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 allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). 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, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 4/28/26 has been entered.
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, 3-7, 14-18, and 20 is/are directed to statutory computer-readable methods under Step 1 of the eligibility analysis. However, the claims are further directed toward a judicial exception under Step 2A Prong One of the eligibility analysis, namely an abstract idea. Under Step 2A Prong Two of the eligibility analysis, the claim(s) does/do not include additional elements to integrate the exception into a practical application of that exception. Under Step 2B of the eligibility analysis, the claims are not sufficient to amount to significantly more than the judicial exception because nothing in the asserted claims purports to improve the functioning of the computer itself or effect an improvement in any other technology or technical field. The claim(s) is/are directed to the abstract idea of a natural language processing (NLP) model using a confidence score parameter for determining, based on the text classification, a candidate text segment, and determining a context associated with the candidate text segment. This is “organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721”, (see MPEP 2106.04(a)(2)(I)(A)(iv)). In this case the entity-extracted invoice data is converted mathematically into structured invoice data via a computer vision technique, an image augmentation technique, a Natural Language Processing technique, and a text processing technique.
MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity provides that “the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility”. In this case the natural language processing (NLP) model is no more than a well-understood, routine, conventional activities previously known to the industry, recited at a high level of generality. In order to evaluate whether the additional elements are well-understood, routine, and conventional activity, a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity, (see Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)). Provided is a citation to a publication discussing subject matter similar to the claims, i.e. a natural language processing (NLP) model, that demonstrates the well-understood, routine, conventional nature of the additional element(s): v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition). OCR is considered to be a similar technological limitation as NLP. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 7, 14-18, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Raamadhurai et al., U.S. 20180349776 A1.
1. (Currently Amended) A method, performed by an electronic device, for autonomous reconciliation of invoice data, the method comprising:
obtaining an invoice data set, the invoice data set comprising unstructured invoice data, (see Raamadhurai, ¶ 17 “Upon receiving the data, the determination engine may determine whether the data is structured or unstructured.”);
determining, based on the invoice data set and an entity extraction model, an entity extraction set comprising an entity parameter and a first value parameter, wherein the first value parameter is associated with a first confidence score parameter, (see Raamadhurai, ¶ 19, 48 “The confidence score is indicative of matching of a value of the first set of data with a value of the second set of data of the pair.” “the score generator 224 may generate the confidence score for each pair of the values indicating the strength of the matching of each pair of values.”), the entity extraction model comprising a natural language processing (NLP) model, (see Raamadhurai, ¶ 33 “In an example embodiment, the AI technique may include, but is not limited to, Natural Language Processing (NLP)”), and wherein determining the entity extraction set comprises, by the entity extraction model:
applying a text classification to the invoice data set, (see Raamadhurai, ¶ 40 “The OCR converter 204 may differentiate the unstructured data between text and non-text.”);
determining, based on the text classification, a candidate text segment, (see Raamadhurai, ¶ 40 “The OCR converter 204 may determine whether the unstructured data is machine-readable.”);
determining a context associated with the candidate text segment, (see Raamadhurai, ¶ 45 “In order to understand the functionality of the rule parser 220 and the interpreter 222, a rule The Bank Statement description would specify the type of Transaction and the date of the transaction.”); and
determining the entity extraction set based on the candidate text segment and the context associated with the candidate text segment, (see Raamadhurai, ¶ 45 “If this information matches a transaction Description and date along with the Transaction Amount, then a correspondence exists between the two” may be considered.”);
outputting, based on the entity extraction set, an information extraction result, (see Raamadhurai, ¶ 47 “The interpreter 222 may process the atomic rules and then execute the compound rule for obtaining an output for which the compound rule is created.”); and
one or more of:
reconciling the invoice data based at least in part on the information extraction result; or
causing, based on the information extraction result, an invoice payment system to process an invoice associated with the invoice data set.
3. (Currently Amended) The method according to claim 1, wherein the obtaining comprises extracting the invoice data set from the invoice data using an information extraction technique, (see, Raamadhurai, ¶ 24 “the AI engine has the capability to extract the Reconciliation rules from the data itself”).
4. (Original) The method according to claim 3, wherein the information extraction technique comprises one or more of: a computer vision technique, an image augmentation technique, a Natural Language Processing technique, and a text processing technique, (see Raamadhurai, ¶ 33 “the rule extraction engine 108 may determine the relations based on an Artificial Intelligence (AI) technique. In an example embodiment, the AI technique may include, but is not limited to, Natural Language Processing (NLP)”).
