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 .
Priority
This application is a continuation of, and claims priority to, U.S. patent application Ser. No. 17/805,954, filed on Jun. 8, 2022, entitled “ONBOARDING OF ENTITY DATA”, which is a continuation of, and claims priority to, U.S. patent application Ser. No. 16/621,583, filed on Dec. 11, 2019, entitled “IMPROVED ONBOARDING OF ENTITY DATA”, now U.S. Pat. No. 11,361,227, which is a 35 U.S.C. § 371 National Phase Entry Application from PCT/US2017/062859, filed on Nov. 21, 2017, entitled “IMPROVED ONBOARDING OF ENTITY DATA”, the disclosures of which are incorporated by reference herein in their entirety.
DETAILED ACTION
This Office Action is in response to an Amendment Application on 07/14/2026. In the Application, claims 2, 7, 9, 15, 17-18, and 20 have been amended. Claims 3-6, 8, 10-14, 16, 19, and 21 remain original. Claim 1 remain cancelled. No new claim has been added.
For this Office Action, claims 2-21 have been received for consideration and have been examined.
Response to Arguments
Claim Rejections – 35 USC § 101
Applicants’ amendments to claims 2, 9, and 15 have been reviewed, however, amended claims still recite an Abstract Idea which falls into two recognized sub-categories of abstract ideas, i.e., Mathematical Concepts / Mental Processes & Certain Methods of Organizing Human Activity.
The limitations focus entirely on collecting, analyzing, and manipulating data without any specific technical improvements to hardware or software functionality.
As mentioned above, the limitations recite two recognized sub-categories of abstract ideas as follows:
Mathematical Concepts / Mental Processes: Organizing data by identifiers or relationships, calculating statistics, and evaluating a "confidence measure" against a criterion are steps that can be performed conceptually or via human mental activity.
Certain Methods of Organizing Human Activity: "Onboarding" entities, managing a data graph, and triggering remedial actions are high-level concepts for managing data workflows.
Claim limitations have been mapped to show the Abstract Idea as follows:
Claim Limitation
Abstract Category / Equivalent
Receive a request to onboard...
Data Collection: Merely receiving or gathering input data.
Analyze entity data... to identify a common identifier...
Data Classification: Sorting, filtering, or matching data based on shared traits.
Determine whether a statistic represents a failure...
Mathematical/Logical Comparison: Comparing a metric against a rule or threshold.
Cause at least one remedial action... onboarding the synthetic data...
Conditional Execution: Taking generic business or administrative action based on the result of the analysis.
In order to make the claim patent-eligible (under Step 2B or the second prong of Step 2A), the claim must demonstrate a technical solution to a technical problem. Right now, the limitations are written in functional "what-it-does" language rather than "how-it-technically-does-it."
Additionally, the claims lack to show following steps:
Improve computer functionality: For example, does onboarding synthetic data reduce memory consumption of the knowledge graph? Does it optimize database query speeds?
Use a specific, non-conventional implementation: If the "confidence measure" or the way "synthetic data" is generated uses a highly specific, unconventional algorithmic architecture (rather than generic "if/then" rules), emphasizing that specific mechanism in the claims can overcome the rejection.
Based on above explanations, the amended claim still recite an Abstract Idea and therefore this rejection has been maintained.
Double Patenting
The amended claims do not change the scope of the nonstatutory double patenting rejection. Therefore, the rejection has been maintained.
Claim Rejections - 35 USC § 101 (Abstract Idea)
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more analyzed according to MPEP 2106.
Independent claims 2, 9, and 15 nominally fall within statutory categories:
Claim 2 recites a system and therefore falls within the “machine” category.
Claim 9 recites a computer-implemented method and therefore falls within the “process” category.
Claim 15 recites a non-transitory computer-readable medium and therefore falls within the “manufacture” category.
The claims are therefore evaluated under Step 2A of the Alice/Mayo eligibility analysis.
Independent claims 2, 9, and 15
Independent claims 2, 9, and 15 recite substantially corresponding limitations. Accordingly, the claims are analyzed together.
Step 2A, Prong One: Judicial exception
Claims 2, 9, and 15 recite the abstract idea of:
Evaluating entity information for compliance with rules and selecting a remedial action based on the evaluation.
