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
The present application is being examined under the claims filed on 12/5/2023.
Claims 1-20 are rejected.
Claims 1-20 are pending.
Specification
The specification filed on 12/5/2023 is acceptable for examination purposes.
Drawings
The drawings filed on 12/5/2023 are acceptable for examination purposes.
Claim Objections
Claim 12 is objected to because of the following informalities:
In claim 12, line 3, “on each of the entity identifies” should read “on each of the entity identities.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Step 1: Claim 1 is a method claim. Therefore, Claims 1-18 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship (mental process - processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship may be performed manually by a user with the aid of pen and paper by observing/analyzing an entity, a relationship between the entity and another entity, and attributes of the relationship and using judgement/evaluation to identify the data elements features. See MPEP 2106.04(a)(2)(III)(C).)
compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity (mental process - compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity may be performed manually by a user with the aid of pen and paper by observing/analyzing the entity, relationship and attributes of the relationship in the data elements and using judgement/evaluation to compile a state data structure in the form of a graph data structure. See MPEP 2106.04(a)(2)(III)(C).)
evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system, [wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions] (mental process - evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system may be performed manually by a user with the aid of pen and paper by observing/analyzing occurrence of a condition defined in a rules-based system by a model and judging/evaluating the state data structure. See MPEP 2106.04(a)(2)(III)(C).)
wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition (mental process - wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition may be performed manually by a user with the aid of pen and paper by observing/receiving further data elements continually or periodically and judging/evaluating the further data elements against the state data structure for occurrence of the condition. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
receiving, from a data source, data elements extracted from a record (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
receiving, from a data source, data elements extracted from a record (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-18. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2,
Step 2A Prong 1:
wherein evaluating the state data structure includes using node and node path similarity algorithms to determine the distance between embedded node-paths relating to an entity in the fact graph database and embedded node-paths in the rules graph database for entities of that particular entity type (mental process - wherein evaluating the state data structure includes using node and node path similarity algorithms to determine the distance between embedded node-paths relating to an entity in the fact graph database and embedded node-paths in the rules graph database for entities of that particular entity type may be performed manually by a user with the aid of pen and paper by observing/analyzing the node and node path similarity algorithms and using judgement/evaluation to determine the distance between embedded node-paths relating to an entity in the fact graph database and embedded node-paths in the rules graph database. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the state data structure is in the form of a graph data structure including a fact graph database and a rules graph database (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the state data structure is in the form of a graph data structure including a fact graph database and a rules graph database (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 3,
Step 2A Prong 1:
wherein identifying features in the data elements includes recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms (mental process - wherein identifying features in the data elements includes recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms may be performed manually by a user with the aid of pen and paper by observing/analyzing the data elements and using judgement/evaluation to classify and/or label the data elements. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 4,
Step 2A Prong 1:
See the rejection of Claim 3 above, which Claim 4 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the one or more entity recognition algorithms include one or both of: conditional random fields; and, hybrid bi-directional long short-term memory / convolutional neural networks (LSTM–CNN) (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the one or more entity recognition algorithms include one or both of: conditional random fields; and, hybrid bi-directional long short-term memory / convolutional neural networks (LSTM–CNN) (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 5,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein identifying features in the data elements includes using one or more classifiers (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein identifying features in the data elements includes using one or more classifiers (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 6,
Step 2A Prong 1:
See the rejection of Claim 5 above, which Claim 6 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein identifying features in the data elements includes using one or more of: an entity-type classifier; an entity-relationship classifier; and, an entity-role, rights and/or obligations classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein identifying features in the data elements includes using one or more of: an entity-type classifier; an entity-relationship classifier; and, an entity-role, rights and/or obligations classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 7,
Step 2A Prong 1:
wherein the attributes of the relationship include one or more of: an entity role in the relationship; an entity obligation in the relationship; and/or an entity right in the relationship (mental process - wherein the attributes of the relationship include one or more of: an entity role in the relationship; an entity obligation in the relationship; and/or an entity right in the relationship may be performed manually by a user with the aid of pen and paper by observing/including an entity role in the relationship, an entity obligation in the relationship and/or an entity right in the relationship. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 8,
Step 2A Prong 1:
wherein the model is an entity-relational model for a rules-based system in which ontological elements include one or more of “entities”, “relationships”, “actions” and “events” (mental process – wherein the model is an entity-relational model for a rules-based system in which ontological elements include one or more of “entities”, “relationships”, “actions” and “events” may be performed manually by a user with the aid of pen and paper by observing/including one or more of entities, relationships, actions and events. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 9,
Step 2A Prong 1:
wherein the condition is a threshold against which data elements within the state data structure are evaluated to determine when the threshold is met (mental process – wherein the condition is a threshold against which data elements within the state data structure are evaluated to determine when the threshold is met may be performed manually by a user with the aid of pen and paper by observing/evaluating data elements against a predetermined threshold. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 10,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 10 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the rules-based system is an entity relational rules-based system, wherein the entity relational rules-based system is a legal system, and wherein the condition is a cause of action arising in the relationship between the entity and another entity (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the rules-based system is an entity relational rules-based system, wherein the entity relational rules-based system is a legal system, and wherein the condition is a cause of action arising in the relationship between the entity and another entity (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 11,
Step 2A Prong 1:
wherein the data elements include or represent natural language phrases extracted from a natural language record (mental process – wherein the data elements include or represent natural language phrases extracted from a natural language record may be performed manually by a user with the aid of pen and paper by observing/extracting natural language phrases from a natural language record. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 12,
Step 2A Prong 1:
