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 .
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 directed to a method and a system which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 and 11 recite(s) the following abstract idea: (The examiner notes that the user device, its components, and its functions are outside the scope of the applicant’s invention and, as such, have been considered part of the abstract idea because they cannot be considered “additional element” of the claimed invention.)
receiving an asset transaction data from a user device associated with a user, wherein the asset transaction data corresponds to a transaction associated with an asset;
processing the asset transaction data;
identifying a transaction characteristic data based on the processing, wherein the transaction characteristic data corresponds to a characteristic associated with the transaction;
storing the transaction characteristic data;
generating a transaction update data based on the transaction characteristic data, wherein the transaction update data corresponds to an update in relation to the transaction; and
transmitting the transaction update data to the client device.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising marketing and sales related activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of a computer comprising a storage device, a communication device, and a processing device executing software (e.g., an OCR module and a trained general-purpose AI module) which is just a general-purpose computer with generic computer components.
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving an asset transaction data from a user device associated with a user, wherein the asset transaction data corresponds to a transaction associated with an asset (receiving data);
processing the asset transaction data (processing data);
storing the transaction characteristic data (storing data); and
transmitting the transaction update data to the client device (transmitting data).
The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer comprising a storage device, a communication device, and a processing device executing software (e.g., an OCR module and a trained general-purpose AI module) which is just a general-purpose computer with generic computer components. to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general-purpose computer (as evidenced from at least paragraphs 66, 103, and 105-109, as well as, figures 1 and 9 of the applicant’s specification; Tableau, What is the history of artificial intelligence (AI), February 27, 2023, https://web.archive.org/web/20230227102129/https://www.tableau.com/data-insights/ai/history, pages 1-10 which discloses on at least page 1, paragraph 1 that artificial intelligence was well-known by at least February 2023; Crown Relocations, The Past & Future of OCR Technology, December 6, 2023, https://web.archive.org/web/ 20231206051739 /https://www.crownrelo.co.nz/the-past-future-of-ocr-technology/, pages 1-8 which discloses on at least page 2, paragraph 1 that OCR was a well-known technology by at least December 2023; and Hussain, ICR Services – Enhancing Business Efficiency in Digital World, August 23, 2022, https://wpblog.com/icr-services-enhancing-business-efficiency-in-digital-world/, pages 1-5 which discloses on at least page 2, Paragraph 2 that combining OCR and artificial intelligence was well-known by at least August 2022); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving an asset transaction data from a user device associated with a user, wherein the asset transaction data corresponds to a transaction associated with an asset (receiving data);
processing the asset transaction data (processing data);
storing the transaction characteristic data (storing data); and
transmitting the transaction update data to the client device (transmitting data).
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
The dependent claims 2-10 and 12-20 appear to merely further limit the abstract idea by further limiting the asset, the asset transaction data, and the generating of the transaction update, as well as, adding the additional steps of analyzing the real estate document, generating a document extracted data which are all considered part of the abstract idea (Claims 2 and 12); adding the additional steps of generating a transaction ID which is considered part of the abstract idea (Claims 3 and 13); further limiting the generating of the transaction update and the storing of the transaction characteristic data which are both considered part of the abstract idea (Claims 4 and 14); further limiting the user device which is outside the scope of the applicant’s invention and as such considered part of the abstract idea, as well as, adding the additional steps of receiving the user input data, processing the user input data, generating a modified transaction update data, and transmitting the modified transaction update data which are all considered part of the abstract idea (Claims 5 and 15); further limiting the asset transaction data, the generating of the transaction update data, as well as, adding an additional step of validating user data which are all considered part of the abstract idea (Claims 6 and 16); adding the additional steps of generating a regulatory query, transmitting the regulatory query, and receiving regulatory data, as well as, further limiting the generating of the transaction update data which are all considered part of the abstract idea (Claims 10 and 20); adding the additional steps of generating a transaction-based additional insight, transmitting the transaction-based additional insight, receiving additional insight data, and processing the additional insight data, as well as, further limiting the generating of the transaction update data which are all considered part of the abstract idea (Claims 7 and 17); adding the additional step of generating a legal advisory data which is considered part of the abstract idea (Claims 8 and 18); and adding the additional steps of generating model training data, training model and storing the trained model which are all considered part of the abstract idea (Claims 9 and 19) , and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 status.