Specifically, the claims recite:
Receiving a request to onboard a plurality of entities to a knowledge graph;
Analyzing entity data to identify an entity having a common identifier or common relationship;
Determining whether a statistic represents a failure of applying a rule to the entity; and
In response to the determination, causing a remedial action to be taken based on the statistic.
The limitations of analyzing entity data to identify common identifiers or relationships and determining whether a statistic represents failure of a rule describe comparisons, evaluations, and judgments. Such operations can be performed in the human mind or with the assistance of pen and paper. For example, a person could review records describing multiple entities, identify entities sharing a common identifier or relationship, apply an established rule to the identified entities, and determine whether the rule has been violated.
Determining a remedial action based on the result constitutes a judgment concerning what corrective action should be taken. These limitations therefore recite a mental process under the “mental processes” grouping of abstract ideas identified in MPEP § 2106.04(a)(2).
To the extent that determining the recited “statistic” requires calculating a numerical value or percentage, the claims additionally recite a mathematical calculation. For purposes of Step 2A, Prong Two and Step 2B, these limitations are considered together as the single abstract idea identified above.
The claimed concept is analogous to the collection and analysis of information held abstract in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353–54 (Fed. Cir. 2016), and the statistical analysis held abstract in SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167–68 (Fed. Cir. 2018).
Accordingly, claims 2, 9, and 15 recite a judicial exception.
Step 2A, Prong Two: Practical application
The additional elements recited by claims 2, 9, and 15 include:
A processor;
A memory or non-transitory computer-readable medium storing instructions;
Computer implementation of the recited operations; and
A knowledge graph as the environment in which the entity information is onboarded.
These elements do not integrate the abstract idea into a practical application.
The processor, memory, and computer-readable medium are recited at a high level of generality and merely provide generic computer components for performing the information-analysis process. The claims do not require a particular processor, memory architecture, storage architecture, or unconventional arrangement of computer components.
The knowledge graph similarly provides the data environment in which the abstract analysis is performed. The claims do not recite:
A particular knowledge-graph data structure;
A specific arrangement of graph nodes or edges;
An improved graph-indexing or traversal mechanism;
A particular semantic-fingerprint algorithm;
A specific algorithm for calculating the failure statistic; or
A particular technical mechanism for performing the remedial action.
Instead, the claims use result-oriented functional language requiring the system to “analyze,” “determine,” and “cause” the stated results without specifying how those results are technically achieved.
Although the specification indicates that the disclosed techniques may improve the efficiency of onboarding data, reduce onboarding errors, and reduce the number of onboarding iterations, the claims do not recite a specific improvement to processor operation, memory operation, database architecture, or knowledge-graph technology. Improving the accuracy or efficiency of an abstract information-analysis process does not, by itself, establish an improvement in computer functionality.
The claims also do not employ a particular machine integral to the abstract idea, transform a physical article into a different state or thing, or otherwise apply the abstract idea in a meaningful manner. Limiting the claimed analysis to knowledge-graph onboarding merely links the exception to a particular technological environment.
Accordingly, claims 2, 9, and 15 do not integrate the judicial exception into a practical application and are directed to the abstract idea.
Step 2B: Inventive concept
The processor, memory, and computer-readable medium perform only their ordinary functions of storing and executing instructions and receiving, analyzing, and storing data. The specification’s description of Figure 8 confirms that the disclosed operations may be implemented using general computing equipment, including workstations, servers, computing clusters, blade servers, server farms, and other data-processing systems.
The remaining limitations—receiving information, comparing entity attributes and relationships, applying rules, evaluating statistics, and selecting remedial action—constitute the abstract idea itself and cannot supply the inventive concept.
When considered individually and as an ordered combination, the additional elements merely instruct a generic computer to implement the abstract evaluation process. No unconventional computer arrangement or particular technical implementation is recited.
Therefore, independent claims 2, 9, and 15 do not recite significantly more than the abstract idea.
Dependent claims 3, 10, and 16
Claims 3, 10, and 16 specify that applying the rule includes satisfaction of a particular identifier or relationship by the entity.
Determining whether an entity possesses a required identifier or relationship further defines the rule-based evaluation constituting the abstract idea. The limitation remains a comparison and judgment that can be performed mentally.
