/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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 04/10/2025.
Claims 1-20 are pending.
Priority
2. This application is a Continuation of 18/216,553 (Patent US 12,299,015), which was filed on 06/29/2023, was acknowledged and considered.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 01/13/2026 complies with the provisions of M.P.E.P. 609. The examiner has considered it.
Double Patenting
4. 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" ranted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type 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 Omum, 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
5. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,299,015. Although the conflicting claims are not identical, they are not patentably distinct from each other.
Instant Application 19176043
Patent US 12,299,015
Claim 1:
A method comprising:
responsive to an input received via a device, causing a first generative language model to
generate a first dialog summary and generate a search query based on the first dialog
summary, wherein the first dialog summary comprises a summary of a first dialog
history of a user;
responsive to search result data produced via an execution of the search query generated by the first generative language model, causing a second generative language model to generate
and output a response to the input based on the search result data and a second dialog
summary, wherein the second dialog summary comprises a summary of a second dialog history of the user; and
causing providing the response for presentation to the user via the device.
Claim 1:
A method comprising:
generating a first search prompt based on a first input portion of an online dialog involving a user of a computing device, wherein the first search prompt comprises a dialog summarization instruction to generate and output a dialog summary, and the dialog summary comprises a machine-generated summary based on at least one of a dialog history, attribute data associated with the user, or online activity data associated with the user;
sending the first search prompt to a first large language model;
receiving a first search query, wherein, in response to the first search prompt, the first search query is generated and output by the first large language model based on the dialog summary;
sending the first search query to a search system;
receiving search result data, wherein the search result data is determined based on an execution of the first search query by the search system;
including at least some of the search result data in a first output portion of the online dialog, wherein the first output portion is configured to be displayed at the computing device in response to the first input portion of the online dialog;
generating a first response prompt based on the first input portion of the online dialog, the dialog summary, and the search result data;
sending the first response prompt to a second large language model;
receiving a first response, wherein the first response is generated and output by the second large language model based on the first response prompt; and
including the first response in the first output portion of the online dialog.
Claim 13:
A system comprising:
a processor; and
a memory comprising instructions that when executed by the processor cause the processor to:
responsive to an input received via a device, cause a first generative language model to generate a first dialog summary and generate a search query based on the first dialog summary, wherein the first dialog summary comprises a summary of a first dialog history of a user;
responsive to search result data produced via an execution of the search query generated by the first generative language model, cause a second generative language model to generate
and output a response to the input based on the search result data and a second dialog
summary, wherein the second dialog summary comprises a summary of a second dialog history of the user; and
cause the response to be provided for presentation to the user via the device.
Claim 10:
A system comprising:
at least one processor; and
at least one memory device coupled to the at least one processor, wherein the at least one memory device comprises at least one instruction that, when executed by the at least one processor, cause the at least one processor to perform at least one operation comprising:
generating a first search prompt based on a first input portion of an online dialog involving a user of a computing device, wherein the first search prompt comprises a dialog summarization instruction to generate and output a dialog summary, and the dialog summary comprises a machine-generated summary based on at least one of a dialog history, attribute data associated with the user, or online activity data associated with the user;
sending the first search prompt to a first large language model;
receiving a first search query, wherein, in response to the first search prompt, the first search query is generated and output by the first large language model based on the dialog summary;
sending the first search query to a search system; receiving search result data, wherein the search result data is determined based on an execution of the first search query by the search system; including at least some of the search result data in a first output portion of the online dialog, wherein the first output portion is configured to be displayed at the computing device in response to the first input portion of the online dialog;
generating a first response prompt based on the first input portion of the online dialog, the dialog summary, and the search result data;
sending the first response prompt to a second large language model;
receiving a first response, wherein the first response is generated and output by the second large language model based on the first response prompt; and
including the first response in the first output portion of the online dialog.
Claim 19:
A non-transitory machine readable storage medium comprising instructions that, when executed by a processor, cause the processor to:
responsive to an input received via a device, cause a first generative language model to generate a first dialog summary and generate a search query based on the first dialog summary, wherein the first dialog summary comprises a summary of a first dialog history of a user;
responsive to search result data produced via an execution of the search query generated by the first generative language model, cause a second generative language model to generate
and output a response to the input based on the search result data and a second dialog
summary, wherein the second dialog summary comprises a summary of a second dialog history of the user; and
cause the response to be provided for presentation to the user via the device.
