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 .
Claims 1-20 have been presented for examination and are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 and of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy was filed on 02/25/2026.
Specification
The disclosure is objected to because of the following informalities: typographical error in paragraph [0003], “the current method of pushing information has a problem that the use acquires the information in low efficiency”, it should be user. Appropriate correction is required.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
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Claims 1-20 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of prior of co-pending Application No. US 18/797,460, hereinafter ‘460. This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
The Instant application - 18/797,465
Co-Pending Application - 18/797,460
Claim 1. A method of pushing information, comprising:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres;
for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
Claim 1. A method of pushing information, comprising:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres;
for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
.
Claim 2. The method of claim 1, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises:
event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 2. The method of claim 1, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises:
event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 3. The method of claim 1, wherein the acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 3. The method of claim 1, wherein the acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 4. The method of claim 1, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 4. The method of claim 1, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 5. The method of claim 4, wherein the filtering the text information corresponding to the each broadcast subject from the candidate text information, comprises:
clustering the candidate text information to obtain candidate text information groups respectively corresponding to a plurality of events under the each broadcast subject; and filtering a target text information group satisfying a target filtering condition from the candidate text information groups corresponding to the plurality of the events respectively, wherein the target text information group comprises the text information corresponding to the each broadcast subject.
Claim 5. The method of claim 4, wherein the filtering the text information corresponding to the each broadcast subject from the candidate text information, comprises:
clustering the candidate text information to obtain candidate text information groups respectively corresponding to a plurality of events under the each broadcast subject; and
filtering a target text information group satisfying a target filtering condition from the candidate text information groups corresponding to the plurality of the events respectively, wherein the target text information group comprises the text information corresponding to the each broadcast subject.
Claim 6.
The method of claim 1, further comprising: storing candidate text information and vector data corresponding to the candidate text information, in association, into a vector database; in response to acquiring questioning information sent by the target user after pushing the target broadcast content to the target user, filtering target text information associated with the questioning information from the vector database based on the questioning information and the vector data;
inputting the target text information into the content generation model to obtain an answer result associated with the questioning information; and
pushing the answer result to the target user.
Claim 6. The method of claim 1, further comprising: storing candidate text information and vector data corresponding to the candidate text information, in association, into a vector database;
in response to acquiring questioning information sent by the target user after pushing the target broadcast content to the target user, filtering target text information associated with the questioning information from the vector database based on the questioning information and the vector data;
inputting the target text information into the content generation model to obtain an answer result associated with the questioning information; and pushing the answer result to the target user.
Claim 7. 7. The method of claim 6, further comprising: pushing pieces of alternative questioning information to the target user; and
wherein the acquiring the questioning information sent by the target user, comprises:
in response to the target user triggering target candidate questioning information in the pieces of the alternative questioning information, determining the target alternative questioning information as the questioning information.
Claim 7. The method of claim 6, further comprising: pushing pieces of alternative questioning information to the target user; and
wherein the acquiring the questioning information sent by the target user, comprises:
in response to the target user triggering target candidate questioning information in the pieces of the alternative questioning information, determining the target alternative questioning information as the questioning information.
Claim 8. The method of claim 6, wherein the filtering target text information associated with the questioning information from the vector database based on the questioning information and the vector data, comprises: performing keyword extraction processing on the questioning information to obtain a query keyword corresponding to the questioning information; and
filtering the target text information associated with the questioning information from the vector database based on an association degree between a keyword vector corresponding to the query keyword and the vector data stored in the vector database.
Claim 8. The method of claim 6, wherein the filtering target text information associated with the questioning information from the vector database based on the questioning information and the vector data, comprises:
performing keyword extraction processing on the questioning information to obtain a query keyword corresponding to the questioning information; and
filtering the target text information associated with the questioning information from the vector database based on an association degree between a keyword vector corresponding to the query keyword and the vector data stored in the vector database.
Claim 9. The method of claim 1, wherein the push trigger condition comprises at least one selected from the group consisting of:
the target user subscribing to the broadcast content corresponding to the target broadcast subject; receiving a push request sent by the target user corresponding to the target broadcast subject; and
a content display page corresponding to the target broadcast subject being opened.
Claim 9. The method of claim 1, wherein the push trigger condition comprises at least one selected from the group consisting of:
the target user subscribing to the broadcast content corresponding to the target broadcast subject;
receiving a push request sent by the target user corresponding to the target broadcast subject; and
a content display page corresponding to the target broadcast subject being opened.