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) 1-7, 9, 11-17, and 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Davis (PGPUB: 2023/0206364).
Claims 1 and 11: Davis discloses a method and a system of facilitating an automated asset transaction comprising:
receiving, using a communication device, an asset transaction data from a user device associated with a user, wherein the asset transaction data corresponds to a transaction associated with an asset (Paragraph 160: a user (e.g., contracting party) uploads a legal prose contract to an application interface for processing; Paragraph 152: legal prose contract may be an asset purchase agreement or a lease (e.g., a real estate lease));
processing, using a processing device, the asset transaction data (Paragraph 160: scans are performed to convert the contract into an electronic version using optical character recognition (OCR)); Paragraph 149: one or more machine learning algorithms are used to generate hybrid legal contract file and/or hybrid journal entries (e.g., hybrid journal entries) from pre-existing legal prose contract);
identifying, using the processing device, a transaction characteristic data based on the processing, wherein the transaction characteristic data corresponds to a characteristic associated with the transaction (Paragraph 150: Classification engine may classify portions of legal prose contract based on a document type, a clause type, an identity of a party, a subject of a legal document, and so on. Paragraph 151: the identifier classification engine may classify an identity within the legal prose and then resolve a DID to obtain the DID Document (e.g., DPKI metadata); Paragraph 152: document classification engine may classify legal prose contract as an asset purchase agreement, a license (e.g., synchronization license), a will, a lease (e.g., a real estate lease), and so on.; Paragraph 153: a clause classification engine classifies different types of clauses within legal prose contract such as a license grant clause (e.g., a grant of rights), an indemnity clause, an arbitration clause, a force majeure clause, an escalation clause, a confidentiality clause, a non-compete clause, an intellectual property rights clause, warranty clause, a payment clause, a term/termination clause, and the like; Paragraph 154: a data model classification engine classifies the components of a data model (e.g., variable names, data types, and values of data model) used to generate hybrid legal contract file);
storing, using a storage device, the transaction characteristic data (Paragraph 156: the journal entry classification engine generates hybrid journal entries directly from legal prose contract);
generating, using the processing device, a transaction update data based on the transaction characteristic data, wherein the transaction update data corresponds to an update in relation to the transaction, wherein the generating is further based on an Al module (Paragraph 158: machine learning algorithms may use data model classification engine to classify the markup language that will apply to legal prose contract and generate a markup version of a given clause or entire legal prose contract; Paragraph 159 and 161: decentralized application cross-references verifiable data registry to obtain DIDs (e.g., asset DIDs and/or service provider DIDs) that are the subject of legal prose contract; if a DID is available, machine learning algorithm obtains the DID and populates the DID in the shared data model. If a given identity in legal prose contract does not have a DID, machine learning may alert the user to this determination and guide the user in creating a DID, if applicable; machine learning algorithms populate the parameters for the data model from the legal prose to generate hybrid legal contract file; Paragraph 162: decentralized application populates data model using the parameters extracted by classification engines; decentralized application populates hybrid legal contract file, which may include original legal prose contract, a markup language and data model, and/or any logic implemented in code that contracting party a would like to include; decentralized application populates hybrid journal entry based on parameters in shared data model and/or information generated by classification engines; Paragraph 163: the user performs an iterative process to correct any errors made by classification engines) ; and
transmitting, using the communication device, the transaction update data to the client device. (Paragraph 163: both data structures (hybrid legal contract file and hybrid journal entry) are routed back to application interface for review by contracting party)
Claims 2 and 12: Davis discloses the method of claim 1 and the system of claim 11,
wherein the asset comprises a real estate asset (Paragraph 80: assets may be tangible (e.g., cash, inventory, accounts receivable, land, buildings, equipment, etc.) such as real estate),
wherein the asset transaction data comprises a real estate document data corresponding to a document associated with the real estate asset (Paragraph 152: legal prose contract may be an asset purchase agreement or a lease (e.g., a real estate lease); Paragraph 80: assets may be tangible assets such as real estate),