Claim 15:
At least one non-transitory machine readable storage medium comprising at least one instruction that, when executed by at least one processor, causes the at least one processor to perform at least one operation comprising:
generating a first search prompt based on a first input portion of an online dialog involving a user of a computing device, wherein the first search prompt comprises a dialog summarization instruction to generate and output a dialog summary, and the dialog summary comprises a machine-generated summary based on at least one of a dialog history, attribute data associated with the user, or online activity data associated with the user; sending the first search prompt to a first large language model;
receiving a first search query, wherein, in response to the first search prompt, the first search query is generated and output by the first large language model based on the dialog summary;
sending the first search query to a search system;
receiving search result data, wherein the search result data is determined based on an execution of the first search query by the search system;
including at least some of the search result data in a first output portion of the online dialog, wherein the first output portion is configured to be displayed at the computing device in response to the first input portion of the online dialog;
generating a first response prompt based on the first input portion of the online dialog, the dialog summary, and the search result data;
sending the first response prompt to a second large language model;
receiving a first response, wherein the first response is generated and output by the second large language model based on the first response prompt; and
including the first response in the first output portion of the online dialog.
Examiner’s Note
6. A generative language model (According to Google): “A generative language model is a type of AI, often built on transformer architectures, that learns patterns from massive datasets to generate new, human-like text, code, or other content in response to prompts. These models predict the next token in a sequence, acting as advanced pattern-matching systems.”
Example of ‘dialog summary’ (Paragraph 97 of the instant specification): “to the input portion of the dialog, one or more skills from a summary of the user's dialog context (e.g., a summary of the user's online profile)”
Thomson et al, US 20220122587, [Thomson: Paragraph 167 (“Alternatively or additionally, the transcription system 108 may provide a summary of one or both sides of the conversation to one or both parties”, i.e., ‘dialog summary’)] [Thomson: Paragraphs 260 and 263 (“the decoder 510 may use a first language model that may be configured to run quickly or to use memory efficiently such as a trigram model. In these and other embodiments, decoder 510 may render results in a rich format and transmit the results to the rescorer 512. The rescorer 512 may use a second language model, such as an RNNLM, 6-gram model or other model that covers longer n-grams, to rescore the output of the decoder 510 and create a transcription. The first language model may be smaller and may run faster than the second language model”, i.e., ‘first generative language model’ and ‘second generative language model’)] [Thomson: Paragraph 326 (“model may be determined by querying databases such as user account or profile records, transcription party customer registration records, from a lookup table, by examining out-of-band signals, or based on signal analysis.” and “First user account status and history, such as number of times he/she called to complain, number of communication sessions to customer care or technical support, number of months as a user, payment history and status”, i.e., ‘dialog history of a user’ and “transcription” is the ‘dialog summary’)] [Thomson: Paragraph 529 (“to direct a CA client of the revoiced first transcription unit 1914a to display the second transcription or a summary of the second transcription over a preceding period of time. Displaying the second transcription may provide the CA performing the revoicing for the revoiced first transcription unit 1914a context for the communication session”, i.e., “first transcription” and “second transcription” are ‘first dialog summary’ and ‘second dialog summary’)] [Thomson: Paragraphs 840-841 and 845 (“The output may be converted by a Language Model (LM) converter 4202 to a grammar or second language model LM2” and “The second ASR system 4220b may use the second language model LM2 to transcribe the revoiced audio to generate a second transcription. The second ASR system 4220b may further use a third generic language model LM3 to create the second transcription” and “The first ASR system 4220a may use a first language model to transcribe communication session audio into a first transcription and a multiple hypotheses output, such as in the form of a lattice. The LM converter 4202 may convert the multiple hypotheses output to a second language model”, i.e., second language model takes output from first language model as input)].
Kelkar et al, US 20250259021, [Kelkar: Abstract (“A first generative language model processes user messages to determine user intent, while a dialog management model analyzes the intent and conversation context to identify required system actions. The system executes actions by retrieving parameters from context, performing database queries or API calls to obtain response data, and storing results in conversation context variables. A second generative language model generates natural language responses using the action results. The system maintains conversation context including message history, action results, and state information”, i.e., second language model takes output from first language model as input)] [Kelkar: Paragraph 9 (“Dialog Manager is the component that keeps track of the overall progress of the conversation, since it can involve multiple back and forth turns, history of what the user has said and the NLU has understood, and what the NLG has responded with, and uses the history and state of the conversation as context for enabling the Conversational AI system to taking certain actions if needed”, i.e., ‘dialog history of a user’)].
