DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims X are new.
Claims 1-17 are pending and have been examined.
This action is in reply to the papers filed on 12/23/2024 (effective filing date 03/15/2017).
Information Disclosure Statement
The information disclosure statement(s) submitted: 08/01/2025, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Terminal Disclaimer
The terminal disclaimer filed on xxx disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Pat. No. xxxx has been reviewed and has been placed in the file.
Examiner acknowledges Applicant’s filed Terminal Disclaimer to prior art patent McCauley et al. US Pat. No. 5,930,775. A terminal disclaimer may be filed to overcome or obviate a nonstatutory double patenting rejection (37 CFR 1.321; MPEP 706.02; 1490).
Double Patenting - Withdrawn
The double patenting rejection is withdrawn per the filed terminal disclaimer noted above.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
Double Patenting
The nonstatutory 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-17 are rejected on the ground of anticipatory-nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11,861,751.
19/000,629 – Claim 1. A computer-implemented method comprising:
US 11,861,751 – Claim 1. A computer-implemented method comprising:
19/000,629 – Claim 1. defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract;
US 11,861,751 – Claim 1. defining in computer memory an object model containing a structural representation of events and artefacts through which contracts are created, changed and brought to an end, the object model having at least three object types: contract objects, contract transaction objects and contract document objects;
19/000,629 – Claim 1. associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document;
US 11,861,751 – Claim 1. associating the contract objects with one or more contract transaction objects corresponding to one or more actions taken within a contract; associating contract document objects containing a corresponding contract document with one or more corresponding contract transaction objects, wherein the contract transaction object comprises contract data variables having contract data values;
19/000,629 – Claim 1. accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets;
US 11,861,751 – Claim 1. accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry specific rule sets, customer specific rule sets and user-created rule sets;
19/000,629 – Claim 1. applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document;
US 11,861,751 – Claim 1. applying the plurality of rule sets to words of each contract document to identify whether the words contain one or more core attributes pertaining to details of the contract, wherein the one or more core attributes may comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities or contextual dependencies;
19/000,629 – Claim 1. determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables;
US 11,861,751 – Claim 1. determining prevailing terms of the contract by evaluating all child contract transaction objects to build a single set of contract data variables and values;
19/000,629 – Claim 1. evaluating the contract data variables and assigning a contract risk value; and
US 11,861,751 – Claim 1. evaluating the contract data variables and assigning a contract data risk value to one or more of the contract data values;
19/000,629 – Claim 1. when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device.
US 11,861,751 – Claim 1. evaluating a maximum risk for each contract data variable; and presenting a sum of the contract data risk values for the contract data risk variables and a sum of the maximum contract data risk values for each contract data variable.
19/000,629 – Claim 2. The computer-implemented method of claim 1, wherein the one or more core attributes comprise one or more of legal classifications subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies.
US 11,861,751 – Claim 1. applying the plurality of rule sets to words of each contract document to identify whether the words contain one or more core attributes pertaining to details of the contract, wherein the one or more core attributes may comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities or contextual dependencies;
19/000,629 – Claim 3. The computer-implemented method of claim 1, wherein the object model further includes contract objects associated with one or more of the contract transaction objects, wherein associating the contract objects with one or more of the contract transaction objects comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type of at least one of the contract transaction objects is one of: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction, or Novate Transaction.
US 11,861,751 – Claim 2. The method of claim 1, wherein the step of associating the contract objects comprises the step of associating the contract objects with one or more contract transaction objects corresponding to one or more actions taken within the contract, wherein at least one of the contract transaction objects is of one of the following types: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction and Novate Transaction.
19/000,629 – Claim 4. The computer-implemented method of claim 3, wherein the object model further includes project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
US 11,861,751 – Claim 10. The method of claim 1 further wherein the object types further comprise project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
19/000,629 – Claim 5. The computer-implemented method of claim 1, further comprising transforming each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, assigning a confidence score to the legal classification, and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm, wherein the legal classification comprises one of an obligation, a right, a representation, an act, or a definition.
US 11,861,751 – Claim 3. The method of claim 1, further comprising transforming documents into a collection of single sentences, assigning a legal classification to the sentences, and assigning a confidence score to the legal classification; presenting sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm.
US 11,861,751 – Claim 1. applying the plurality of rule sets to the words of each contract document containing legal classifications to determine a type of the legal classification, wherein at least one of the types of legal classification is an obligation, a right, a representation, an act or deed, and a definition;
19/000,629 – Claim 6. The computer-implemented method of claim 1, wherein applying the plurality of rule sets to one or more words of each corresponding contract document comprises evaluating sentence pairs of each corresponding contract document.
US 11,861,751 – Claim 5. The method of claim 1 wherein the step of applying the plurality of rule sets to words of each contract document comprises the step of evaluating sentence pairs of each contract document.
19/000,629 – Claim 7. The computer-implemented method of claim 3, further comprising storing the prevailing terms of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format.
US 11,861,751 – Claim 6. The method of claim 1 further comprising storing the prevailing terms in the contract object in XML format.
US 11,861,751 – Claim 7. The method of claim 1 further comprising storing the prevailing terms in the contract object in JSON format.
US 11,861,751 – Claim 8. The method of claim 1 further comprising storing the prevailing terms in the contract object in a triple store format.
19/000,629 – Claim 8. The computer-implemented method of claim 1, further comprising: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets.
19/000,629 – Claim 9. One or more non-transitory computer-readable storage media storing one or more sequences of instructions which, when executed by one or more processors, cause of the one or more processors to execute: defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract; associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document; accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets; applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document; determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables; evaluating the contract data variables and assigning a contract risk value; and when a contract risk value exceeds a threshold value, communicating an alert via email or text message.