wherein processing the data elements includes assigning pseudonymized identifiers to entity identities identified in the data elements by performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier (mathematical concept - wherein processing the data elements includes assigning pseudonymized identifiers to entity identities identified in the data elements by performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier may be performed by mathematical process, performing a cryptographic operation on each of the entity identifiers and generating a corresponding pseudonymized identifier. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: specification, page 13, lines 4-6, “Assigning pseudonymized identifiers may be by using a deterministic pseudonymization algorithm (e.g., a cryptographic or encryption algorithm) […]”)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 13,
Step 2A Prong 1:
wherein assigning pseudonymized identifiers to entity identities identified in the data elements includes creating a pseudonymized value register of all extracted entities by performing the cryptographic operation on an entity item at the information point at which the entity item has been recognized or extracted (mathematical concept - wherein assigning pseudonymized identifiers to entity identities identified in the data elements includes creating a pseudonymized value register of all extracted entities by performing the cryptographic operation on an entity item at the information point at which the entity item has been recognized or extracted may be performed by mathematical process, creating a pseudonymized value register of all extracted entities by performing the cryptographic operation on an entity item at which the entity item has been recognized or extracted. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: specification, page 13, lines 4-6, “Assigning pseudonymized identifiers may be by using a deterministic pseudonymization algorithm (e.g., a cryptographic or encryption algorithm) […]”)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 14,
Step 2A Prong 1:
See the rejection of Claim 12 above, which Claim 14 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein assigning pseudonymized identifiers includes transmitting the pseudonymized identifiers to an entity register for co-referencing standardization (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein assigning pseudonymized identifiers includes transmitting the pseudonymized identifiers to an entity register for co-referencing standardization (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 15,
Step 2A Prong 1:
See the rejection of Claim 14 above, which Claim 15 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein transmitting pseudonymized entity identifiers includes transmitting a standardized name to the information point from which the entity item was extracted (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein transmitting pseudonymized entity identifiers includes transmitting a standardized name to the information point from which the entity item was extracted (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 16,
Step 2A Prong 1:
See the rejection of Claim 15 above, which Claim 16 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
including recording pseudonymized entity related information at one or more data locations in a federated database system (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
including recording pseudonymized entity related information at one or more data locations in a federated database system (MPEP 2106.05(d)(II) indicates that merely “Recording a customer’s order” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 17,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 17 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the alert is transmitted to and output via a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the alert is transmitted to and output via a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 18,
Step 2A Prong 1:
See the rejection of Claim 17 above, which Claim 18 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the alert includes a confidence or proximity score associated with the identification (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the alert includes a confidence or proximity score associated with the identification (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 19,
Step 1: Claim 19 is a system claim. Therefore, Claim 19 is directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship (mental process - processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship may be performed manually by a user with the aid of pen and paper by observing/analyzing an entity, a relationship between the entity and another entity, and attributes of the relationship and using judgement/evaluation to identify the data elements features. See MPEP 2106.04(a)(2)(III)(C).)
compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity (mental process - compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity may be performed manually by a user with the aid of pen and paper by observing/analyzing the entity, relationship and attributes of the relationship in the data elements and using judgement/evaluation to compile a state data structure in the form of a graph data structure. See MPEP 2106.04(a)(2)(III)(C).)
evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system (mental process - evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system may be performed manually by a user with the aid of pen and paper by observing/analyzing occurrence of a condition defined in a rules-based system by a model and judging/evaluating the state data structure. See MPEP 2106.04(a)(2)(III)(C).)
wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition (mental process - wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition may be performed manually by a user with the aid of pen and paper by observing/receiving further data elements continually or periodically and judging/evaluating the further data elements against the state data structure for occurrence of the condition. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a non-transitory computer-readable storage medium; and one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed on the one or more processors, cause the system to perform operations comprising (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receiving, from a data source, data elements extracted from a record (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a non-transitory computer-readable storage medium; and one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed on the one or more processors, cause the system to perform operations comprising (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receiving, from a data source, data elements extracted from a record (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 19 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 20,
Step 1: Claim 20 is a product claim. Therefore, Claim 20 is directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship (mental process - processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship may be performed manually by a user with the aid of pen and paper by observing/analyzing an entity, a relationship between the entity and another entity, and attributes of the relationship and using judgement/evaluation to identify the data elements features. See MPEP 2106.04(a)(2)(III)(C).)
compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity (mental process - compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity may be performed manually by a user with the aid of pen and paper by observing/analyzing the entity, relationship and attributes of the relationship in the data elements and using judgement/evaluation to compile a state data structure in the form of a graph data structure. See MPEP 2106.04(a)(2)(III)(C).)
evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system (mental process - evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system may be performed manually by a user with the aid of pen and paper by observing/analyzing occurrence of a condition defined in a rules-based system by a model and judging/evaluating the state data structure. See MPEP 2106.04(a)(2)(III)(C).)
wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition (mental process - wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition may be performed manually by a user with the aid of pen and paper by observing/receiving further data elements continually or periodically and judging/evaluating the further data elements against the state data structure for occurrence of the condition. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
A computer program product for machine learning-based identification of a condition defined in a rules-based system, the computer program product comprising a non-transitory computer-readable medium having stored computer-readable program code for performing the steps of (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receiving, from a data source, data elements extracted from a record (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A computer program product for machine learning-based identification of a condition defined in a rules-based system, the computer program product comprising a non-transitory computer-readable medium having stored computer-readable program code for performing the steps of (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receiving, from a data source, data elements extracted from a record (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 20 is rejected as being directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 9, 11, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya et al. (US 20210109995 A1) (hereinafter Mihindukulasooriya), in view of Tang (US 11238065 B1).
