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 .
Response to Arguments
Applicant’s arguments, see remarks, filed 11/28/2025, with respect to 103 rejection have been fully considered and are persuasive. The 103 rejection of claims 1-10, 12-16, 18-20 has been withdrawn.
See reasons for allowance below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,809,829 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because In re Karlson, 136 USPQ 184 (1963): “Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before”.
11,809,829 B2
18/485,665
1. A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS, wherein the content is associated with a relevance threshold for identifying one or more likely portions of the content and an additional relevance threshold for identifying a highly relevant portion of the one or more portions of the content; determining a relevance of the content based on a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; identifying the highly relevant portion from a sub-portion of the one or more likely-relevant portions of the content based on both the relevance threshold and the additional relevance threshold, wherein the sub-portion is associated with the additional relevance threshold that is greater than the relevance threshold; and generating a notification associated with the one or more identified likely-relevant portions of the content and the highly relevant portion of the content.
2. The system of claim 1, wherein the method further comprises: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating a response to the one or more likely-relevant portions further based on the one or more content features.
3. The system of claim 1, wherein the method further comprises: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating a response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
4. The system of claim 1, wherein the method further comprises: generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, wherein when the system is operated in a semi-autonomous mode, providing the response to the one or more likely-relevant portions of the content to the first user; and when the system is operated in an autonomous mode, providing the response to the one or more likely-relevant portions of the content to the CS; receiving user feedback based on the response to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback.
5. The system of claim 4, wherein the method further comprises: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating a response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
6. The system of claim 1, wherein the method further comprises: determining one or more content-substance features to encode in the response based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in the response based on other content-style features encoded in the likely-relevant portions of the content; and generating a response to the one or more likely-relevant portions of the content such that the response encodes the one or more content-substance features and the one or more content-style features.
7. The system of claim 1, wherein the method further comprises: generating a response to the highly-relevant portion of the content based on the response-generation model for the first user; and providing the notification of the identified highly-relevant portion of the content and the response to the highly-relevant portion of the content to the first user.
8. A method comprising: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS, wherein the content is associated with a relevance threshold for identifying one or more likely portions of the content and an additional relevance threshold for identifying a highly relevant portion of the one or more portions of the content; determining a relevance of the content based on a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; identifying the highly relevant portion from a sub-portion of the one or more likely-relevant portions of the content based on both the relevance threshold and the additional relevance threshold, wherein the sub-portion is associated with the additional relevance threshold that is greater than the relevance threshold; and generating a notification associated with the one or more identified likely-relevant portions of the content and the highly relevant portion of the content.
9. The method of claim 8, further comprising: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating a response to the one or more likely-relevant portions further based on the one or more content features.
10. The method of claim 8, further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating a response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
11. The method of claim 8, further comprising: generating a response to the highly-relevant portion of the content based the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant portion of the content and the response to the highly-relevant portion of the content to the first user.
12. The method of claim 8, further comprising: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating a response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
13. The method of claim 12, further comprising: receiving user feedback based on the response to the one or more likely-relevant portions of the content; and updating at least one of the response-generation model, the content-substance model, or the content-style model based on the user feedback.
14. The method of claim 8, further comprising: generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, when operated in a semi-autonomous mode, providing the response to the one or more likely-relevant portions of the content to the first user; and when operated in an autonomous mode, providing the response to the one or more likely-relevant portions of the content to the CS.
15. One or more computer-readable media having instructions stored thereon, wherein the instructions, when executed by a processor of a computing device, cause the computing device to perform actions including: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS, wherein the content is associated with a relevance threshold for identifying one or more likely portions of the content and an additional relevance threshold for identifying a highly relevant portion of the one or more portions of the content; determining a relevance of the content based on a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; identifying the highly relevant portion from a sub-portion of the one or more likely-relevant portions of the content based on both the relevance threshold and the additional relevance threshold, wherein the sub-portion is associated with the additional relevance threshold that is greater than the relevance threshold; and generating a notification associated with the one or more identified likely-relevant portions of the content and the highly relevant portion of the content.
16. The media of claim 15, the actions further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating a response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
17. The media of claim 15, wherein the actions further comprise: generating a response to the highly-relevant portion of the content based the response-generation model for the first user; and providing the notification of the identified highly-relevant portion of the content and the response to the highly-relevant portion of the content to the first user.
18. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating a response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
19. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features to encode in a response based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in the response based on other content-style features encoded in the likely-relevant portions of the content; and generating the response to the one or more likely-relevant portions of the content such that the response encodes the one or more content-substance features and the one or more content-style features.