19/000,629 – Claim 10. The one or more non-transitory computer-readable storage media of claim 9, wherein the one or more core attributes comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies.
19/000,629 – Claim 11. The one or more non-transitory computer-readable storage media of claim 9, wherein the object model further includes contract objects associated with one or more of the contract transaction objects, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute associating the contract objects with one or more of the contract transaction objects comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type of at least one of the contract transaction objects is one of: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction, or Novate Transaction.
19/000,629 – Claim 12. The one or more non-transitory computer-readable storage media of claim 9, wherein the object model further includes project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
19/000,629 – Claim 13. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute transforming each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, and assigning a confidence score to the legal classification; and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm.
19/000,629 – Claim 14. The one or more non-transitory computer-readable storage media of claim 13, wherein the legal classification comprises one of an obligation, a right, a representation, an act, and a definition.
19/000,629 – Claim 15. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute applying the plurality of rule sets to one or more words of each corresponding contract document by evaluating sentence pairs of each corresponding contract document.
19/000,629 – Claim 16. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute storing the prevailing terms of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format.
19/000,629 – Claim 17. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets.
The remaining independent claims contain feature similar to that of claim 1 and are rejected accordingly. The dependent claims are further rejected for their dependency upon a rejected independent base claim.
Claims 1-17 are rejected on the ground of anticipatory-nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,416,956.
19/000,629 – Claim 1. A computer-implemented method comprising:
US 11,416,956 – Claim 1. A method of machine recognition and tracking of contract terms over the lifetime of a contract using a machine learning algorithm, the method comprising:
19/000,629 – Claim 1. defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract;
US 11,416,956 – Claim 1. defining an object model containing a structural representation of the events and artefacts through which contracts are created, changed and brought to an end, the object model having at least three object types: contract objects, contract transaction objects and contract document objects;
19/000,629 – Claim 1. associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document;
US 11,416,956 – Claim 1. associating the contract objects with one or more contract transaction objects corresponding to one or more actions taken within the contract, wherein at least one of the contract transaction objects is of one of the following types: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction and Novate Transaction; associating contract document objects containing a corresponding contract document with one or more corresponding contract transaction objects;
19/000,629 – Claim 1. accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets;
US 11,416,956 – Claim 1. accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training the machine learning algorithm by processing documents, clauses, sentences, and phrases from contracts to create the plurality of rule sets, the plurality of rule sets comprising one or more of: document rule sets, contract transaction rule sets, clause classification rule sets, universal contract module rule sets, industry specific rule sets, and customer specific rule sets;
19/000,629 – Claim 1. applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document;
US 11,416,956 – Claim 1. applying the plurality of rule sets to words of each contract document to identify whether the words contain one or more core attributes pertaining to details of the contract, wherein the one or more core attributes may comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities or contextual dependencies;
19/000,629 – Claim 1. determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables;
US 11,416,956 – Claim 1. determining prevailing terms of a first version of the contract by evaluating all child contract transaction objects, and the data contained therein, chronologically to build a single set of terms for storage in the contract object;
19/000,629 – Claim 1. evaluating the contract data variables and assigning a contract risk value; and
US 11,416,956 – Claim 7. The method of claim 1 further comprising the step of calculating a risk score associated with the prevailing terms of the contract.
19/000,629 – Claim 1. when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device.
US 11,416,956 – Claim 1. transforming documents into a collection of single sentences, assigning a legal classification to the sentences, and assigning a confidence score to the legal classification; presenting sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm;
19/000,629 – Claim 2. The computer-implemented method of claim 1, wherein the one or more core attributes comprise one or more of legal classifications subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies.
US 11,416,956 – Claim 1. applying the plurality of rule sets to words of each contract document to identify whether the words contain one or more core attributes pertaining to details of the contract, wherein the one or more core attributes may comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities or contextual dependencies;
19/000,629 – Claim 3. The computer-implemented method of claim 1, wherein the object model further includes contract objects associated with one or more of the contract transaction objects, wherein associating the contract objects with one or more of the contract transaction objects comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type of at least one of the contract transaction objects is one of: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction, or Novate Transaction.
US 11,416,956 – Claim 1. associating the contract objects with one or more contract transaction objects corresponding to one or more actions taken within the contract, wherein at least one of the contract transaction objects is of one of the following types: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction and Novate Transaction;
19/000,629 – Claim 4. The computer-implemented method of claim 3, wherein the object model further includes project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
US 11,416,956 – Claim 8. The method of claim 1 further wherein the object types further comprise project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
19/000,629 – Claim 5. The computer-implemented method of claim 1, further comprising transforming each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, assigning a confidence score to the legal classification, and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm, wherein the legal classification comprises one of an obligation, a right, a representation, an act, or a definition.
US 11,416,956 – Claim 1. transforming documents into a collection of single sentences, assigning a legal classification to the sentences, and assigning a confidence score to the legal classification; presenting sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm;
US 11,416,956 – Claim 9. The method of claim 1 wherein the legal classification comprises one of an obligation, a right, a representation, an act, and a definition.
19/000,629 – Claim 6. The computer-implemented method of claim 1, wherein applying the plurality of rule sets to one or more words of each corresponding contract document comprises evaluating sentence pairs of each corresponding contract document.
US 11,416,956 – Claim 3. The method of claim 1 wherein the step of evaluating words of each contract document comprises the step of evaluating sentence pairs of each contract document.
19/000,629 – Claim 7. The computer-implemented method of claim 3, further comprising storing the prevailing terms of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format.
US 11,416,956 – Claim 4. The method of claim 1 wherein the step of storing the prevailing terms in the contract object comprises the step of storing the prevailing terms in the contract object in XML format.
