DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The application has been examined. Claims 1-20 are pending.
Allowable Subject Matter
Claims 6-8 are objected as being allowable if claims overcome the Obviousness Double Patenting Rejection, claim objections and 35 USC § 102 Rejection.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/15/2024 and 07/15/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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); and 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 §§ 706.02(l)(1) - 706.02(l)(3) 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/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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-19 of the U.S. Patent No. 11,270,237. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claims 1-20 of the instant application and claims 1-19 of the U.S. Patent No. 11,270,237 is that the claims of the instant application discloses the scope of the invention to be broader than to the scope of the U.S. Patent No. 11,270,237.
Claim 1 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of the U.S. Patent No. 11,270,237. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 1 of the instant application and claim 1 of the U.S. Patent No. 11,270,237 is that the claims of the instant application discloses method steps which are broader to the method steps of the U.S. Patent No. 11,270,237.
Claim 19 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 18 of the U.S. Patent No. 11,270,237. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 19 of the instant application and claim 18 of the U.S. Patent No. is that the claims of the instant application which are broader to the claims of the U.S. Patent No. 11,270,237.
Claim 20 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 19 of the U.S. Patent No. 11,270,237. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 20 of the instant application and claim 19 of the U.S. Patent No. 11,270,237 is that the claims of the instant application discloses computer-readable non-transitory storage medium implementing the method steps which are broader to the computer-readable non-transitory storage medium implementing the method steps of the U.S. Patent No. 11,270,237.
Claims Comparison Table
Instant Application:
18/773,414
U.S. Patent No. 11,270,237 B2
(common inventive entity and assignee)
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user from at least one data source; receiving validation data for the user from at least one data enricher; processing the contextual data and the validation data through a first machine-learning model to determine contact information for the user and a plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data of a plurality of other users; processing the contextual data, the validation data, and the determined contact information through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data of the plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one individual from the plurality of individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 2:
The method of claim 1, wherein the first machine-learning model is retrained with the determined quantifiable measures.
Claim 2:
The method of claim 1, wherein the second machine-learning model is retrained with the determined quantifiable measures.
Claim 3:
The method of claim 1, wherein the quantifiable measures of dyadic ties are determined based on user contact detail quality, a frequency of communication, information within communications, information capacity and bandwidth, physical distance, social network ties, or timeliness of when contact information was updated.
Claim 3:
The method of claim 1, wherein the quantifiable measures of dyadic ties are determined based on user contact detail quality, a frequency of communication, information within communications, information capacity and bandwidth, physical distance, social network ties, or timeliness of when contact information was updated.
Claim 4:
The method of claim 1, wherein the first machine-learning model determines the quantifiable measures based on a compounding impact of individual elements from the contextual data.
Claim 4:
The method of claim 1, wherein the second machine-learning model determines the quantifiable measures based on a compounding impact of individual elements from the contextual data.
Claim 5:
The method of claim 1, wherein the first machine-learning model classifies relationships between the user and each of the individuals according to type, length, and age of the respective parties at a time when the respective relationship began, and wherein the first machine-learning model comprises weighted values for the classifications.
Claim 5:
The method of claim 1, wherein the second machine-learning model classifies relationships between the user and each of the individuals according to type, length, and age of the respective parties at a time when the respective relationship began, and wherein the second machine-learning model comprises weighted values for the classifications.
Claim 6:
The method of claim 1, comprising: before processing the contextual data through the first machine-learning model: receiving validation data for the user from at least one data enricher; and processing the contextual data and the validation data through a second machine-learning model to determine contact information for the user and the individuals, the second machine-learning model trained with previously received validation data and the previously received contextual data of the other users, wherein the received validation data and the determined contact information is processed through the first machine-learning model to determine the quantifiable measures.
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user from at least one data source; receiving validation data for the user from at least one data enricher; processing the contextual data and the validation data through a first machine-learning model to determine contact information for the user and a plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data of a plurality of other users; processing the contextual data, the validation data, and the determined contact information through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data of the plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one individual from the plurality of individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 7:
The method of claim 6, wherein the second machine-learning model merges the processed data to determine and verify current and previous contact information for the user and the individuals and to determine a chorological order of the contact information.
