.
Claims 1, 13, and 14 have been amended.
Claims 2-4 have been cancelled.
Claims 1, and 5-14 are pending.
The earliest priority date of the claimed invention is April 27, 2023.
Response to Amendment
Amendments to Claims 1 and 13-14 are acknowledged. Amendments to Claims 1 and 13-14 are sufficient to overcome the 35 USC 103 rejection of Claims 1 and 5-14.
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 and 5-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
101 Analysis:
Step 1:
Claim 1 (and claims 13 and 14) is directed to “a method for parsing journal entries.” A method is a statutory process under 35 U.S.C. § 101.
Step 2A, Prong 1:
A. Certain Methods of Organizing Human Activity
The claim is directed to parsing journal entries, specifically debit and credit records, to identify sub-journal entries that balance or “foot” to zero.
The following claim language is relevant: “receiving input data comprising a plurality of journal entries”, “selecting a first subset of records … comprising either one or more debit records or one or more credit records”, “wherein the first subset of records and the second subset of records are corresponding subsets that sum to zero”, “recording the first subset of records and second subset of records as a first sub-journal entry”.
The specification confirms that the claimed activity is in the field of financial reporting and auditing: The disclosure relates to “financial reporting” and “auditing of financial data.” The system parses general ledger data. The output may include indications of misstatements, high-risk transactions, financial reporting errors, business inefficiencies, offsets to transactions, and similar audit/accounting determinations.
Under the 2019 PEG, fundamental economic practices, commercial interactions, and financial activities fall within the grouping of certain methods of organizing human activity. Parsing debit and credit accounting records to identify balanced sub-journal entries is a financial/auditing activity. Accordingly, the claim recites an abstract idea in this grouping.
B. Mathematical Concepts
Claim 1 also recites mathematical concepts.
The relevant language includes: “generating a hash representation of the first subset of records”, “determining that the hash representation associated with the first subset of records matches a hash representation associated with a second subset of records”
“wherein the first subset of records and the second subset of records are corresponding subsets that sum to zero”.
The “sum to zero” limitation expressly requires a mathematical relationship between selected debit and credit subsets. The claim also uses hash representations as mathematical/computational identifiers for subsets. The specification further explains that the identifiers may be generated using a hash function, a sum, or an absolute value of the subset.
Dependent claims reinforce this mathematical character:
Claim 11 recites that identifiers are generated using a hash function.
Claim 12 recites that the identifiers comprise absolute values of record subsets.
Thus, the claims recite mathematical calculations, mathematical relationships, and mathematical representations.
C. Mental Processes
Several claimed operations can be characterized, at least at a high level, as observations, evaluations, judgments, or determinations that could be performed mentally or with pen and paper for a smaller data set.
Relevant language includes: “selecting a first subset of records”, “determining that the hash representation … matches”, “recording the first subset of records and second subset of records as a first sub-journal entry”, “identifying one or more subsets of records”, “removing the identified one or more subsets of records … from consideration”.
Although the claim uses hash representations and data structures, the underlying logic of selecting debit/credit subsets, determining whether corresponding amounts balance, recording the balanced grouping, and removing that grouping from further consideration is an evaluation and bookkeeping procedure that, in principle, could be performed manually for smaller entries.
The use of a computer to perform the process at greater scale does not, by itself, remove the claim from the mental-process grouping.
Step 2A, Prong 1 Conclusion
Claim 1 recites judicial exceptions, including:
Certain methods of organizing human activity — financial reporting/auditing of journal entries;
Mathematical concepts — generating hash representations, comparing representations, and determining whether debit/credit subsets sum to zero; and
Mental processes — selecting, comparing, identifying, recording, and removing accounting record subsets from further consideration.
Step 2A, Prong 2:
Because claim 1 recites a judicial exception, the next question is whether the claim as a whole integrates the exception into a practical application.
The strongest eligibility argument for claim 1 is that it recites a more specific data-processing technique than merely “analyzing financial data.” In particular, claim 1 recites: “generating a hash representation of the first subset of records”; “identifying one or more subsets of records in the first sub-journal entry in one or more data structures”; “removing the identified one or more subsets of records from the one or more data structures by deleting the identified one or more subsets of records from the one or more data structures”; “wherein the one or more data structures are progressively depopulated … thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures”.
