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 .
Response to Amendment
The Amendment filed 04/22/2026 has been entered. Claims 1-20 remain pending in the application.
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.
Claims 1-3, 5-6, 8-10, 12-13, 15-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bonat et al. (US 12045843 B2 hereinafter Bonat) in view of Peng (US 11271754 B2) and Hudson et al. (US 20210019667 A1 hereinafter Hudson)
As to independent claim 1, Bonat teaches a system comprising one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more processors and including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: [software, medium and processors Col. 14 ln. 44-65]
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
Peng and Bonat do not specifically teach wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters.
However, Hudson teaches wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and [training data with legal documents such as contracts with attributes (labels, polarity, ) ¶59 " The labelled public data store 24 is for storing data that is labelled with labels that are recognizable or interpretable by all of the computers 20a,20b,20c,20d in such a way that it can be made public without losing the confidentiality of the document it represents. Thus, the data is normalized. The private label store is for storing data that is labelled with labels that are only recognizable or interpretable by the specific computer of the computers 20a,20b,20c,20d that generate the private labels. Interpretable or recognizable means that the meaning of the label is directly understood. As part of the normalization, each of the client computers 20a,20b,20c,20d also includes a polarity token generator. This is an important part of embodiments of the present invention. The polarity token generator is implemented in software in each client computer. The polarity token generator automatically generates and applies a polarity token to the labelled public data and the labelled private data. A polarity token or reference indicates the obligations of the parties to a legal contract being processed. In other words, whether a reference to a party in the contract is made with respect to an obligor (own party or customer), obligee (counterparty or supplier) or whether it is reciprocal (applies to all of the parties)"]
training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters. [trains with attributes (labels) and recognizes patterns ¶59, ¶61, ¶65 " exploit observed semantic patterns in polarity references of the data."…"models to provide an indication of risk of one or more parties to a contract or a draft contract"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat and Peng by incorporating the wherein the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters disclosed by Hudson because all techniques address the same field of tracking data and by incorporating Hudson into Bonat and Peng enables more logical and consistent decisions based on patterns with low processing requirements [Hudson ¶5, ¶8]
As to dependent claim 2, the rejection of claim 1 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 3, the rejection of claim 1 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 5, the rejection of claim 1 is incorporated Bonat, Peng and Hudson further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 6, the rejection of claim 1 is incorporated Bonat, Peng and Hudson further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
As to independent claim 8, Bonat teaches a method, comprising:
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
Peng and Bonat do not specifically teach wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters.
However, Hudson teaches wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and [training data with legal documents such as contracts with attributes (labels, polarity, ) ¶59 " The labelled public data store 24 is for storing data that is labelled with labels that are recognizable or interpretable by all of the computers 20a,20b,20c,20d in such a way that it can be made public without losing the confidentiality of the document it represents. Thus, the data is normalized. The private label store is for storing data that is labelled with labels that are only recognizable or interpretable by the specific computer of the computers 20a,20b,20c,20d that generate the private labels. Interpretable or recognizable means that the meaning of the label is directly understood. As part of the normalization, each of the client computers 20a,20b,20c,20d also includes a polarity token generator. This is an important part of embodiments of the present invention. The polarity token generator is implemented in software in each client computer. The polarity token generator automatically generates and applies a polarity token to the labelled public data and the labelled private data. A polarity token or reference indicates the obligations of the parties to a legal contract being processed. In other words, whether a reference to a party in the contract is made with respect to an obligor (own party or customer), obligee (counterparty or supplier) or whether it is reciprocal (applies to all of the parties)"]
training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters. [trains with attributes (labels) and recognizes patterns ¶59, ¶61, ¶65 " exploit observed semantic patterns in polarity references of the data."…"models to provide an indication of risk of one or more parties to a contract or a draft contract"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat and Peng by incorporating the wherein the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters disclosed by Hudson because all techniques address the same field of tracking data and by incorporating Hudson into Bonat and Peng enables more logical and consistent decisions based on patterns with low processing requirements [Hudson ¶5, ¶8]
As to dependent claim 9, the rejection of claim 8 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 10, the rejection of claim 8 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 12, the rejection of claim 8 is incorporated Bonat, Peng and Hudson further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 13, the rejection of claim 8 is incorporated Bonat, Peng and Hudson further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
As to independent claim 15, Bonat teaches one or more computer-readable non-transitory storage media embodying instructions that, when executed by a processor, cause the processor to perform operations comprising: [software, medium and processors Col. 14 ln. 44-65]
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
Peng and Bonat do not specifically teach wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters.