20. The actions of claim 15, wherein the actions further comprise: generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, providing the response to the one more likely-relevant portions of the content to one or more other users that are separate from the first user and participating in the CS; receiving user feedback, from at least one of the one or more other users, based on the response to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback received from the at least one of the one or more other users.
1. A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS;
determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user; and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
2. The system of claim 1, wherein the method further comprises: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the plurality of alternative responses to one or more likely-relevant portions further based on the one or more content features.
3. The system of claim 1, wherein the method further comprises: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
4. The system of claim 1, wherein the method further comprises: identifying user feedback based on the user selection from the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback.
5. The system of claim 4, wherein the method further comprises: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
6. The system of claim 1, wherein the method further comprises: determining one or more content-substance features to encode in each of the plurality of alternative responses based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in each of the plurality of alternative responses based on other content-style features encoded in the likely-relevant portions of the content; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content such that each of the plurality of alternative responses encodes the one or more content-substance features and the one or more content-style features.
7. The system of claim 1, wherein the method further comprises: when the system is operated in a semi-autonomous mode, providing the plurality of alternative responses to the one or more likely-relevant portions of the content to the first user.
8. A method comprising: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user; and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
9. The method of claim 8, further comprising: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the plurality of alternative responses to one or more likely-relevant portions further based on the one or more content features.
10. The method of claim 8, further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
11. The method of claim 8, further comprising: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
12. The method of claim 8, further comprising: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
13. The method of claim 12, further comprising: identifying user feedback based on the user selection from the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating at least one of the plurality of alternative responses-generation model, the content-substance model, or the content-style model based on the user feedback.
14. The method of claim 8, further comprising: when operated in a semi-autonomous mode, providing the plurality of alternative responses to the one or more likely-relevant portions of the content to the first user.
15. One or more computer-readable media having instructions stored thereon, wherein the instructions, when executed by a processor of a computing device, cause the computing device to perform actions including: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user; and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
16. The media of claim 15, the actions further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
17. The media of claim 15, wherein the actions further comprise: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
18. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
19. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features to encode in each of the plurality of alternative responses based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in each of the plurality of alternative responses based on other content-style features encoded in the likely-relevant portions of the content; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content such that the plurality of alternative responses encodes the one or more content-substance features and the one or more content-style features.
20. The actions of claim 15, wherein the actions further comprise: providing the plurality of alternative responses to the one more likely-relevant portions of the content to one or more other users that are separate from the first user and participating in the CS; receiving user feedback, from at least one of the one or more other users, based on the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback received from the at least one of the one or more other users.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10,585,991 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because In re Karlson, 136 USPQ 184 (1963): “Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before”.
10,585,991
18/485,665
1. A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS;
determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user;
identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user;
generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, the generated response configured for participation in the conversation in place of the first user;
and causing the generated response that is configured for participation in the conversation in place of the first user to be presented.
2. The system of claim 1, wherein the method further comprises: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the response to one or more likely-relevant portions further based on the one or more content features.
3. The system of claim 1, wherein the method further comprises: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
4. The system of claim 1, wherein the method further comprises: receiving user feedback based on the response to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback.
5. The system of claim 4, wherein the method further comprises: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
6. The system of claim 1, wherein the method further comprises: determining one or more content-substance features to encode in the response based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in the response based on other content-style features encoded in the likely-relevant portions of the content; and generating the response to the one or more likely-relevant portions of the content such that the response encodes the one or more content-substance features and the one or more content-style features.
7. The system of claim 1, wherein the method further comprises: when the system is operated in a semi-autonomous mode, providing the response to the one or more likely-relevant portions of the content to the first user; and when the system is operated in an autonomous mode, providing the response to the one or more likely-relevant portions of the content to the CS.
8. A method comprising: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, the generated response configured for participation in the conversation in place of the first user; and causing the generated response that is configured for participation in the conversation in place of the first user to be presented.
9. The method of claim 8, further comprising: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the response to one or more likely-relevant portions further based on the one or more content features.
10. The method of claim 8, further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
11. The method of claim 8, further comprising: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based on the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
12. The method of claim 8, further comprising: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
13. The method of claim 12, further comprising: receiving user feedback based on the response to the one or more likely-relevant portions of the content; and updating at least one of the response-generation model, the content-substance model, or the content-style model based on the user feedback.
14. The method of claim 8, further comprising: when operated in a semi-autonomous mode, providing the response to the one or more likely-relevant portions of the content to the first user; and when operated in an autonomous mode, providing the response to the one or more likely-relevant portions of the content to the CS.
