DETAILED ACTION
This action is in response to the amendment filed 05/21/2026. Claims 1-10, 12, and 14-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, regarding claim 1, claim 1 recites “the list of input fields including the second field.” Examiner notes that the specification supports a list of input field with “the biography field 335 may be associated with metadata (not shown) that indicates the source or type of the value (e.g., ‘computed’), a selected computation (e.g., ‘Write an advisor bio with AI’), and a list of the input fields” from paragraph 0092. The specification does not support, however that a second field is included in the list of input fields.
Specifically, regarding claim 1, claim 1 recites “using the metadata associated with the first field, execute the selected computation using the value of the second field.” Examiner notes that the specification supports metadata with “the biography field 335 may be associated with metadata (not shown) that indicates the source or type of the value (e.g., ‘computed’), a selected computation (e.g., ‘Write an advisor bio with AI’), and a list of the input fields” from paragraph 0092. The specification does not support, however using the metadata to execute the selected computation.
Specifically, regarding claim 1, claim 1 recites “a change in value of the second field included in the list of input fields.” Examiner notes that the specification supports a list of input field with “the biography field 335 may be associated with metadata (not shown) that indicates the source or type of the value (e.g., ‘computed’), a selected computation (e.g., ‘Write an advisor bio with AI’), and a list of the input fields” from paragraph 0092. The specification does not support, however that a change in value of the second field is included in the list of input fields.
Specifically, regarding claim 1, claim 1 recites “using the metadata associated with the first field, execute the selected computation using the changed value of the second field.” Examiner notes that the specification supports metadata with “the biography field 335 may be associated with metadata (not shown) that indicates the source or type of the value (e.g., ‘computed’), a selected computation (e.g., ‘Write an advisor bio with AI’), and a list of the input fields” from paragraph 0092. The specification does not support, however using the metadata to execute the selected computation.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
generate a knowledge graph comprising a plurality of nodes, each node comprising at least one field to store data, and being associated with at least one other node (This limitation is a mental process as it encompasses a human mentally generating a knowledge graph and is thus an evaluation.)
at a particular node, define a plurality of fields, wherein a first field in the plurality of fields has a dependence on a second field in the plurality of fields (This limitation is a mental process as it encompasses a human mentally defining a plurality of fields and is thus an evaluation.)
and wherein metadata associated with the first field identifies the first field as a computed field, and specifies a selected computation for generating values of the first field, and a list of input fields for the selected computation, the list of input fields including the second field (This limitation is a mental process as it encompasses a human mentally associating metadata, specifying a computation and a list of input fields and is thus an evaluation.)
using the received data, define a value of the second field at the particular node in the knowledge graph (This limitation is a mental process as it encompasses a human mentally define a value and is thus an evaluation.)
using the metadata associated with the first field, execute the selected computation using the value of the second field to… generate a computed value for the first field, based on the dependence (This limitation is a mental process as it encompasses a human mentally generating a computed value and is thus an evaluation.)
in response to the indication, using the metadata associated with the first field, execute the selected computation using the changed value of the second field to generate an updated computed value for the first field based on the dependence; (This limitation is a mental process as it encompasses a human mentally generating an updated computed value and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A system comprising: at least one processor; and a non-transitory computer-readable storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
receive data at the knowledge graph (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
…automatically generate a computed value… (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Store the computed value as a current value of the first field at the particular node in the knowledge graph (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Receive an indication at the particular node in the knowledge graph, wherein the indication is triggered by a change in the value of the second field (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Store the updated computed value as a current value of the first field at the particular node in the knowledge graph (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A system comprising: at least one processor; and a non-transitory computer-readable storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receive data at the knowledge graph is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
…automatically generate a computed value… uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Store the computed value as a current value of the first field at the particular node in the knowledge graph is the well understood, routine, and conventional activity of “Storing and retrieving information in memory” (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93)
Receive an indication at the particular node in the knowledge graph, wherein the indication is triggered by a change in the value of the second field is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Store the updated computed value as a current value of the first field at the particular node in the knowledge graph is the well understood, routine, and conventional activity of “Storing and retrieving information in memory” (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93)
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
wherein generating the computed value for the first field comprises performing an operation, wherein the value of the second field is an input to the operation, and the computed value for the first field is an output of the operation (This limitation is a mental process as it encompasses a human mentally generating computed value and is thus an evaluation.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 does not further recite any additional elements. Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 2 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
wherein the operation comprises a transform, and performing the operation comprises applying the transform to the value of the second field (This limitation is a mental process as it encompasses a human mentally performing an operation and is thus an evaluation.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
wherein the operation is defined by a user (This limitation is a mental process as it encompasses a human mentally performing an operation and is thus an evaluation.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
wherein the operation is one of a plurality of pre-defined operations (This limitation is a mental process as it further describes the mental process of performing an operation as claimed in claim 2.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
executing the instructions further cause the at least one processor to receive, at the particular node in the knowledge graph, a selection of the operation from the plurality of pre-defined operations. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
executing the instructions further cause the at least one processor to receive, at the particular node in the knowledge graph, a selection of the operation from the plurality of pre-defined operations is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
generating the computed value for the first field using the response from the external data source. (This limitation is a mental process as it encompasses a human mentally generating the computed value and is thus an evaluation.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
generating a request to an external data source, the request comprising the value of the second field (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
providing the request to the external data source (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving a response from the external data source (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
generating a request to an external data source, the request comprising the value of the second field is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
providing the request to the external data source is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receiving a response from the external data source is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the same abstract ideas as claim 6. Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
wherein the external data source is a machine learning model (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
the request is a prompt to the machine learning model, and the response is an output of the machine learning model to the prompt. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the external data source is a machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
The request is a prompt to the machine learning model, and the response is an output of the machine learning model to the prompt is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites the same abstract ideas as claim 6. Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
Wherein the external data source is a database (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
the request is a query to the database, and the response is a reply of the database to the query. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
Wherein the external data source is a database uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the request is a query to the database, and the response is a reply of the database to the query is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites the same abstract ideas as claim 6. Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
Wherein the external data source is an application programming interface (API) (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
the request is an input to the API, and the response is an output of the API to the input. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
Wherein the external data source is an application programming interface (API) uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the request is an input to the API, and the response is an output of the API to the input is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 9 is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites the same abstract ideas as claim 1. Therefore, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements of
wherein the received data is received from a user. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 10 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the received data is received from a user is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 10 is subject-matter ineligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites the same abstract ideas as claim 1. Therefore, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of
wherein the indication is received from a user. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the indication is received from a user is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 12 is subject-matter ineligible.
