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 3/11/2025.
Claims 1-20 are presented for examination.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,277,540. Although the claims at issue are not identical, they are not patentably distinct from each other. For example:
Claim 1 of the present application recites:
A computing system for mapping products to taxability categories, comprising: a computing device including one or more processors configured to execute instructions using portions of associated memory to implement, in a run-time inference phase: an artificial intelligence model configured to receive as run-time input a sequence of tokens associated with a product, and output a run-time output including a predicted tax category for the product, wherein the artificial intelligence model includes a neural network that has been pretrained to compute embeddings for the run-time input via one or more embedding layers, and the predicted tax category is output from the artificial intelligence model and stored in a product taxability record.
Claim 1 of U.S. Patent No. 12,277,540 recites:
A computing system for mapping products to taxability categories, comprising: a computing device including one or more processors configured to execute instructions using portions of associated memory to implement, in a run-time inference phase: an artificial intelligence model configured to receive as run-time input product text including a product name and product description from a product catalog associated with a product, and output a run-time output including a predicted tax category for the product; a taxability category mapping engine configured to link a taxability driver to the product; and a taxability category driver record association engine configured to create a taxability category mapping drivers record including the taxability driver linked to the product, wherein the product catalog includes a product code and taxpayer data related to the product, the taxability driver is linked to the product based on the product code and the taxpayer data, the predicted tax category output from the artificial intelligence model is displayed in a graphical user interface, and the predicted tax category and the taxability category mapping drivers record are stored in a product taxability record.
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 and 12 as representative recite:
a computing device including one or more processors configured to execute instructions using portions of associated memory to implement, in a run-time inference phase: an artificial intelligence model configured to receive as run-time input a sequence of tokens associated with a product, and output a run-time output including a predicted tax category for the product, wherein the artificial intelligence model includes a neural network that has been pretrained to compute embeddings for the run-time input via one or more embedding layers, and the predicted tax category is output from the artificial intelligence model and stored in a product taxability record.
.
The claims as a whole recite classifying a product into a tax category. These limitations recite the concepts directed to receiving information, determining what tax category it belongs to, storing and using that determination to calculate taxes. Accordingly, under step 2A (prong 1) the claim limitations recite the abstract idea exception of “Mental processes”. For example, a human tax professional has classified products into taxability categories for centuries, consulting reference guides, applying business rules and recording the result in a ledger. MPEP § 2106.04(a)(2)(III).
The claim limitations recite an abstract idea because the claim recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas, such as commercial activity, specifically tax compliance and product taxability determination. (Ultramercial v. Hulu, buySafe v. Google).
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 and 12 recite additional elements including computing device, processors, artificial intelligence model, tokens, neural network, embedding layers, run-time output. 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.
In view of the above, under Step 2A (prong 2), claims 1 and 12 do not integrate the recited exception into a practical application (see again: 2019 PEG) because the blockchain is used only as a data repository for storing transaction data and linked transaction records.
Even considered as an ordered combination (as a whole), the additional elements of dependent claims 2-11, 13-20 do not add anything further than when they are considered individually. Claim 4 recite transaction tax engine which is a generic tax calculation system and “configuration settings for the transaction tax engine are extracted from the product taxability record” – for example data from a record is read and used by a downstream system. Claim 5 adds a “transaction device” sending a request and receiving a response which is a generic client-server architecture and does not add significantly more than the abstract idea. Claims 3 and 14 add an evaluation module with user confirmation – displaying a result to a human and receiving their confirmation is itself an abstract mental process or human judgment applied to a computer output. Claim 11 recites feedback training – collecting user feedback and suing it to retrain a machine learning models is a generic abstract iterative improvement concept which is a mental process automated on a computer without any technical implementation details distinguishing from the generic idea of supervised learning.
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 and 12 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 specification identifies a problem “it may require large amounts of time and money for a company to manually map its products to a tax category”, (see [2]). This is a business efficiency problem and the claimed solution – automating the manual process using AI.
The additional elements are generic computer components performing their well-understood routing and conventional functions. An ordered combination of conventional steps does not amount to significantly more than the abstract idea. 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. The specification itself confirms by citing off-the-shelf tokenizer (BlazingText, Word2Vec, WordPiece) and describing standard supervised training with backpropagation, a loss function and standard neural network architectures (see [25-26]).
For example, the additional elements of claims 1 and 12 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network
storing and retrieving information in memory
performing repetitive calculations
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 2-11, 13-20 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
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 1-3, 6-7, 11-14, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Tater (Tool for automated tax coding of invoices), in view of Sevrens (U.S. Patent Publication No. 20180300608).
Regarding claims 1 and 12, Tater teaches a computing system for mapping products to taxability categories, comprising: a computing device including one or more processors configured to execute instructions using portions of associated memory, (Automatically arriving at a tax-code for a given product accurately and efficiently..the proposed system determines the most relevant tax code for an invoice using attributes such as item description, vendor details, shipping and delivery location, see abstract pg 15185);
to implement, in a run-time inference phase: an artificial intelligence model configured to receive as run-time input a sequence of tokens associated with a product, (when an invoice is received, the accounts payable agent validates if all required information is available on the invoice. After this, the agent performs three-way matching between purchase order, in voice and goods received, or assign accounting codes to process the invoice in system, along with determining the tax code…The tax module picks the description of the invoice along with the shipping addresses, including origin and destination countries, company code details, and vendor details and uses the model trained on the training data to determine the tax code, 15186-15187, see Table1);
and output a run-time output including a predicted tax category for the product, (See Table 1 (7N, 7O and ID)… for an invoice using attributes such as item description, vendor details, shipping and delivery location, abstract);
and the predicted tax category is output from the artificial intelligence model and stored in a product taxability record, (the appropriate tax code is updated for each item in the invoice, which is then processed for payment, pg. 15186, see also training data characteristics on pg. 15188).