Claim 10. The method of claim 2, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 10. The method of claim 2, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 11. The method of claim 3, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 11. The method of claim 3, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 12. 12. A computer device, comprising:
at least one processor; and
at least one memory;
wherein the memory stores machine-readable instructions that are executable by the at least one processor, the at least one processor is configured to execute the machine-readable instructions stored in the memory, and when the machine-readable instructions are executed by the at least one processor, the at least one processor executes a method of pushing information, and the method of pushing information comprises:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres;
for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
Claim 12. A computer device, comprising:
at least one processor; and
at least one memory;
wherein the memory stores machine-readable instructions that are executable by the at least one processor, the at least one processor is configured to execute the machine-readable instructions stored in the memory, and when the machine-readable instructions are executed by the at least one processor, the at least one processor executes a method of pushing information, and the method of pushing information comprises:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres; for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
Claim 13. The computer apparatus of claim 12, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises:
event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 13. The computer apparatus of claim 12, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises:
event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 14. The computer device of claim 12, wherein the acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 14. The computer device of claim 12, wherein the acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 15. The computer device of claim 12, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 15. The computer device of claim 12, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 16. The computer device of claim 15, wherein the filtering the text information corresponding to the each broadcast subject from the candidate text information, comprises:
clustering the candidate text information to obtain candidate text information groups respectively corresponding to a plurality of events under the each broadcast subject; and
filtering a target text information group satisfying a target filtering condition from the candidate text information groups corresponding to the plurality of the events respectively, wherein the target text information group comprises the text information corresponding to the each broadcast subject.
Claim 16. The computer device of claim 15, wherein the filtering the text information corresponding to the each broadcast subject from the candidate text information, comprises:
clustering the candidate text information to obtain candidate text information groups respectively corresponding to a plurality of events under the each broadcast subject; and
filtering a target text information group satisfying a target filtering condition from the candidate text information groups corresponding to the plurality of the events respectively, wherein the target text information group comprises the text information corresponding to the each broadcast subject.
Claim 17. A non-transient computer-readable storage medium, wherein computer programs are stored on the non-transient computer-readable storage medium, and when the computer programs are run by a computer device, the computer device executes a method of pushing information, and the method of pushing information comprises:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres;
for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
Claim 17. A non-transient computer-readable storage medium, wherein computer programs are stored on the non-transient computer-readable storage medium, and when the computer programs are run by a computer device, the computer device executes a method of pushing information, and the method of pushing information comprises:
acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres;
for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject;
inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject;
and
in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.
Claim 18. The non-transient computer-readable storage medium of claim 17, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises: event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 18. The non-transient computer-readable storage medium of claim 17, wherein the content generation model is trained by:
acquiring a content template corresponding to each broadcast subject of the at least one broadcast subject, wherein the content template comprises:
event contents organized in a preset format and respectively associated with a plurality of events corresponding to the each broadcast subject; and
generating the content generation model by using the content template to train a model to be trained.
Claim 19. The non-transient computer-readable storage medium of claim 17, wherein the acquiring a plurality of real-time multimedia contents corresponding to the each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 19. The non-transient computer-readable storage medium of claim 17, wherein the acquiring a plurality of real-time multimedia contents corresponding to the each broadcast subject of at least one broadcast subject, comprises:
for each broadcast subject, determining a target information source corresponding to the each broadcast subject, wherein a confidence degree corresponding to the target information source satisfies a preset confidence degree condition; and
acquiring the multimedia contents corresponding to the each broadcast subject and published by the target information source in real time.
Claim 20. The non-transient computer-readable storage medium of claim 17, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Claim 20. The non-transient computer-readable storage medium of claim 17, wherein the generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject, comprises:
performing parse processing on the multimedia contents corresponding to the each broadcast subject to obtain candidate text information corresponding to the multimedia contents; and
filtering the text information corresponding to the each broadcast subject from the candidate text information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH KASSA whose telephone number is (571)270-0567. The examiner can normally be reached on Monday -Friday 9 AM -6 PM.
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02/28/2026
/ELIZABETH KASSA/Examiner, Art Unit 2457
/ARIO ETIENNE/Supervisory Patent Examiner, Art Unit 2457