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites the same abstract ideas as claim 1. Therefore, claim 14 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 further recites additional elements of
wherein the indication is automatically triggered after a period of time. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 14 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the indication is automatically triggered after a period of time is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 14 is subject-matter ineligible.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
wherein the node is a first node that is associated with a second node in the knowledge graph (This limitation is a mental process as it encompasses a human mentally associating a first node with a second node and is thus an evaluation.)
update a field of the second node based on the computed value (This limitation is a mental process as it encompasses a human mentally updating a field and is thus an evaluation.)
Therefore, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of
executing the instructions further cause the at least one processor to update a field (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination because
executing the instructions further cause the at least one processor to update a field uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 15 is subject-matter ineligible.
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites
perform the plurality of operations to generate a plurality of outputs (This limitation is a mental process as it encompasses a human mentally performing operations to generate outputs and is thus an evaluation.)
Therefore, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 further recites additional elements of
wherein executing the instructions further cause the at least one processor to: … perform the plurality of operations to generate a plurality of outputs (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
provide the computed value for the first field as an input to a plurality of operations (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 16 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein executing the instructions further cause the at least one processor to: … perform the plurality of operations to generate a plurality of outputs uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
provide the computed value for the first field as an input to a plurality of operations is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 16 is subject-matter ineligible.
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 17 recites
wherein defining the value of the second field comprises …transform the received data (This limitation is a mental process as it encompasses a human mentally defining a value by transformed data and is thus an evaluation.)
defining the value of at least the second field based on the transformed data (This limitation is a mental process as it encompasses a human mentally defining a value and is thus an evaluation.)
Therefore, claim 17 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 17 further recites additional elements of
applying a data model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
wherein the data model provides a mapping from the received data to the plurality of fields of the particular node (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 17 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 17 do not provide significantly more than the abstract idea itself, taken alone and in combination because
applying a data model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
wherein the data model provides a mapping from the received data to the plurality of fields of the particular node uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 17 is subject-matter ineligible.
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites
and defining the value of the second field comprises applying the profile to the second field (This limitation is a mental process as it encompasses a human mentally applying the profile to the second field and is thus an evaluation.)
Therefore, claim 18 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 18 further recites additional elements of
wherein the received data comprises a profile (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 18 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 18 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the received data comprises a profile is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 18 is subject-matter ineligible.
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites
wherein updating the first field using the computed value comprises performing a validation on the computed value, wherein the validation comprises at least one rule to which any value of the first field must conform (This limitation is a mental process as it encompasses a human mentally performing a validation on the computed value and is thus an evaluation.)
Therefore, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 does not further recite any additional elements. Therefore, claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 19 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 19 is subject-matter ineligible.
Regarding Claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites the same abstract ideas as claim 1. Therefore, claim 20 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 further recites additional elements of
executing the instructions further cause the at least one processor to provide the computed value to a user as a suggested value, and receive a confirmation from the user to proceed to update the first field using the suggested value (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 20 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 20 do not provide significantly more than the abstract idea itself, taken alone and in combination because
executing the instructions further cause the at least one processor to provide the computed value to a user as a suggested value, and receive a confirmation from the user to proceed to update the first field using the suggested value is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 20 is subject-matter ineligible.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8, 10, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huaman et al. (“Towards Knowledge Graphs Validation through Weighted Knowledge Sources”) (hereafter referred to as Huaman) in view of Liu et al. (US 2022/0357723 A1) (hereafter referred to as Liu) and in further view of Tang et al. (“Learning to Update Knowledge Graphs by Reading News”) (hereafter referred to as Tang).
Regarding claim 1, Huaman teaches
A system comprising: at least one processor (Huaman, page 10, last paragraph, “We validated 2530 politician instances by using the Validator, which compares and computes a confidence score for each triple and instance. To execute this task the Validator required ~15 minutes approximately on a CPU described in Table 1” and Huaman, page 8, Table 1,
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);
define a plurality of fields, wherein a first field in the plurality of fields has a dependence on a second field in the plurality of fields (Huaman, page 5, 2nd paragraph, “The user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address)” where “we compare the name value of an instance of the KG against the name value of the same instance in an external source. We repeat this process for every triple of an instance and we compute a triple confidence score, the triple confidence scores are later added to an aggregated confidence score for the instance” (Huaman, page 5, 1st paragraph). Examiner notes that the second field is the instance type and the first field is the aggregated confidence score.)
and wherein metadata associated with the first field identifies the first field as a computed field, and specifies a selected computation for generating values of the first field, and a list of input fields for the selected computation, the list of input fields including the second field (Huaman, page 6, 5th paragraph – page 7, 1st paragraph, “We define a set of knowledge sources as S, S = {s1,…, sm}, si
∈
S with 1 ≤ i ≤ m. The user’s KG g consists of a set of instances that are to be validated against the set of knowledge sources S. A knowledge source si consists of a set of instances E={e1,…,en}, ej
∈
E with 1≤ j ≤ n and an instance ej consists of a set of attribute values P = {p1,…,pm}, pk
∈
P for 1≤ k ≤ M. Furthermore, sim is a similarity function used to compare attribute pair k for two instances. We compute the similarity of an attribute value of two instances a, b. Where a represents an instance in the user’s KG g, denoted g(a), and b represents an instance in the knowledge source si denoted si(b).