Claim 6:
The method of claim 1, wherein the second machine-learning model merges the processed data to determine and verify current and previous contact information for the user and the individuals and to determine a chorological order of the contact information.
Claim 8:
The method of claim 7, comprising: receiving, from the user-interface, corrections for the determined contact information, wherein the first machine-learning model is retrained with the corrections.
Claim 7:
The method of claim 6, comprising: receiving, from the user-interface, corrections for the determined contact information, wherein the second machine-learning model is retrained with the corrections.
Claim 9:
The method of claim 1, comprising: receiving, from the user-interface, instructions to remove the access to at least one of the determined measures.
Claim 8:
The method of claim 1, comprising: receiving, from the user-interface, instructions to remove the access to at least one of the determined measures.
Claim 10:
The method of claim 1, wherein the contextual data is received from at least one source data provider via an application programming interface (API).
Claim 9:
The method of claim 1, wherein the contextual data is received from at least one source data provider via an application programming interface (API).
Claim 11:
The method of claim 10, wherein the at least on source data provider comprises a social media provides, an email provider, the user's phone contacts, a messaging provider, a provider of at least one forum, a provider of an auction or selling site, or a provider of a recreational site.
Claim 10:
The method of claim 9, wherein the at least on source data provider comprises a social media provides, an email provider, the user's phone contacts, a messaging provider, a provider of at least one forum, a provider of an auction or selling site, or a provider of a recreational site.
Claim 12:
The method of claim 1, wherein the contextual data is received on a periodic basis.
Claim 11:
The method of claim 1, wherein the contextual data is received on a periodic basis.
Claim 13:
The method of claim 1, wherein the grouping includes the user.
Claim 12:
The method of claim 1, wherein the grouping includes the user.
Claim 14:
The method of claim 1, comprising: providing, to the user-interface, an industry strength scoring for the user determined according to the determined quantifiable measures or a regional strength scoring for the user determined according to the determined quantifiable measures.
Claim 13:
The method of claim 1, comprising: providing, to the user-interface, an industry strength scoring for the user determined according to the determined quantifiable measures or a regional strength scoring for the user determined according to the determined quantifiable measures.
Claim 15:
The method of claim 1, comprising: providing, to the user-interface, a best path to a decision maker determined according to the determined quantifiable measures.
Claim 14:
The method of claim 1, comprising: providing, to the user-interface, a best path to a decision maker determined according to the determined quantifiable measures.
Claim 16:
The method of claim 1, comprising: providing, to the user-interface, a validation of a recruiting rolodex determined according to the determined quantifiable measures.
Claim 15:
The method of claim 1, comprising: providing, to the user-interface, a validation of a recruiting rolodex determined according to the determined quantifiable measures.
Claim 17:
The method of claim 1, comprising: providing the access to the determined quantifiable measures to a calendar application or an email client accessible by at least one of the members of the grouping or the user.
Claim 16:
The method of claim 1, comprising: providing the access to the determined quantifiable measures to a calendar application or an email client accessible by at least one of the members of the grouping or the user.
Claim 18:
The method of claim 1, wherein the contextual data comprises digital footprint data for the user and digital path data for the user.
Claim 17:
The method of claim 1, wherein the contextual data comprises digital footprint data for the user and digital path data for the user.
Claim 19:
A dyadic ties measurement system, comprising: a user-interface; one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through the user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 18:
A dyadic ties measurement system, comprising: a user-interface; one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; receiving validation data for the user from at least one data enricher; processing the contextual data and the validation data through a first machine-learning model to determine contact information for the user and a plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data of a plurality of other users; processing the contextual data, the validation data, and the determined contact information through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data of the plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one individual from the plurality of individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 20:
One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 19:
One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; receiving validation data for the user from at least one data enricher; processing the contextual data and the validation data through a first machine-learning model to determine contact information for the user and a plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data of a plurality of other users; processing the contextual data, the validation data, and the determined contact information through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data of the plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one individual from the plurality of individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of the U.S. Patent No. 11,694,131. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claims 1-20 of the instant application and claims 1-20 of the U.S. Patent No. 11,694,131 is that the claims of the instant application discloses the scope of the invention to be broader than to the scope of the U.S. Patent No. 11,694,131.