The claims do not specify a particular technical mechanism for locating, comparing, authenticating, or validating the identifier or relationship. Accordingly, these limitations neither integrate the exception into a practical application nor provide significantly more than the abstract idea.
Claims 3, 10, and 16 are therefore ineligible under § 101.
Dependent claims 4, 11, and 17
Claims 4, 11, and 17 require the statistic to be included in a failure report that further includes percentages associated with other entities having different common identifiers or relationships and violating a rule.
Calculating percentages constitutes a mathematical operation. Organizing those percentages into a failure report constitutes organizing and presenting information. These limitations further refine the abstract analysis and communicate its results.
Generating a report following an abstract evaluation is insignificant post-solution activity and does not improve the operation of a computer or knowledge graph. See Electric Power Group, 830 F.3d at 1354–55.
Accordingly, claims 4, 11, and 17 do not integrate the abstract idea into a practical application or provide an inventive concept.
Dependent claims 5, 12, and 18
Claims 5, 12, and 18 require comparing an entity schema associated with the entity data to a knowledge-based schema of the knowledge graph.
Comparing two schemas constitutes comparing, classifying, and evaluating information. The claims do not recite:
A particular schema structure;
A particular schema-mapping algorithm;
A particular data-conversion process;
A specific mechanism for resolving schema conflicts; or
An improvement to the manner in which a computer stores or retrieves graph data.
The schema-comparison limitation therefore adds another abstract data-comparison operation rather than a particular improvement to database or knowledge-graph technology.
Accordingly, claims 5, 12, and 18 do not integrate the exception into a practical application or add significantly more than the abstract idea.
Dependent claims 6, 13, and 19
Claims 6, 13, and 19 specify that the remedial action includes providing the statistic to a source of the entity data.
Providing the result of the abstract analysis to an information source constitutes insignificant post-solution activity. The claims do not specify a particular communication protocol, network architecture, security mechanism, or improved transmission technique. The limitation merely communicates the result produced by the abstract evaluation.
Accordingly, claims 6, 13, and 19 do not integrate the exception into a practical application or add significantly more than the abstract idea.
Dependent claims 7, 14, and 20
Claims 7, 14, and 20 require onboarding the plurality of entities with the knowledge graph. The remedial action includes automatically onboarding synthetic data for an entity that violated a rule because it lacked a particular identifier or relationship. The synthetic data is selected based on the missing identifier or relationship.
Recognizing that an entity lacks required information and selecting substitute information based on the missing information constitute evaluation and selection of informational content. These operations form part of the abstract rule-based remediation.
The recitation of automatically onboarding the synthetic data does not specify how the onboarding is technically performed. The claims do not recite:
How the synthetic data is generated;
How the accuracy or reliability of the synthetic data is determined;
How conflicting synthetic and existing data are resolved;
How the synthetic data is mapped to particular graph nodes or edges;
How graph integrity is preserved;
How the graph structure is technically modified; or
A particular data structure or algorithm that improves knowledge-graph operation.
The synthetic data constitutes informational content. Selecting that content based on a missing identifier or relationship and storing it in a knowledge graph does not constitute a technological improvement merely because the operation is performed automatically.
The claims are distinguishable from claims directed to a specific improvement in database architecture, such as the self-referential table considered in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336–39 (Fed. Cir. 2016). Claims 7, 14, and 20 do not recite a new database structure or a particular improvement in the manner in which the computer stores, organizes, or retrieves data.
Further, an alleged improvement in the completeness or quality of information stored in a database does not necessarily constitute an improvement in the functioning of the database itself. See BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1287–88 (Fed. Cir. 2018).
The synthetic-data selection and onboarding limitations therefore constitute the abstract idea itself or merely instruct a generic computer to apply the abstract idea by storing selected information.
Accordingly, claims 7, 14, and 20 do not integrate the exception into a practical application or provide significantly more than the abstract idea.
Dependent claims 8 and 21
Claims 8 and 21 specify that the entity data is in JavaScript Object Notation (“JSON”) format.
JSON merely defines the format of the information being received and analyzed. Limiting an abstract data-analysis process to information expressed in a particular conventional format does not improve computer functionality or meaningfully limit how the analysis is performed.