15. One or more computer-storage media having instructions stored thereon, wherein the instructions, when executed by a processor of a computing device, cause the computing device to perform actions including: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a response to the one or more likely-relevant portions of the content based on a response-generation model for the first user, the generated response configured for participation in the conversation in place of the first user; and causing the generated response that is configured for participation in the conversation in place of the first user to be presented.
16. The media of claim 15, the actions further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the response to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
17. The media of claim 15, wherein the actions further comprise: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based on the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
18. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the response to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
19. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features to encode in the response based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in the response based on other content-style features encoded in the likely-relevant portions of the content; and generating the response to the one or more likely-relevant portions of the content such that the response encodes the one or more content-substance features and the one or more content-style features.
20. The actions of claim 15, wherein the actions further comprise: providing the response to the one more likely-relevant portions of the content to one or more other users that are separate from the first user and participating in the CS; receiving user feedback, from at least one of the one or more other users, based on the response to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback received from the at least one of the one or more other users.
1. A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS;
determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user;
identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user;
generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user;
and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
2. The system of claim 1, wherein the method further comprises: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the plurality of alternative responses to one or more likely-relevant portions further based on the one or more content features.
3. The system of claim 1, wherein the method further comprises: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
4. The system of claim 1, wherein the method further comprises: identifying user feedback based on the user selection from the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback.
5. The system of claim 4, wherein the method further comprises: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
6. The system of claim 1, wherein the method further comprises: determining one or more content-substance features to encode in each of the plurality of alternative responses based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in each of the plurality of alternative responses based on other content-style features encoded in the likely-relevant portions of the content; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content such that each of the plurality of alternative responses encodes the one or more content-substance features and the one or more content-style features.
7. The system of claim 1, wherein the method further comprises: when the system is operated in a semi-autonomous mode, providing the plurality of alternative responses to the one or more likely-relevant portions of the content to the first user.
8. A method comprising: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user; and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
9. The method of claim 8, further comprising: determining one or more content features based on the content and one or more natural language models, wherein the one or more content features indicate one or more intended semantics of the one or more natural language expressions; determining the relevance of the content further based on the content features; and generating the plurality of alternative responses to one or more likely-relevant portions further based on the one or more content features.
10. The method of claim 8, further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
11. The method of claim 8, further comprising: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
12. The method of claim 8, further comprising: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
13. The method of claim 12, further comprising: identifying user feedback based on the user selection from the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating at least one of the plurality of alternative responses-generation model, the content-substance model, or the content-style model based on the user feedback.
14. The method of claim 8, further comprising: when operated in a semi-autonomous mode, providing the plurality of alternative responses to the one or more likely-relevant portions of the content to the first user.
15. One or more computer-readable media having instructions stored thereon, wherein the instructions, when executed by a processor of a computing device, cause the computing device to perform actions including: receiving content that is exchanged within a communication session (CS), wherein the content includes one or more natural language expressions that encode a portion of a conversation carried out by a plurality of users participating in the CS; determining a relevance of the content based on a user-interest model for a first user of the plurality of users and a content-relevance model for the first user; identifying one or more likely-relevant portions of the content based on the relevance of the content and one or more relevance thresholds, wherein the one or more identified likely-relevant portions of the content are likely-relevant to the first user; generating a plurality of alternative responses to the one or more likely-relevant portions of the content based on a response-generation model for the first user; causing the plurality of alternative responses to be presented to the first user; and receiving a user selection that identifies an alternative response from the plurality of alternative responses for the communication session.
16. The media of claim 15, the actions further comprising: receiving metadata associated with the CS; determining one or more contextual features of the CS based on the received metadata and a CS context model, wherein the one or more contextual features indicate a context of the conversation for the first user; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more contextual features of the CS.
17. The media of claim 15, wherein the actions further comprise: identifying a sub-portion of the one or more likely-relevant portions of the content based on the relevance of the content and an additional relevance threshold, wherein the identified sub-portion of the one or more portions of the content is highly-relevant to the first user; generating a response to the highly-relevant content based the response-generation model for the first user; and providing a real-time notification of the identified highly-relevant content and the response to the highly-relevant content to the first user.
18. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features based on the content and a content-substance model included in the one or more natural language models, wherein the one or more content-substance features indicate one or more topics discussed in the conversation; and determining one or more content-style features based on the content and a content-style model included in the one or more natural language models, wherein the one or more content-style features indicate an emotion of at least one of the plurality of the users; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content further based on the one or more content-substance features and the one or more content-style features of the content.
