DETAILED ACTION
This office action is in response to the communication filed on July 21, 2026. Claims 1-18 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed on July 21, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Page 5 of the Remarks argues that the system of amended claim 1 is not “software per se”, accordingly, the 101 rejection directed to “software per se” should be withdrawn.
Examiner agrees that the amended features of claim 1 overcomes the pending “software per se” 101 rejection. Therefore, the “software per se” 101 rejection has been withdrawn.
Applicant in Page 6 of the Remarks argues that the claims do not recite a mental process because the human mind is not equipped to “ingest and filter data from one or more data sources” and then “automatically validate, catalogue, index, tag, assign a confidence score along with document links, or a combination thereof”, and even assuming arguendo, that a judicial exception were recited, the claims integrate any such judicial exception into a practical application.
Examiner respectfully disagrees.
It is important to note that the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements (MPEP 2106.05(a)).
If the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement (MPEP 2106.04(d)(1)).
Dependent claim 2, which dependent on independent claim 1, covers several steps, such as the “ingest and filter data from the one or more data source” and “automatically validate, catalogue, index, tag, assign a confidence score along with document links, or a combination thereof” steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which is discussed in detail in the current 101 rejection below.
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, are recognized as a well understood, routine, and conventional activity within the field of computer functions, and are applying the exception using generic computer components, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Additionally, dependent claims 7, 9, and 11-15 cover additional steps that also recite an abstract idea within the “Mental Processes” grouping of abstract ideas, as discussed in detail in the 101 rejection below.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Pages 7-8 of the Remarks argues that the cited prior art Oleinikov does not teach or even suggest the feature "providing an automated source of truth", as recited in independent claim 1.
Examiner respectfully disagrees.
Oleinikov in [0003], [0069], and [0076] discloses a system configured to access data from a plurality of data source providers, the system including a data ingestor configured to ingest data from the plurality of data source providers, the system ingesting and enriching data.
Oleinikov in [0069], [0084], and [0592] discloses as more and more data is ingested and processed by the system, the data can be used to populate missing fields or add new values to existing fields in response to specific queries to provide data for fields, reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points, and identify patterns or make deductions.
Oleinikov in [0512], [0603], and [0734] discloses the data processing system retrieving record objects through an API call, searching for and identifying relationships responsive to ingesting data, requesting data via API calls.
Oleinikov in [0594] discloses by linking activities to node profiles increasing the accuracy and validity of the profiles, increasing a likelihood that each profile represents the true state of the world.
Oleinikov in [0746] and [0773] discloses generating filtered data sets that only include single ground truth values used to calculate insights, updated value of a filtered data set becomes the ground of truth.
Therefore, Oleinikov discloses "providing an automated source of truth".
For the above reasons, Examiner states that rejection of the current Office action is proper.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Dependent claims 2-18 recite a system, which is directed to a statutory
category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Dependent claim 2 recites the limitations:
“ingest and filter data from the one or more data sources”;
A person can mentally or using a pen and paper ingest and mentally or using a pen and paper filter data from one or more data sources.
“automatically validate, catalogue, index, tag, assign a confidence score along with document links, or a combination thereof”.
A person can mentally or using a pen and paper automatically validate, catalogue, index, tag, assign a confidence score along with document links, or a combination thereof.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Dependent claim 2 depends on independent claim 1, and independent claim 1 recites the limitations:
“wherein the system is configured to provide an automated source of truth”, which is a step of providing or outputting data. The step is recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a system for providing a database engine, the system comprising a computer entity configured to provide: a data ingestion layer including at least one connector coupling the system to one or more data sources; a processing element configured to provide a data processing and enrichment layer; and a knowledge serving layer including a search application programming interface (API)” and “wherein the system is configured to” in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “the system of claim 1, wherein the system is configured to:” and “one or more data sources” in the steps in claim 2 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Dependent claims 2-18 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Dependent claim 2 depends on independent claim 1, and independent claim 1 recites the limitation:
“wherein the system is configured to provide an automated source of truth”, which is a step of providing or outputting data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitation is not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 3 recites additional limitations, such as:
“wherein the data includes unstructured data”;
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper ingest and filter unstructured data from one or more data sources, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 recites additional limitations, such as:
“wherein the data ingestion layer further comprises: a data repository”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 recites additional limitations, such as:
“wherein the data processing and enrichment layer comprises at least one of: a de-duplication engine; a conflict resolution module; a trustworthiness scoring module; and a tagging and indexing service”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 recites additional limitations, such as:
“wherein the de-duplication engine comprises a machine-learning algorithm”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitations, such as:
“….de-duplicate the data at a factoid level”.