The specification identifies JSON as one of several formats in which third-party entity data may be provided, along with XML and other human-readable formats. Thus, the JSON limitation merely confines the abstract idea to a conventional data format.
Accordingly, claims 8 and 21 do not integrate the exception into a practical application or provide an inventive concept.
Conclusion
Claims 2–21 recite the abstract idea of evaluating entity information for compliance with rules and selecting remedial action based on the evaluation. The processor, memory, computer-readable medium, knowledge graph, schema comparison, failure report, transmission, synthetic-data onboarding, and JSON limitations merely implement, communicate, or further refine that abstract information-analysis process using generic computer functionality.
When considered individually and as ordered combinations, the claims do not recite:
A specific improvement to computer or knowledge-graph functionality;
Use of a particular machine integral to the exception;
A transformation of a particular article;
An unconventional arrangement of computer components; or
Other meaningful limitations that amount to significantly more than the abstract idea.
Accordingly, claims 2–21 are rejected under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter.
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.
Claim(s) 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Estes et al., (US9348815B1) in view of Ouzzani et al., (US20160364325A1) and further in view of Gupta et al., (WO2014150214A2).
Regarding claim 2, Estes discloses:
A system comprising at least one processor and at least one memory having instructions stored thereon.” (col. 2, ll. 1–26; claim 8 — describes a system having processors and memory storing executable instructions for performing knowledge-processing operations.
receive a request to onboard a plurality of entities to a knowledge graph (col. 8, ll. 35–56 — describes batch and streaming ingestion and incremental incorporation of newly received data into a knowledge base; col. 9, ll. 23–35 — describes receiving create, read, and update commands as parameters in a server request body).
analyze entity data associated with the plurality of entities (col. 7, ll. 1–38 — describes ingesting structured and unstructured data, extracting entities and facts, and resolving relationships; (col. 8, ll. 35–55 — describes applying global analytics to newly ingested data).
identify at least one entity . . . having a common identifier (col. 13, l. 65–col. 14, l. 42 — describes grouping names, aliases, pronouns, and other mentions into a common entity when they refer to the same real-world person or object).
or a common relationship (col. 11, ll. 1–45 — describes entity nodes linked by assertion edges representing interactions and relationships, and clustering entities into common concept nodes).
Estes fails to disclose:
determine whether a statistic represents a failure of applying at least one rule; in response to determining the statistic represents a failure, cause at least one remedial action to be taken based on the statistic.
However, Ouzzani discloses:
determine whether a statistic represents a failure of applying at least one rule (¶¶11–13 — describes applying quality rules, storing rule violations, and calculating an error value indicating the probability that an attribute violates a rule; ¶¶23–29 — describes checking data against rules, recording violations, and assigning probability or contribution scores to erroneous tuples; ¶¶66–69 — describes functions that detect cells failing to satisfy quality rules and identify the most likely erroneous tuples);
in response to determining the statistic represents a failure, cause at least one remedial action to be taken based on the statistic (¶¶8–10 — describes using error information to correct a transformation or problematic data in the originating data source; ¶¶20–22 — provides the corresponding method for correcting transformations or source data based on identified errors; ¶¶92–93 — describes a repair function that identifies changes to database cells necessary to resolve a rule violation).
It would have been obvious to an ordinary skill in the art before the effective filing date of the claimed invention to modify Estes’s knowledge-graph ingestion and include Ouzzani’s quality rule validation.
The motivation to combine Estes and Ouzzani is to have a system for checking data for errors and identifying the origin of the errors.
The combination of Estes and Ouzzani fails to disclose:
in response to determining a confidence measure associated with synthetic data satisfies a criterion.
However, Gupta discloses:
in response to determining a confidence measure associated with synthetic data satisfies a criterion (¶¶68–71 — describes calculating confidence measures for replacement information and declining to use the information when the confidence measure does not exceed a specified threshold).
It would have been obvious to apply Ouzzani’s quality-rule validation to Estes’s knowledge-graph ingestion and to use Gupta’s confidence-gated replacement information to remedy incomplete entities. The combination would predictably improve graph accuracy and completeness.