US 11,416,956 – Claim 5. The method of claim 1 wherein the step of storing the prevailing terms in the contract object comprises the step of storing the prevailing terms in the contract object in JSON format.
US 11,416,956 – Claim 6. The method of claim 1 wherein the step of storing the prevailing terms in the contract object comprises the step of storing the prevailing terms in the contract object in a triple store format.
19/000,629 – Claim 8. The computer-implemented method of claim 1, further comprising: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets.
19/000,629 – Claim 9. One or more non-transitory computer-readable storage media storing one or more sequences of instructions which, when executed by one or more processors, cause of the one or more processors to execute: defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract; associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document; accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets; applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document; determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables; evaluating the contract data variables and assigning a contract risk value; and when a contract risk value exceeds a threshold value, communicating an alert via email or text message.
19/000,629 – Claim 10. The one or more non-transitory computer-readable storage media of claim 9, wherein the one or more core attributes comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies.
19/000,629 – Claim 11. The one or more non-transitory computer-readable storage media of claim 9, wherein the object model further includes contract objects associated with one or more of the contract transaction objects, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute associating the contract objects with one or more of the contract transaction objects comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type of at least one of the contract transaction objects is one of: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction, or Novate Transaction.
19/000,629 – Claim 12. The one or more non-transitory computer-readable storage media of claim 9, wherein the object model further includes project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
19/000,629 – Claim 13. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute transforming each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, and assigning a confidence score to the legal classification; and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm.
19/000,629 – Claim 14. The one or more non-transitory computer-readable storage media of claim 13, wherein the legal classification comprises one of an obligation, a right, a representation, an act, and a definition.
19/000,629 – Claim 15. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute applying the plurality of rule sets to one or more words of each corresponding contract document by evaluating sentence pairs of each corresponding contract document.
19/000,629 – Claim 16. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute storing the prevailing terms of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format.
19/000,629 – Claim 17. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets.
The remaining independent claims contain feature similar to that of claim 1 and are rejected accordingly. The dependent claims are further rejected for their dependency upon a rejected independent base claim.
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 1-17 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method and computer readable medium for the machine evaluation of contract terms.
Claim 1 recites [a] computer-implemented method comprising: defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract; associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document; accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets; applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document; determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables; evaluating the contract data variables and assigning a contract risk value; and when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-8 recite a method and, therefore, are directed to the statutory class of a process. Claims 19-17 recite a non-transitory computer readable medium/computer product and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A computer-implemented method comprising:
No additional elements are positively claimed.
defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract;
This limitation includes the step(s) of: defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract.
But for the computer memory, this limitation is directed to processing and/or communicating known information to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
defining in a computer memory an object model…
associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document;
This limitation includes the step(s) of: associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets;
This limitation includes the step(s) of: accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving and transmitting information) to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document;
This limitation includes the step(s) of: applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving and transmitting information) to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables;
This limitation includes the step(s) of: determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
evaluating the contract data variables and assigning a contract risk value; and
This limitation includes the step(s) of: evaluating the contract data variables and assigning a contract risk value.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device.
This limitation includes the step(s) of: when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device.
But for the mobile computing device, this limitation is directed to processing and/or communicating known information (e.g., receiving and transmitting information) to facilitate the machine evaluation of contract terms which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
… communicating an alert via email or mobile computing device
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of ‘machine evaluation of contract terms’, which, pursuant to MPEP 2106.04, is aptly categorized as a mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a processor and computer readable medium for implementing the CRM claims and processor and memory for implementing the method claims. Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: a processor and computer readable medium for implementing the CRM claims and processor and memory for implementing the method claims. These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Examiner further notes that the “offer intelligence tool” is nothing more than a software application being executed on a generic retailer computer. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data. Furthermore, Applicant’s Specification (PGPub. 2025/0124530 [0172]) refers to a general computer system, but they do not include any technically-specific computer algorithm or code.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; 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).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent CRM claim 9 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-8 and 10-17 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims defining an object model, associating objects, accessing a classifier, applying rules, determining terms, evaluating variables, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from implementing machine evaluation of contract terms. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7, 9-12, 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Young et al. 2002/0111922; in view of Batra et al. 2018/0137465; in further view of Chang et al. 2016/0140123.
