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 .
DETAILED ACTION
This office action is in response to communication filed on 4/30/2024.
Claims 1-20 are presented for examination.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-20, under Step 2A, recites a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claims 1, 11 and 19 as representative recite:
identifying a plurality of orders, each order including an associated set of item names and an associated set of receipt labels extracted from an associated receipt image; for each order in the plurality of orders:
generating a set of optimized label-name pairs for the order by fuzzy matching the associated set of item names and the associated set of receipt labels;
generating statistical association scores for a set of candidate label-name pairs based on receipt-order item pairs, the statistical association scores describing a relative likelihood that a receipt label in the label-name pair occurs with an item name in the label-name pair relative to the sets of optimized label-name pairs for the plurality of orders;
selecting a set of training label-name pairs from the set of candidate label-name pairs based on the statistical association scores; and fine-tuning a language model with the training label-name pairs; and
applying the fine-tuned language model to generate a similarity score between a target item and a target receipt label.
These steps constitute data collection, data analysis, statistical evaluation which are abstract ideas of organizing human activity and mathematical concepts. The claim recites collecting transaction data (orders, item names, receipt labels), analyzing the data using mathematical concepts (fuzzy matching, statistical likelihood scoring), selecting data based on numeric comparisons and generating similarity scores.
Under Step 2A (prong 2), viewed individually or as a whole the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claims 1, 11, 19 recite additional elements, including receipt image, language model, processors, computer program. Although reciting additional elements, these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware or, merely uses a computer as a tool to perform an abstract idea. Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as computers or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. The results of the method (optimizes label-name pair, statistical association scores, similarity scores) are informational outputs not technical effects on a computer or other technology.
In view of the above, under Step 2A (prong 2), claims 1, 11, 19 do not integrate the recited exception into a practical application (see again: 2019 PEG).
Even considered as an ordered combination (as a whole), the additional elements of dependent claims 2-10, 12-18, 20 do not add anything further than when they are considered individually. In view of the above, claims 1-20 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 11, 190 taken individually or as a whole the additional elements do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
The additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, fuzzy matching and similarity scoring are well-known mathematical concepts, statistical association scores and probability based likelihood calculations are conventional, selecting data based on threshold values is routing data filtering, training or fin-tuning a language model using labeled data is a conventional machine learning practice, generating s similarity score is a numeric calculation.
Further, see MPEP 2106.05(f), “Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);”.
See MPEP 2106.05(d), “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
Even considered as an ordered combination (as a whole), the additional elements of dependent claims 2-10, 12-18, 20 do not add anything further than when they are considered individually.
In view of the above, claims 1-20 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas (U.S. Patent Publication No. 2012/0330971), in view of Durazo (U.S. Patent No. 11,257,049).
Regarding claims 1, 10, 19, Thomas teaches identifying a plurality of orders, each order including an associated set of item names and an associated set of receipt labels extracted from an associated receipt image; see (receipts data include description and a price of each item purchased, [19-21], extracted values include item names and description, [28], scanned receipts have images of physical store receipts, [29], labels include item description and item price, [49-53].
for each order in the plurality of orders: and fine-tuning a language model with the training label-name pairs, (he Receipt Language Model is trained using the labels that were applied to the training receipts by the initial features and weights, [22], This manually corrected training example defined by the BPO Analyst is typically saved to the system, and used to update the Receipt Language Model with updated training data, [24], updating Receipt Language Model 62, Fig. 10, [133].
Thomas substantially discloses extracting and label receipts tokens but does not disclose generating a set of optimized label-name pairs for the order by fuzzy matching the associated set of item names and the associated set of receipt labels; However, Durazo teaches the mapping component 220 can identify keywords of the extracted text using the data model(s).. the training database 228) includes text extracted from historical receipts.. trained data model can output a similarity score indicative of whether the particular description is similar to a historical description from a receipt that is similar to the current receipt, Col.13 ln 8-35, the mapping component 220 can provide a confidence score representing how likely it is that a keyword that is identified using the data model is in fact an appropriate match to the item description and/or receipt item, Col.14 ln 30-38.
Thomas substantially discloses extracting and label receipts tokens but does not disclose generating statistical association scores for a set of candidate label-name pairs based on receipt-order item pairs, the statistical association scores describing a relative likelihood that a receipt label in the label-name pair occurs with an item name in the label-name pair relative to the sets of optimized label-name pairs (receipt keyword, item description) for the plurality of orders. However, Durazo teaches a confidence score represents probability (statistical likelihood) that a keyword extracted from receipt data corresponds to an item, Col.14 ln 33-37)
Thomas substantially discloses training a receipt language model but does not disclose selecting a set of training label-name pairs from the set of candidate label-name pairs based on the statistical association scores; However, Durazo teaches the confidence scores may be compared to a threshold, the confidence score is less than the threshold confidence score, the keyword(s) may be omitted from further tasks related to the list (e.g., querying with the keyword to identify an item identifier), Col.6 ln 35-44, Col. 13 ln 55-60.
Thomas substantially discloses the model to extract labels but does not disclose applying the fine-tuned language model to generate a similarity score between a target item and a target receipt label. However, Durazo teaches applying the trained model to receipt text, outputting confidence scores indicating likelihood between receipt text and items, See at least Col. 13 ln 55-60, Col. 18 ln 20-40.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the receipt language model of Thomas, to include high confidence matches, as taught by Durazo, since both references address the same problem of accurate identification of items from receipt text using machine learning and the combination applies known techniques for their intended purposes and yields predictable results.
Reading claims 2, 11, 20, Durazo discloses generating fuzzy matching scores for each receipt label with each item name and pairing label-name pairs based on the fuzzy matching scores, (comparing receipt keywords to item descriptions, confidence scores represent probability (fuzzy), Col.13 -14, 18).
Reading claims 3, 12, Thomas discloses fine-tuning the language model with the training label-name pairs comprises training the language model to receive a receipt image comprising receipt labels and to generate corresponding predicted item names, [22, 28-31, 49-53].
Reading claims 4, 13, Thomas in view of Durazo disclose applying the language model to receive an order comprising item name and to generate corresponding predicted receipt labels. Thomas teaches trained receipt language model maps between receipt labels and item information and Durazo teaches bidirectional comparison between receipt keywords and item descriptions, Col. 13 ln 8-45.
Reading claims 5, 14, Durazo discloses applying the fine-tuned language model comprises applying the language model to receive an order comprising item names and an associated receipt comprising receipt labels and to generate a set of optimized label-name pairs, (generating correspondence matches between receipt text and item description…omitting low-confidence matches (optimizing), Col.13 ln 46-60).
Reading claims 6, 15, Durazo discloses applying the fine-tuned language model comprises applying the language model to receive an order comprising item name and an associated receipt comprising receipt labels and to generate a pair score for the order and associated receipt, (confidence scores, Col.13 ln 46-60).
Reading claims 7, 16, Durazo discloses the pair score for the order and associated receipt is based on fuzzy matching scores for the item names and receipt labels, Col.13 ln 46-60.
Reading claims 8, 17, Thomas discloses the language model is a large language model, (receipt language model, (language-based NLP system). Note: selection of more larger complex language is a routing design choice, [022]).
Reading claims 9, 18, Durazo discloses selecting a set of training label-name pairs from the set of candidate label-name pairs based on the statistical association scores comprises selecting label-name pairs having a statistical association score above a threshold value, (confidence score compared to threshold, Col.13 ln 46-60).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MILENA RACIC whose telephone number is (571)270-5933. The examiner can normally be reached M-F 7:30am-4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian (Ryan) Zeender can be reached at (571)272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MILENA RACIC/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627