Claim 1 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of the U.S. Patent No. 11,694,131. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 1 of the instant application and claim 1 of the U.S. Patent No. 11,694,131 is that the claims of the instant application are broader to the claims of the U.S. Patent No. 11,694,131.
Claim 19 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 19 of the U.S. Patent No. 11,694,131. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 19 of the instant application and claim 19 of the U.S. Patent No. 11,694,131 is that the claims of the instant application are broader to the claims of the U.S. Patent No. 11,694,131.
Claim 20 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 20 of the U.S. Patent No. 11,694,131. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 20 of the instant application and claim 20 of the U.S. Patent No. 11,694,131 is that the claims of the instant application discloses computer-readable non-transitory storage medium implementing the method steps which are broader to the computer-readable non-transitory storage medium implementing the method steps of the U.S. Patent No. 11,694,131.
Claims Comparison Table
Instant Application:
18/773,414
U.S. Patent No. 11,694,131 B2
(common inventive entity and assignee)
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals; receiving validation data for the plurality of individuals from at least one data enricher; processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users; processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users; and providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
Claim 2:
The method of claim 1, wherein the first machine-learning model is retrained with the determined quantifiable measures.
Claim 3:
The method of claim 1, wherein the second machine-learning model is retrained with the determined quantifiable measures of dyadic ties.
Claim 3:
The method of claim 1, wherein the quantifiable measures of dyadic ties are determined based on user contact detail quality, a frequency of communication, information within communications, information capacity and bandwidth, physical distance, social network ties, or timeliness of when contact information was updated.
Claim 4:
The method of claim 1, wherein the quantifiable measures of dyadic ties are determined based on a quality of the contact information determined according to the mapping, a source of the contextual data for the user, a frequency of communication, information within communications, information capacity and bandwidth, physical distance, social network ties, or timeliness of when the contact information was updated.
Claim 4:
The method of claim 1, wherein the first machine-learning model determines the quantifiable measures based on a compounding impact of individual elements from the contextual data.
Claim 5:
The method of claim 1, wherein the second machine-learning model determines the quantifiable measures of dyadic ties based on a compounding impact of individual elements from the contextual data for the user and the mapping.
Claim 5:
The method of claim 1, wherein the first machine-learning model classifies relationships between the user and each of the individuals according to type, length, and age of the respective parties at a time when the respective relationship began, and wherein the first machine-learning model comprises weighted values for the classifications.
Claim 6:
The method of claim 1, wherein the second machine-learning model classifies relationships between the user and each of the individuals according to type, length, and age of the respective parties at a time when the respective relationship began, and wherein the second machine-learning model comprises weighted values for the classifications.
Claim 6:
The method of claim 1, comprising: before processing the contextual data through the first machine-learning model: receiving validation data for the user from at least one data enricher; and processing the contextual data and the validation data through a second machine-learning model to determine contact information for the user and the individuals, the second machine-learning model trained with previously received validation data and the previously received contextual data of the other users, wherein the received validation data and the determined contact information is processed through the first machine-learning model to determine the quantifiable measures.
Claim 1:
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising: receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals; receiving validation data for the plurality of individuals from at least one data enricher; processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users; processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users; and providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
Claim 7:
The method of claim 6, wherein the second machine-learning model merges the processed data to determine and verify current and previous contact information for the user and the individuals and to determine a chorological order of the contact information.
Claim 7:
The method of claim 1, wherein the second machine-learning model merges the processed data to determine and verify current and previous contact information for the user and the individuals and to determine a chorological order of the contact information.