7. (Currently Amended) The method according to claim 2, wherein the obtaining comprises obtaining the invoice data set based on an identification of one or more invoice data patterns indicative of mandatory information for invoice processing in the invoice data set, (see Raamadhurai, ¶ 33 “the CRF technique, may be understood as a probabilistic framework applied in pattern recognition and machine learning for being used for structured prediction”).
14. (Currently Amended) The method according to claim 1, wherein the outputting comprises: determining whether the first confidence score parameter satisfies a criterion; and when it is determined that the first confidence score parameter satisfies the criterion, including the entity parameter and the first value parameter into the information extraction result, (see Raamadhurai, ¶ 20 “In an example embodiment, a threshold value of the confidence score may be defined, and the classification may be performed based on the threshold value”).
15. (Currently Amended) The method according to claim 1, wherein the outputting (S106) comprises: determining whether the first confidence score parameter satisfies a criterion; and when it is determined that the first confidence score parameter does not satisfy the criterion, including the entity parameter associated with the first value parameter into a fine-tuning data set, (see Raamadhurai, ¶ 25 “the system may inform a user whether a rule is satisfied or not in a particular scenario along with a level to which a particular rule is satisfied by providing the confidence scores. This feature of the system may accommodate for variations within the data due to many factors such as, for example, human error, standard procedures of banks, and process specific variations”). Accommodating for variations is considered to be a form of “fine-tuning” as claimed.
16. (Currently Amended) The method according to claim 15, wherein the fine-tuning data set is taken as an input by the entity extraction model and/or is taken to update the one or more invoice data patterns, (see Raamadhurai, ¶ 25, 33 “the system may inform a user whether a rule is satisfied or not in a particular scenario along with a level to which a particular rule is satisfied by providing the confidence scores. This feature of the system may accommodate for variations within the data due to many factors such as, for example, human error, standard procedures of banks, and process specific variations”). Accommodating for variations is considered to be a form of “fine-tuning” as claimed. Whether a rule is satisfied or not in a particular scenario is considered to be a “data pattern” as claimed.
17. (Currently Amended) The method according to claim 14, wherein the criterion is based on a threshold, (see Raamadhurai, ¶ 20 “In an example embodiment, a threshold value of the confidence score may be defined, and the classification may be performed based on the threshold value”).
18. (Currently Amended) The method according to claim 1, the method comprising evaluating the information extraction result based on one or more patterns from historical invoice data, (see Raamadhurai, ¶ 3 “invoice reconciliation”; ¶ 33 “the CRF technique, may be understood as a probabilistic framework applied in pattern recognition and machine learning for being used for structured prediction”).
20. (Currently Amended) An electronic device comprising memory circuitry, processor circuitry, and a wireless interface, wherein the electronic device is configured to perform any of the methods according to claim 1, (see Raamadhurai, ¶ 63-65).
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.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raamadhurai et al., U.S. 20180349776 A1, in view of Hosoai et al., U.S. 20220237707 A1.
Raamadhurai fails to disclose the following features taught by reference to Hosoai:
5. (Currently Amended) The method according to claim 2, wherein the obtaining comprises reducing noise in the invoice data set, (see Hosoai, ¶ 47)(duplicate invoice detection subsystem 120 may filter out, or otherwise remove (i.e. reduce noise), candidate invoice cluster 210 from duplicate invoice database 130).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the features of Alder and Morris because of the following findings of fact:
F. Known Work in One Field of Endeavor May Prompt Variations of It for Use in Either the Same Field or a Different One Based on Design Incentives or Other Market Forces if the Variations Are Predictable to One of Ordinary Skill in the Art
(1) the scope and content of the prior art in the same field of endeavor as that of the applicant’s invention include a similar or analogous device (method, or product), i.e. G06Q 40/12 Accounting;
(2) there were design incentives or market forces which would have prompted adaptation of the known device (method, or product), (see Hosoai, ¶ 93)(block or reduce the number of duplicate invoices that a supplier can willfully generate in an attempt to receive multiple payments for a single instance of a product or service provided by the supplier.);
(3) the differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art;
(4) one of ordinary skill in the art, in view of the identified design incentives or other market forces, could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
This rationale and motivation to combine is applied to the remaining claims below herein by reference.
6. (Currently Amended) The method according to claim 2, wherein the obtaining comprises standardizing the invoice data set, (see Hosoai, ¶ 29)(elements of the networked computer system 170 may each comprise an interface compatible with the network 140 and are programmed or configured to use standardized protocols for communication across the networks).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUSSELL S GLASS whose telephone number is (571)272-7285. The examiner can normally be reached M-F, 9-5.
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/RUSSELL S GLASS/Primary Examiner, Art Unit 3627
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