19/000,629 – Claim 1. Young et al. 2002/0111922 teaches A computer-implemented method comprising: defining in a computer memory an object model containing a structural representation of events (Young et al. 2002/0111922 [0055 - FIG. 14 is a block diagram of a menu structure of the contract management system] FIG. 14 is a block diagram of a menu structure of the contract management system according to the invention; [0070 - FIG. 29 is a diagram of the typical states of contract document, object and task that occur throughout development of a contract] FIG. 29 is a diagram of the typical states of contract document, object and task that occur throughout development of a contract; [0076 - Client computers 20 may include any type of Internet-compatible communications or interface device, e.g., a personal computer (PC), a laptop computer, or a wireless device such as…] FIG. 2 is a management overview diagram of the contract management system according to the present invention. The invention can be used in any situation where interactive business processes which are facilitated through collaboration and span multiple organizations and/or departments within a single organization. The system, through a web server 14 and application server 16, communicates via the Internet 18 to a plurality of client computers 20. Client computers 20 may include any type of Internet-compatible communications or interface device, e.g., a personal computer (PC), a laptop computer, or a wireless device such as a personal digital assistant (PDA) or cellular telephone (cell phone). [0105 - events]) and artifacts through which contracts are created (Young et al. 2002/0111922 [0033 - contract management module having several functional dimensions that support the creation, collaboration, negotiation, approval, and analytics of complex contracts…] The invention disclosed herein provides a method and system to enable, streamline, and enhance the cross-organizational processes of contract management. The system includes a contract management module having several functional dimensions that support the creation, collaboration, negotiation, approval, and analytics of complex contracts typical for highly customized products or services. The contract management module preferably is a Java based, n-tier, object-orientated application that is configurable and scalable. [0049 - FIG. 8 depicts a simplified functional process for creating a contract…] FIG. 8 depicts a simplified functional process for creating a contract or master agreement;), changed, and brought to an end, the object model including contract transaction objects and contract document objects (Young et al. 2002/0111922 [0083 – transaction management][0120 - Marketplaces is to facilitate general transactions][0137 - any object in the system (e.g., the contract itself, a Delivery Order/Purchase Order, an Action Item, etc.) can be attached directly to the object] In addition to capturing the actual contractual data, contract management module 22 provides the tools for each user to collaborate with users in multiple organizations to facilitate the creation of accurate and comprehensive documents. Contract management module 22 preferably provides tools to document questions, concerns, and discussions as well as to assign and monitor Action and Issue items across multiple organizations. In addition, any supplemental information such as text documents, engineering drawings, project plans, and so on, that may be required for clarification of any object in the system (e.g., the contract itself, a Delivery Order/Purchase Order, an Action Item, etc.) can be attached directly to the object, Every object has an audit trail of the events that have occurred to the object and the versions of its attachments. As a result, many problems that exist today in the manually managed contracting process, such as version control of both electronic and hardcopies and the difficulty of collecting and accessing all relevant information, are eliminated. [0148 - attached to the specific contract object] As mentioned above, an Action Item is used to capture, assign, and monitor specific tasks that need to be completed by a specific date. For example, if User 1 from Company A is working on a Contract with User 2 from Company B, and User 2 must provide some specific data, for example, verification of the term of the contract, then User 1 can create an Action Item, that is attached to the specific contract object, which outlines what tasks must be completed and assigns this Action Item to User 2. User 2 will be notified that he/she has been assigned a new Action Item by the system. Once the tasks are complete, User 2 will close the Action Item and it will remain associated with the Contract as historical data. [0281 – stores transaction information and contract data]), wherein the contract transaction objects correspond to one or more actions associated with a contract (Young et al. 2002/0111922 [0007 - business rules, predefined events for processing object-contextual actions…][0080 - Contract management module 22 facilitates collaboration by offering: … Action and Issue Items … with automatic notification of new and modified Action Items … relevant to the user…][0137 - assign and monitor Action and Issue items] In addition to capturing the actual contractual data, contract management module 22 provides the tools for each user to collaborate with users in multiple organizations to facilitate the creation of accurate and comprehensive documents. Contract management module 22 preferably provides tools to document questions, concerns, and discussions as well as to assign and monitor Action and Issue items across multiple organizations. In addition, any supplemental information such as text documents, engineering drawings, project plans, and so on, that may be required for clarification of any object in the system (e.g., the contract itself, a Delivery Order/Purchase Order, an Action Item, etc.) can be attached directly to the object, Every object has an audit trail of the events that have occurred to the object and the versions of its attachments. As a result, many problems that exist today in the manually managed contracting process, such as version control of both electronic and hardcopies and the difficulty of collecting and accessing all relevant information, are eliminated. [0148 - User 1 can create an Action Item, that is attached to the specific contract object] As mentioned above, an Action Item is used to capture, assign, and monitor specific tasks that need to be completed by a specific date. For example, if User 1 from Company A is working on a Contract with User 2 from Company B, and User 2 must provide some specific data, for example, verification of the term of the contract, then User 1 can create an Action Item, that is attached to the specific contract object, which outlines what tasks must be completed and assigns this Action Item to User 2. User 2 will be notified that he/she has been assigned a new Action Item by the system. Once the tasks are complete, User 2 will close the Action Item and it will remain associated with the Contract as historical data. [0105; 0122; 0275]); associating one or more of the contract document objects with one or more corresponding contract transaction objects (Young et al. 2002/0111922 [0123 - contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities] A contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities or organizations in the future. When an organization requires the transaction of the goods/services referenced in the contract, a Delivery Order (DO) or Purchase Order (PO) must be created and executed to release the products/goods from the contract. Delivery Orders/Purchase Orders define in more detail the products/services that are to be delivered as well as the specific details (e.g. delivery instructions) for those products/services ordered. [0124 – Purchase order associated with the contract] Interface module 28 facilitates business messages transformation, delivery and basic mapping functions between multiple contract management module 22 systems as depicted in FIG. 31. In that figure it can be seen that one organization's intranet 278 communicates with another organization's intranet 280 via the Internet 18. According to the invention, each intranet 278,280 includes a contract management module 22 and an interface module 28. Each contract management module includes a web GUI/JSP 282 which is capable of communicating with a subscriber's web browser 284 and a database 286 that stores transaction information and contract data. Contract management module 22 further includes an outbound API 288 and an inbound API 290 for communicating with an inbound API 292 and an outbound API 294, respectively, of interface module 28. [0132 - specific data relating to the contract management process] To ensure the leveragability of contract management module 22 across all sizes and types of organizations, manual entry is available for all data fields. In addition, data that is commonly used on every contract and that remains relatively constant for an organization (e.g., user contact information, billing addresses, etc.) is maintained in a master data file. Each subscriber