19. The media of claim 15, wherein the actions further comprise: determining one or more content-substance features to encode in each of the plurality of alternative responses based on other content-substance features encoded in the likely-relevant portions of the content; determining one or more content-style features to encode in each of the plurality of alternative responses based on other content-style features encoded in the likely-relevant portions of the content; and generating the plurality of alternative responses to the one or more likely-relevant portions of the content such that the plurality of alternative responses encodes the one or more content-substance features and the one or more content-style features.
20. The actions of claim 15, wherein the actions further comprise: providing the plurality of alternative responses to the one more likely-relevant portions of the content to one or more other users that are separate from the first user and participating in the CS; receiving user feedback, from at least one of the one or more other users, based on the plurality of alternative responses to the one or more likely-relevant portions of the content; and updating the response-generation model based on the user feedback received from the at least one of the one or more other users.
Allowable Subject Matter
Claims 11 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The combination of Bangalore, Green, and Ingram fails to teach or suggest the features of amended independent claim 1 (e.g., “wherein a selected alternative response is transmittable to a participant in the communication session (CS) that is ongoing; the selected alternative response is transmittable based on a context of the CS for participant-directed message transmission to at least one of the plurality of users participating in the CS.”) Bangalore analyzes utterances but never performs participant-directed transmission within a live CS or uses contextual state to decide when or to whom a response should be sent. Green is even further removed: it ranks objects for search and recommendation and has no disclosure of multi-party communication, no live sessions, and no contextual transmission logic. Ingram delivers content asynchronously using heuristics, not within an active conversation or based on a maintained conversational state. None of the references disclose a persistent conversational context that governs participant-specific message delivery in real time, and nothing in the cited combination would motivate a skilled artisan to retrofit asynchronous targeting or search embeddings into a real-time, context-aware communication architecture. As amended, the claim recites a structural and functional interaction—context-based, participant-directed transmission within an ongoing CS—that is absent from the art and irreconcilable with the teachings of the cited references. Bangalore fails to disclose this limitation. Bangalore focuses on analyzing user utterances to update a conversation context, but does not disclose transmitting a selected response to another participant within an ongoing CS or performing any transmission based on the context of that session. Its comparison of user utterances to conversation history is limited to contextual understanding and does not involve context-dependent message delivery among multiple users. Green describes ranking objects using reconstructed embeddings and personalization scores, but it does not disclose multi-user transmission or contextual communication within an active session. Green’s framework for object relevance is static and focused on search and recommendation—not on participant-directed response transmission during a live conversation. Ingram similarly fails to bridge the gap. Ingram teaches asynchronous targeting and content delivery through heuristics or query strategies, but not real-time or context-coupled communication. Its “quick lookup” mechanism relates to static content targeting, not adaptive, live-session message routing among CS participants. Neither Bangalore, Green, nor Ingram teach or suggest such a real-time, context-driven, participant-directed communication mechanism. Accordingly, the cited combination lacks the structures and logic necessary to render the amended claims obvious. The references do not contemplate transmission within an ongoing session based on contextual state modeling, and thus fail to teach or suggest the claimed invention. Independent claims 8 and 15 recite novel features of the claimed invention and, for at least the reasons set forth above with respect to independent claim 1, the cited references also fails to teach or suggest the features of independent claims 8 and 15. Each of claims 2-7, 9-14, and 16-20 depends, either directly or indirectly, from one of independent claims 1, 8 and 15.
Claims 11 and 17 both include similar recitations to the allowable parent applications, claims include "identifying the highly relevant portion from a sub-portion of the one or more likely- relevant portions of the content based on both the relevance threshold and the additional relevance threshold, wherein the sub-portion is associated with the additional relevance threshold that is greater than the relevance threshold". The cited art to Aravamudan discloses "receiving first input intended to identify a desired content item among content items associated with metadata, determining that an input portion has an importance measure exceeding a threshold, and providing feedback identifying the input portion. Abstract and FIG. 4." There is no discussion of a two thresholds such as (1) a relevance threshold; and (2) an additional relevance threshold Input is merely evaluated to determine is a threshold is met. The combination of references is silent on "an additional relevance threshold" (e.g., a determined importance or temporal urgency of a sub-portion of the likely-relevant portion). The cited reference to Baldwin is also silent with regards to (a) an additional relevance threshold; and (b) a highly relevant portion identified based on the additional relevance threshold. As such, the combination of references fails to teach or suggest "identifying the highly relevant portion from a sub-portion of the one or more likely-relevant portions of the content based on both the relevance threshold and the additional relevance threshold, wherein the sub-portion is associated with the additional relevance threshold that is greater than the relevance threshold."
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL ORTIZ-SANCHEZ/Primary Examiner, Art Unit 2656