However, Hudson teaches wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and [training data with legal documents such as contracts with attributes (labels, polarity, ) ¶59 " The labelled public data store 24 is for storing data that is labelled with labels that are recognizable or interpretable by all of the computers 20a,20b,20c,20d in such a way that it can be made public without losing the confidentiality of the document it represents. Thus, the data is normalized. The private label store is for storing data that is labelled with labels that are only recognizable or interpretable by the specific computer of the computers 20a,20b,20c,20d that generate the private labels. Interpretable or recognizable means that the meaning of the label is directly understood. As part of the normalization, each of the client computers 20a,20b,20c,20d also includes a polarity token generator. This is an important part of embodiments of the present invention. The polarity token generator is implemented in software in each client computer. The polarity token generator automatically generates and applies a polarity token to the labelled public data and the labelled private data. A polarity token or reference indicates the obligations of the parties to a legal contract being processed. In other words, whether a reference to a party in the contract is made with respect to an obligor (own party or customer), obligee (counterparty or supplier) or whether it is reciprocal (applies to all of the parties)"]
training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters. [trains with attributes (labels) and recognizes patterns ¶59, ¶61, ¶65 " exploit observed semantic patterns in polarity references of the data."…"models to provide an indication of risk of one or more parties to a contract or a draft contract"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat and Peng by incorporating the wherein the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters disclosed by Hudson because all techniques address the same field of tracking data and by incorporating Hudson into Bonat and Peng enables more logical and consistent decisions based on patterns with low processing requirements [Hudson ¶5, ¶8]
As to dependent claim 16, the rejection of claim 15 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 17, the rejection of claim 15 is incorporated Bonat, Peng and Hudson further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 19, the rejection of claim 15 is incorporated Bonat, Peng and Hudson further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 20, the rejection of claim 15 is incorporated Bonat, Peng and Hudson further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bonat in view of Peng and Hudson as applied to the rejection of claim 1 and 8 above, and further in view of Jia et al. (US 20120185493 A1 hereinafter Jia)
As to dependent claim 4, the combination of Bonat, Peng and Hudson teach all the limitations of claim 1 that is incorporated.
Bonat, Peng and Hudson do not specifically teach wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value.
However, Jia teaches wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; [data objects that conform to model and elements (attributes) ¶4 " receiving a data object that conforms to a second data model. The method then selects one or more of the mapping rules. The mapping rules provide a mapping between a set of elements of the second data model and a corresponding set of elements of the first data model. The method then applies the selected mapping rules to transform a set of elements of the received data object into a corresponding set of elements of a target data object conforming to the first data model"]
each of the descriptive attributes is associated with a variable name, a type, and a value [name, type and value for a rule ¶27 "rule templates may be provided for mapping changes in attribute name, type, and value respectively"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat, Peng and Hudson by incorporating the wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value disclosed by Jia because all techniques address the same field of tracking data and by incorporating Jia into Bonat, Peng and Hudson more effectively identify duplicates in data and improve versioning [Jia ¶2]
As to dependent claim 11, the combination of Bonat, Peng and Hudson teach all the limitations of claim 8 that is incorporated.
Bonat, Peng and Hudson do not specifically teach wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value.