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…. We compute the weighted triple confidence as follows
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.” Examiner notes that the metadata associated with the first field is equation 1. Examiner notes that equation 1 computes the first field through a specified computation and the list of inputs including g, a, and b, which are instance types.)
receive data at the knowledge graph (Huaman, page 5, 2nd paragraph, “At first step, a user is required to provide a KG to be validated. For this, the user has two options, a) to provide a SPARQL endpoint where to fetch the data from or b) to load a dataset in a Turtle format. Moreover, the user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address).”);
using the received data, define a value of the second field (Huaman, page 5, 2nd paragraph, “The user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address)” where “Based on the DS defined in the input, the validator maps the input KG and the external sources to a common format, e.g., a telephone number of a hotel can be stored with different property names across the knowledge sources: phone, telephone, or phone_number. The validator provides a basic mapping feature to map the input KG and external data sources to a common attribute space” (Huaman, page 5 last paragraph). Examiner notes that the second field is the instance type.);
Using the metadata associated with the first field, execute the selected computation using the value of the second field to automatically generate a computed value for the first field, based on the dependence (Huaman, page 6, 2nd paragraph, “Computing a confidence value can get complicated as the number of instances and their features can get out of hand quickly. Therefore, a means to automatically validate KGs is desirable. To compute a confidence value for an instance, the confidence value for each of its triples has to be evaluated first” where “Triple validation calculates a confidence score of whether a property value on various external sources matches the property value in the user’s KG. For example, the user’s KG contains the Hotel Alpenhof instance and statements about it; Hotel Alpenhof’s phone is +4352878550 and Hotel Alpenhof’s address is Hintertux 750” (Huaman, page 6, 3rd paragraph) and where “We define a set of knowledge sources as S, S = {s1,…, sm}, si
∈
S with 1 ≤ i ≤ m. The user’s KG g consists of a set of instances that are to be validated against the set of knowledge sources S. A knowledge source si consists of a set of instances E={e1,…,en}, ej
∈
E with 1≤ j ≤ n and an instance ej consists of a set of attribute values P = {p1,…,pm}, pk
∈
P for 1≤ k ≤ M. Furthermore, sim is a similarity function used to compare attribute pair k for two instances. We compute the similarity of an attribute value of two instances a, b. Where a represents an instance in the user’s KG g, denoted g(a), and b represents an instance in the knowledge source si denoted si(b).
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…. We compute the weighted triple confidence as follows
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” (Huaman, page 6, 5th paragraph – page 7, 1st paragraph). Examiner notes that the triple confidence score is the computed value for the first field and equation 1 is the metadata used to execute the computation of equation 1.);
store the computed value as a current value of the first field (Huaman, page 5, 1st paragraph, “We compute a triple confidence score, the triple confidence scores are later added to an aggregated confidence score for the instance.” Examiner notes that the triple confidence score is the computed value for the first field and the aggregated confidence score is the first field);
Huaman does not explicitly disclose, but Liu does disclose
and a non-transitory computer-readable storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to (Liu, page 4, paragraph 0092, “The instructions for implementing processes or methods described herein may be provided on non-transitory computer-readable storage media or memories, such as a cache, buffer, RAM, FLASH, removable media, hard drive, or other computer readable storage media. A processor performs or executes the instructions to train and/or apply a trained model for controlling a system.”):
generate a knowledge graph comprising a plurality of nodes, each node comprising at least one field to store data, and being associated with at least one other node (Liu, page 4, paragraph 0045, “User data and/or project configuration data with can be used for an engineering project Pin order to (re-) configure an industrial system comprising various components. These data are multi-relational data that can be represented as a knowledge graph KG whose entities representing components correspond to nodes and whose relations correspond to edges in the graph” where “components of a set of components are represented by graph nodes and relations between two components which are represented by edges between the corresponding nodes” (Liu, page 1, abstract) and “latent feature representation methods e.g. [4] that learn embeddings for each entity and relations an predict links by calculating scores (entity, relation, entity)-triples” (Liu, page 3, paragraph 0009). Examiner notes that the knowledge graph comprises nodes and edges that make up triples. Examiner further notes that the at least one field to store data is the node representing an entity.);
at a particular node, define a plurality of fields (Liu, page 4, paragraph 0045, “User data and/or project configuration data with can be used for an engineering project Pin order to (re-) configure an industrial system comprising various components. These data are multi-relational data that can be represented as a knowledge graph KG whose entities representing components correspond to nodes and whose relations correspond to edges in the graph” where “components of a set of components are represented by graph nodes and relations between two components which are represented by edges between the corresponding nodes” (Liu, page 1, abstract) and “latent feature representation methods e.g. [4] that learn embeddings for each entity and relations an predict links by calculating scores (entity, relation, entity)-triples” (Liu, page 3, paragraph 0009). Examiner notes that the plurality of fields are the triples.)
define a value…at the particular node in the knowledge graph (Liu, page 4, paragraph 0045, “User data and/or project configuration data with can be used for an engineering project Pin order to (re-) configure an industrial system comprising various components. These data are multi-relational data that can be represented as a knowledge graph KG whose entities representing components correspond to nodes and whose relations correspond to edges in the graph” where “components of a set of components are represented by graph nodes and relations between two components which are represented by edges between the corresponding nodes” (Liu, page 1, abstract) and “latent feature representation methods e.g. [4] that learn embeddings for each entity and relations an predict links by calculating scores (entity, relation, entity)-triples” (Liu, page 3, paragraph 0009). Examiner notes that the value at a node is the scores.)
Huaman and Liu are analogous to the claimed invention because they both disclose updating knowledge graphs. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented the knowledge graph on a non-transitory computer readable storage medium. Thus, this would be applying a known technique (updating knowledge graphs) to a known device (computer readable storage medium) ready for improvement to yield predictable results (an updated knowledge graph) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way). It also would have been obvious to a person having ordinary skill in the art prior to the effective filing date to use the knowledge graph in Liu in place of Huaman. Thus, this would be a simple substitution of one known element (Huaman’s knowledge graph) for another (Liu’s knowledge graph) to obtain predictable results (an updated knowledge graph) (MPEP 2143 I. (B) Simple substitution of one known element for another to obtain predictable results).
Huaman and Liu do not disclose, but Tang discloses
receive an indication at the particular node in the knowledge graph (Tang, page 3, Figure 2
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where “we design a novel neural methodl, GUpdater, to read text snippets, which features an attention mechanism to selectively control the message passing over KG structures. This novel architecture enables us to perform both link-adding and link-deleting to ensure the KG up-to-date.” (Tang, page 2, 2nd column, 2nd point). Examiner notes that the text snippet is the indication.);
wherein the indication is triggered by a change in a value of the second field included in the list of input fields, the change resulting in a changed value of the second field(Tang, page 3, Figure 2
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where “We first formally define the task. Given a knowledge graph G = (E, R, T), where E, R and T are the entity set, relation set and KG triple set, respectively, and a new text snippet S = {w1, w2,…, w|S|}, for which entity linking has been performed to build the mentioned entity set L
⊂
E, the text-based knowledge graph updating task is to read the news snippet S and update T accordingly to get the final triple set T’ and the updated graph G’ = (E, R, T’)” (Tang, page 2, 2nd column, Section 2 Task Formulation, 1st paragraph). Examiner notes that the second field is the relation set and the indication is the text snippet. Examiner further notes that the relation set changes within the text snippet, and the text snippet is sent as an indication to the nodes of the knowledge graph).