Regarding claim 9, it is a method claim and recite similar subject matter as claim 2 and therefore rejected under similar ground of rejection.
Regarding claim 15, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 2 and therefore rejected under similar ground of rejection.
Regarding claim 3, the combination of Estes, Ouzzani and Gupta discloses:
applying the at least one rule includes satisfaction of a particular identifier or a particular relationship (Ouzzani, ¶¶82–90 — describes functional dependencies, conditional functional dependencies, check constraints, and validation rules directed to particular data attributes.
Estes, col. 10, ll. 36–65 — describes entity information represented as properties, identifiers, and subject-object assertions; col. 11, ll. 1–45 — describes entity relationships represented by graph edges).
It would have been obvious to apply Ouzzani’s attribute-validation rules to Estes’s entity identifiers and relationship properties.
Regarding claim 10, it is a method claim and recite similar subject matter as claim 3 and therefore rejected under similar ground of rejection.
Regarding claim 16, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 3 and therefore rejected under similar ground of rejection.
Regarding claim 4, the combination of Estes, Ouzzani and Gupta discloses:
The system of claim 2, wherein the statistic is included in a failure report that further includes percentages of other entities of the plurality of entities that:
have at least one of a different common identifier, or a different common relationship than the at least one entity; and violate a rule of the at least one rule (Ouzzani: ¶¶3–5 — describes a target report containing data affected by rules and errors; ¶¶46–48 — describes explanations that summarize tuples involved in rule violations; ¶¶56–57 — describes generating descriptive and prescriptive explanations from a stored violation table).
It would have been obvious to apply Ouzzani’s attribute-validation rules to Estes’s entity identifiers and relationship properties.
Regarding claim 11, it is a method claim and recite similar subject matter as claim 4 and therefore rejected under similar ground of rejection.
Regarding claim 17, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 4 and therefore rejected under similar ground of rejection.
Regarding claim 5, the combination of Estes, Ouzzani and Gupta discloses:
The system of claim 2, wherein the instructions, when executed by the at least one processor, further cause the system to compare an entity schema associated with the entity data to a knowledge base schema of the knowledge graph (Gupta: ¶5 — describes comparing properties associated with an entity reference to a schema table associated with the entity type; ¶54 — describes comparing properties expected under an entity-type schema with properties actually present in the knowledge graph; ¶66; claims 2 and 8 — describes identifying a missing property when the entity has fewer properties than required by its schema).
Regarding claim 12, it is a method claim and recite similar subject matter as claim 5 and therefore rejected under similar ground of rejection.
Regarding claim 18, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 5 and therefore rejected under similar ground of rejection.
Regarding claim 6, the combination of Estes, Ouzzani and Gupta discloses:
The system of claim 2, wherein the at least one remedial action includes providing the statistic to a source of the entity data (Ouzzani: ¶¶9 and 12 — describes identifying the data source containing or originating a problematic attribute; ¶¶46–48 — describes generating an explanation that summarizes the detected errors; ¶¶56–57 — describes generating descriptive and prescriptive explanations associated with the source data).
Regarding claim 13, it is a method claim and recite similar subject matter as claim 6 and therefore rejected under similar ground of rejection.
Regarding claim 19, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 6 and therefore rejected under similar ground of rejection.
Regarding claim 7, the combination of Estes, Ouzzani and Gupta discloses:
The system of claim 2, wherein the instructions, when executed by the at least one processor, further cause the system to:
“onboard the plurality of entities with the knowledge graph (Estes, col. 8, ll. 35–56 — describes ingesting new data and incrementally incorporating it into the knowledge base; Estes, col. 10, ll. 1–12 — describes automatically constructing a knowledge graph from public and private data).
the entity violated a given rule . . . as a result of lacking a particular identifier or a particular relationship (Gupta, ¶54 — describes detecting a missing entity property by comparing the entity with its expected schema; Gupta, ¶66 — gives an example in which an entity has four of five required schema properties and the fifth property is identified as missing; Ouzzani: ¶¶82–90 — describes treating failure to satisfy an attribute dependency or constraint as a rule violation).
the synthetic data is selected based on the particular identifier or relationship (Gupta, ¶¶59–61 — describes generating a query containing the particular property whose value is missing; Gupta, ¶¶63 and 67–69 — describes receiving information responsive to the missing property and using that information to update the knowledge graph).