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper de-duplicate the data at a factoid level, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the machine-learning algorithm is configured to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 recites additional limitations, such as:
“wherein the de-duplication engine is configured to combine the machine-learning algorithm with at least one other de-duplication approach”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 9 recites additional limitations, such as:
“….assess conflicting information and determine a most likely truth”.
These limitations are directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper assess conflicting information and the person can mentally or using a pen and paper determine a most likely truth, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the conflict resolution module is configured to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 recites additional limitations, such as:
“wherein the conflict resolution module comprises a machine learning algorithm”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 recites additional limitations, such as:
“….determine the most likely truth through at least one of analyzing source credibility, analyzing source recency, and corroborating across multiple sources”.
These limitations are directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper determine a most likely truth through at least one of analyzing source credibility, analyzing source recency, and corroborating across multiple sources, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the machine learning algorithm is trained to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 recites additional limitations, such as:
“…assign a confidence score to the most likely truth”.
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper assign a confidence score to a most likely truth, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the trustworthiness scoring module is configured to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 recites additional limitations, such as:
“wherein the confidence score is based upon at least one of source reliability, data age, level of agreement across sources, and/or the outcome of the conflict resolution process”.
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper assign a confidence score based upon at least one of source reliability, data age, level of agreement across sources, and/or the outcome of a conflict resolution process, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 14 recites additional limitations, such as:
“…automatically extract metadata from the data”.
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper automatically extract metadata from data, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the tagging and indexing service is configured to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 15 recites additional limitations, such as:
“…extracts the metadata using natural language processing”.
This limitation is directed to the same abstract idea under the mental processes grouping as dependent claim 2, because a person can mentally or using a pen and paper automatically extract metadata from data using natural language processing, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the tagging and indexing service” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 recites additional limitations, such as:
“the API is configured to provide an interface for querying indexed data from the data processing and enrichment layer”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 17 recites additional limitations, such as:
“wherein the search API is configured to provide search results with at least one of an identified 'truth,' a confidence score, and a link to original source artifacts in the data”, which is a step of providing or outputting data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
The additional elements “wherein the search API is configured to” in the step are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 18 recites additional limitations, such as:
“wherein the knowledge serving layer further comprises a training interface configured to enable review of low-confidence data”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 3-18 are also directed to abstract idea without significantly more and are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oleinikov (US Pub 2022/0345543).
With respect to claim 1, Oleinikov discloses a system for providing a database engine, the system comprising a computing entity (Oleinikov in [0003], [0069], and [0076] discloses a system comprising one or more processors configured by machine-readable instructions to access data from a plurality of systems of record of a plurality of data source providers, the system comprising a computer device including a database) configured to provide:
a data ingestion layer including at least one connector coupling the system to one or more data sources (Oleinikov in [0003], [0069], and [0076] discloses a system configured to access data from a plurality of data source providers, the system including a data ingestor configured to ingest data from the plurality of data source providers);
a processing element configured to provide a data processing and enrichment layer (Oleinikov in [0003], [0069], and [0076] discloses a system configured to access data from a plurality of data source providers, the system including a data ingestor configured to ingest data from the plurality of data source providers, the system ingesting and enriching data); and
a knowledge serving layer including a search application programming interface (API) (Oleinikov in [0069], [0084], and [0592] discloses as more and more data is ingested and processed by the system, the data can be used to populate missing fields or add new values to existing fields in response to specific queries to provide data for fields, reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points, and identify patterns or make deductions; Oleinikov in [0512], [0603], and [0734] discloses the data processing system retrieving record objects through an API call, searching for and identifying relationships responsive to ingesting data, requesting data via API calls);
wherein the system is configured to provide an automated source of truth (Oleinikov in [0594] discloses by linking activities to node profiles increasing the accuracy and validity of the profiles, increasing a likelihood that each profile represents the true state of the world; Oleinikov in [0746] and [0773] discloses generating filtered data sets that only include single ground truth values used to calculate insights, updated value of a filtered data set becomes the ground of truth).
With respect to claim 2, Oleinikov discloses the system of claim 1, wherein the system is configured to:
ingest and filter data from one or more data sources (Oleinikov in [0069] and [0076] discloses a system including a data ingestor configured to ingest data from a plurality of data source providers); and
automatically validate, catalogue, index, tag, assign a confidence score along with document links, or a combination thereof (Oleinikov in [0069] and [0084] discloses as more and more data is ingested and processed by the system, the data can be used to populate missing fields or add new values to existing fields, reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points, and identify patterns or make deductions).
With respect to claim 3, Oleinikov discloses the system of claim 2, wherein the data includes unstructured data (Oleinikov in [0623] and [0732] discloses identifying values from unstructured data of record objects, generating master data set in structured or unstructured format).