preferably has the ability to update and maintain this master information manually or automatically via integration with existing systems. The master data is made readily available throughout the process via drop down boxes and/or auto-population of specified fields. For organizations that utilize interface module 28 to integrate to their existing back-end systems, Enterprise Resource Planning (ERP) systems, or other applications, specific data relating to the contract management process can be automatically imported into contract management module 22. The contract management module provides the donating organization with a view of the data that is specifically relevant to the contract management process as well as the ability for the donating organization to share this information with another company or other entity without forfeiting control of the information. The owner of the information maintains control of the information throughout the entire contract management process and determines, by criteria established in the business logic of contract management module 22 at step 58, when the information is ready to be shared and collaborated on with other organizations.), wherein the one or more of the contract document objects each contain a corresponding contract document (Young et al. 2002/0111922 [0054 - contract document] FIG. 13 depicts a functional process by which approved contract information becomes accepted information for incorporation into a finalized contract document using the contract management system according to the invention; [0070 - diagram of the typical states of contract document, object and task] FIG. 29 is a diagram of the typical states of contract document, object and task that occur throughout development of a contract; [0080 - attachment of supplemental documents to any business object in the system] Collaboration is a critical component in the interactive process of contract management. Contract management module 22 facilitates collaboration by offering: role based permissions, cross-organizational Action and Issue Items (described later) with automatic notification of new and modified Action Items and Issue Items relevant to the user, attachment of supplemental documents to any business object in the system, real-time statuses of business objects, configurable business rules to govern the use of information assets, easily administrated user groups and permissions, modifiable approvals and audit functions, and automated calculation of key metrics such as cycle time. As a result, contract management module 22 fosters close customer relationships by enhancing each user's ability to make more informed decisions which, in turn, increases work accuracy and efficiency and reduces redundant activities. Complex contracts may be executed more rapidly which reduces the time to market and time to revenue. The system and its method of operation also reduces the cost of the contracting process and provides the analytics for continuous cross-organizational process improvements. [0137;0140]); accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases (Young et al. 2002/0111922 [0292; 0297 – rule sets]), the plurality of rule sets comprising document rule sets, contract transaction rule sets (Young et al. 2002/0111922 [0105 - rules][0292;0297 – rule sets]), and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets (Young et al. 2002/0111922 [0292;0297 – rule sets]); applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document (Young et al. 2002/0111922 [0083 – core business logic]); determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables (Young et al. 2002/0111922 [0123 - contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities or organizations] A contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities or organizations in the future. When an organization requires the transaction of the goods/services referenced in the contract, a Delivery Order (DO) or Purchase Order (PO) must be created and executed to release the products/goods from the contract. Delivery Orders/Purchase Orders define in more detail the products/services that are to be delivered as well as the specific details (e.g. delivery instructions) for those products/services ordered.); evaluating the contract data variables and assigning a contract risk value (Young et al. 2002/0111922 [0139 - alerts to notify managers of information such as deadlines that are in danger of being missed and significant delays caused by lack of action by a user][0210; 0211; 0253 - variables]); and when a contract risk value exceeds a threshold value, communicating an alert via email or mobile computing device (Young et al. 2002/0111922 [0139 - alerts to notify managers of information such as deadlines that are in danger of being missed and significant delays caused by lack of action by a user]).
Young et al. 2002/0111922 may not expressly disclose the “contract risk” features, however, Batra et al. 2018/0137465 teaches (Batra et al. 2018/0137465 [0036 - determining whether the risk exceeds a predetermined threshold level 672, and updating the smart contract on the blockchain when the requirements of the smart contract are supported by the event and the risk does not exceed the predetermined threshold level 674. … an alert may be created and used to alert an interested party regarding the failure and why the contract is not maintained in this event. For example, the alert may include the variable which had the highest weight, the variables which were violated…] FIG. 6C illustrates a method 660 that includes one or more of identifying a metric configuration associated with a smart contract stored in a blockchain 662, logging an event which is part of the metric configuration 664, determining a risk associated with the event 668, determining whether the risk exceeds a predetermined threshold level 672, and updating the smart contract on the blockchain when the requirements of the smart contract are supported by the event and the risk does not exceed the predetermined threshold level 674. If the risk level calculated does exceed the threshold level then the transaction may not be logged in the blockchain and instead an alert may be created and used to alert an interested party regarding the failure and why the contract is not maintained in this event. For example, the alert may include the variable which had the highest weight, the variables which were violated, etc.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Young et al. 2002/0111922 to include the features as taught by Batra et al. 2018/0137465. One of ordinary skill in the art would have been motivated to do so to implement tools and features well known to the machine evaluation of contract terms which should prove to improve user experience, maximize profits, and optimize revenue.
Young et al. 2002/0111922 may not expressly disclose the “accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts” features, however, Chang et al. 2016/0140123 teaches (Chang et al. 2016/0140123 [0018 - receiving a natural language input … input can be processed to detect a sentence, identify words in the sentence, and tag the words with corresponding part of speech types … techniques can implement a machine learning classifier to predict, based on a pattern formed by the groupings, that one clause is associated with particular groupings while another clause is associated with other groupings …] The embodied techniques include receiving a natural language input, such as an utterance searching for information. This input can be processed to detect a sentence, identify words in the sentence, and tag the words with corresponding part of speech types (e.g., to indicate whether a word is a noun, verb, adjective, etc.). The techniques further include generating groupings from the part of speech types. For example, each grouping can group one or more tags (e.g., noun tags, verb tags, adjective tags, etc.) identifying the part of speech type(s) of the corresponding words. Rather than analyzing the words to determine what word belongs to what clause of the query, the techniques involve using the groupings to do so. For example, the techniques can implement a machine learning classifier to predict, based on a pattern formed by the groupings, that one clause is associated with particular groupings while another clause is associated with other groupings. Once the prediction is available, words can then be added accordingly to the clauses to generate the query. For example, if the prediction indicates that a noun grouping (e.g., a grouping containing a noun tag corresponding to a noun) belongs to a select clause of an SQL query, the noun corresponding to that noun grouping can be added to the select clause. In comparison, if the prediction was for a where clause, the noun would be added to the where clause instead.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Young et al. 2002/0111922 to include the features as taught by Chang et al. 2016/0140123. One of ordinary skill in the art would have been motivated to do so to implement tools and features well known to the machine evaluation of contract terms which should prove to improve user experience, maximize profits, and optimize revenue.