The specification also asserts that the disclosed approach avoids brute-force computation and reduces the number of candidate debit/credit combinations.
However, under the 2019 PEG, the inquiry is not merely whether the abstract idea is performed efficiently. The question is whether the claim applies the exception in a manner that imposes a meaningful limit and reflects a technological improvement or another practical application beyond the abstract idea itself.
A. No Sufficiently Claimed Improvement to Computer Functionality
The claim does not appear to recite a specific improvement to the functioning of a computer itself. The claimed improvement is primarily an improvement to the financial/auditing analysis of journal entries, namely identifying debit and credit subsets that sum to zero while reducing the candidate search space.
The specification describes the problem as follows: Auditors need to parse journal entries; Brute-force combinations become computationally unworkable; The disclosed method reduces the number of candidate combinations by removing already-matched debit and credit records from consideration.
This is a computational-efficiency assertion, but the claim does not recite enough technical detail to establish an improvement to computer technology itself. For example, claim 1 does not recite: A specific non-conventional hash-table architecture; A particular memory layout; A collision-resolution mechanism; A reverse-index structure for deletion; A technical data schema improving computer retrieval; A concurrency, caching, or memory-management technique; A specific transformation of computer operation independent of the financial/auditing content.
The “thereby iteratively reducing” clause states the intended result of reducing candidate combinations, but the claim largely recites the result at a functional level.
Accordingly, the claim appears to improve the efficiency of performing an abstract financial/mathematical analysis, rather than improving the functioning of the computer itself.
B. Generic Data Structures and Hashing Do Not Alone Provide Integration
The claim recites “one or more data structures” and “hash representation.” Dependent claims 5 and 6 specify hash tables:
Claim 5: “the first data structure comprises a first hash table comprising one or more subsets of debit records … and wherein the first hash table does not include credit records.”
Claim 6: “the second data structure comprises a second hash table comprising one or more subsets of credit records … and wherein the second hash table does not include debit records.”
These limitations provide some implementation detail, but the specification characterizes the relevant data structures broadly and conventionally. For example, the specification states that the data structures may be: Hash tables; Hash sets; B-trees; Sorted arrays; Unsorted arrays; or Other suitable data structures.
The specification further states that hash tables are data structures that map keys to values and allow efficient lookup and comparison of data. It also states that the hash tables can be generated according to known methods.
Thus, the claimed hash tables are used as generic data structures for storing and looking up financial record subsets. The claim does not recite a new hash table structure, new hashing technique, or particularized computer-memory organization that itself improves computer functionality.
C. Extra-Solution Activity
Several limitations amount to data gathering, output, or bookkeeping ancillary to the abstract idea.
For example: “receiving input data comprising a plurality of journal entries from one or more data sources”. This is conventional data gathering.
Similarly, claim 9 recites: “generating an output, the output comprising an indication of the debit records and credit records forming the sub-journal entry.” This is post-solution output of the result of the analysis.
Claim 10 further recites outputs such as: “an indication of a misstatement”, “an indication of a high-risk transaction”, “indications of inefficiencies in business operations”, “indications of errors in financial reporting”.
These are business/audit conclusions or reports generated from the abstract analysis. They do not integrate the abstract idea into a technological practical application.
D. No Particular Machine or Transformation
The claim does not require a particular machine beyond generic computing components implied by the use of data structures and hash representations.
The specification describes generic host/client computing systems, processors, databases, I/O devices, networks, storage, and software. The claim itself does not require a specific machine configuration or specialized hardware.
The claim also does not transform an article into a different state or thing. The “records,” “journal entries,” “subsets,” and “hash representations” are information. Manipulating or reorganizing information, without more, is not a transformation of an article for purposes of Step 2A, Prong 2.
Step 2A, Prong 2 Conclusion
Claim 1 does not appear to integrate the judicial exception into a practical application.
The claim uses generic data structures, hash representations, and deletion of stored record subsets to carry out an abstract financial/accounting and mathematical matching process more efficiently. The alleged improvement is primarily to the efficiency of the abstract analysis itself — parsing journal entries and reducing candidate debit/credit combinations — rather than to computer functionality or another technology.
