DETAILED ACTION
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 .
The preliminary amendment filed on May 23, 2025 has been acknowledged. Claims 1-20 have been cancelled. New claims 21-40 have been added. As a result, claims 21-40 are pending in this office action.
Drawings
The drawings received on 1 April 2025 are accepted by the Examiner.
This Office Action is Non-Final.
Priority
Application 19/097,349 is a continuation of application 18/047,613 filed on 10/18/2022 now US Patent No. 12287766 18/047,613 claims benefit of provisional application # 63/256,996 10/18/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 1, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Abstract Objections
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21 and 33 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 13 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 21 and 33 of the instant application are obvious variants of claims 1 and 13 of US Patent No 12,287,766 .
Claim 29-32 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9-12 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 29-32 of the instant application are obvious variants of claims 9-12 of US Patent No 12,287,766 .
Claims 22 and 34 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 and 14 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 22 and 34 of the instant application are obvious variants of claims 2 and 14 of US Patent No 12,287,766.
Claims 23 and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3 and 15 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 23 and 35 of the instant application are obvious variants of claims 3 and 15 of US Patent No 12,287,766.
Claims 24 and 36 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4 and 16 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 24 and 36 of the instant application are obvious variants of claims 4 and 16 of US Patent No 12,287,766.
Claims 25 and 37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5 and 17 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 25 and 37 of the instant application are obvious variants of claims 5 and 17 of US Patent No 12,287,766.
Claims 26 and 38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6 and 18 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 26 and 38 of the instant application are obvious variants of claims 6 and 18 of US Patent No 12,287,766.
Claims 27 and 39 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7 and 19 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 27 and 39 of the instant application are obvious variants of claims 7 and 19 of US Patent No 12,287,766.
Claims 28 and 40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8 and 20 of US Patent 12,287,766. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 28 and 40 of the instant application are obvious variants of claims 8 and 20 of US Patent No 12,287,766.
Claims Comparison Table
19097349
U.S. Patent No. 12,287,766
19097349
U.S. Patent No. 12,287,766
21. A method for matching organizations to unique identifiers and
managing the unique identifiers, comprising:
ingesting, by a company matching computer program, a plurality of records
from a plurality of data sources;
identifying, by the company matching computer program, a company
associated with each of the plurality of records;
assigning, by the company matching computer program, unique identifiers to
each uniquely identified company;
matching, by the company matching computer program, each of the plurality
of records to one of the uniquely identified companies using a trained company
matching machine learning engine, the trained company matching machine
learning engine being trained on a matching record truth set;
matching, by a contact matching computer program, each of the plurality of
records to a contact using a trained contact matching machine learning engine, the
trained contact matching machine learning engine being trained on a matching
contact truth set;
generating, by an identity management computer program, a graph database
using the unique identifiers as lattice work comprising information about a
connection of the unique identifiers and the plurality of records;
receiving, by the company matching computer program, the contact
matching computer program, and the identity management computer program,
feedback on the matching companies, and matching contacts;
updating, by the identity management computer program, the graph database
based on a quality assessment by a quality scoring computer program, the quality
assessment comprising a number of matched independent sources; and
updating, by the company matching computer program, the trained company
matching machine learning engine, and updating, by the contact matching
computer program, the trained contact matching machine learning engine.