19/000,629 – Claim 9. Young et al. 2002/0111922 further teaches One or more non-transitory computer-readable storage media (Young et al. 2002/0111922 [0077 - storage][0114 - communications network medium]) storing one or more sequences of instructions which (Young et al. 2002/0111922 [0105 – processing instructions]), when executed by one or more processors, cause of the one or more processors to execute (Young et al. 2002/0111922 [0009 - business logic is downloaded to the clients for execution][0076 – wireless device]): …. communicating an alert via email or text message (Young et al. 2002/0111922 [0108 – an email may be sent to a set of users][0285 - as broadcast messages and cross organizational alerts]).
Young et al. 2002/0111922 may not expressly disclose the “non-transitory computer-readable storage media” features, however, Batra et al. 2018/0137465 teaches (Batra et al. 2018/0137465 [0040 - computer readable medium may be a non-transitory computer readable medium that includes tangible hardware components, such as memory, that can store software] As illustrated in FIG. 7, a memory 710 and a processor 720 may be discrete components of a network entity 700 that are used to execute an application or set of operations as described herein. The application may be coded in software in a computer language understood by the processor 720, and stored in a computer readable medium, such as, a memory 710. The computer readable medium may be a non-transitory computer readable medium that includes tangible hardware components, such as memory, that can store software. Furthermore, a software module 730 may be another discrete entity that is part of the network entity 700, and which contains software instructions that may be executed by the processor 720 to effectuate one or more of the functions described herein. In addition to the above noted components of the network entity 700, the network entity 700 may also have a transmitter and receiver pair configured to receive and transmit communication signals (not shown).). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Young et al. 2002/0111922 to include the features as taught by Batra et al. 2018/0137465. One of ordinary skill in the art would have been motivated to do so to implement tools and features well known to the machine evaluation of contract terms which should prove to improve user experience, maximize profits, and optimize revenue.
defining in a computer memory an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end, the object model including contract transaction objects and contract document objects, wherein the contract transaction objects correspond to one or more actions associated with a contract; associating one or more of the contract document objects with one or more corresponding contract transaction objects, wherein the one or more of the contract document objects each contain a corresponding contract document; accessing a machine learning classifier comprising a plurality of rule sets that classify natural language words and clauses of contracts, the plurality of rule sets having been formed via training a machine learning algorithm on contract documents, contract clauses, contract sentences and contract phrases, the plurality of rule sets comprising document rule sets, contract transaction rule sets, and clause classification rule sets, the machine learning classifier further comprising one or more of industry-specific rule sets or customer-specific rule sets; applying the plurality of rule sets to one or more words of each corresponding contract document to identify one or more core attributes of the one or more words pertaining to details of the corresponding contract document; determining prevailing terms of each corresponding contract document by evaluating one or more child contract transaction objects to build a single set of contract data variables; evaluating the contract data variables and assigning a contract risk value; and when a contract risk value exceeds a threshold value, communicating an alert via email or text message.
The remaining features/limitations of Claim 9, has similar features/limitations as of Claim 1, therefore those features/limitations and the claims are REJECTED under the same rationale as Claim 1.
19/000,629 – Claim 2. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 1, wherein the one or more core attributes comprise one or more of legal classifications subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies (Young et al. 2002/0111922 [0123 – contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities or organizations…][0291 - within the context of the contract management application…]).
19/000,629 – Claim 10. The one or more non-transitory computer-readable storage media of claim 9, wherein the one or more core attributes comprise one or more of legal classifications, subject classifications, party directions, timing contingencies, conditionalities, or contextual dependencies.
Claim 10, has similar limitations as of Claim 2, therefore it is REJECTED under the same rationale as Claim 2.
19/000,629 – Claim 3. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 1, wherein the object model further includes contract objects (Young et al. 2002/0111922 [0151 - contract-related objects…] FIG. 16 displays a standard list page or screen 140 for easy access and availability of several objects of the same type, e.g., contracts. A similar type of screen may be made available for delivery order lists, purchase order lists or other contract-related objects that may be associated with individual contracts being tracked by the system. Each screen 120 (FIG. 15), 140 (FIG. 16) and 160 (FIG. 17) preferably includes a context sensitive menu, usually at or near the top of the screen that indicates where the list of objects is located within the hierarchy of the system. [0234 - Model object] If the form is being "updated", an update( ) method subclass of BaseDataForm is called, the appropriate Model object is created and filled with data from the form. A Wrapper is then created for the Model and the Wrapper create( ) method is called.) associated with one or more of the contract transaction objects (Young et al. 2002/0111922 [0143 - contracts and associated business objects] The Collaboration Menu 82 groups together the functionality specifically related to contract management and collaboration. A contract list area 96 provides a comprehensive list of all contracts in the system that the specific user has permission to view. A contract line item number (CLIN) list area 98 allows users to create, read, update, and edit specific contract line items. A collaboration report area 100 provides a relational report of contracts and associated business objects. [0298]), wherein associating the contract objects with one or more of the contract transaction objects (Young et al. 2002/0111922 [0070 - contract document, object and task…] FIG. 29 is a diagram of the typical states of contract document, object and task that occur throughout development of a contract;) comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type (Young et al. 2002/0111922 [0160 - TYPE field] Since the BASE_OBJECT table is linked to all other tables, it is the place in which fields common to all objects (created_date, created_by_id, etc.) are placed. It also contains a TYPE field which helps identify which other table in the system contains the corresponding row of data. In object-oriented terms, the BASE_OBJECT table acts as a super class for the other tables in the system.) of at least one of the contract transaction objects (Young et al. 2002/0111922 [0151; 0161 – object types]) is one of: Create Transaction (Young et al. 2002/0111922 [0049 - creating a contract or master agreement] FIG. 8 depicts a simplified functional process for creating a contract or master agreement;), Terminate Transaction (Young et al. 2002/0111922 [0215 - Cancel]), Amend Transaction, Order Transaction (Young et al. 2002/0111922 [0123-128 – Deliver Order and Purchase Order interpreted as Order Transaction]), Renew Transaction, Assign Transaction (Young et al. 2002/0111922 [0148 - assign]), or Novate Transaction.