Step 2B:
Because claim 1 recites a judicial exception and does not integrate the exception into a practical application, Step 2B asks whether the claim includes additional elements, individually or as an ordered combination, that amount to significantly more than the judicial exception.
A. Additional Elements Beyond the Abstract Idea
The additional elements include: “one or more data sources”; “hash representation”; “one or more data structures”; storing, recording, adding, identifying, and deleting records; generating an output; use of hash tables in dependent claims 5 and 6; use of a hash function in dependent claim 11.
These elements are computer/data-processing implementation details, but they appear to be generic and conventional on the present record.
B. Factual Basis for WURC
The specification provides support that the computer components and data structures are well-understood, routine, and conventional.
For example: Generic Computing Architecture
The specification describes a host computing system and client computing system using generic components: databases; processors; I/O devices; network connections; storage; communication devices; software executed by processors.
The specification also describes the computing device as any suitable microprocessor-based device, including a personal computer, workstation, server, phone, or tablet. This supports that the hardware implementation is generic.
Conventional Data Structures
The specification states that the data structures may include: hash tables; hash sets; B-trees; sorted arrays; unsorted arrays; or other suitable data structures.
The specification further explains that these data structures are used for quick lookup and comparison of data. Additionally, the specification states that the hash tables may be generated according to known methods. This supports a finding that the claimed data structures and hash functionality are conventional computer tools used for their ordinary purpose.
Conventional Software Implementation
The specification states that software may be written in suitable programming languages such as C, C++, Java, or Python and may be deployed in client/server, web-browser, or web-service configurations. This further supports that the implementation uses generic software and computing arrangements.
C. Ordered Combination
The ordered combination of claim 1 is more specific than simply “analyze financial data on a computer.” The claim recites a sequence of selecting subsets, generating hash representations, matching debit and credit subset representations, recording a sub-journal entry, and deleting matched subsets from data structures to reduce subsequent candidate combinations.
However, the ordered combination appears to be the abstract idea itself: an accounting/mathematical rule for reducing the search space while identifying balanced debit and credit subsets. The improvement is achieved by the claimed financial/mathematical logic, not by a non-conventional arrangement of computer components.
The specification’s performance discussion demonstrates that the algorithm may reduce the number of candidate combinations compared with brute-force computation. But improved performance of an abstract calculation, implemented on conventional data structures, generally does not supply an inventive concept unless the claim recites a specific technological improvement to the computer or another technology.
Here, the claim does not recite sufficient technical details to show that the ordered combination is non-conventional in the computer-technology sense.
Overall Conclusion Under § 101
Claim 1 is directed to a statutory process, but it recites abstract ideas including:
A financial/auditing method of organizing human activity;
Mathematical concepts involving hash representations, matching, and zero-sum relationships; and
Mental processes involving selecting, comparing, identifying, recording, and removing accounting record subsets.
The claim does not appear to integrate those exceptions into a practical application because the recited data structures and hash representations are used generically to perform financial/auditing analysis more efficiently. The claim does not recite a specific improvement to computer functionality or another technology.
The additional elements do not amount to significantly more than the abstract idea because they are generic computer/data-processing elements and conventional data structures used according to their ordinary functions.
Conclusion for claim 1: Claim 1 is likely ineligible under 35 U.S.C. § 101.
A § 101 rejection is recommended for at least claim 1.
Claim 2 recites repeating steps when additional subsets remain. This merely recites iteration of the same abstract process and does not provide a technological improvement.
Claims 3 and 4 recite identifying and removing debit or credit subsets from respective data structures. These limitations specify bookkeeping within data structures but remain directed to manipulating financial data subsets. They do not recite a non-generic technical implementation.
Claims 5 and 6 recite separate hash tables for debit and credit records. These limitations are more specific than claim 1 and provide the best potential eligibility support among the dependent claims. However, on this record, they still appear to use conventional hash tables in a conventional manner to store and retrieve financial records. The claims do not recite an improved hash-table structure or other computer-functionality improvement.
Claim 7 recites adding an unmatched subset to a first or second data structure depending on whether the subset comprises debit or credit records. This is data organization based on accounting classification and does not integrate the abstract idea into a practical application.