1. A method for matching organizations to unique identifiers and managing the unique identifiers, comprising:
ingesting, by a company matching computer program, a plurality of records from a plurality of data sources;
identifying, by the company matching computer program, a company associated with each of the plurality of records;
assigning, by the company matching computer program, a unique identifiers to each uniquely identified company;
matching, by the company matching computer program, each of the plurality of records to one of the uniquely identified companies using a trained company matching machine learning engine, the trained company matching machine learning engine being trained on a matching record truth set;
identifying, by the company matching computer program, a primary company record in the matching records and associating other matching records with the primary company record; matching, by a contact matching computer program, each of the plurality of records to a contact using a trained contact matching machine learning engine, the trained contact matching machine learning engine being trained on a matching contact truth set; identifying, by the contact matching computer program, a primary contact record in the matching records and associating other matching records with the primary contact record; generating, by an identity management computer program, a graph database using the unique identifiers as lattice work comprising information about a connection of the unique identifiers and the plurality of records; receiving, by the company matching computer program, the contact matching computer program, and the identity management computer program, feedback on the matching companies, and matching contacts; updating, by the identity management computer program, the graph database based on a quality assessment by a quality scoring computer program, the quality assessment comprising a number of matched independent sources; updating, by the identity management computer program, the graph database based on the feedback on the matching companies and matching contacts; updating, by the identity management computer program, the graph database based on a reliability of the plurality of data sources; updating, by the company matching computer program, the trained company matching machine learning engine, and updating, by the contact matching computer program, the trained contact matching machine learning engine; and generating, by a user interface executed on a computing device and in operative communication with the identity management computer program, a display of the graph database and a feedback interface.
29. A method for quality scoring unique identifiers, comprising:
receiving, by a quality scoring computer program, source records from each
of a plurality of data sources;
scoring, by the quality scoring computer program, the source records based
on a propensity to match a unique identifier, resulting in a first score that indicates
whether the source record is describing a well-known entity;
scoring, by the quality scoring computer program, the source records based
on direct user feedback, resulting in a second score that indicates whether the
source of the record has shown a general level of quality;
aggregating, by the quality scoring computer program, the first score and the
second score into an aggregated score;
outputting, by the quality scoring computer program, the aggregated score;
generating, by an identity management computer program, a graph database
using the unique identifiers as lattice work comprising information about a
connection of the unique identifiers, the source records, and the aggregated score;
and
determining, by a machine learning engine in operative communication with
the identity management program, a pattern of feedback of the aggregated scores
and updating the aggregated scores based on a prediction of the aggregated scores
based on the pattern of feedback.
A method for quality scoring unique identifiers, comprising:
receiving, by a quality scoring computer program, source records from each of a plurality of data sources;
scoring, by the quality scoring computer program, each of the source records based on a value of content in the source records, resulting in a first score that indicates whether the source record has valuable information;
scoring, by the quality scoring computer program, the source records based on a propensity to match a unique identifier, resulting in a second score that indicates whether the source record is describing a well-known entity; scoring, by the quality scoring computer program, the source records based on direct user feedback, resulting in a third score that indicates whether the source of the record has shown a general level of quality; scoring, by the quality scoring computer program, the unique identifiers based on a number of different data sources attached to each unique identifier, resulting in a fourth score that indicates a reliability of the unique identifier; aggregating, by the quality scoring computer program, the first score, the second score, the third score, and the fourth score; outputting, by the quality scoring computer program, the aggregated scores; generating, by an identity management computer program, a graph database using the unique identifiers as lattice work comprising information about a connection of the unique identifiers, the source records, and the aggregated scores; generating, by a user interface executed on a computing device and in operative communication with the identity management computer program, a display of the graph database and a feedback interface; and determining, by a machine learning engine in operative communication with the identity management program, a pattern of feedback of the aggregated scores received through the feedback interface and updating the aggregated scores based on a prediction of the aggregated scores based on the pattern of feedback.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21, line 7, recites "assigning, by the company matching computer program, unique identifiers to each uniquely identified company". It is not the clear if the invention assigns multiple identifiers, as unique identifiers to each uniquely identified company or if it assigns only one unique identifier for each uniquely identified company.
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.
Claim 29 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the limitations of, scoring, by a quality scoring computer program, the source records based on a propensity to match a unique identifier and scoring, by the quality scoring computer program, the unique identifiers based on a number of different data sources attached to each unique identifier, aggregating, by the quality scoring computer program, the first score, the second score, the third score, and the fourth score” .