19/000,629 – Claim 11. Young et al. 2002/0111922 further teaches The one or more non-transitory computer-readable storage media (Young et al. 2002/0111922 [0077 - storage][0114 - communications network medium]) of claim 9, wherein the object model further includes contract objects associated with one or more of the contract transaction objects (Young et al. 2002/0111922 [0123 - contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities] A contract represents a master agreement that establishes the terms, conditions, total value and/or quantity of goods and/or services that will be transacted between entities or organizations in the future. When an organization requires the transaction of the goods/services referenced in the contract, a Delivery Order (DO) or Purchase Order (PO) must be created and executed to release the products/goods from the contract. Delivery Orders/Purchase Orders define in more detail the products/services that are to be delivered as well as the specific details (e.g. delivery instructions) for those products/services ordered. [0124 – Purchase order associated with the contract] Interface module 28 facilitates business messages transformation, delivery and basic mapping functions between multiple contract management module 22 systems as depicted in FIG. 31. In that figure it can be seen that one organization's intranet 278 communicates with another organization's intranet 280 via the Internet 18. According to the invention, each intranet 278,280 includes a contract management module 22 and an interface module 28. Each contract management module includes a web GUI/JSP 282 which is capable of communicating with a subscriber's web browser 284 and a database 286 that stores transaction information and contract data. Contract management module 22 further includes an outbound API 288 and an inbound API 290 for communicating with an inbound API 292 and an outbound API 294, respectively, of interface module 28. [0132 - specific data relating to the contract management process] To ensure the leveragability of contract management module 22 across all sizes and types of organizations, manual entry is available for all data fields. In addition, data that is commonly used on every contract and that remains relatively constant for an organization (e.g., user contact information, billing addresses, etc.) is maintained in a master data file. Each subscriber preferably has the ability to update and maintain this master information manually or automatically via integration with existing systems. The master data is made readily available throughout the process via drop down boxes and/or auto-population of specified fields. For organizations that utilize interface module 28 to integrate to their existing back-end systems, Enterprise Resource Planning (ERP) systems, or other applications, specific data relating to the contract management process can be automatically imported into contract management module 22. The contract management module provides the donating organization with a view of the data that is specifically relevant to the contract management process as well as the ability for the donating organization to share this information with another company or other entity without forfeiting control of the information. The owner of the information maintains control of the information throughout the entire contract management process and determines, by criteria established in the business logic of contract management module 22 at step 58, when the information is ready to be shared and collaborated on with other organizations.), further comprising sequences of instructions which (Young et al. 2002/0111922 [0105 – processing instructions]), when executed using the one or more processors (Young et al. 2002/0111922 [0009 - business logic is downloaded to the clients for execution][0076 – wireless device]), cause the one or more processors to execute …
associating the contract objects with one or more of the contract transaction objects comprises associating the contract objects with one or more contract transaction objects corresponding to the one or more actions associated with the contract, wherein a type of at least one of the contract transaction objects is one of: Create Transaction, Terminate Transaction, Amend Transaction, Order Transaction, Renew Transaction, Assign Transaction, or Novate Transaction.
Young et al. 2002/0111922 may not expressly disclose the “non-transitory computer-readable storage media” features, however, Batra et al. 2018/0137465 teaches (Batra et al. 2018/0137465 [0040 - computer readable medium may be a non-transitory computer readable medium that includes tangible hardware components, such as memory, that can store software] As illustrated in FIG. 7, a memory 710 and a processor 720 may be discrete components of a network entity 700 that are used to execute an application or set of operations as described herein. The application may be coded in software in a computer language understood by the processor 720, and stored in a computer readable medium, such as, a memory 710. The computer readable medium may be a non-transitory computer readable medium that includes tangible hardware components, such as memory, that can store software. Furthermore, a software module 730 may be another discrete entity that is part of the network entity 700, and which contains software instructions that may be executed by the processor 720 to effectuate one or more of the functions described herein. In addition to the above noted components of the network entity 700, the network entity 700 may also have a transmitter and receiver pair configured to receive and transmit communication signals (not shown).). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Young et al. 2002/0111922 to include the features as taught by Batra et al. 2018/0137465. One of ordinary skill in the art would have been motivated to do so to implement tools and features well known to the machine evaluation of contract terms which should prove to improve user experience, maximize profits, and optimize revenue.
The remaining features/limitations of Claim 11, has similar features/limitations as of Claim 3, therefore those features/limitations and the claims are REJECTED under the same rationale as Claim 3.
19/000,629 – Claim 4. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 3, wherein the object model further includes project objects, user objects (Young et al. 2002/0111922 [0080]), group objects, workflow objects (Young et al. 2002/0111922 [0034 - workflow]), organization objects (Young et al. 2002/0111922 [0118; 0137]), legal entity objects, and product objects (Young et al. 2002/0111922 [0174 – user objects][0232; 0234; 0267 – Model Objects]).