Claim 8 recites that the input data comprises general ledger data. This is a field-of-use limitation directed to financial/accounting data.
Claims 9 and 10 recite generating outputs and financial/audit indications. These are post-solution reporting steps and/or further business/audit determinations. They do not provide a technological practical application.
Claims 11 and 12 recite hash-function generation and absolute-value identifiers. These limitations further recite mathematical operations or conventional computer implementation of mathematical/data identifiers. They do not, without more, supply a technological improvement.
Prior Art
The prior arts of record fail to teach the overall combination as claimed in Claims 1 and 5-14. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A method for parsing journal entries comprising:
receiving input data comprising a plurality of journal entries from one or more data sources; and
parsing a first journal entry of the plurality of journal entries, wherein parsing the first journal entry comprises iteratively:
selecting a first subset of records from a first journal entry of the plurality of journal entries, the first subset of records comprising either one or more debit records or one or more credit records;
generating a hash representation of the first subset of records;
determining that the hash representation associated with the first subset of records matches a hash representation associated with a second subset of records, the second subset of records comprising one or more credit records if the first subset of records comprises one or more debit records and the second subset of records comprising one or more debit records if the first subset of records comprises one or more credit records, wherein the first subset of records and the second subset of records are corresponding subsets that sum to zero; and
in accordance with determining that the hash representation associated with the first subset of records matches the hash representation associated with the second subset of records:
recording the first subset of records and second subset of records as a first sub- journal entry;
identifying one or more subsets of records in the first sub-journal entry in one or more data structures; and
removing the identified one or more subsets of records from the one or more data structures by deleting the identified one or more subsets of records from the one or more data structures, wherein the one or more data structures are progressively depopulated such that records contained in the first sub-journal entry are removed from consideration in subsequent iterations of parsing the first journal entry, thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
35 USC 103
US Pat Pub 2024/0013100 “Havel” discloses unmatched records in databases be matched automatically by a computing device may receiving source records and target records to be matched together; determining, based on a machine learning model and for each of the plurality of target records, a distance value between the respective target record, and a subset of the plurality of source records; generating a matched record that identifies the subset of the plurality of source records and a selected one or more target records; and updating records in a database. Havel fails to disclose one or more data structures are progressively depopulated such that records contained in the first sub-journal entry are removed from consideration in subsequent iterations of parsing the first journal entry, thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures.
US Pat Pub 2012/0265655 “Stroh” teaches processing a transaction document including one or more financial transaction entries, including a record generation subsystem having data processing means configured to a) receive or generate a complete character string characterizing the financial transaction entries displayed on the document; b) analyze each complete character string to identify known transaction identifiers used in financial transactions; c) analyze each complete character string to identify transaction data strings associated with each identified transaction character string; and d) store transaction data, including the transaction character strings and associated transaction data strings, for each single financial transaction in a database. Stroh fails to teach one or more data structures are progressively depopulated such that records contained in the first sub-journal entry are removed from consideration in subsequent iterations of parsing the first journal entry, thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures.
US Pat Pub 2019/0122307 “Sayed” teaches receiving a current account balance for a user; receiving historical transaction data for the user; determining, based on the current account balance and the historical transaction data, historical cash balances for the user; determining forecasted transactions for the user; updating a database, the updating based on the historical cash balances and the forecasted transactions; generating, based on the database, data to display; and transmitting the data to a computing device for display in a user interface. Sayed fails to teach one or more data structures are progressively depopulated such that records contained in the first sub-journal entry are removed from consideration in subsequent iterations of parsing the first journal entry, thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures.
US Pat 5,390,113 “Sampson” teaches electronically performing bookkeeping upon a plurality of accounting journal entries having at least one account number and at least one data component associated with each account number, electronically reading a chart of accounts having account numbers and opening balances associated with the plurality of journal entries, creating a set of account-section numbers for each account number, reading the journal entries, and assigning one of the account-section numbers to each account number. Sampson fails to teach one or more data structures are progressively depopulated such that records contained in the first sub-journal entry are removed from consideration in subsequent iterations of parsing the first journal entry, thereby iteratively reducing a number of candidate combinations between subsets of credit records and debit records that are stored in the one or more data structures.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