The limitation of “scoring, by the quality scoring computer program, the source records based on a propensity to match a unique identifier, resulting in a first score that indicates whether the source record is describing a well-known entity;
scoring, by the quality scoring computer program, the source records based
on direct user feedback, resulting in a second score that indicates whether the
source of the record has shown a general level of quality; scoring, by the quality scoring computer program, the source records based on a propensity to match a unique identifier, resulting in a first score that indicates whether the source record is describing a well-known entity;
scoring, by the quality scoring computer program, the source records based
on direct user feedback, resulting in a second score that indicates whether the
source of the record has shown a general level of quality”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a quality scoring computer program,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a quality scoring computer program” language, “scoring” in the context of this claim encompasses the user thinking and manually assigning the first score based on a value of content in the source records and/or manual adding the scores using pen and paper.
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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites two additional elements – receiving, by a quality scoring computer program, source records from each of a plurality of data sources and outputting, by the quality scoring computer program, the aggregated scores. The receiving and outputting steps are recited at a high level of generality (i.e., as a general means of receiving data for score and outputting the score) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The quality scoring computer program that performs the receiving and outputting steps is also recited at a high level of generality and merely automates the receiving, scoring and outputting steps. Each of the recitations of the quality scoring computer program do not integrate the judicial exception into a practical application such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving and outputting using the quality scoring computer program to perform the scoring and aggregating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities, amount to no more than implementing the abstract idea with a computerized system. The receiving and outputting steps were considered to be insignificant extra-solution activities (mere data gathering and manipulating”. The Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim 30 recites additional abstract idea of wherein in the third score is based on the source record matching a name, an address, and/or a zip code in on or the unique identifiers, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Claim 30 does not recite any additional elements.
Claim 31 recites additional abstract idea of “identifying, by the quality scoring computer program, one of the source records with direct feedback that is similar to one of the source records that does not have direct user feedback; and assigning the source record that does not have direct user feedback with similar user feedback as the identified source record”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Claim 31 does not recite any additional elements.
Claim 32 recites additional abstract idea of “assigning, by the quality scoring computer program, a weight to each of the first score and the second score based on feedback received on the aggregated score”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Claim 32 does not recite any additional elements.
Claims rejection 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-28 and 33-40 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Gao et al. (US 20200327136 A1) in view of Rogynskyy et al. (US 10489462 B1) further in view of Nguyen et al (US 7269605 B1) further in view of Cheng et al. (US 20190266528 A1).
Regarding claims 21 and 33 Gao discloses a method for matching organizations to unique identifiers and managing the unique identifiers, comprising:
matching, by the company matching computer program, each of the plurality of records to one of the uniquely identified companies using a trained company matching machine learning engine (see Gao paragraph [0005], use the match scores to rank candidates and keep a predetermined number of top predetermined matches, and display the predetermined number of top predetermined matches using a user interface), the trained company matching machine learning engine being trained on a matching record truth set (see Gao paragraph [0019], Addresses are parsed into constituent components using a suitable machine learning model trained for the task. The constituent components include building name, building number, road name, road type, directional, suite or unit number, city, state, postcode and country.);
matching, by a contact matching computer program, each of the plurality of records to a contact using a trained contact matching machine learning engine; the trained contact matching machine learning engine being trained on a matching contact truth set (see Gao paragraph [0019], Addresses are parsed into constituent components using a suitable machine learning model trained for the task. The constituent components include building name, building number, road name, road type, directional, suite or unit number, city, state, postcode and country).