With respect to claim 4, Oleinikov discloses the system of claim 2, wherein the data ingestion layer further comprises: a data repository (Oleinikov in [0065], [0069] and [0076] discloses a system including a data ingestor configured to ingest data from a plurality of data source providers and storing the data in a data store).
With respect to claim 5, Oleinikov discloses the system of claim 2, wherein the data processing and enrichment layer comprises at least one of:
a de-duplication engine (Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0201] and [0480] discloses perform identity resolution or deduplication based on unique identifiers associated with a profile, generating profile data using independent, factual, and objective source of activity information; Oleinikov in [0206] and [0393] discloses periodically perform deduplication by comparing each node to every other node to determine if two nodes can be merged);
a conflict resolution module (Oleinikov in [0303] and [0421] discloses resolving conflicts between record objects and field values in different systems of records that include different data, selecting between conflicting data by selecting data that has the highest likelihood of being accurate, maintain confidence scores of different values of fields to determine a likelihood of the value being accurate, confidence score based on contribution score serving as evidence of a value, contribution score based on recency and a trust score indicating how trustworthy a source is, trustworthiness of a source based on a health score determined based on how many values of record objects of the system of record match values the system knows to be true or accurate, resolve conflicts using rules, policies, or a confidence score of a value of an attribute or field);
a trustworthiness scoring module (Oleinikov in [0303] and [0421] discloses resolving conflicts between record objects and field values in different systems of records that include different data, selecting between conflicting data by selecting data that has the highest likelihood of being accurate, maintain confidence scores of different values of fields to determine a likelihood of the value being accurate, confidence score based on contribution score serving as evidence of a value, contribution score based on recency and a trust score indicating how trustworthy a source is, trustworthiness of a source based on a health score determined based on how many values of record objects of the system of record match values the system knows to be true or accurate, resolve conflicts using rules, policies, or a confidence score of a value of an attribute or field); and
a tagging and indexing service (Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index).
With respect to claim 6, Oleinikov discloses the system of claim 5, wherein the de-duplication engine comprises a machine-learning algorithm (Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0201] and [0480] discloses perform identity resolution or deduplication based on unique identifiers associated with a profile, generating profile data using independent, factual, and objective source of activity information; Oleinikov in [0206] and [0393] discloses periodically perform deduplication by comparing each node to every other node to determine if two nodes can be merged).
With respect to claim 7, Oleinikov discloses the system of claim 6, wherein the machine-learning algorithm is configured to de-duplicate the data at a factoid level (Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0201] and [0480] discloses perform identity resolution or deduplication based on unique identifiers associated with a profile, generating profile data using independent, factual, and objective source of activity information; Oleinikov in [0206] and [0393] discloses periodically perform deduplication by comparing each node to every other node to determine if two nodes can be merged).
With respect to claim 8, Oleinikov discloses the system of claim 6, wherein the de-duplication engine is configured to combine the machine-learning algorithm with at least one other de-duplication approach (Oleinikov in [0018] discloses storing filtered data of data source providers, training a machine learning model based on the filtered data; Oleinikov in [0084] discloses reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points; Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0201] and [0480] discloses perform identity resolution or deduplication based on unique identifiers associated with a profile, generating profile data using independent, factual, and objective source of activity information; Oleinikov in [0206] and [0393] discloses periodically perform deduplication by comparing each node to every other node to determine if two nodes can be merged; Oleinikov in [0784] discloses training a machine learning model with data set with confidence scores).
With respect to claim 9, Oleinikov discloses the system of claim 5, wherein the conflict resolution module is configured to assess conflicting information and determine a most likely truth (Oleinikov in [0303] and [0421] discloses resolving conflicts between record objects and field values in different systems of records that include different data, selecting between conflicting data by selecting data that has the highest likelihood of being accurate, maintain confidence scores of different values of fields to determine a likelihood of the value being accurate, confidence score based on contribution score serving as evidence of a value, contribution score based on recency and a trust score indicating how trustworthy a source is, trustworthiness of a source based on a health score determined based on how many values of record objects of the system of record match values the system knows to be true or accurate, resolve conflicts using rules, policies, or a confidence score of a value of an attribute or field).
With respect to claim 10, Oleinikov discloses the system of claim 9, wherein the conflict resolution module comprises a machine learning algorithm (Oleinikov in [0018] discloses storing filtered data of data source providers, training a machine learning model based on the filtered data; Oleinikov in [0084] discloses reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points; Oleinikov in [0784] discloses training a machine learning model with data set with confidence scores).
With respect to claim 11, Oleinikov discloses the system of claim 10, wherein the machine learning algorithm is trained to determine the most likely truth through at least one of analyzing source credibility, analyzing source recency, and corroborating across multiple sources (Oleinikov in [0018] discloses storing filtered data of data source providers, training a machine learning model based on the filtered data; Oleinikov in [0084] discloses reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points; Oleinikov in [0784] discloses training a machine learning model with data set with confidence scores).