In response to the indication, using the metadata associated with the first field, execute the selected computation using the changed value of the second field to generate an updated computed value for the first field based on the dependence (Tang, page 3, Figure 2
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where “We first formally define the task. Given a knowledge graph G = (E, R, T), where E, R and T are the entity set, relation set and KG triple set, respectively, and a new text snippet S = {w1, w2,…, w|S|}, for which entity linking has been performed to build the mentioned entity set L
⊂
E, the text-based knowledge graph updating task is to read the news snippet S and update T accordingly to get the final triple set T’ and the updated graph G’ = (E, R, T’)” (Tang, page 2, 2nd column, Section 2 Task Formulation, 1st paragraph) and “we use DistMult (Yang et al., 2014), which is known to have good performance on standard KG completion tasks, followed by sigmoid function, as the decoder i.e., for each possible triple (ei, rk, ej) where ei, ej
∈
E1hop, rk
∈
R, the probability of this triple to appear in the final KG is computed as follow:
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” where “Many recent works focus on embedding a KG into a continuous vector space, which can be used to fill in missing links in KGs….However, most of them learn from static KGs, thus unable to help with our task, since they can not dynamically add new links or delete obsolete links according to extra text information. In this paper, we propose a novel neural model, GUpdater, to tackle this problem, which features a graph based encoder to learn latent KG representations with the guidance from the news text, and a decoder to score candidate triples with reconstructing the KG as the objective” (Tang, page 1, 2nd column, last paragraph – Tang, page 2, 1st column, 2nd paragraph ). Examiner notes that the first field is the triple set and the indication is the text snippet. Examiner notes that the metadata is DistMult and the sigmoid function used to compute the triple set probabilities. Examiner further notes that the probability of the triple to appear shown by the thickness of the dashed lines in Figure 2 is the computed value. Additionally, reconstructing the KG with the probability of the triple included in the processing is using the changed value of the second field to generate an updated computed value. Examiner further notes that the probability of the triple depends on the relation set and thus the updated computed value is based on the dependence.).
Store the updated computed value as the current value of the first field at the particular node in the knowledge graph (Tang, page 3, Figure 2 (see below)
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where “We first formally define the task. Given a knowledge graph G = (E, R, T), where E, R and T are the entity set, relation set and KG triple set, respectively, and a new text snippet S = {w1, w2,…, w|S|}, for which entity linking has been performed to build the mentioned entity set L
⊂
E, the text-based knowledge graph updating task is to read the news snippet S and update T accordingly to get the final triple set T’ and the updated graph G’ = (E, R, T’)” (Tang, page 2, 2nd column, Section 2 Task Formulation, 1st paragraph) and “we use DistMult (Yang et al., 2014), which is known to have good performance on standard KG completion tasks, followed by sigmoid function, as the decoder i.e., for each possible triple (ei, rk, ej) where ei, ej
∈
E1hop, rk
∈
R, the probability of this triple to appear in the final KG is computed as follow:
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” Examiner notes that the first field is the triple set and the indication is the text snippet. Examiner further notes that the probability of the triple to appear shown by the thickness of the dashed lines in Figure 2 is the computed value. Additionally, the triple set, or first field, is updated by using the probability of the triple to appear as shown in Figure 2.).
Huaman, Liu, and Tang are considered analogous to the claimed invention because they update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman and Liu to have received an indication. Doing so is advantageous because it “enables us to perform both link-adding and link-deleting to ensure the KG up-to-date” (Tang, page 2, 2nd column, 2nd point).
Regarding claim 2, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein generating the computed value for the first field comprises performing an operation, wherein the value of the second field is an input to the operation, and the computed value for the first field is an output of the operation (Huaman, page 6, 2nd to last paragraph, “We compute the similarity of an attribute value of two instances a, b. Where a represents an instance in the user’s KG g, denoted g(a), and b represents an instance in the knowledge source si, denoted si(b)” and Huaman, page 7, Equation (2),
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Examiner notes that the instances, or second field, are input to equation 2 and the triple confidence, or computed value for the first field is output. ).
Regarding claim 3, Huaman in view of Liu and Tang teach the system of claim 2. Huaman further teaches
wherein the operation comprises a transform, and performing the operation comprises applying the transform to the value of the second field (Huaman, page 6, 2nd to last paragraph, “We compute the similarity of an attribute value of two instances a, b. Where a represents an instance in the user’s KG g, denoted g(a), and b represents an instance in the knowledge source si, denoted si(b)” and Huaman, page 7, Equation (2),
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Examiner notes equation 2 is the operation comprising a transform. Examiner further notes that the operation is performed on the instance or the second field. ).
Regarding claim 4, Huaman in view of Liu and Tang teach the system of claim 2. Huaman further teaches
wherein the operation is defined by a user (Huaman, page 6 last paragraph – page 7, first paragraph, “Next, users have to set an external weight for each knowledge source si, W = {w1,…,wm} is a set of weights over the knowledge sources, such as wi defines a weight of importance for si, 0 ≤ i ≤ m, wi
∈
W with wi
∈
[0,1] where 0 is the minimum degree of importance and a value of 1 is the maximum degree.”).
Regarding claim 5, Huaman in view of Liu and Tang teach the system of claim 2. Huaman further teaches
wherein the operation is one of a plurality of pre-defined operations (Huaman, page 2, 2nd paragraph, “We developed an approach to validate a KG against different knowledge sources. Our approach involves (1) mapping the different knowledge sources to a common schema (e.g. Schema.org3), (2) instance matching that ensures that we are comparing the same entity across the different knowledge sources, (3) confidence measurement, which computes a confidence score for each triple and instance in the KG, and (4) visualization that offers an interface to interact with.” Examiner notes that numbers 1-4 of the approach are pre-defined operations and the confidence measurement is the operation.),
and executing the instructions further cause the at least one processor to receive, … a selection of the operation from the plurality of pre-defined operations (Huaman, page 6, 2nd paragraph, “Computing a confidence value can get complicated as the number of instances and their feature can get out of hand quickly. Therefore, a means to automatically validate KGs is desirable. To compute a confidence value for an instance, the confidence value for each of its triples has to be evaluated first” where “to execute this task the Validator required ~15 minutes approximately on a CPU described in Table 1” (Huaman, page 10, last paragraph). Examiner notes that the selection of the operation is the execution of evaluating the confidence value for each of its triples.)