Tater does not explicitly disclose a neural network that has been pretrained to compute embeddings for the run-time input via one or more embedding layers.
However, Sevrens teaches the language models 108 include parameters of different neural network models..the language models 108 can include learnable parameters in an RNN. The learnable parameters in the RNN can include weights, biases, word embeddings and character embeddings, [22].. Feeding each transaction in both directions allows both tokens prior to the current token and tokens after the current token to be bases for classifying the current token, [45], see embedding instances, [41]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to substitute the IR-based semantic similarity module of Tater, with Sevrens’s pretrained neural network embedding structure. Tater explicitly teaches semantic similarity component “The decoupling of different steps ensures that this en gine can be later replaced by some other semantic similarity module if experiments prove that to be better for a different client, region, or language”, see pg 15189. Tater identifies “item descriptions are not well-formed sentences, computing semantic similarity is a daunting task”, pg 15187. A person of ordinary skilled in the art would have had reasonable expectation to substitute Sevrens’s embedding based neural network for Tater’s IR module as such substitution involves replacing one text classification component with another that was well-understood and known to produce improved results.
Regarding claims 2-3, 13-14, Tater teaches the sequence of tokens associated with the product includes tokens representing taxpayer data, including a taxpayer code and taxpayer partition, see Table 1; an evaluation module configured to display the predicted tax category and to receive a user input indicating that the predicted tax category is correct prior to adopting the predicted tax category for usage in a transaction tax engine, (The agent sees the invoice copy along with the details of items and the predicted tax code.. i.e. Cp < Cmin, the item is sent back to the workflow for manual entry of tax code, pg 15188).
Regarding claim 6, 17, Tater teaches the artificial intelligence model is trained with a training data set including a plurality of training data pairs, each training data pair including training product information associated with a training product catalog and a ground truth classification indicating a tax category for the product associated with the training product catalog, (see training data characteristics, pg. 15188).
Regarding claim 7, 18, Tater teaches represents product text including a product name and product description extracted from a product catalog, but does not explicitly disclose the sequence of tokens associated with the product and the artificial intelligence model includes a tokenizer configured to tokenize the product text to thereby produce tokenized text. Sevrens teaches tokens and word and character embedding, [39-45].
Regarding claim 11, Tater teaches collect user feedback via an implicit or explicit user feedback interface, and perform feedback training of the artificial intelligence model based at least in part on the user feedback. (to give an upvote, implying that the assigned tax code is correct, or give a downvote to choose the most suitable tax code. This feedback is used to enhance the model. In case the predicted confidence is lower than the minimum defined thresh old, i.e. Cp < Cmin, the item is sent back to the workflow for manual entry of tax code, pg. 15188).
Claims 4-5, 8-10, 15-16, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tater and Sevrens combination and further in view of Sullivan (U.S. Patent Publication No. 2003/0093320).
Regarding claim 4, 15, the combination does not explicitly teach a transaction tax engine, wherein transaction tax engine configuration settings for the transaction tax engine are extracted from the product taxability record. Sullivan teaches a transaction tax compliance system comprising a tax transaction calculator 202, address manager 270, tax rate 272, [37, 48]. Sullivan further discloses attach catalog numbers SKU representing products or services stores info in a seller database 204..the stored record containing commodity codes and reason codes, [49-58].
Regarding claim 5, 16, the combination does not explicitly teach a transaction device, wherein the transaction tax engine is configured to receive a tax calculation request from the transaction device, process the tax calculation request according to the transaction tax engine configuration settings, and transmit a tax calculation response to the transaction device. Sullivan teaches Users, including sellers and/or purchasers, of the transaction tax compliance system preferably first configure records of the transaction tax compliance system according to their business, [39], Sellers and purchasers, through their billing or purchasing systems, cash registers, and/or websites, may transmit transaction data to one or more centralized processors through telecommunications technology or via their own computer networks, [5].
Regarding claim 8, 19, the combination does not explicitly teach a taxability category mapping engine configured to link a taxability driver to the product; and a taxability category driver record association engine configured to create a taxability category mapping drivers record including the taxability driver linked to the product, (attach catalog control numbers (“SKU's”) representing products or services that they sell or purchase to an applicable commodity code of the transaction tax compliance system, [40]).
Regarding claim 9-10, 20, the product catalog includes a product code and taxpayer data related to the product, and the taxability driver is linked to the product based on the product code and the taxpayer data; the taxability category mapping drivers record is stored in the product taxability record, (seller/purchaser company code, seller identifier, seller name, and seller/purchaser division codes, [44]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the method of combination, to include the method, as taught by Sullivan, in order to calculate the accurate transaction tax liability for all consummated transactions, [3].
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
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