Regarding Claim 1,
Mihindukulasooriya teaches:
“A computer-implemented method for machine learning-based identification of a condition defined in a rules-based system comprising:” (preamble)
“receiving, from a data source, data elements extracted from a record” (Mihindukulasooriya, 104 and 108 in Fig. 1 and Paragraph [0029], “[…] via an extraction component 108, extract one or more terms (e.g., words, phrases, numbers, other alphanumeric objects, and so on) from the document collection 104.”; Examiner’s note: receiving (i.e. extracting), from a data source, data elements (i.e. one or more terms) extracted from a record (i.e. the document collection) is taught.)
“processing the data elements, including identifying in the data elements features including one or more of: an entity; a relationship between the entity and another entity; and, attributes of the relationship” (Mihindukulasooriya, Paragraph [0032], “the context-component 116 can be trained to generate a context-based word embedding that detects, captures, or predicts one or more particular relations (e.g., hypernymy, hyponymy, synonymy, antonymy, partonomy, supplier, entailment, and so on) between terms in the document collection 104.“; Examiner’s note: processing the data elements, including identifying in the data elements features (i.e. generating a context-based word embedding) including one or more of: an entity (i.e. term), a relationship between the entity and another entity and attributes of the relationship (i.e. one or more particular relations) is taught.)
“evaluating the state data structure for occurrence of a condition defined in a rules-based system by a model which represents a collection of conditions defined in the rules-based system” (Mihindukulasooriya, 102 and 602 in Fig. 6 and Paragraph [0006], “Based on the embedding, the vectors of two or more extracted terms can be compared (e.g., via cosine similarity, Euclidean distance, and so on). If this comparison shows that the extracted terms are insufficiently similar as used in the input corpus (e.g., a similarity value is below a threshold), the relationship retrieved between nodes in the knowledge graph that correspond to the extracted terms can be filtered out as irrelevant.”; Examiner’s note: evaluating the state data structure (i.e. comparing two or more extracted terms through cosine similarity or Euclidean distance) for occurrence of a condition defined (i.e. a threshold) in a rules-based system by a model (i.e. Spurious relationship filtration system 102 in Fig. 6) which represents a collection of conditions defined in the rules-based system is taught.)
“wherein the model is trained using machine learning applied to training data comprising corpora of information which include labelled data elements relating to: entities, relationships, attributes of relationships and one or more conditions of the collection of conditions” (Mihindukulasooriya, Fig. 6 and Paragraph [0055], “the trained embedding algorithm 502 can employ artificial intelligence and/or machine learning methodologies (e.g., neural networks) to generate word embeddings (e.g., via Word2Vec, Continuous Bag of Words, Skip Gram, GloVe (Global Vectors), BERT (Bidirectional Encoder Representations from Transformers), and so on). Because machine learning can be involved, the trained embedding algorithm 502 can be trained to generate embeddings that detect, capture, and/or predict one or more particular relations between and/or among terms in the document collection 104 […] the similarity value should be higher than the threshold if higher values indicate more similarity, or the similarity value should be lower than the threshold if lower values indicate more similarity […]”; Examiner’s note: wherein the model is trained using machine learning (i.e. neural networks) applied to training data comprising corpora of information which include labelled data elements relating to: entities (i.e. terms), relationships, attributes of relationships (i.e. one or more particular relations between terms) and one or more conditions of the collection of conditions (i.e. the threshold) is taught.)
Mihindukulasooriya does not explicitly teach:
“compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity”
“wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition”
“outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition”
Tang teaches:
“compiling a state data structure in the form of a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, wherein the state data structure represents the relationship between the entity and another entity” (Tang, 830, 922, 924 and 926 in Fig. 9, “
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“; Examiner’s note: compiling a state data structure in the form of a graph data structure (i.e. generating knowledge graph data structure) based on the entity (i.e. node), relationship (i.e. edge) and attributes of the relationship identified in the data elements (i.e. determined attributes), wherein the state data structure represents the relationship between the entity and another entity (i.e. knowledge graph data structure) is taught in Fig. 9.)
“wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition” (Tang, Fig. 10C and Col. 13, Lines 33-42, “In step 1056, server 140 may determine whether a predetermined threshold of node changes is surpassed […] If the percentage of changed clusters exceeds the predetermined threshold (“No” in step 1056), then subroutine 1032 may return to step 1046.”; Examiner’s note: determining whether a predetermined threshold of node changes is surpassed and returning to the previous step when exceeding the predetermined threshold teaches wherein evaluating the state data structure by the model includes continually or periodically receiving further data elements and evaluating the further data elements against the state data structure for occurrence of the condition.