Huaman does not explicitly disclose, but Liu does disclose
receive, at a particular node in the knowledge graph, a selection of the operation from the plurality of pre-defined operations (Liu, page 4, paragraph 0025, “The mining process usually starts from the source node of the knowledge graph, along edges of the knowledge graph, to target components represented by target nodes of the knowledge graph, to extract different paths between the source node and the target nodes, wherein each logical rule derived from an extracted path is assigned to said confidence value. The knowledge graph usually contains information about historical configuration solutions, components along with their technical features, user data, user order history, and background information considering the components and configurations (e.g., compatibility, availability)” where “when a set of product components and their technical features are provided a user selects components that are compatible with each other and fulfill the functional requirements of the project configuration” (Liu, page 3, paragraph 0004). Examiner notes that mining uses a selection from the user at a particular node in a knowledge graph.)
Huaman and Liu are analogous to the claimed invention because they both disclose updating knowledge graphs. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to use the knowledge graph in Liu in place of Huaman. Thus, this would be a simple substitution of one known element (Huaman’s knowledge graph) for another (Liu’s knowledge graph) to obtain predictable results (an updated knowledge graph) (MPEP 2143 I. (B) Simple substitution of one known element for another to obtain predictable results).
Regarding claim 6, Huaman in view of Liu and Tang teach the system of claim 2. Huaman further teaches
wherein the operation comprises: generating a request to an external data source, the request comprising the value of the second field (Huaman, page 6, 5th paragraph, “We define a set of knowledge sources as S, S = {s1,…, sm}, si
∈
S with 1 ≤ i ≤ m. The user’s KG g consists of a set of instances that are to be validated against the set of knowledge sources S. A knowledge source si consists of a set of instances E={e1,…,en}, ej
∈
E with 1≤ j ≤ n and an instance ej consists of a set of attribute values P = {p1,…,pm}, pk
∈
P for 1≤ k ≤ M.” Examiner notes that generating a request to an external data source is defining a set of knowledge sources.);
providing the request to the external data source (Huaman, page 6, 5th paragraph, “We define a set of knowledge sources as S, S = {s1,…, sm}, si
∈
S with 1 ≤ i ≤ m. The user’s KG g consists of a set of instances that are to be validated against the set of knowledge sources S. A knowledge source si consists of a set of instances E={e1,…,en}, ej
∈
E with 1≤ j ≤ n and an instance ej consists of a set of attribute values P = {p1,…,pm}, pk
∈
P for 1≤ k ≤ M” where “Internally, the Validator has been set up to fetch data from different external sources (e.g. Wikidata, DBpedia), which were selected based on their domain coverage for the task at hand and their widely use [8]” (Huaman, page 5, 2nd paragraph). Examiner notes that providing a request to an external data source is defining a set of knowledge sources and fetching the data from different external sources.);
receiving a response from the external data source (Huaman, page 4, Figure 1,
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Examiner notes that the receiving a response from the external data source is the arrow from Google, OpenStreetMap, and Yandex.);
and generating the computed value for the first field using the response from the external data source (Huaman, page 4, Figure 1,
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Examiner notes that the response from the external data source is the arrow from Google, OpenStreetMap, and Yandex. Examiner further notes that the triple confidence score is generated using the response since the triple validation is further down the pipeline.).
Regarding claim 8, Huaman in view of Liu and Tang teach the system of claim 6. Huaman further teaches
wherein the external data source is a database, the request is a query to the database, and the response is a reply of the database to the query (Huaman, page 6, 5th paragraph, “We define a set of knowledge sources as S, S = {s1,…, sm}, si
∈
S with 1 ≤ i ≤ m. The user’s KG g consists of a set of instances that are to be validated against the set of knowledge sources S. A knowledge source si consists of a set of instances E={e1,…,en}, ej
∈
E with 1≤ j ≤ n and an instance ej consists of a set of attribute values P = {p1,…,pm}, pk
∈
P for 1≤ k ≤ M” where “Internally, the Validator has been set up to fetch data from different external sources (e.g. Wikidata, DBpedia), which were selected based on their domain coverage for the task at hand and their widely use [8]” (Huaman, page 5, 2nd paragraph) and “Wikidata query service raised timeout errors when querying data, so we decided to fetch the maximum allowed number of politician instances from Wikidata and stored them locally” (Huaman, page 11, 2nd paragraph). Examiner notes that providing a request to an external data source is defining a set of knowledge sources and fetching the data from different external sources. Examiner further notes that the request is a query to Wikidata and the reply is fetching the instances.).
Regarding claim 10, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein the received data is received from a user (Huaman, page 5, 2nd paragraph, “At first step, a user is required to provide a KG to be validated. For this, the user has two options, a) to provide a SPARQL endpoint where to fetch the data from or b) to load a dataset in a Turtle format. Moreover, the user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address).”).
Regarding claim 15, Huaman, Liu, and Tang teach the system of claim 1. Huaman in view of Liu and Tang further teaches
wherein the node is a first node that is associated with a second node in the knowledge graph, and executing the instructions further cause the at least one processor to update a field of the second node based on the computed value (Tang, page 3, Figure 2
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where “We first formally define the task. Given a knowledge graph G = (E, R, T), where E, R and T are the entity set, relation set and KG triple set, respectively, and a new text snippet S = {w1, w2,…, w|S|}, for which entity linking has been performed to build the mentioned entity set L
⊂
E, the text-based knowledge graph updating task is to read the news snippet S and update T accordingly to get the final triple set T’ and the updated graph G’ = (E, R, T’)” (Tang, page 2, 2nd column, Section 2 Task Formulation, 1st paragraph) and “we use DistMult (Yang et al., 2014), which is known to have good performance on standard KG completion tasks, followed by sigmoid function, as the decoder i.e., for each possible triple (ei, rk, ej) where ei, ej
∈
E1hop, rk
∈
R, the probability of this triple to appear in the final KG is computed as follow:
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”. Examiner notes that the first field is the triple set, the indication is the text snippet, the first node is JB [Jimmy Butler] and the second node is The Minnesota Timberwolves. Examiner further notes that the probability of the triple to appear shown by the thickness of the dashed lines in Figure 2 is the computed value.).