Regarding claim 14, it is a method claim and recite similar subject matter as claim 7 and therefore rejected under similar ground of rejection.
Regarding claim 20, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 7 and therefore rejected under similar ground of rejection.
Regarding claim 8, the combination of Estes, Ouzzani and Gupta discloses:
The system of claim 2, wherein the entity data is in JavaScript Object Notation format (Estes, col. 9, ll. 13–21 — expressly describes a JSON-based knowledge-base query language; Estes, col. 9, ll. 23–35 — describes placing input parameters into a JSON object, transmitting the JSON object in a server request body, and returning results in JSON format).
Regarding claim 21, it is a non-transitory computer-readable medium claim and recite similar subject matter as claim 8 and therefore rejected under similar ground of rejection.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting over claims 1-20 of U.S. Patent No. US11769064B2 since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
The subject matter claimed in the instant application is broad in nature and is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
Instant Application # 18/472,570
US Patent # US11769064B2
2. (Amended) A system comprising:
at least one processor; and
at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, cause the system to:
receive a request to onboard a plurality of entities to a knowledge graph;
analyze entity data associated with the plurality of entities to identify at least one entity of the plurality of entities having a common identifier or a common relationship;
determine whether a statistic represents a failure of applying at least one rule to the at least one entity; and
in response to determining the statistic represents a failure, cause at least one remedial action to be taken based on the statistic, the at least one remedial action including:
in response to determining a confidence measure associated with synthetic data satisfies a criterion, onboarding the synthetic data to the knowledge graph.
1. A system comprising:
at least one processor; and
at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, cause the system to:
receive, at the system, a request to onboard, to a knowledge graph accessible by the system, a plurality of entities, each entity having at least one associated identifier, and at least one relationship with at least one other entity of the plurality of entities;
receive, at the system, entity data that describes the plurality of entities, the entity data including the at least one associated identifier and the at least one relationship;
analyze the entity data to identify a subset of the plurality of entities having a common identifier or a common relationship;
determine, for the subset, results related to the analyzing of the entity data, the results including a statistic representing failure of applying at least one rule to the subset; and
cause at least one remedial action to be taken based on the statistic.
3. (New) The system of claim 2, wherein applying the at least one rule includes satisfaction of a particular identifier or a particular relationship by the at least one entity.
3. The system of claim 1, wherein the applying of the at least one rule includes analyzing a particular identifier or a particular relationship against the at least one rule to determine whether the at least one rule is violated.
4. (New) The system of claim 2, wherein the statistic is included in a failure report that further includes percentages of other entities of the plurality of entities:
have at least one of a different common identifier, or a different common relationship than the at least one entity; and
violates a rule of the at least one rule.
4. The system of claim 2, wherein the statistic is included in a failure report that further includes percentages of other subsets that:
have at least one of a different common identifier, or a different common relationship than the subset; and
violate a rule of the at least one rule.
5. (New) The system of claim 2, wherein the instructions, when executed by the at least one processor, further cause the system to compare an entity schema associated with the entity data to a knowledge base schema of the knowledge graph.
5. The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to compare an entity schema associated with the entity data to a knowledge base schema of the knowledge graph.
6. (New) The system of claim 2, wherein the at least one remedial action includes providing the statistic to a source of the entity data.
6. The system of claim 1, wherein the at least one remedial action includes providing the statistic to a source of the entity data.
7. (New) The system of claim 2, wherein the instructions, when executed by the at least one processor, further cause the system to:
onboard the plurality of entities with the knowledge graph, wherein:
the at least one remedial action includes automatically onboarding synthetic data associated with a given entity of the plurality of entities;
the given entity violated a given rule of the at least one rule, as a result of lacking a particular identifier or a particular relationship; and
the synthetic data is selected based on the particular identifier or relationship.
7. The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to:
onboard the plurality of entities with the knowledge graph,
wherein:
the at least one remedial action includes automatically onboarding synthetic data associated with a given entity of the plurality of entities;
the given entity violated the at least one rule, as a result of lacking a particular identifier or a particular relationship; and
the synthetic data is selected based on the particular identifier or the particular relationship.