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“outputting an alert when the occurrence of a condition is identified or approximated, wherein the alert includes an indication of the condition” (Tang, 1056 and 1058 in Fig. 10C and Col. 13, Lines 37-40, “If the percentage change is beneath the threshold (“Yes” in step 1056, then in step 1058 server 140 may output the cluster memberships, and add the memberships to the hierarchical cluster tree structure.”; Examiner’s note: outputting cluster memberships if the percentage change is beneath the threshold teaches outputting an alert when the occurrence of a condition is identified or approximated (the percentage change beneath the threshold), wherein the alert includes an indication of the condition (outputting cluster membership indicates the percentage change is below the threshold condition).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the filtering spurious knowledge graph relationships in Mihindukulasooriya, and the systems/methods for generating and implementing knowledge graphs for knowledge representation and analysis as taught in Tang. Mihindukulasooriya teaches the filtering spurious knowledge graph relationships. Tang teaches creating a graph data structure based on the entity, relationship and attributes of the relationship identified in the data elements, continually or periodically receiving further data elements, evaluating the further data elements against the state data structure for occurrence of the condition and outputting an alert when the occurrence of a condition is identified or approximated. One of ordinary skill would have motivation to combine Mihindukulasooriya and Tang to “generate knowledge graph-based models that overcome previous technical problems while increasing […] accuracy and utility” (Tang, Col. 2, Lines 35-37).
Regarding Claim 2,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
“wherein the state data structure is in the form of a graph data structure including a fact graph database and a rules graph database” (Tang, Col. 9, Lines 31-33 and 47-51, “Structured data may identify one or more fields in the data, and the associated values for each given field […] For example, server 140 may employ one or more rules for identifying address information in parsed data, by searching the parsed data for a predetermined format of [house number] [street name] [road label (st./rd./ln./pl./ave., etc.)].”; Examiner’s note: structured data identifying one or more fields in the data teaches a fact graph database and one or more rules for identifying address information further teaches a rules graph database.)
“wherein evaluating the state data structure includes using node and node path similarity algorithms to determine the distance between embedded node-paths relating to an entity in the fact graph database and embedded node-paths in the rules graph database for entities of that particular entity type” (Mihindukulasooriya, Paragraph [0075], “the similarity value can be a function (e.g., cosine similarity, Euclidean distance, and so on) of the vector representations of the one or more first terms (e.g., the vectors representing Terms B to H).”; Mihindukulasooriya, Paragraph [0056], “the trained embedding algorithm 502 can, in various instances, generate word embeddings that detect, capture, and/or predict one or more particular relations between terms in the document collection 104. These trained embeddings can be leveraged to determine the relevance of relationships, which can be of the same types as the particular relations, that are retrieved from the knowledge graph 106.”; Examiner’s note: cosine similarity or Euclidean distance teaches node and node path similarity algorithms to determine the distance between embedded node-paths. The trained embedding algorithm generating vectors where two similar vectors with two words consistent with the existence of a partonomy relation teaches embedded node-paths relating to an entity in the fact graph database (word embeddings) and embedded node-paths in the rules graph database for entities of that particular entity type (the relevance of relationships, which can be of the same types as the particular relations).)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 9,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
“wherein the condition is a threshold against which data elements within the state data structure are evaluated to determine when the threshold is met” (Mihindukulasooriya, 102 and 602 in Fig. 6 and Paragraph [0006], “Based on the embedding, the vectors of two or more extracted terms can be compared (e.g., via cosine similarity, Euclidean distance, and so on). If this comparison shows that the extracted terms are insufficiently similar as used in the input corpus (e.g., a similarity value is below a threshold), the relationship retrieved between nodes in the knowledge graph that correspond to the extracted terms can be filtered out as irrelevant.”; Examiner’s note: comparing two or more extracted terms through cosine similarity or Euclidean distance with a threshold teaches wherein the condition is a threshold against which data elements within the state data structure are evaluated to determine when the threshold is met (similarity value higher than a threshold).)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 11,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
“wherein the data elements include or represent natural language phrases extracted from a natural language record” (Mihindukulasooriya, 104 and 108 in Fig. 1 and Paragraph [0029], “[…] via an extraction component 108, extract one or more terms (e.g., words, phrases, numbers, other alphanumeric objects, and so on) from the document collection 104.”; Examiner’s note: wherein the data elements (i.e. one or more terms) include or represent natural language phrases (e.g., words, phrases, and so on) extracted from a natural language record (i.e. the document collection).)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 19,
Claim 19 recites substantially the same limitations as Claim 1, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 20,
Claim 20 is a computer program product to perform the method of Claim 1, therefore, it is rejected under the same rationale.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang as applied in claim 1, and further in view of Ma et al. (“End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF”) (hereinafter Ma).
Regarding Claim 3,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
The combination of Mihindukulasooriya and Tang does not explicitly teach:
“wherein identifying features in the data elements includes recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms”
Ma teaches:
“wherein identifying features in the data elements includes recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms” (Ma, Section 1, “We first use convolutional neural networks (CNNs) (LeCun et al., 1989) to encode character-level information of a word into its character-level representation. Then we combine character- and word-level representations and feed them into bi-directional LSTM (BLSTM) to model context information of each word. On top of BLSTM, we use a sequential CRF to jointly decode labels for the whole sentence. We evaluate our model on two linguistic sequence labeling tasks - POS [...] and NER [...]”; Examiner’s note: wherein identifying features in the data elements (i.e. encoding character-level information of a word into its character-level representation and combining character- and word-level representations and feeding them into bi-directional LSTM (BLSTM) to model context information of each word) includes recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms (i.e. using a sequential CRF to jointly decode labels for the whole sentence and evaluating our model on two linguistic sequence labeling tasks – POS and NER (CRF, BLSTM, BLSTM-CNN)) are taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, and the end-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF as taught in Ma. The combination of Mihindukulasooriya and Tang teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition and outputting an alert. Ma teaches recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang and Ma to “achieve[] state-of-the-art performance on two linguistic sequence labeling tasks” (Ma, Section 6).