With respect to claim 12, Oleinikov discloses the system of claim 9, wherein the trustworthiness scoring module is configured to assign a confidence score to the most likely truth (Oleinikov in [0303] and [0421] discloses resolving conflicts between record objects and field values in different systems of records that include different data, selecting between conflicting data by selecting data that has the highest likelihood of being accurate, maintain confidence scores of different values of fields to determine a likelihood of the value being accurate, confidence score based on contribution score serving as evidence of a value, contribution score based on recency and a trust score indicating how trustworthy a source is, trustworthiness of a source based on a health score determined based on how many values of record objects of the system of record match values the system knows to be true or accurate, resolve conflicts using rules, policies, or a confidence score of a value of an attribute or field).
With respect to claim 13, Oleinikov discloses the system of claim 12, wherein the confidence score is based upon at least one of source reliability, data age, level of agreement across sources, and/or the outcome of the conflict resolution process (Oleinikov in [0303] and [0421] discloses resolving conflicts between record objects and field values in different systems of records that include different data, selecting between conflicting data by selecting data that has the highest likelihood of being accurate, maintain confidence scores of different values of fields to determine a likelihood of the value being accurate, confidence score based on contribution score serving as evidence of a value, contribution score based on recency and a trust score indicating how trustworthy a source is, trustworthiness of a source based on a health score determined based on how many values of record objects of the system of record match values the system knows to be true or accurate, resolve conflicts using rules, policies, or a confidence score of a value of an attribute or field).
With respect to claim 14, Oleinikov discloses the system of claim 5, wherein the tagging and indexing service is configured to automatically extract metadata from the data (Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index; Oleinikov in [0089], [0090] and [0222] discloses a parser applying natural language processing to identify patterns, words or phrases, or the type of content used for analysis, filtering, tagging, classifying, deduplication, and other functions, extracting features using natural language processing, parser identifying nodes associated with activity, parsing metadata of the activity to identify the nodes, metadata including fields and other information; Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index).
With respect to claim 15, Oleinikov discloses the system of claim 14, wherein the tagging and indexing service extracts the metadata using natural language processing (Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index; Oleinikov in [0089], [0090] and [0222] discloses a parser applying natural language processing to identify patterns, words or phrases, or the type of content used for analysis, filtering, tagging, classifying, deduplication, and other functions, extracting features using natural language processing, parser identifying nodes associated with activity, parsing metadata of the activity to identify the nodes, metadata including fields and other information; Oleinikov in [0090] discloses applying natural language processing to identify content used for analysis, filtering, tagging, classifying, deduplication, and other functions; Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index).
With respect to claim 16, Oleinikov discloses the system of claim 2, wherein the API configured to provide an interface for querying indexed data from the data processing and enrichment layer (Oleinikov in [0018] and [0147] discloses storing filtered data of data source providers, identifying data in an index; Oleinikov in [0512], [0603], and [0734] discloses the data processing system retrieving record objects through an API call, searching for and identifying relationships responsive to ingesting data, requesting data via API calls; Oleinikov in [0592] and [0665] discloses providing data for fields in response to specific queries, using confidence scores as a threshold for selecting node profiles responsive to queries).
With respect to claim 17, Oleinikov discloses the system of claim 16, wherein the search API is configured to provide search results with at least one of an identified 'truth,' a confidence score, and a link to original source artifacts in the data (Oleinikov in [0387] and [0603] discloses searching for and identifying relationships responsive to ingesting data, data that is less accurate will have low confidence scores while data that is accurate will have higher confidence scores; Oleinikov in [0509] and [0512] discloses accessing stored data and retrieving data through an API; Oleinikov in [0734] and [0766] discloses requesting data and generating filtered data set in response to API call, different requests for data in a short time span results in receiving different results; Oleinikov in [0746] and [0773] discloses generating filtered data sets that only include single ground truth values used to calculate insights, updated value of a filtered data set becomes the ground of truth).
With respect to claim 18, Oleinikov discloses the system of claim 16, wherein the knowledge serving layer further comprises a training interface configured to enable review of low-confidence data (Oleinikov in [0018] discloses storing filtered data of data source providers, training a machine learning model based on the filtered data; Oleinikov in [0084] discloses reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points; Oleinikov in [0784] discloses training a machine learning model with data set with confidence scores).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Remarks
The relevant prior art of record that are not used in claim rejections but are pertinent to the claims or disclosure are:
Parikh (US Pat 12,652,302), which discloses accessing knowledge base, such as databases or APIs and ingesting data, .
Singh (US Pat 12,537,836), which discloses data ingestion and accessing databases via APIs, and data enrichment and filtering.
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/R.M/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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