analyzing, using the processing device, the real estate document data, wherein the analyzing is based on an OCR module (Paragraph 160: scans are performed to convert the legal prose contract into an electronic version using optical character recognition (OCR));
generating, using the processing device, a document extract data based on the analyzing, wherein the document extract data corresponds to an extract of the real estate document (Paragraph 150: Classification engine may classify portions of legal prose contract based on a document type, a clause type, an identity of a party, a subject of a legal document, and so on. Paragraph 151: identifier classification engine a may classify an identity within the legal prose and then resolve a DID to obtain the DID Document (e.g., DPKI metadata); Paragraph 152: document classification engine may classify legal prose contract as an asset purchase agreement, a license (e.g., synchronization license), a will, a lease (e.g., a real estate lease), and so on.; Paragraph 153: a clause classification engine classifies different types of clauses within legal prose contract such as a license grant clause (e.g., a grant of rights), an indemnity clause, an arbitration clause, a force majeure clause, an escalation clause, a confidentiality clause, a non-compete clause, an intellectual property rights clause, warranty clause, a payment clause, a term/termination clause, and the like; Paragraph 154: a data model classification engine classifies the components of a data model (e.g., variable names, data types, and values of data model) used to generate hybrid legal contract file),
wherein the generating of the transaction update is further based on the document extract data. (Paragraph 158: machine learning algorithms may use data model classification engine to classify the markup language that will apply to legal prose contract and generate a markup version of a given clause or entire legal prose contract; Paragraph 159 and 161: decentralized application cross-references verifiable data registry to obtain DIDs (e.g., asset DIDs and/or service provider DIDs) that are the subject of legal prose contract; if a DID is available, machine learning algorithm obtains the DID and populates the DID in the shared data model. If a given identity in legal prose contract does not have a DID, machine learning may alert the user to this determination and guide the user in creating a DID, if applicable; machine learning algorithms populate the parameters for the data model from the legal prose to generate hybrid legal contract file; Paragraph 162: decentralized application populates data model using the parameters extracted by classification engines; decentralized application populates hybrid legal contract file, which may include original legal prose contract, a markup language and data model, and/or any logic implemented in code that contracting party a would like to include; decentralized application populates hybrid journal entry based on parameters in shared data model and/or information generated by classification engines; Paragraph 163: the user performs an iterative process to correct any errors made by classification engines)
Claims 3 and 13: Davis discloses the method of claim 1 and the system of claim 11 further comprising generating, using the processing device, a transaction ID data based on the identifying of the transaction characteristic data, wherein the transaction ID data represents a transaction ID associated with the transaction, wherein the transaction ID data is comprised in the transaction update data. (Paragraphs 41-42: the hybrid legal document transfers ownership of an asset from the second party to the first party, wherein the asset is associated with an asset DID, populating the first party as a new controller of the asset DID, and/or removing the second party as a controller of the asset DID; Paragraph 231: the financial settlement information included within the hybrid journal entry includes a transaction ID; Paragraph 162: decentralized application populates data model using the parameters extracted by classification engines; decentralized application populates hybrid legal contract file, which may include original legal prose contract, a markup language and data model, and/or any logic implemented in code that contracting party a would like to include; decentralized application populates hybrid journal entry based on parameters in shared data model and/or information generated by classification engines)