Regarding Claim 4,
The combination of Mihindukulasooriya, Tang and Ma teaches:
“The method as claimed in claim 3,” (preamble)
“wherein the one or more entity recognition algorithms include one or both of: conditional random fields; and, hybrid bi-directional long short-term memory / convolutional neural networks (LSTM–CNN)” (Ma, Section 1, “We first use convolutional neural networks (CNNs) (LeCun et al., 1989) to encode character-level information of a word into its character-level representation. Then we combine character- and word-level representations and feed them into bi-directional LSTM (BLSTM) to model context information of each word. On top of BLSTM, we use a sequential CRF to jointly decode labels for the whole sentence.”; Examiner’s note: conditional random fields (CRF); and, hybrid bi-directional long short-term memory / convolutional neural networks (LSTM-CNN) are taught.)
The reasons of obviousness have been noted in the rejection of Claim 3 above and applicable herein.
Regarding Claim 5,
The combination of Mihindukulasooriya, Tang and Ma teaches:
“The method as claimed in claim 1,” (preamble)
“wherein identifying features in the data elements includes using one or more classifiers” (Ma, Fig. 3,
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; Examiner’s note: CRF layer, bi-directional LSTM and CNN teach using one or more classifiers.)
The reasons of obviousness have been noted in the rejection of Claim 3 above and applicable herein.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang, and further in view of Ma as applied in claim 3, and further in view of Gao et al. (“Extracting Normative Relationships from Business Contracts”) (hereinafter Gao).
Regarding Claim 6,
The combination of Mihindukulasooriya, Tang and Ma teaches:
“The method as claimed in claim 5,” (preamble)
wherein identifying features in the data elements includes using one or more of: an entity-type classifier; an entity-relationship classifier (Mihindukulasooriya, Paragraph [0032], “the context-component 116 can be trained to generate a context-based word embedding that detects, captures, or predicts one or more particular relations (e.g., hypernymy, hyponymy, synonymy, antonymy, partonomy, supplier, entailment, and so on) between terms in the document collection 104.“; Mihindukulasooriya, Paragraph [0048], “the extraction component 108 can extract (e.g., recognize, identify, detect, mine, and so on) one or more of Terms A through H from the document collection 104.“; Mihindukulasooriya, Paragraph [0055], “the trained embedding algorithm 502 can be trained to generate embeddings that detect, capture, and/or predict one or more particular relations between and/or among terms in the document collection 104 […]”; Examiner’s note: wherein identifying features in the data elements (generating a context-based word embedding) includes using one or more of: an entity-type classifier (i.e. the extraction component extracting one or more of terms); an entity-relationship classifier (i.e. generating embeddings that predict one or more particular relations between and/or among terms in the document collection) is taught.)
The combination of Mihindukulasooriya, Tang and Ma does not explicitly teach:
wherein identifying features in the data elements includes using one or more of: an entity-role, rights and/or obligations classifier
Gao teaches:
wherein identifying features in the data elements includes using one or more of: an entity-role, rights and/or obligations classifier (Gao, Section 1, “[…] extracting norms and allied concepts from contracts.“; Gao, Section 2, “a norm has four core elements – subject, object, antecedent, and consequent. Norms in our approach are of the following main types. A commitment means that its subject commits to its object to ensure the consequent if the antecedent holds […] An authorization means that its subject is authorized by its object for bringing about the consequent if the antecedent holds […] A power means that its subject is empowered by its object to bring about the consequent if the antecedent holds […] A prohibition means that its subject is forbidden by its object from bringing about the consequent if the antecedent holds.“; Gao, Section 6, “We introduce an automatic approach to extract norms from business contracts and further classify norm types.”; Examiner’s note: wherein identifying features in the data elements (i.e. extracting norms and allied concepts from contracts) includes using one or more of: an entity-role, rights and/or obligations classifier (i.e. norms having core elements subject and object with main types, a commitment, an authorization, a power and a prohibition) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, Ma and using one or more of an entity-role, rights and/or obligations classifier as taught in Gao. The combination of Mihindukulasooriya, Tang and Ma teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition, outputting an alert and recognizing, classifying and/or labelling the data elements using one or more entity recognition algorithms. Gao teaches using one or more of an entity-role, rights and/or obligations classifier. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang, Ma and Gao to “greatly facilitate a business in complying with its contracts and monitoring its counterparties for their compliance” (Gao, Section 2).