Rogynskyy expressly discloses ingesting, by a company matching computer program, a plurality of records from a plurality of data sources (see Rogynskyy col. 11, lines 35-40 The electronic activity ingestor 205 can be any script, file, program, application, set of instructions, or computer-executable code that is configured to enable a computing device on which the electronic activity ingestor 205 is executed to perform one or more functions of the electronic activity ingestor 205 described herein. The electronic activity ingestor 205 can be configured to ingest electronic activities from the plurality of data source providers);
identifying, by the company matching computer program, a company associated with each of the plurality of records (see Rogynskyy col. 9, lines 20-30, The data processing system 9300 can further match (9340) the ingested electronic activities to one or more record objects maintained in one or more systems of record instances of the data source provider from which the electronic activity was received. The data processing system 9300 can further synchronize the electronic activity matched to record objects to update the system of record instances of the data source provider (9350));
assigning, by the company matching computer program, unique identifiers to each uniquely identified company (see Rogynskyy col. 14, lines 39-50, the electronic activity parser 210 can be configured to identify a signature in a body of an electronic message. The parser 210 can identify the signature by utilizing a signature detection policy that includes logic for identifying patterns of signatures. In some embodiments, a signature can include one or more values of attributes, such as values for attributes including but not limited to a name, a phone number, a company name, a company division, a company address, a job title, one or more social network handles or links, an email address, among other).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to have ingesting a plurality of records from a plurality of data sources. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Nguyen expressly discloses receiving, by the company matching computer program, the contact matching computer program, and/or the identity management computer program, feedback on the matching companies and/or matching contacts (see Nguyen col. 3, lines 30-50, a contact table 132 for storing contact information synchronized from the PIM folders 122, a company table 133 for storing information about companies discerned by the facility from the synchronized contact information; a contact/company association table 134 for storing associations between contacts and companies; a contact update table 135 identifying contacts whose contents may have been changed using the PIM, which are to be re-synchronized; and a company form 136 for inputting, displaying, or changing information for company items);
updating, by the identity management computer program, the graph database based on a quality assessment by a quality scoring computer program , the quality assessment comprising a number of matched independent sources (see Nguyen col. 3, lines 30-50, a contact table 132 for storing contact information synchronized from the PIM folders 122, a company table 133 for storing information about companies discerned by the facility from the synchronized contact information; a contact/company association table 134 for storing associations between contacts and companies; a contact update table 135 identifying contacts whose contents may have been changed using the PIM, which are to be re-synchronized; and a company form 136 for inputting, displaying, or changing information for company items) and
updating, by the company matching computer program, the trained company matching machine learning engine, or updating, by the contact matching computer program, the trained contact matching machine learning engine (see Nguyen col. 3, lines 30-50, a contact table 132 for storing contact information synchronized from the PIM folders 122, a company table 133 for storing information about companies discerned by the facility from the synchronized contact information; a contact/company association table 134 for storing associations between contacts and companies; a contact update table 135 identifying contacts whose contents may have been changed using the PIM, which are to be re-synchronized; and a company form 136 for inputting, displaying, or changing information for company items).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Nguyen into the method of Gao to have synchronizing the plurality of records in a graph database using the unique identifier. Here, combining Nguyen with Gao, which are both related to company matching to unique identifiers improves Gao, by system for coordinating the activity of a PIM with a third-party add-in and synchronizing shared data between a PIM and a third-party PIM add-in (see Nguyen col. 1, lines 48-51).
Cheng expressly generating, by an identity management computer program, a graph database using the unique identifiers as lattice work comprising information about a connection of the unique identifiers and the plurality of records (see Cheng paragraph [0004], A system and accompanying methods for discovering hidden correlation relationships for risk analysis using graph-based machine learning are disclosed. In particular, the system and accompanying methods utilize machine learning to detect hidden correlation relationships based on the knowledge learned from the data of a number of clients, which are stored using graph database….Correlation relationships may be extracted, and a graph may be created with the correlation relationships and other information about the clients. The vertex of the graph may be a company, an individual, or any type of entity. The edge may represent a certain kind of correlation relationship. The edge may be directional or unidirectional and it may also have a weight. On certain occasions, there could be one or multiple edges between two vertexes indicating one or multiple correlation relationships. From the graph database, a set of features may be computed that is indicative of the proximity of two vertexes. The closer the two vertexes are, the more likely that they may have a certain correlation relationship. Training data may be created from the data available at the graph database. A machine learning system may be built using the training data and may predict the probability of a hidden correlation relationship between pairs of nodes from the graph, i.e., companies and individuals).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cheng into the method of Gao to have generating, by an identity management computer program, a graph database. Here, combining Cheng with Gao, which are both related to company matching to unique identifiers improves Gao, by providing a system and method for discovering hidden correlation relationships for risk analysis using graph-based machine learning (see Cheng paragraph [0002]).