Claims 4 and 14. Davis discloses the method of claim 1 and the system of claim 11, wherein each of the generating of the transaction update data and the storing of the transaction characteristic data is based on an execution of a smart contract, wherein the smart contract is associated with a block-chain network. (Paragraph 211: once hybrid legal contract file is signed, the source code implementing the logical functions may be loaded into compute environment agreed to by the parties; this process is documented in hybrid legal contract file; in some embodiments, decentralized application performs this process; if contracting party and/or asset owner are uploading legal logic to a distributed ledger, the parties may use a multi-signature distributed ledger transaction to “deposit” legal logic into a location on the distributed ledger (e.g., in a “smart contract”); once on the distributed ledger, legal logic may potentially interact with other legal logic in other “smart contracts” on the same distributed ledger or potentially other distributed ledgers; for example, legal logic may use “call” functions that query other “smart contract” states; Paragraph 155: the classification engines include a logic classification engine that classifies the logic used to generate the hybrid legal contract file; for example, if legal prose contract includes ongoing legal logic that can be automated, machine learning algorithm may be trained to classify logic within legal prose contract itself; once classified, machine learning algorithms may search a logic repository for potential code that can be executed in a compute environment)
Claims 5 and 15. Davis discloses the method of claim 1 and the system of claim 11,
wherein the user device comprises a user presentation device configured for presenting the transaction update data to the user, wherein the user device further comprises a user input device configured for generating a user input data corresponding to a user input in relation to the transaction, wherein the user device further comprises a user communication device configured for transmitting the user input data to the communication device, (Paragraphs 78-79, and 88: user device has a display with a graphical user interface with which allows the user to review the hybrid legal contract file and the hybrid journal entry; user device includes digital buttons, a digital keyboard, physical buttons, a physical keyboard, one or more touch screen components, a graphical user interface (GUI), and/or the like; user device includes can receive and communicate information via an application interface);
receiving, using the communication device, the user input data from the user device; processing, using the processing device, the user input data; generating, using the processing device, a modified transaction update data based on the processing of the user input data, wherein the modified transaction update data corresponds to a modification associated with the update in relation to the transaction; and transmitting, using the communication device, the modified transaction update data to the user device. (Paragraphs 139-141: the hybrid legal contract includes variable names in which a user can input values; Paragraphs 163 and 169: the hybrid legal contract file and hybrid journal entry are transmitted for review by contracting party (e.g., user review), and an iterative process is used to correct any errors made by the classification engines; once contracting party has finalized hybrid legal contract file and hybrid journal entry is transmitted back to a location as set forth in the DPKI metadata of contracting party; contracting parties may make changes, communicate comments and/or questions, and/or take whatever steps are necessary to satisfy the pre-existing legal relationship when converting legal prose contract into hybrid legal contract file; Paragraph 170: once contracting parties have agreed to the “layer 1” natural language prose, the “layer 2” data model and markup, and/or any “layer 3” legal logic, entire hybrid legal contract file may be digitally signed by contracting parties; Paragraph 192-193: a transaction interface is presented to a contracting party to assist in populating the “deal points” that generate the values in empty data model; the inputs from contracting party are populated into populated data model and the values are populated into legal prose contract through the markup language; application interface may present various options for contracting party to submit the transaction request; in each of these options, the ask is to populate data model with the contractual “deal points” that can then be integrated into legal prose contract; Paragraphs 196-204: once a first contracting party makes changes to the legal prose contract, the modified contract is provided to a second contracting party who can make changes, this process continues until a deal is negotiated and the hybrid legal contract file is executed; Paragraph 207: both the first contracting party and the second contracting party then digital sign the entire hybrid legal contract file)
Claims 6 and 16: Davis discloses the method of claim 1 and the system of claim 11,
wherein the asset transaction data comprises a user data corresponding to the user associated with the transaction (Paragraphs 159-160: the received asset transaction data includes a DID and DPKI metadata associated with one or more contracting parties),
validating, using the processing device, the user data (Paragraph 111-112 and 159: a verifiable data registry is used to validate the user data),