Regarding Claim 7,
The combination of Mihindukulasooriya, Tang, Ma and Gao teaches:
“The method as claimed in claim 6,” (preamble)
“wherein the attributes of the relationship include one or more of: an entity role in the relationship; an entity obligation in the relationship; and/or an entity right in the relationship” (Gao, Section 2, “a norm has four core elements – subject, object, antecedent, and consequent. Norms in our approach are of the following main types. A commitment means that its subject commits to its object to ensure the consequent if the antecedent holds […] An authorization means that its subject is authorized by its object for bringing about the consequent if the antecedent holds […] A power means that its subject is empowered by its object to bring about the consequent if the antecedent holds […] A prohibition means that its subject is forbidden by its object from bringing about the consequent if the antecedent holds.“; Gao, Section 2.3, “Given a sentence from a contract that expresses a norm, extract the elements (subject, object, antecedent, consequent) of the norm.“; Gao, Section 6, “We introduce an automatic approach to extract norms from business contracts and further classify norm types.”; Examiner’s note: extracting the elements (subject, object, antecedent, consequent) of the norm teaches an entity role in the relationship. Classification and extraction of norm types such as a commitment and a prohibition further teach an entity obligation in the relationship. Classification and extraction of norm types such as an authorization and a power further teach an entity right in the relationship.)
The reasons of obviousness have been noted in the rejection of Claim 6 above and applicable herein.
Regarding Claim 8,
The combination of Mihindukulasooriya, Tang, Ma and Gao teaches:
“The method as claimed in claim 1,” (preamble)
“wherein the model is an entity-relational model for a rules-based system in which ontological elements include one or more of “entities”, “relationships”, “actions” and “events”” (Gao, Section 1, “A designer would use the extracted norms and produce a specification for a multiagent system.”; Gao, Section 2, “a norm has four core elements – subject, object, antecedent, and consequent. Norms in our approach are of the following main types. A commitment means that its subject commits to its object to ensure the consequent if the antecedent holds […] An authorization means that its subject is authorized by its object for bringing about the consequent if the antecedent holds […] A power means that its subject is empowered by its object to bring about the consequent if the antecedent holds […] A prohibition means that its subject is forbidden by its object from bringing about the consequent if the antecedent holds.“; Gao, Section 6.2, “The consequent of one norm may be the antecedent of another: a chain of dependencies often exists in contracts whereby the success or failure of one business action triggers another“; Examiner’s note: wherein the model is an entity-relational model (i.e. norms having core elements, subject, object, antecedent and consequent with the main types) for a rules-based system (i.e. extracting the norms and producing a specification for a multiagent system) in which ontological elements include one or more of entities (i.e. subject and object), relationships (i.e. norm types), actions and events (i.e. the consequent of one norm may be the antecedent of another - success or failure of one business action triggering another) are taught.)
The reasons of obviousness have been noted in the rejection of Claim 6 above and applicable herein.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang as applied in claim 1, and further in view of Wyner et al. (“A legal case OWL ontology with an instantiation of Popov v. Hayashi”) (hereinafter Wyner).
Regarding Claim 10,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
“wherein the rules-based system is an entity relational rules-based system” (Mihindukulasooriya, 102, 108, 112, 502 and 600 in Fig. 6
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; Examiner’s note: wherein the rules-based system (i.e. 600) is an entity relational rules-based system (i.e. spurious relationship filtration system (102)) is taught.)
The combination of Mihindukulasooriya and Tang does not explicitly teach:
“wherein the entity relational rules-based system is a legal system, and wherein the condition is a cause of action arising in the relationship between the entity and another entity”
Wyner teaches:
“wherein the entity relational rules-based system is a legal system” (Wyner, Fig. 2 and Section 3.1, “In Fig. 2, we represent the Case class, which is equivalent to: the class comprised of individuals which have a defendant, a plaintiff, and a judge; the class comprised of the union of decided and undecided cases. The Case class is also a subclass of classes defined by object and data properties such as having solicitors, a hearing, a name, and a jurisdiction.”
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; Examiner’s note: The ontology models the legal case domain defining classes such as Plaintiff, Judge, Defendant, Sollicitor, Hearing, Decided Case and Undecided Case.)
“wherein the condition is a cause of action arising in the relationship between the entity and another entity” (Wyner, Fig. 10 and Section 3.6.2, “A cause of action is the plaintiff’s claim which expresses a legal theory and which justifies bringing the case to court […] In Popov v. Hayashi, the judge outlines each of the causes of action, the claims of fact, and other legally defined elements so as to determine whether the cause of action is satisfied and the plaintiff wins the case.”
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; Examiner’s note: the judge outlining each of the causes of action, the claims of fact, and other legally defined elements teach wherein the condition is a cause of action arising in the relationship between the entity and another entity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, and the legal case OWL ontology with an instantiation of Popov v. Hayashi as taught in Wyner. The combination of Mihindukulasooriya and Tang teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition and outputting an alert. Wyner teaches the entity relational rules-based system being a legal system, and the condition being a cause of action arising in the relationship between the entity and another entity. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang and Wyner to “make[] explicit the conceptual knowledge of the legal case domain, support[] reasoning about the domain, and can be used for information retrieval and text annotation” (Wyner, Section 1).
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang as applied in claim 1, and further in view of Schnell et al. (“Privacy-preserving record linkage using Bloom filters”) (hereinafter Schnell).