Regarding claims 22 and 34, Rogynskyy discloses wherein the plurality of data sources comprise proprietary data sources, structured public data sources, unstructured public data sources, and third party data sources (see Rogynskyy col. 8, line 65- col. 9, lines 1-5, Each data source provider 9350 can include one or more data sources 9355a-n and/or one or more system of record instances 9360. Examples of data sources can include electronic mail servers, telephone log servers, contact servers, other types of servers and end-user applications that may receive or maintain electronic activity data or profile data relating to one or more nodes).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to have a graph database using the unique identifier. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Regarding claims 23 and 35, Rogynskyy discloses, wherein the company matching computer program identifies each company based on a company name, a company address, a company identifier, and a company uniform resource locator (see Rogynskyy col. 12, line 1-5, The other data may also be stored by the one or more servers that hosts, processes, stores or manages electronic activities. This data can include contact data, such as Names, addresses, phone numbers, Company information, titles, among others).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to identifiy each company based on a company name, a company address, a company identifier, and/or a company uniform resource locator. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Regarding claims 24 and 36, Rogynskyy discloses wherein the step of assigning the unique identifier to each uniquely identified company comprises: determining, by the company matching computer program, that the uniquely identified company has not been assigned the unique identifier; generating, by the company matching computer program, the unique identifier for the uniquely identified company; and storing, by the company matching computer program, an association between the unique identifier and the uniquely identified company (see Rogynskyy col. 13, lines 25-35, The node graph generation system 200 can be configured to periodically regenerate or recalculate the node graph. The node graph generation system 200 can do so responsive to additional data being ingested by the system 200. When new electronic activities or data is ingested by the node graph generation system 200, the system 200 can be configured to recalculate the node graph as the confidence scores (as will be described later) can change based on the information included in the new electronic activities).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to identifiy each company based on a company name, a company address, a company identifier, and/or a company uniform resource locator. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Regarding claims 25 and 37, Gao discloses, wherein the company matching computer program matches each of the plurality of records to one of the uniquely identified companies using Locality Sensitive Hashing (LSH) (see Gao paragraph [0107], the company matching system may use locality-sensitive hashing techniques, such as SimHash, to generate a hash code for webpage text. Locality-sensitive hashing functions exhibit the property that similar input text hashes to the same or similar hash codes, in the sense that their Hamming distance is small).
Regarding claims 26 and 38, Nguyen discloses, wherein the company matching computer program identifies the primary company record and a primary contact record based on an ownership hierarchy of the record (see Nguyen col. 3, lines 30-50, a contact table 132 for storing contact information synchronized from the PIM folders 122, a company table 133 for storing information about companies discerned by the facility from the synchronized contact information; a contact/company association table 134 for storing associations between contacts and companies; a contact update table 135 identifying contacts whose contents may have been changed using the PIM, which are to be re-synchronized; and a company form 136 for inputting, displaying, or changing information for company items).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Nguyen into the method of Gao to have synchronizing the plurality of records in a graph database using the unique identifier. Here, combining Nguyen with Gao, which are both related to company matching to unique identifiers improves Gao, by system for coordinating the activity of a PIM with a third-party add-in and synchronizing shared data between a PIM and a third-party PIM add-in (see Nguyen col. 1, lines 48-51).
Regarding claims 27 and 39, Gao discloses wherein the company matching computer program identifies the primary company record and a primary contact record based on a likelihood of matching records from the plurality of sources (see Gao paragraph [0005], the analytic system provides a method of matching company names, the method including generating a provider-side company dataset, for each record in a client's company records, identify a list of match candidates from the provider-side company dataset that has overlapping company signature fragments, for each client-side company, compute match scores against the list of provider-side match candidates on each signature fragment and form an overall match score, for each client-side company, use the match scores to rank candidates and keep a predetermined number of top predetermined matches, and display the predetermined number of top predetermined matches using a user interface).