wherein the generating of the transaction update data is further based on the validating (Paragraphs 151 and 165-166: the identifier classification engine classifies and/or resolves the identifiers (e.g., DIDs) by cross-referencing the verifying name registry,
wherein the user data comprises at least one of a user name data corresponding to a name associated with the user and a user signature data corresponding to a signature associated with the user. (Paragraph 117-118, 159-161, 170-170, 207-209, and 213-214: the verifiable credentials include human meaningful names, such as user legal names, that are cryptographically tied to the identifier, the verifiable credential can be reference to obtain the associated DID; the user data can include as a digital signature of the contracting party)
Claims 7 and 17: Davis discloses the method of claim 1 and the system of claim 11 further comprising: generating, using the processing device, a transaction-based additional insight query data based on the processing of the asset transaction data, wherein the transaction-based additional insight query data corresponds to an additional insight query in relation to the transaction associated with the asset; transmitting, using the communication device, the transaction-based additional insight query data to an external database; and receiving, using the communication device, an additional insight data from the external database based on the transmitting of the transaction-based additional insight query data; processing, using the processing device, the additional insight data, wherein the generating of the transaction update data is further based on the processing of the additional insight data. (Paragraphs 58-59, 83-86, 96-98, 143, 155, and 201-204: the structured data of the hybrid legal contract’s data is used as input to logical functions (e.g., generating a query) to implement legal logic contained within the legal prose of the contract to access data in various external databases (e.g., data stores, distributed ledgers, repositories, and data registries to obtain additional insights such as legal documents other than contracts such as written consent of a board of directors relating to the authority of a corporate officer that are mapped to verifiable credentials, automating payments, upon the occurrence of a certain event, a change of control of the asset and the asset identity upon the occurrence of a certain event, creating identifiers, cryptographic keys, and/or revocation registries, wherein one or more of these additional insight information are used to generate the hybrid legal contract file and hybrid journal entry (e.g., the transaction update data))
Claims 9 and 19: Davis discloses the method of claim 1 and the system of claim 11 further comprising:
generating, using the processing device, a module training data based on each of the transaction update data and the transaction characteristic data; training, using the processing device, the AI module to obtain a trained AI module, wherein the training is based on the module training data; and storing, using the storage device, the trained AI module. (Paragraph 157 and 258-264, 270-274: machine learning algorithms train one or more classification engines separately; in some embodiments, machine learning algorithms simultaneously train one or more classification engines; for example, machine learning algorithms may simultaneously train one or more classification engines using techniques such as multi-output architectures, multitask learning, etc.)
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 status.
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 8, 10, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Davis (PGPUB: 2023/0206364) in view of Maluchnik (PGPUB: 2025/0299136).
Claims 8 and 18: The method of claim 1 and the system of claim 11 further comprising generating, using the processing device, a legal advisory data based on the processing of the asset transaction data, wherein the generating of the legal advisory data is based on the AI module, wherein the legal advisory data is comprised in the transaction update data.
Davis discloses the method of claim 1 and the system of claim 11.
Davis does not disclose generating a legal advisory data based on the processing of the asset transaction data, wherein the generating of the legal advisory data is based on the AI module, wherein the legal advisory data is comprised in the transaction update data.
However, the analogous art of Muluchnik discloses that it is known to generating a legal advisory data based on the processing of the asset transaction data, wherein the generating of the legal advisory data is based on the AI module, wherein the legal advisory data is comprised in the transaction update data in at least paragraphs 48-52, 56-60, 177, 180-181, and 184-194.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Davis to include the generating of legal advisory data as disclosed by Muluchnik.
The motivation for doing so is to ensure the contracts adhere to the latest legal standards and corporate policies, as well as, to ensure that the contracts comply with a myriad of international laws and regulations, including tax laws, anti-corruption laws, and data protection statutes. (Muluchnik – Paragraphs 5-6).