Regarding Claim 12,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
The combination of Mihindukulasooriya and Tang does not explicitly teach:
“wherein processing the data elements includes assigning pseudonymized identifiers to entity identities identified in the data elements by performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier”
Schnell teaches:
“wherein processing the data elements includes assigning pseudonymized identifiers to entity identities identified in the data elements by performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier” (Schnell, Page 4, Col. 1, Lines 1-9, “If we want to compute the similarity between those strings without revealing bigrams, we must use an encryption. Our protocol for privacy-preserving record linkage uses a Bloom filter for this task. To accomplish this, we store the q-grams of each name in a separate bit array (a Bloom filter) using k multiple cryptographic mappings (hash functions) respectively. Then we compare the Bloom filters bit by bit and calculate a similarity coefficient.”; Examiner’s note: using an encryption to compute the similarity between the strings without revealing bigrams teaches processing the data elements (computing the similarity between the strings) includes assigning pseudonymized identifiers to entity identities identified in the data elements (encrypting strings/identifiers) by performing a cryptographic operation on each of the entity (using k multiple cryptographic mappings (hash functions)) identifies to generate a corresponding pseudonymized identifier (encrypted strings/identifiers).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, and the privacy-preserving record linkage using Bloom filters as taught in Schnell. The combination of Mihindukulasooriya and Tang teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition and outputting an alert. Schnell teaches performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang and Schnell to “have[] a low computational burden” (Schnell, Conclusion).
Regarding Claim 13,
The combination of Mihindukulasooriya, Tang and Schnell teaches:
“The method as claimed in claim 12,” (preamble)
“wherein assigning pseudonymized identifiers to entity identities identified in the data elements includes creating a pseudonymized value register of all extracted entities by performing the cryptographic operation on an entity item at the information point at which the entity item has been recognized or extracted.” (Schnell, Page 4, Col. 1, Lines 1-3, “If we want to compute the similarity between those strings without revealing bigrams, we must use an encryption.”; Schnell, Page 4, Col. 2, Lines 4-10, “A Bloom filter is a bit array of length l with all bits initially set to 0. Furthermore, k independent hash functions h1,…, hk are defined, each mapping on the domain between 0 and l – 1. In order to store the set S = {x1, x2,…,xn} in the Bloom filter, each element xi ϵ S is hash coded using the k hash functions and all bits having indices hj(xi) for 1 <= j <= k are set to 1.”; Examiner’s note: using an encryption teaches assigning pseudonymized identifiers to entity identities identified in the data elements (encrypting the strings/identifiers). k independent hash functions defined and each element in the Bloom filter is hash coded using k hash functions in order to store the set further teach creating a pseudonymized value register of all extracted entities (hash coded each element) by performing the cryptographic operation (k has functions) on an entity item at the information point at which the entity item has been recognized or extracted (each mapping on the domain).)
The reasons of obviousness have been noted in the rejection of Claim 12 above and applicable herein.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang, and further in view of Schnell as applied in claim 12, and further in view of Lassoued et al. (US 20200089766 A1) (hereinafter Lassoued).
Regarding Claim 14,
The combination of Mihindukulasooriya, Tang and Schnell teaches:
“The method as claimed in claim 12,” (preamble)
The combination of Mihindukulasooriya, Tang and Schnell does not explicitly teach:
“wherein assigning pseudonymized identifiers includes transmitting the pseudonymized identifiers to an entity register for co-referencing standardization”
Lassoued teaches:
“wherein assigning pseudonymized identifiers includes transmitting the pseudonymized identifiers to an entity register for co-referencing standardization” (Lassoued, Paragraph [0082], “A lexicon of entities may be learned from the ontology using entity names, labels, and properties (e.g., job, role, nickname, etc.), as in block 1108. A lexicon of relationship types may be learned from a semantic network (e.g., mother of, father of, etc.), as in block 1110. A co-reference resolution may be applied/performed, as in block 1112. An entity lexicon may be used to identify/spot entity mentions and link them to the relevant entities, as in block 1114. A relationship lexicon may be used to identify/spot mentions, following or preceding possessives (e.g., his dad, the father of X, X's father, etc.) and link the possessives to the relevant ontology relationships, as in block 1116. The results of the co-reference resolution, entity linking, and relationship linking, in conjunction with the ontological relationships between entities, to fully resolve semantic references, as in block 1118.”; Examiner’s note: using an entity lexicon to identify/spot entity mentions and linked them to the relevant entities teach assigning pseudonymized identifiers including transmitting the pseudonymized identifiers (entity mentions) to an entity register (entity lexicon) for co-referencing standardization (co-reference resolution).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, Schnell and the co-reference resolution and entity linking as taught in Lassoued. The combination of Mihindukulasooriya, Tang and Schnell teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition, outputting an alert and performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier. Lassoued teaches transmitting the pseudonymized identifiers to an entity register for co-referencing standardization. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang, Schnell and Lassoued to “provide[] for resolving person/entity co-references using a domain knowledge ontology” (Lassoued, Paragraph [0019]).
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang, and further in view of Schnell, and further in view of Lassoued as applied in claim 14, and further in view of Sarzynski et al. (US 20210281600 A1) (hereinafter Sarzynski).