8. (New) The system of claim 2, wherein the entity data is in JavaScript Object Notation format.
2. The system of claim 1, wherein the entity data is received in JavaScript Object Notation (“JSON”) format.
Remaining instant independent claims 9, and 15 and their respective dependent claims in the instant application recite similar subject matter as claimed in the patented application # US11769064B2.
Although the instant claims are not identical, the subject matter recited in the instant claims are not different from the subject matter already patented claims. Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Claims 2-21 are rejected on the ground of nonstatutory double patenting over claims 1-2, 5-6, and 8 of U.S. Patent No. US11361227B2 since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
The subject matter claimed in the instant application is broad in nature and is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
Instant Application # 18/472,570
US Patent # US11361227B2
2. (Amended) A system comprising:
at least one processor; and
at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, cause the system to:
receive a request to onboard a plurality of entities to a knowledge graph;
analyze entity data associated with the plurality of entities to identify at least one entity of the plurality of entities having a common identifier or a common relationship;
determine whether a statistic represents a failure of applying at least one rule to the at least one entity; and
in response to determining the statistic represents a failure, cause at least one remedial action to be taken based on the statistic, the at least one remedial action including:
in response to determining a confidence measure associated with synthetic data satisfies a criterion, onboarding the synthetic data to the knowledge graph.
1. A method implemented by one or more processors, comprising:
receiving, at a computing system associated with an existing knowledge graph, a request from a third party to onboard, with the existing knowledge graph, a plurality of entities, each entity having one or more associated identifiers and relationships with one or more other entities of the plurality of entities, wherein the existing knowledge graph is accessible to one or more users via one or more automated assistants;
receiving, at the computing system from the third party, first third party entity data that describes the plurality of entities and associated identifiers and relationships;
analyzing the first third party entity data to identify one or more semantic fingerprints, wherein each semantic fingerprint of the one or more semantic fingerprints matches a respective subset of the plurality of entities;
determining, for a given semantic fingerprint of the semantic fingerprints, results related to the analyzing, wherein the results include a statistic representing success or failure of applying one or more rules to the respective subset of entities that match the given semantic fingerprint;
causing the statistic to be conveyed at one or more output components of one or more computing devices associated with the third party;
receiving, at the computing system from the third party, second third party entity data that once again describes the plurality of entities and associated identifiers and relationships, wherein the second third party entity data is modified based on the statistic;
onboarding the plurality of entities with the existing knowledge graph;
receiving, from one or more of the automated assistants, a request to perform a task related to a given entity of the plurality of entities;
identifying, in the knowledge graph, a node representing the given entity; and
causing the task related to the given entity to be performed.
3. (New) The system of claim 2, wherein applying the at least one rule includes satisfaction of a particular identifier or a particular relationship by the at least one entity.
5. The method of claim 1, wherein the one or more rules include satisfaction of a particular identifier or relationship.
4. (New) The system of claim 2, wherein the statistic is included in a failure report that further includes percentages of other entities of the plurality of entities that:
have at least one of a different common identifier, or a different common relationship than the at least one entity; and
violates a rule of the at least one rule.
6. The method of claim 1, wherein the failure statistic includes an indication of a percentage of a subset of entities that violated one or more of the rules.
5. (New) The system of claim 2, wherein the instructions, when executed by the at least one processor, further cause the system to compare an entity schema associated with the entity data to a knowledge base schema of the knowledge graph.
8. The method of claim 1, wherein the analyzing includes comparing a third party entity schema associated with the third party entity data to a knowledge graph schema associated with the existing knowledge graph.
8. (New) The system of claim 2, wherein the entity data is in JavaScript Object Notation format.
2. The method of claim 1, wherein the first and second third party entity data are received in JavaScript Object Notation (“JSON”) format.
Remaining instant independent claims 9, and 15 and their respective dependent claims in the instant application recite similar subject matter as claimed in the patented application # US11361227B2.
Although the instant claims are not identical, the subject matter recited in the instant claims are not different from the subject matter already patented claims. Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED M AHSAN whose telephone number is (571)272-5018. The examiner can normally be reached 8:30 AM - 6:00 PM.
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/SYED M AHSAN/Primary Examiner, Art Unit 2491
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