Regarding claims 28 and 40 Gao discloses matching, by a property matching computer program, each of the plurality of records to a property using a trained property matching machine learning engine (see Gao paragraph [0019], Addresses are parsed into constituent components using a suitable machine learning model trained for the task. The constituent components include building name, building number, road name, road type, directional, suite or unit number, city, state, postcode and country. These components are also normalized. Some of these normalizations are as follows. Road name words are normalized by lowercasing, replacing certain abbreviations, and standardizing numerics that may be spelled out); and identifying, by the contact matching computer program, a primary property record in the matching records and associating other matching records with the primary property record (see Gao paragraph [0005], the analytic system provides a method of matching company names, the method including generating a provider-side company dataset, for each record in a client's company records, identify a list of match candidates from the provider-side company dataset that has overlapping company signature fragments, for each client-side company, compute match scores against the list of provider-side match candidates on each signature fragment and form an overall match score, for each client-side company, use the match scores to rank candidates and keep a predetermined number of top predetermined matches, and display the predetermined number of top predetermined matches using a user interface).
Claims 29-30 and 32 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Gao et al. (US 20200327136 A1) in view of Rogynskyy et al. (US 10489462 B1) further in view of Cheng et al. (US 20190266528 A1).
Regarding claim 29, Gao discloses a method for quality scoring unique identifiers, comprising:
scoring, by a quality scoring computer program, the source records based on a propensity to match a unique identifier, resulting in a first score that indicates whether the source record is describing a well-known entity (see Gao paragraph [0107], the company matching system may use locality-sensitive hashing techniques, such as SimHash, to generate a hash code for webpage text. Locality-sensitive hashing functions exhibit the property that similar input text hashes to the same or similar hash codes, in the sense that their Hamming distance is small).
Rogynskyy expressly discloses receiving, by a quality scoring computer program, source records from each of a plurality of data sources (see Rogynskyy col. 11, lines 35-40 The electronic activity ingestor 205 can be any script, file, program, application, set of instructions, or computer-executable code that is configured to enable a computing device on which the electronic activity ingestor 205 is executed to perform one or more functions of the electronic activity ingestor 205 described herein. The electronic activity ingestor 205 can be configured to ingest electronic activities from the plurality of data source providers);
scoring, by the quality scoring computer program, the source records based on direct user feedback, resulting in a second score that indicates whether the source of the record has shown a general level of quality (see Rogynskyy col. 14, lines 39-50, the electronic activity parser 210 can be configured to identify a signature in a body of an electronic message. The parser 210 can identify the signature by utilizing a signature detection policy that includes logic for identifying patterns of signatures. In some embodiments, a signature can include one or more values of attributes, such as values for attributes including but not limited to a name, a phone number, a company name, a company division, a company address, a job title, one or more social network handles or links, an email address, among other).
aggregating, by the quality scoring computer program, the first score and the second score into an aggregated score (see Rogynskyy col. 49, lines 35-40, The record object manager 255 can function as a systems of record object aggregator that is configured to aggregate data points from many systems of record, calculate the contribution score of each data point, and a timeline of the contribution score of each of those data points. The record object manager 255 or the system 200 in general can then enrich the node graph generated and maintained by the node graph generation system 200 by updating node profiles using the data points and their corresponding contribution scores)
determining, by a machine learning engine in operative communication with the identity management program, a pattern of feedback of the aggregated scores and updating the aggregated scores based on a prediction of the aggregated scores based on the pattern of feedback (see Rogynskyy col. 49, lines 35-40, The record object manager 255 can function as a systems of record object aggregator that is configured to aggregate data points from many systems of record, calculate the contribution score of each data point, and a timeline of the contribution score of each of those data points. The record object manager 255 or the system 200 in general can then enrich the node graph generated and maintained by the node graph generation system 200 by updating node profiles using the data points and their corresponding contribution scores).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to identifiy each company based on a company name, a company address, a company identifier, and/or a company uniform resource locator. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Cheng expressly generating, by an identity management computer program, a graph database using the unique identifiers as lattice work comprising information about a connection of the unique identifiers and the plurality of records (see Cheng paragraph [0004], A system and accompanying methods for discovering hidden correlation relationships for risk analysis using graph-based machine learning are disclosed. In particular, the system and accompanying methods utilize machine learning to detect hidden correlation relationships based on the knowledge learned from the data of a number of clients, which are stored using graph database….Correlation relationships may be extracted, and a graph may be created with the correlation relationships and other information about the clients. The vertex of the graph may be a company, an individual, or any type of entity. The edge may represent a certain kind of correlation relationship. The edge may be directional or unidirectional and it may also have a weight. On certain occasions, there could be one or multiple edges between two vertexes indicating one or multiple correlation relationships. From the graph database, a set of features may be computed that is indicative of the proximity of two vertexes. The closer the two vertexes are, the more likely that they may have a certain correlation relationship. Training data may be created from the data available at the graph database. A machine learning system may be built using the training data and may predict the probability of a hidden correlation relationship between pairs of nodes from the graph, i.e., companies and individuals).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cheng into the method of Gao to have generating, by an identity management computer program, a graph database. Here, combining Cheng with Gao, which are both related to company matching to unique identifiers improves Gao, by providing a system and method for discovering hidden correlation relationships for risk analysis using graph-based machine learning (see Cheng paragraph [0002]).