19/000,629 – Claim 12. The one or more non-transitory computer-readable storage media of claim 9, wherein the object model further includes project objects, user objects, group objects, workflow objects, organization objects, legal entity objects, and product objects.
Claim 12, has similar limitations as of Claim 4, therefore it is REJECTED under the same rationale as Claim 4.
19/000,629 – Claim 7. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 3, further comprising storing the prevailing terms (Young et al. 2002/0111922 [0123; 0148 - terms]) of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format (Young et al. 2002/0111922 [0081; 0091; 0103; 0257; 0277 – XML format]).
19/000,629 – Claim 16. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute storing the prevailing terms of each corresponding contract document in the contract objects associated with each corresponding contract document in any of an XML format, JSON format, or triple store format.
Claim 16, has similar limitations as of Claim 7, therefore it is REJECTED under the same rationale as Claim 7.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Young et al. 2002/0111922; in view of Batra et al. 2018/0137465; in further view of Chang et al. 2016/0140123; in view of Hieber et al. 2015/0106076.
19/000,629 – Claim 6. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 1, wherein applying the plurality of rule sets (Young et al. 2002/0111922 [0080; 0105; 0115 - rules][0292;0297 – rule sets]) to one or more words of each corresponding contract document comprises evaluating sentence pairs of each corresponding contract document (Young et al. 2002/0111922 [0054; 0140 – contract document]).
Young et al. 2002/0111922 may not expressly disclose the “evaluating sentence pairs” features, however, Hieber et al. 2015/0106076 teaches (Hieber et al. 2015/0106076 [0019 – sentence pairs are evaluated…] These sentence pairs are evaluated by human translators who may edit or change the target sentence unit of a sentence pair to correct errors in the machine translation. These changes are referred to as post-edits of target sentence units.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Young et al. 2002/0111922 to include the features as taught by Hieber et al. 2015/0106076. One of ordinary skill in the art would have been motivated to do so to implement tools and features well known to the machine evaluation of contract terms which should prove to improve user experience, maximize profits, and optimize revenue.
19/000,629 – Claim 15. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute applying the plurality of rule sets to one or more words of each corresponding contract document by evaluating sentence pairs of each corresponding contract document.
Claim 15, has similar limitations as of Claim 6, therefore it is REJECTED under the same rationale as Claim 6.
No Prior-art Rejection / Potentially Allowable
Claims 5, 8, 13, 14, 17 cannot be rejected with prior-art. Individual claimed features are taught in the prior-art, however, the unique combination of features and elements are not taught by the prior-art without hindsight reasoning. These claims are further rejected to as being dependent upon a rejected base claim but might possibly be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
19/000,629 – Claim 5. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 1, further comprising transforming (Young et al. 2002/0111922 [0281 - transformation]) each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, assigning a confidence score to the legal classification, and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification (Young et al. 2002/0111922 [0148 - verification]) comprising receiving a correction and feeding the correction to the machine learning algorithm, wherein the legal classification comprises one of an obligation, a right, a representation (Young et al. 2002/0111922 [0094 - representation]), an act, or a definition (Young et al. 2002/0111922 [0007; 0078 - definitions]).
19/000,629 – Claim 8. Young et al. 2002/0111922 further teaches The computer-implemented method of claim 1, further comprising: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets (Young et al. 2002/0111922 ).
19/000,629 – Claim 13. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute transforming each corresponding contract document into a collection of single sentences, assigning a legal classification to each of the single sentences, and assigning a confidence score to the legal classification; and presenting each of the single sentences with the confidence score below a predetermined threshold for verification, the verification comprising receiving a correction and feeding the correction to the machine learning algorithm.
19/000,629 – Claim 14. The one or more non-transitory computer-readable storage media of claim 13, wherein the legal classification comprises one of an obligation, a right, a representation, an act, and a definition.
19/000,629 – Claim 17. The one or more non-transitory computer-readable storage media of claim 9, further comprising sequences of instructions which, when executed using the one or more processors, cause the one or more processors to execute: using optical character recognition to convert a plurality of digitally stored contract documents to text and processing the contract documents after conversion using a trained ML/AI algorithm to pre-classify the contract documents into contract transaction types with confidence scores, and storing the documents in a training corpus; parsing the documents of the training corpus to transform into single sentences, using trained ML/AI models to classify the single sentences using legal classifiers with confidence scores, and storing the single sentences with the legal classifiers and confidence scores in a clause corpus; refining one or more clause classification models by passing the sentences of the clause corpus through one or more neural networks; training one or more machine learning models using the clause corpus as a training dataset to yield a plurality of universal contract model rules; deploying the plurality of universal contract model rules, after the training, with industry-specific rules and customer-specific rules, as the plurality of rule sets.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Schlachter et al. 2017/0228590 [0005 - a computer program product for identifying information in a document … scan a document using optical character recognition; analyze the scanned document … apply a rule based analysis on the text block to identify a label and a field containing a value …]
Fahey 2009/0012834 [0058 - Document attributes include both core attributes and custom attributes]
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
R. Pandita, X. Xiao, H. Zhong, T. Xie, S. Oney and A. Paradkar, "Inferring method specifications from natural language API descriptions," 2012 34th International Conference on Software Engineering (ICSE), Zurich, Switzerland, 2012, pp. 815-825, doi: 10.1109/ICSE.2012.6227137.
THIS ACTION IS MADE FINAL
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW T. SITTNER whose telephone number is (571) 270-7137 and email: matthew.sittner@uspto.gov. The examiner can normally be reached on Monday-Friday, 8:00am - 5:00pm (Mountain Time Zone). Please schedule interview requests via email: matthew.sittner@uspto.gov
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b