Claim 8:
The method of claim 7, comprising: receiving, from the user-interface, corrections for the determined contact information, wherein the first machine-learning model is retrained with the corrections.
Claim 3:
The method of claim 1, wherein the second machine-learning model is retrained with the determined quantifiable measures of dyadic ties.
Claim 9:
The method of claim 1, comprising: receiving, from the user-interface, instructions to remove the access to at least one of the determined measures.
Claim 8:
The method of claim 1, comprising: receiving, from the user-interface, instructions to remove the access to at least one of the determined measures of dyadic ties.
Claim 10:
The method of claim 1, wherein the contextual data is received from at least one source data provider via an application programming interface (API).
Claim 10:
The method of claim 1, wherein the contextual data is received from at least one source data provider via an application programming interface (API).
Claim 11:
The method of claim 10, wherein the at least on source data provider comprises a social media provides, an email provider, the user's phone contacts, a messaging provider, a provider of at least one forum, a provider of an auction or selling site, or a provider of a recreational site.
Claim 11:
The method of claim 10, wherein the at least on source data provider comprises a social media provides, an email provider, the user's phone contacts, a messaging provider, a provider of at least one forum, a provider of an auction or selling site, or a provider of a recreational site.
Claim 12:
The method of claim 1, wherein the contextual data is received on a periodic basis.
Claim 12:
The method of claim 10, wherein the contextual data is received on a periodic basis.
Claim 14:
The method of claim 1, comprising: providing, to the user-interface, an industry strength scoring for the user determined according to the determined quantifiable measures or a regional strength scoring for the user determined according to the determined quantifiable measures.
Claim 13:
The method of claim 1, comprising: providing, to the plurality of members of the group through the user-interface, access to an industry strength scoring for the user determined according to the determined quantifiable measures of dyadic ties or a regional strength scoring for the user determined according to the determined quantifiable measures of dyadic ties.
Claim 15:
The method of claim 1, comprising: providing, to the user-interface, a best path to a decision maker determined according to the determined quantifiable measures.
Claim 14:
The method of claim 1, comprising: providing, to the plurality of members of the group through the user-interface, a best path to a decision maker determined according to the determined quantifiable measures of dyadic ties.
Claim 16:
The method of claim 1, comprising: providing, to the user-interface, a validation of a recruiting rolodex determined according to the determined quantifiable measures.
Claim 15:
The method of claim 1, comprising: providing, to the plurality of members of the group through the user-interface, a validation of a recruiting rolodex determined according to the determined quantifiable measures of dyadic ties.
Claim 17:
The method of claim 1, comprising: providing the access to the determined quantifiable measures to a calendar application or an email client accessible by at least one of the members of the grouping or the user.
Claim 16:
The method of claim 1, comprising: providing the access to the determined quantifiable measures of dyadic ties to a calendar application or an email client accessible by at least one of the members of the group.
Claim 18:
The method of claim 1, wherein the contextual data comprises digital footprint data for the user and digital path data for the user.
Claim 17:
The method of claim 1, wherein the contextual data comprises digital footprint data for the user and digital path data for the user.
Claim 19:
A dyadic ties measurement system, comprising: a user-interface; one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through the user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 19:
A dyadic ties measurement system, comprising: a user-interface; one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals; receiving validation data for the plurality of individuals from at least one data enricher; processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users; processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users; and providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
Claim 20:
One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claim 20:
One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals; receiving validation data for the plurality of individuals from at least one data enricher; processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users; processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users; and providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
Claim Objections
Claims 1, 4-7, 9, 11, and 18-20 are objected to because of the following informalities: lack of terminology consistency
Claim 1, line 4, recites “processing the contextual data” and should be changed to -- processing the received contextual data --.
Similar changes are suggested for subsequent claims.
Claim 1, line 6, recites “with previously received” and should be changed to -- with the previously received --.
Similar changes are suggested for subsequent claims.