Regarding Claim 15,
The combination of Mihindukulasooriya, Tang, Schnell and Lassoued teaches:
“The method as claimed in claim 14,” (preamble)
The combination of Mihindukulasooriya, Tang, Schnell and Lassoued does not explicitly teach:
“wherein transmitting pseudonymized entity identifiers includes transmitting a standardized name to the information point from which the entity item was extracted”
Sarzynski teaches:
“wherein transmitting pseudonymized entity identifiers includes transmitting a standardized name to the information point from which the entity item was extracted” (Sarzynski, Paragraph [0195], “entity names found by the entity name extraction engine 1230 are checked by a comparison engine 1235 to determine whether an extracted entity name has a corresponding entity pseudonym in the pseudonym table 1220. In certain embodiments, entity names found in the pseudonym table 1220 by the comparison engine 1235 are replaced by corresponding entity pseudonyms by a pseudonym enrichment engine 1240 […] the pseudonym enrichment engine 1240 enriches the event with the pseudonym name corresponding to the original entity name found in the event to generate pseudonymized events 1245.”; Examiner’s note: entity names found by the entity name extraction engine being checked by a comparison engine to determine whether an extracted entity name has a corresponding entity pseudonym teach transmitting a standardized name (extracted entity name) to the information point from which the entity item was extracted (entity name extraction engine). Replacing entity names with corresponding entity pseudonyms and generating pseudonymized events with the replaced pseudonym name further teach transmitting pseudonymized entity identifiers (corresponding entity pseudonyms).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, Schnell, Lassoued and the security system using pseudonyms to anonymously identify entities and corresponding security risk related behaviors as taught in Sarzynski. The combination of Mihindukulasooriya, Tang, Schnell and Lassoued teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition, outputting an alert, performing a cryptographic operation on each of the entity identifies to generate a corresponding pseudonymized identifier and transmitting the pseudonymized identifiers to an entity register for co-referencing standardization. Sarzynski teaches transmitting a standardized name to the information point from which the entity item was extracted. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang, Schnell, Lassoued and Sarzynski to “allow[] an organization to address security issues in a private manner without damage to the reputation of the entity” (Sarzynski, Paragraph [0194]).
Regarding Claim 16,
The combination of Mihindukulasooriya, Tang, Schnell, Lassoued and Sarzynski teaches:
“The method as claimed in claim 15,” (preamble)
“including recording pseudonymized entity related information at one or more data locations in a federated database system” (Sarzynski, Paragraph [0161], “[…] the persistent datastore of event data may be implemented as a relational database management system (RDBMS), a structured query language (SQL) RDBMS, a not only SQL (NoSQL) database, a graph database, or other database approaches familiar to those of skill in the art.”; Sarzynski, Paragraphs [0192] and [0193], “[…] the entity names and corresponding pseudonyms are stored in a pseudonym table […] the pseudonym table 1220 is encrypted and/or stored on a separate hardware device so that the entity names and corresponding entity pseudonyms are available only to select authorized users, such as a security administrator.”; Examiner’s note: other database approaches, storing the entity names and corresponding pseudonyms in a pseudonym table and encrypted pseudonym table teach recording pseudonymized entity related information (storing the entity names and corresponding pseudonyms) at one or more data locations in a federated database system (other database approach and storing the pseudonym table teach the federated database system).)
The reasons of obviousness have been noted in the rejection of Claim 15 above and applicable herein.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mihindukulasooriya in view of Tang as applied in claim 1, and further in view of Sarzynski.
Regarding Claim 17,
The combination of Mihindukulasooriya and Tang teaches:
“The method as claimed in claim 1,” (preamble)
The combination of Mihindukulasooriya and Tang does not explicitly teach:
“wherein the alert is transmitted to and output via a user device”
Sarzynski teaches:
“wherein the alert is transmitted to and output via a user device” (Sarzynski, Fig. 16 and Paragraph [0203], “[…] a risk graph 1604 displayed within a user interface (UI) window 1602 depicts the fluctuation of risk scores 1606 at different points in time within a particular date range 1608 […] the fluctuation of risk scores 1606 displayed within the risk graph 1604 corresponds to the potential risk associated with a particular user entity 1610 at various points in time within the date range 1608.”
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; Examiner’s note: Displaying a risk graph with the fluctuation of risk scores within a user interface (UI) window teaches the alert is transmitted to and output via a user device.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mihindukulasooriya, Tang, and the security system using pseudonyms to anonymously identify entities and corresponding security risk related behaviors as taught in Sarzynski. The combination of Mihindukulasooriya and Tang teaches the filtering spurious knowledge graph relationships, knowledge graph data structure, receiving the further data elements, evaluating the further data elements against the knowledge graph data structure for occurrence of the condition and outputting an alert. Sarzynski teaches the alert being transmitted to and output via a user device. One of ordinary skill would have motivation to combine Mihindukulasooriya, Tang and Sarzynski to “allow[] an organization to address security issues in a private manner without damage to the reputation of the entity” (Sarzynski, Paragraph [0194]).
Regarding Claim 18,
The combination of Mihindukulasooriya, Tang and Sarzynski teaches:
“The method as claimed in claim 17,” (preamble)
“wherein the alert includes a confidence or proximity score associated with the identification” (Sarzynski, Paragraph [0173], “[…] the score 934 may be implemented to represent the unlikelihood of the occurrence of a particular feature associated with event ‘1’ 930 […] the score may be used by a probability distribution analysis system to generate a risk score […] the risk score may be implemented to reflect possible anomalous, abnormal, unexpected or malicious behavior by an entity, as described in greater detail herein.”; Examiner’s note: the alert is taught in supra claim 17. The score representing the unlikelihood of the occurrence of a particular feature associated with event and the score used to generate a risk score teach a confidence or proximity score (risk score) associated with the identification (particular feature associated with event).)
The reasons of obviousness have been noted in the rejection of Claim 17 above and applicable herein.
Conclusion
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/YONG DOO RHO/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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