Regarding claim, 30 Rogynskyy expressly discloses, wherein the third score is based on the source record matching a name, an address, and a zip code in one of the unique identifiers (see Rogynskyy col. 12, line 1-5, The other data may also be stored by the one or more servers that hosts, processes, stores or manages electronic activities. This data can include contact data, such as Names, addresses, phone numbers, Company information, titles, among others).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to identifiy each company based on a company name, a company address, a company identifier, and/or a company uniform resource locator. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Regarding claim 32, Rogynskyy discloses assigning, by the quality scoring computer program, a weight to each of the first score, the second score based on feedback received on the aggregated scores (see Rogynskyy col. 49, lines 35-40, The record object manager 255 can function as a systems of record object aggregator that is configured to aggregate data points from many systems of record, calculate the contribution score of each data point, and a timeline of the contribution score of each of those data points. The record object manager 255 or the system 200 in general can then enrich the node graph generated and maintained by the node graph generation system 200 by updating node profiles using the data points and their corresponding contribution scores).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Rogynskyy into the method of Gao to identifiy each company based on a company name, a company address, a company identifier, and/or a company uniform resource locator. Here, combining Rogynskyy with Gao, which are both related to company matching to unique identifiers improves Gao, by entering the large volume of heterogeneous electronic communications transmitted between devices, inputting the information regarding each electronic communication into a system of record efficiently (see Rogynskyy col. 1, lines 30-37).
Claim 31 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Gao et al. (US 20200327136 A1) in view of Rogynskyy et al. (US 10489462 B1) further in view of Cheng et al. (US 20190266528 A1) further in view of Nguyen et al (US 7269605 B1).
Regarding claim 31, Nguyen expressly discloses identifying… one of the source records with direct feedback that is similar to one of the source records that does not have direct user feedback; and assigning the source record that does not have direct user feedback with similar user feedback as the identified source record (see Nguyen col. 3, lines 30-50, a contact table 132 for storing contact information synchronized from the PIM folders 122, a company table 133 for storing information about companies discerned by the facility from the synchronized contact information; a contact/company association table 134 for storing associations between contacts and companies; a contact update table 135 identifying contacts whose contents may have been changed using the PIM, which are to be re-synchronized; and a company form 136 for inputting, displaying, or changing information for company items).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Nguyen into the method of Gao to have synchronizing the plurality of records in a graph database using the unique identifier. Here, combining Nguyen with Gao, which are both related to company matching to unique identifiers improves Gao, by system for coordinating the activity of a PIM with a third-party add-in and synchronizing shared data between a PIM and a third-party PIM add-in (see Nguyen col. 1, lines 48-51).
.
Remarks
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Vergo (US 20200233872 A1) building a corpus of company information which includes a combination of textual information about the company and structured information about the company, using multiple query entities, calculating a set of similar companies for each company on the list of each query entities, employing a voting scheme to rank the results of the calculation, ordering a final set of the results based on the voting scheme and presenting them back to the user as a first ranked list, iteratively repeating the calculation by adding a second set of new companies and recalculating a second ranked list of recommended companies based on the updated query list.
Conclusion
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/DINKU W GEBRESENBET/Primary Examiner, Art Unit 2164