Huaman, Liu, and Tang are considered analogous to the claimed invention because they update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman and Liu to have received an indication. Doing so is advantageous because it “enables us to perform both link-adding and link-deleting to ensure the KG up-to-date” (Tang, page 2, 2nd column, 2nd point).
Regarding claim 16, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein executing the instructions further cause the at least one processor to: provide the computed value for the first field as an input to a plurality of operations (Huaman, page 7, last paragraph, “Instance validation computes the aggregated score from the attribute space of an instance. Given and instance a that consists of a set of attribute values P = {p1,… pm}, pk
∈
P for 1 ≤ k ≤ M:
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” Examiner notes that the tripleconfidence is the computed value for the first field and the plurality of operations is equation 3.);
and perform the plurality of operations to generate a plurality of outputs (Huaman, page 10, last paragraph, “We validated 2530 politician instances by using the Validator, which compares and computes a confidence score for each triple and instance” where “Instance validation computes the aggregated score from the attribute space of an instance. Given and instance a that consists of a set of attribute values P = {p1,… pm}, pk
∈
P for 1 ≤ k ≤ M:
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” (Huaman, page 7, last paragraph) Examiner notes that a instanceconfidence is generated for each instance in which the plurality of confidence scores is the plurality of outputs and equation 3 is the plurality of operations.).
Regarding claim 17, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein defining the value of the second field comprises: applying a data model to transform the received data (Huaman, page 5, 2nd paragraph, “The user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address)” where “Based on the DS defined in the input, the validator maps the input KG and the external sources to a common format, e.g., a telephone number of a hotel can be stored with different property names across the knowledge sources: phone, telephone, or phone_number. The validator provides a basic mapping feature to map the input KG and external data sources to a common attribute space” (Huaman, page 5 last paragraph). Examiner notes that the second field is the instance type and the data model is the validator.);
and defining the value of at least the second field based on the transformed data, wherein the data model provides a mapping from the received data to the plurality of fields of the particular node (Huaman, page 5, 2nd paragraph, “The user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address)” where “Based on the DS defined in the input, the validator maps the input KG and the external sources to a common format, e.g., a telephone number of a hotel can be stored with different property names across the knowledge sources: phone, telephone, or phone_number. The validator provides a basic mapping feature to map the input KG and external data sources to a common attribute space” (Huaman, page 5 last paragraph) and “the Validator requests to define at least two or more properties (e.g., name and geo coordinates) that are to be used for the instance matching process, which is constrained to strict matches on the defined property values. The resulting matched instance is returned to the Validator and processed to measure its confidence” (Huaman, page 6, 1st paragraph). Examiner notes that the second field is the instance type and the data model is the validator. Examiner further notes that the transformed data is the matched instance.).
Regarding claim 18, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein the received data comprises a profile, and defining the value of the second field comprises applying the profile to the second field (Huaman, page 5, 2nd paragraph, “The user is required to select, from a list of DSs (Domain Specification), a DS that defines an instance type (e.g., Hotel, Person) and their corresponding properties (e.g., name, address)” where “Based on the DS defined in the input, the validator maps the input KG and the external sources to a common format, e.g., a telephone number of a hotel can be stored with different property names across the knowledge sources: phone, telephone, or phone_number. The validator provides a basic mapping feature to map the input KG and external data sources to a common attribute space” (Huaman, page 5 last paragraph). Examiner notes that the second field is the instance type.).
Regarding claim 19, Huaman in view of Liu and Tang teach the system of claim 1. Huaman further teaches
wherein updating the first field using the computed value comprises performing a validation on the computed value, wherein the validation comprises at least one rule to which any value of the first field must conform (Huaman, page 7, last paragraph, “Instance validation computes the aggregated score from the attribute space of an instance. Given and instance a that consists of a set of attribute values P = {p1,… pm}, pk
∈
P for 1 ≤ k ≤ M:
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The instance confidence measures the degree to which an instance is correct based on the triple confidence of each of its attributes. The instance confidence score is compared against a threshold14 t
∈
[0,1]. If instanceconfidence > t indicates its degree of correctness” where “We compute a triple confidence score, the triple confidence scores are later added to an aggregated confidence score for the instance” (Huaman, page 5, 1st paragraph). Examiner notes that the tripleconfidence is the computed value for the first field and the validation is comparing the instance confidence score, which depends on the triple confidence, against a threshold. Examiner further notes that the rule is to be above the threshold.).
Claim(s) 7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huaman in view of Liu and Tang and in further view of Fuerst et al. (US 2021/0357767 A1) (hereafter referred to as Fuerst).
Regarding claim 7, Huaman in view of Liu and Tang teaches the system of claim 6. Huaman in view of Liu and Tang does not teach, but Fuerst does teach
wherein the external data source is a machine learning model, the request is a prompt to the machine learning model, and the response is an output of the machine learning model to the prompt (Fuerst, page 18, paragraph 0054, “According to an embodiment of the present invention, the architecture 100 includes external knowledge sources 102 (e.g., ontologies, domain knowledge, physical models, and/or knowledge bases), a knowledge base 104, one or more user interfaces 106, a knowledge infusion device 108, and a knowledge fusion model 110” where “During the execution phase (e.g., after generating the knowledge fusion model 110 from FIG. 1) new data 202 may processed by both the ML model and the knowledge model of the knowledge fusion model 110. As described previously, the knowledge fusion model 110 permits the correction of potential wrong outputs of the ML model such that it improves overall robustness and is used to calculate an uncertainty value that enables knowledge infusion system… to understand if reality has drifted away from the ML model” (Fuerst, page 19, paragraph 0055) and Fuerst, page 5, FIG. 4
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Examiner notes that the ML model in the Knowledge fusion model is the external data source. Examiner further notes that the request is the new sensor values from the smart city platform and the response is the updated knowledge model to the smart city platform.).