Claims 10 and 20: The method of claim 6 and the system of claim 16 further comprising: generating, using the processing device, a regulatory query data based on the processing of the asset transaction data, wherein the regulatory query data corresponds to a query associated with a regulatory content in relation to the transaction; transmitting, using the communication device, the regulatory query data to a regulatory database; and receiving, using the communication device, a regulatory data from the regulatory database, wherein the generating of the transaction update data is further based on the regulatory data.
Davis discloses the method of claim 6 and the system of claim 16 further comprising:
Davis does not disclose generating, using the processing device, a regulatory query data based on the processing of the asset transaction data, wherein the regulatory query data corresponds to a query associated with a regulatory content in relation to the transaction; transmitting, using the communication device, the regulatory query data to a regulatory database; and receiving, using the communication device, a regulatory data from the regulatory database, wherein the generating of the transaction update data is further based on the regulatory data.
However, the analogous art of Muluchnik discloses that it is known to generating, using the processing device, a regulatory query data based on the processing of the asset transaction data, wherein the regulatory query data corresponds to a query associated with a regulatory content in relation to the transaction; transmitting, using the communication device, the regulatory query data to a regulatory database; and receiving, using the communication device, a regulatory data from the regulatory database, wherein the generating of the transaction update data is further based on the regulatory data in at least paragraphs 48-52, 56-60, 177, 180-181, and 184-194.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Davis to include the querying of a regulatory database to obtain relevant regulatory data associated with the transaction and incorporate the regulatory data in the transaction update data as disclosed by Muluchnik.
The motivation for doing so is to ensure the contracts adhere to the latest legal standards and corporate policies, as well as, to ensure that the contracts comply with a myriad of international laws and regulations, including tax laws, anti-corruption laws, and data protection statutes. (Muluchnik – Paragraphs 5-6).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Padilla et al. (PGPUB: 2025/0191480) which discloses performing industry specific contract analysis using AI model(s) trained to evaluate contracts documents from the same perspective as an experienced attorney would actually review such contracts, issues of varying significance can be identified and brought to the attention of parties, generally those lacking the legal expertise to spot such issues themselves, providing recommendations, guidance and/or insights regarding issues associated with the contract and changes that might be made, wherein the specific industry includes real estate.
Anders et al. (PGPUB: 2022/0058336) which discloses receiving a document from a requestor application; extracting layout information and text from the document; extracting, based on the layout information, values of one or more predefined data items from the text of the document; producing a document validation result by analyzing the one or more data items; embedding, into the document, one or more human-readable comments reflecting the document validation result; and forwarding, to the requestor application, the document comprising the one or more human readable comments.
Padmashali et al. (PGPUB: 2025/0148020) which discloses analyzing and summarizing legal documents using artificial intelligence wherein a legal document such as a contract or agreement is received; multiple AI models in combination to process and summarize the document including identifying the type of legal document using a natural language model, and then extracting key information like parties, dates, and monetary values; related clauses within the document are consolidated using a clause consolidation algorithm even if they are not contiguous; summaries are generated for each section of the document; a question-answering module preemptively generates common questions and answers about the agreement to highlight key considerations for the parties involved.
Hunn et. al. (PGPUB: 2024/0330605) which discloses using AI to assist in contract management, where in the AI may provide a summary of deviations between: versions of an electronic document; or between a version of an electronic document and a standard document used by an organization (e.g., a playbook), an industry (e.g., Software As A Service), a customer, a technology, and other logical divisions; recommend suggestions for generating or modifying an electronic document based on the summary of deviation; and provide a summary of the changes.
Wodetzki et. al. (PGPUB: 2018/0268506) which discloses performing a machine evaluation of contract terms to provide visibility into contractual risks, rights and processes, and generating a trusted summary of the terms.
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/John Van Bramer/Primary Examiner, Art Unit 3622