However, Jia teaches wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; [data objects that conform to model and elements (attributes) ¶4 " receiving a data object that conforms to a second data model. The method then selects one or more of the mapping rules. The mapping rules provide a mapping between a set of elements of the second data model and a corresponding set of elements of the first data model. The method then applies the selected mapping rules to transform a set of elements of the received data object into a corresponding set of elements of a target data object conforming to the first data model"]
each of the descriptive attributes is associated with a variable name, a type, and a value [name, type and value for a rule ¶27 "rule templates may be provided for mapping changes in attribute name, type, and value respectively"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat, Peng and Hudson by incorporating the wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value disclosed by Jia because all techniques address the same field of tracking data and by incorporating Jia into Bonat, Peng and Hudson more effectively identify duplicates in data and improve versioning [Jia ¶2]
As to dependent claim 18, the combination of Bonat, Peng and Hudson teach all the limitations of claim 15 that is incorporated.
Bonat, Peng and Hudson do not specifically teach wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value.
However, Jia teaches wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; [data objects that conform to model and elements (attributes) ¶4 " receiving a data object that conforms to a second data model. The method then selects one or more of the mapping rules. The mapping rules provide a mapping between a set of elements of the second data model and a corresponding set of elements of the first data model. The method then applies the selected mapping rules to transform a set of elements of the received data object into a corresponding set of elements of a target data object conforming to the first data model"]
each of the descriptive attributes is associated with a variable name, a type, and a value [name, type and value for a rule ¶27 "rule templates may be provided for mapping changes in attribute name, type, and value respectively"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat, Peng and Hudson by incorporating the wherein: each of the descriptive attributes represents a characteristic that defines a data object of the data model; each of the descriptive attributes is associated with a variable name, a type, and a value disclosed by Jia because all techniques address the same field of tracking data and by incorporating Jia into Bonat, Peng and Hudson more effectively identify duplicates in data and improve versioning [Jia ¶2]
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bonat in view of Peng and Hudson as applied to the rejection of claim 1 and 8 above, and further in view of Cella et al. (US 20220198562 A1 hereinafter Cella)
As to dependent claim 7, the combination of Bonat, Peng and Hudson teach all the limitations of claim 1 that is incorporated.
Bonat, Peng and Hudson do not specifically teach wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document.
However, Cella teaches wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document. [digital ledger with smart contracts including government agreement ¶915, licensing and copyrights ¶918, smart will, estate ¶1832-1833 employment ¶1847, purchases and leases ¶1039]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat, Peng and Hudson by incorporating the wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document disclosed by Cella because all techniques address the same field of tracking data and by incorporating Cella into Bonat, Peng and Hudson provides smarter contracts for improved controls of rights facilitating audits and verification [Cella ¶29]
As to dependent claim 14, the combination of Bonat, Peng and Hudson teach all the limitations of claim 8 that is incorporated.
Bonat, Peng and Hudson do not specifically teach wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document.
However, Cella teaches wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document. [digital ledger with smart contracts including government agreement ¶915, licensing and copyrights ¶918, smart will, estate ¶1832-1833 employment ¶1847, purchases and leases ¶1039]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat, Peng and Hudson by incorporating the wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document disclosed by Cella because all techniques address the same field of tracking data and by incorporating Cella into Bonat, Peng and Hudson provides smarter contracts for improved controls of rights facilitating audits and verification [Cella ¶29]
Response to Arguments
Applicant's arguments filed 04/22/2026, with respect to 101, these rejections have been withdrawn.
Applicant's arguments filed 04/22/2026. In the remark, applicant argues that:
Bonat and Peng fail to teach "wherein: the training dataset comprises descriptive attributes obtained from a data model; and the descriptive attributes are associated with legal parameters of a legal contract; and training a machine learning model using the descriptive attributes to recognize patterns between the legal parameters." See Bonat (col. 7 ln. 12-17)
As to point (1) applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection made under rejected under 35 U.S.C. 103 as being unpatentable over Bonat view of Peng and Hudson as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Muffat et al. (US 20200250139 A1) teaches feature extraction with PII consideration in data with legal data (see ¶69)
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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