Huaman, Liu, Tang, and Fuerst are considered analogous to the claimed invention because they all update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman, Liu, and Tang to use the ML model in Fuerst. Doing so is advantageous because “the knowledge fusion model 110 permits the correction of potential wrong outputs of the ML model such that it improves overall robustness and is used to calculate an uncertainty value that enables knowledge infusion system… to understand if reality has drifted away from the ML model” (Fuerst, page 19, paragraph 0055).
Regarding claim 9, Huaman in view of Liu and Tang teaches the system of claim 6. Huaman in view of Liu and Tang does not teach, but Fuerst does teach
wherein the external data source is an application programming interface (API), the request is an input to the API, and the response is an output of the API to the input (Fuerst, page 18, paragraph 0054, “As shown, external knowledge sources 102 may be an application programming interface (API) such as a weather API that is directly accessed by the knowledge functions for the knowledge infusion device 108” and Fuerst, page 2, FIG. 1
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Examiner notes that the knowledge functions accessing the API is the request as input and the arrows from 102 to 108 in FIG. 1 are the response as an output.).
Huaman, Liu, Tang, and Fuerst are considered analogous to the claimed invention because they all update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman, Liu, and Tang to use the API in Fuerst. Doing so is advantageous because “embodiments of the present invention use weak and strong knowledge functions that include a reasoning part and a query interface to internal knowledge graphs and external knowledge sources, which enables these functions to adapt automatically based on new knowledge without any user involvement” (Fuerst, page 15, paragraph 0022).
Regarding claim 14, Huaman, Liu and Tang teach the system of claim 1. Huaman in view of Liu and Tang does not teach, but Fuerst does teach
wherein the indication is automatically triggered after a period of time (Fuerst, page 22, paragraph 0097, “The weak and strong knowledge functions may include a reasoning part and a query interface to the in internal knowledge graph and external knowledge. This may enable them to adapt automatically based on the new knowledge without any human involvement. Through that, the same knowledge infusion system may be used across different deployments and it will automatically update through time” where “each time a pre-defined time period elapses, the computing device and/or the user interface 106 may provide a query to the external knowledge sources 102 and/or knowledge base 104 indicating whether any knowledge functions are to be updated. Based on the query, the computing device and/or user interface 106 may receive updated information from the external knowledge sources 102 and/or the knowledge base 104 and may use the updated information to update the weak and strong functions” (Fuerst, page 23, paragraph 0111). Examiner notes that the indication is the updated information or new knowledge.).
Huaman, Liu, Tang, and Fuerst are considered analogous to the claimed invention because they all update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman, Liu, and Tang to automatically trigger the indication after a period of time like in Fuerst. Doing so is advantageous because “this may enable them to adapt automatically based on the new knowledge without any human involvement” (Fuerst, page 22, paragraph 0097).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huaman in view of Liu and in further view of Tang and Chandak et al. (“Building a knowledge graph to enable precision medicine”) (hereafter referred to as Chandak).
Regarding claim 12, Huaman, Liu and Tang teach the system of claim 1. Huaman in view of Liu and Tang does not teach, but Chandak does teach
wherein the indication is received by a user (Chandak, page 11, 7th paragraph, “We used ClinicalBERT to extract word embeddings for disease group names identified during string matching. We also defined the similarity between two disease names as the cosine distance between their ClinicalBERT embeddings. Then, after applying an empirically chosen cutoff of similarity ≥0.98, we manually approved the suggested disease matches and assigned names to the new groups. Finally, these groupings were applied to the knowledge graph” where “disease definitions from the MONDO Disease Ontology were directly extracted from the ontology file and unique for each ‘node_id’. Disease descriptions extracted from UMLS were mapped from Concept Unique Identifier (CUI) terms to MONDO and, as a result, numerous for each ‘node_id’” (Chandak, page 8, 1st paragraph) and “We extracted the following disease features from the Mayo Clinic’s knowledgebase: symptoms, causes, risk factors, complications, and prevention. Since the Mayo Clinic web-scrapping did not provide a unique identifier in any ontology, we mapped disease names in Mayo Clinic to those in MONDO Disease Ontology. To develop this mapping, we used a strategy for grouping disease names described in detail in the Technical Validation section. Briefly, we conducted automated string matching followed by manual approval of all disease name mappings based on their Bidirectional Encoder Representations from Transformers (BERT) model embedding similarity” (Chandak, page 8, 2nd paragraph). Examiner notes that the indication is the group of disease names and features that is provided to the user to manually approve. ).
Huaman, Liu, Tang, and Chandak are considered analogous to the claimed invention because they all update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman, Liu, and Tang to automatically have the user receive the indication like in Chandak. Doing so is advantageous because “users can update individual resources as necessary” (Chandak, page 10, paragraph second to last paragraph).
Regarding claim 20, Huaman, Liu, and Tang teach the system of claim 1. Huaman in view of Liu, and Tang does not teach, but Chandak does teach
wherein executing the instructions further cause the at least one processor to provide the computed value to a user as a suggested value, and receive a confirmation from the user to proceed to update the first field using the suggested value (Chandak, page 11, 7th paragraph, “We used ClinicalBERT to extract word embeddings for disease group names identified during string matching. We also defined the similarity between two disease names as the cosine distance between their ClinicalBERT embeddings. Then, after applying an empirically chosen cutoff of similarity ≥0.98, we manually approved the suggested disease matches and assigned names to the new groups. Finally, these groupings were applied to the knowledge graph” where “disease definitions from the MONDO Disease Ontology were directly extracted from the ontology file and unique for each ‘node_id’. Disease descriptions extracted from UMLS were mapped from Concept Unique Identifier (CUI) terms to MONDO and, as a result, numerous for each ‘node_id’” (Chandak, page 8, 1st paragraph) and “We extracted the following disease features from the Mayo Clinic’s knowledgebase: symptoms, causes, risk factors, complications, and prevention. Since the Mayo Clinic web-scrapping did not provide a unique identifier in any ontology, we mapped disease names in Mayo Clinic to those in MONDO Disease Ontology. To develop this mapping, we used a strategy for grouping disease names described in detail in the Technical Validation section. Briefly, we conducted automated string matching followed by manual approval of all disease name mappings based on their Bidirectional Encoder Representations from Transformers (BERT) model embedding similarity” (Chandak, page 8, 2nd paragraph). Examiner notes that the computed value as a suggested value is the similarity and the confirmation is the manual approval. Examiner further notes that the features of the disease is the first field. ).