Claim 1, line 9, recites “at least one of the individuals” and should be changed to -- at least one individual of the plurality of individuals --.
Similar changes are suggested for subsequent claims.
Claim 5, line 2, recites “each of the individuals” and should be changed to -- each individual of the plurality of individuals --.
Claim 6, line 5, recites “and the individuals” and should be changed to -- and the plurality of individuals --.
Similar changes are suggested for subsequent claims.
Claim 6, line 7, recites “the other users” and should be changed to -- the plurality of other users --.
Claim 9, line 3, recites “the determined measures” and should be changed to -- the determined quantifiable measures --.
Claim 11, line 1, recites “at least on source data provider” and should be changed to -- at least one source data provider --.
Claim 11, line 2, recites “media provides” and should be changed to -- media provider
Claim 18, line 1, recites “wherein the contextual data” and should be changed to -- wherein the received contextual data --.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 9-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Rogynskyy et al. (2019/0361849, hereinafter Rogynskyy).
Regarding claim 1, Rogynskyy discloses a computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors (Rogynskyy, para. 870) and comprising:
receiving contextual data for a user from at least one data source (Rogynskyy discloses that the system receives and aggregate electronic activities which identifies one or more node profiles of people and constructs a node graph) (Rogynskyy, Fig. 3; para. 69);
processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users (Rogynskyy discloses that the electronic activity parser 210 can parse the electronic activity to identify additional information that can be used to populate values of one or more node profiles; the node profile manager 220 can determine that two node profiles have a personal relationship based on the activities exchanges between them that may be tagged with a personal tag or determine a confidence score for the tag (model) classifying the two node profiles) (Rogynskyy, para. 119, 155);
determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals (Rogynskyy discloses that the nodes can be member nodes or group nodes that have electronic activities that are exchanged between or otherwise involved nodes) (Rogynskyy, para. 77); and
providing, through a user-interface, access to the determined quantifiable measures to members of the grouping (Rogynskyy discloses that the record object manager 255 populates data from the record objects to be shared with or accessed by the shadow systems of record to update the multi-tenant master instance of the systems) (Rogynskyy, para. 303).
Regarding claim 2, Rogynskyy discloses the method of claim 1, wherein the first machine-learning model is retrained with the determined quantifiable measures (Rogynskyy discloses that the matching model 340 (machine learning model) can be updated (retrained) as electronic activities are mated to record objects) (Rogynskyy, para. 242).
Regarding claim 3, Rogynskyy discloses the method of claim 1, wherein the quantifiable measures of dyadic ties are determined based on user contact detail quality, a frequency of communication (Rogynskyy discloses that the node graph generation system 200 can identify all possible domain names of the company based on the frequency of communications between identified members) (Rogynskyy, para. 139), information within communications, information capacity and bandwidth, physical distance (Rogynskyy discloses that the matching condition may specify that a location corresponding to the second group node profile is within a predetermined geographical distance) (Rogynskyy, para. 808), social network ties (Rogynskyy discloses one or more social network handles or links) (Rogynskyy, para. 96), or timeliness of when contact information was updated.
Regarding claim 4, Rogynskyy discloses the method of claim 1, wherein the first machine-learning model determines the quantifiable measures based on a compounding impact of individual elements from the contextual data (Rogynskyy discloses that the matching strategies 1100 and 1104 and the restriction strategies are applied to impact and modify the outcomes of the matching model) (Rogynskyy, para. 251).
Regarding claim 5, Rogynskyy discloses the method of claim 1, wherein the first machine-learning model classifies relationships between the user and each of the individuals according to type, length, and age of the respective parties at a time when the respective relationship began, and wherein the first machine-learning model comprises weighted values for the classifications (Rogynskyy discloses that the node pairing engine 240 determines the connection strength between two nodes by identifying each of the electronic activities that associate the nodes to one another (i.e., amount of activity between the two nodes, how many interactions recently between two nodes, length of time since the two nodes were involved, etc.) (Rogynskyy, para. 194).
Regarding claim 9, Rogynskyy discloses the method of claim 1, comprising: receiving, from the user-interface, instructions to remove the access to at least one of the determined measures (Rogynskyy discloses that the filtering engine 270 can be configured to block, remove, redact, delete, or authorize electronic activities tagged, parsed, or processed by the tagging engine 265) (Rogynskyy, para. 176).
Regarding claim 10, Rogynskyy discloses the method of claim 1, wherein the contextual data is received from at least one source data provider via an application programming interface (API) (Rogynskyy discloses that the data processing system 9300 have the authority to access emails stored on the email server through an API or an HTTP) (Rogynskyy, para. 495).
Regarding claim 11, Rogynskyy discloses the method of claim 10, wherein the at least on source data provider comprises a social media provides, an email provider (Rogynskyy discloses that the one or more data source providers can include electronic messaging or mail server) (Rogynskyy, para. 321), the user's phone contacts, a messaging provider (Rogynskyy discloses that the one or more data source providers can include electronic messaging or mail server) (Rogynskyy, para. 321), a provider of at least one forum, a provider of an auction or selling site, or a provider of a recreational site.
Regarding claim 12, Rogynskyy discloses the method of claim 1, wherein the contextual data is received on a periodic basis (Rogynskyy discloses that the node graph generation system 200 can ingest the electronic activities stored or managed by one or more servers one or repeatedly on a periodic basis) (Rogynskyy, para. 81).
Regarding claim 13, Rogynskyy discloses the method of claim 1, wherein the grouping includes the user (Rogynskyy discloses that the matching strategies for linking electronic activity can correspond to one or more users, groups, accounts and/or companies) (Rogynskyy, para. 266).
Regarding claim 14, Rogynskyy discloses the method of claim 1, comprising: providing, to the user-interface, an industry strength scoring for the user determined according to the determined quantifiable measures or a regional strength scoring for the user determined according to the determined quantifiable measures (Rogynskyy discloses that node pairing engine 240 can generate a connection strength determination model that can be configured to determine the connection strength between two nodes used the model that is trained on node pairs known to have a strong/weak connection strength) (Rogynskyy, para. 199).
Regarding claim 15, Rogynskyy discloses the method of claim 1, comprising: providing, to the user-interface, a best path to a decision maker determined according to the determined quantifiable measures (Rogynskyy discloses that the data processing system calculates a match score between the endpoint profile 3004A and the first node profile 3002 based on their respective field value pairs, and determining that the match score is above a threshold or determining that the match score is a best match score (best path) of a plurality of match scores) (Rogynskyy, para. 715).
Regarding claim 16, Rogynskyy discloses the method of claim 1, comprising: providing, to the user-interface, a validation of a recruiting rolodex determined according to the determined quantifiable measures (Rogynskyy discloses that the node graph generation system 200 can provide an indication of these metrics to the member node as a goal or target metrics to improve the likelihood that the member node advances from the first stage to the second/final stage(s)) (Rogynskyy, para. 727).
Regarding claim 17, Rogynskyy discloses the method of claim 1, comprising: providing the access to the determined quantifiable measures to a calendar application or an email client accessible by at least one of the members of the grouping or the user (Rogynskyy discloses that the node graph generation system 200 determines the average seniority of attendees to the meeting established by the user by parsing activities associated with the meeting (i.e., calendar invite or emails) to identify the attendees) (Rogynskyy, para. 411).
Regarding claim 18, Rogynskyy discloses the method of claim 1, wherein the contextual data comprises digital footprint data for the user and digital path data for the user (Rogynskyy discloses that the activity footprint or behavior may have a regular cadence of meetings with each of their customers) (Rogynskyy, para. 414).
Regarding claim 19, Rogynskyy discloses a dyadic ties measurement system, comprising: a user-interface (Rogynskyy, para. 403); one or more processors (Rogynskyy, para. 870); and a computer-readable storage device (Rogynskyy, para. 871) coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving contextual data for a user from at least one data source (Rogynskyy discloses that the system re