Huaman, Liu, Tang and Chandak are considered analogous to the claimed invention because they all update knowledge graphs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huaman, Liu, and Tang to receive a confirmation from the user like in Chandak. Doing so is advantageous because “users can update individual resources as necessary” (Chandak, page 10, paragraph second to last paragraph).
Response to Arguments
Examiner notes that the previous 112(d) rejection has been withdrawn in light of the instant amendments.
On pages 7-8, Applicant argues:
Here, the specification identifies a technical problem in "content population at scale across a structured content management system." The specification explains that manually populating fields is limited, and that running code outside the core CMS to calculate and populate values is challenging. See, e.g., paragraphs [0030], [0032], [0168]-[0169].
The amended claim addresses this problem through a specific Knowledge Graph data Structure and update mechanism. The claim recites machine-readable metadata associated with a first field at a particular node in the Knowledge Graph. That metadata identifies the first field as a computed field, specifies a selected computation for the first field, and specifies a list of input fields for the selected computation, including the second field.
The claim then recites how the system uses that field-associated metadata. The processor uses the metadata to execute the selected computation using the value of the second field, automatically generates the computed value for the first field, and stores the computed value as the current value of the first field at the particular node. When the second field changes, the processor again uses the metadata to execute the selected computation using the changed value of the second field and stores the updated computed value as the current value of the first field.
This is not merely the abstract idea of generating or updating information. The claim is directed to a Knowledge Graph-specific configuration and execution mechanism for maintaining computed field values in a graph-based data structure. The specification supports this mechanism by describing computed values based on inputs and selected computations, metadata associated with computed fields identifying a selected computation and input fields, and storage of computed outputs as field values. See, e.g., paragraphs [0022]-[0023], [0083]-[0086], [0135], [0141]-[0143], [0169] [ 0171], [0207]-[0209], [0218]-[0223].
This case is similar to Enfish. In Enfish, claims to a self-referential table improved database functionality by defining aspects of the table within the data structure itself Here, the amended claim likewise improves Knowledge Graph functionality by defining, within the node-level field structure, a computed field whose associated metadata identifies the computation and input fields used to maintain that field. The USPTO's Desjardins guidance confirms that data-structure improvements, including improvements in storage, data sets, and structures, can constitute patent-eligible technological improvements.
Accordingly, the amended claim does not merely use a generic computer as a tool to generate and update values. The claim recites a specific Knowledge Graph data-structure/update mechanism that improves how the Knowledge Graph maintains dependent computed fields. Thus, any alleged abstract idea is integrated into a practical application, and withdrawal of the §101 rejection is respectfully requested.
Regarding the Applicant’s arguments that the claims provide an improvement, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that improvements cannot be provided by the judicial exceptions alone (MPEP 2106.05(a)). In this case, the improvements from the specification are reflected in the abstract ideas of generating a knowledge graph, defining a plurality of fields, updating values, and generating values.
Examiner further respectfully notes that the additional elements do not provide significantly more than the abstract idea either. “A system comprising: at least one processor; and a non-transitory computer-readable storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to” and “…automatically generate a computed value…” amounts to mere “apply it on a computer” (see MPEP 2106.05(f)) and thus, uses a computer as a tool to perform the abstract idea which cannot provide significantly more (see MPEP 2106.05(f)). “Receive data at the knowledge graph” and “Receive an indication at the particular node in the knowledge graph, wherein the indication is triggered by a change in the value of the second field” recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)), and thus is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). “Store the computed value as a current value of the first field at the particular node in the knowledge graph” and “store the updated computed value as a current value of the first field at the particular node in the knowledge graph” recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)), and thus is the well understood, routine, and conventional activity of “Storing and retrieving information in memory” (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93)
On pages 8-9, Applicant argues:
Independent claim I has been amended to further recite a specific Knowledge Graph computed-field mechanism. In particular, claim I now recites that metadata associated with the first field identifies the first field as a computed field, specifies a selected computation for the first field, and specifies a list of input fields for the selected computation, including the second field. Claim I also recites using that metadata to execute the selected computation and store the computed value as the current value of the first field at the particular node.
None of the cited references, either alone or in combination, discloses the amended features in claim 1.
Specifically, Huaman is directed to validating a KG by mapping an input KG and external sources to a common format, comparing corresponding values, and computing confidence scores for triples and instances. Huaman's Domain Specification and confidence-score framework are used for validation, not for defining a node-level computed field having metadata that specifies a selected computation and input-field list.
Thus, even if Huaman describes properties such as name, address, or phone, those properties are not the claimed field-associated metadata. Huaman does not disclose metadata associated with a first field that identifies the field as computed, identifies the computation for generating the field value, identifies input fields including a second field, and causes the computed value to be stored back as the current value of the first field at the particular node.
Liu also does not cure this deficiency. Liu is directed to generating industrial-system recommendations by mining logical rules from a KG, assigning confidence scores to those rules, applying the rules to project queries, and outputting recommended components. Liu's logical rules and confidence scores are used to rank or explain recommended components, not to maintain a computed field value at a node using field-associated metadata.
Likewise, Tang is directed to text-based KG updating by reading an external news snippet and updating the KG triple set through link-adding or link-deleting. Tang updates graph links/triples based on an external text snippet; it does not disclose metadata associated with a node-level computed field that specifies a selected computation and input fields for maintaining that field.
Therefore, the cited combination fails to teach or suggest the amended limitations of independent claim 1. Withdrawal of the § 103 rejection is respectfully requested.
Regarding the Applicant’s argument that the prior art of record does not disclose the amended limitations, Examiner respectfully disagrees. Specifically, Examiner notes that a combination of Huaman, Liu, and Tang teach the newly amended limitations. More specifically, Examiner notes that Huaman teaches the metadata under broadest reasonable interpretation since Equation 1 is data that describes the first field or computed field, specifies the selected computation, and specifies a list of input fields.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chaudhri et al. (“An Introduction to Knowledge Graphs”) discusses the generic structures and applications of a knowledge graph.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 8:00 am - 6:00 pm EST.
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/K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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