Claim Rejections - 35 USC § 102
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. 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.
9. Claims 1-6, 13 and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Thomson et al (US 20220122587).
Claim 1:
Thomson suggests a method comprising: responsive to an input received via a device, causing a first generative language model to generate a first dialog summary and generate a search query based on the first dialog summary, wherein the first dialog summary comprises a summary of a first dialog history of a user [Thomson: Paragraphs 260 and 263 (“the decoder 510 may use a first language model that may be configured to run quickly or to use memory efficiently such as a trigram model. In these and other embodiments, decoder 510 may render results in a rich format and transmit the results to the rescorer 512. The rescorer 512 may use a second language model, such as an RNNLM, 6-gram model or other model that covers longer n-grams, to rescore the output of the decoder 510 and create a transcription. The first language model may be smaller and may run faster than the second language model”, i.e., ‘first generative language model’ and ‘second generative language model’)] [Thomson: Paragraph 167 (“Alternatively or additionally, the transcription system 108 may provide a summary of one or both sides of the conversation to one or both parties”, i.e., ‘dialog summary’)] [Thomson: Paragraph 529 (“to direct a CA client of the revoiced first transcription unit 1914a to display the second transcription or a summary of the second transcription over a preceding period of time. Displaying the second transcription may provide the CA performing the revoicing for the revoiced first transcription unit 1914a context for the communication session”, i.e., “first transcription” and “second transcription” are ‘first dialog summary’ and ‘second dialog summary’)] [Thomson: Paragraph 326 (“model may be determined by querying databases such as user account or profile records, transcription party customer registration records, from a lookup table, by examining out-of-band signals, or based on signal analysis.” and “First user account status and history, such as number of times he/she called to complain, number of communication sessions to customer care or technical support, number of months as a user, payment history and status”, i.e., ‘dialog history of a user’ and “transcription” is the ‘dialog summary’)]. Thomson suggests responsive to search result data produced via an execution of the search query generated by the first generative language model, causing a second generative language model to generate and output a response to the input based on the search result data and a second dialog summary, wherein the second dialog summary comprises a summary of a second dialog history of the user [Thomson: Paragraphs 840-841 and 845 (“The output may be converted by a Language Model (LM) converter 4202 to a grammar or second language model LM2” and “The second ASR system 4220b may use the second language model LM2 to transcribe the revoiced audio to generate a second transcription. The second ASR system 4220b may further use a third generic language model LM3 to create the second transcription” and “The first ASR system 4220a may use a first language model to transcribe communication session audio into a first transcription and a multiple hypotheses output, such as in the form of a lattice. The LM converter 4202 may convert the multiple hypotheses output to a second language model”, i.e., second language model takes output from first language model as input)] [Thomson: Paragraph 326 (“model may be determined by querying databases such as user account or profile records, transcription party customer registration records, from a lookup table, by examining out-of-band signals, or based on signal analysis.” and “First user account status and history, such as number of times he/she called to complain, number of communication sessions to customer care or technical support, number of months as a user, payment history and status”, i.e., ‘dialog history of a user’ and “transcription” is the ‘dialog summary’)]. Thomson suggests causing providing the response for presentation to the user via the device [Thomson: Paragraphs 91, 99, 143 and 145 (“the transcription system may be provided back to the device for display to a user of the device”)].
Claim 2:
Thomson suggests wherein the second generative language model is different from the first generative language model [Thomson: Paragraphs 260 and 263 (“the decoder 510 may use a first language model that may be configured to run quickly or to use memory efficiently such as a trigram model. In these and other embodiments, decoder 510 may render results in a rich format and transmit the results to the rescorer 512. The rescorer 512 may use a second language model, such as an RNNLM, 6-gram model or other model that covers longer n-grams, to rescore the output of the decoder 510 and create a transcription. The first language model may be smaller and may run faster than the second language model”, i.e., ‘first generative language model’ and ‘second generative language model’)].
Claim 3:
Thomson suggests wherein the second dialog summary is different from the first dialog summary [Thomson: Paragraph 529 (“to direct a CA client of the revoiced first transcription unit 1914a to display the second transcription or a summary of the second transcription over a preceding period of time. Displaying the second transcription may provide the CA performing the revoicing for the revoiced first transcription unit 1914a context for the communication session”, i.e., “first transcription” and “second transcription” are ‘first dialog summary’ and ‘second dialog summary’)].
Claim 4:
Thomson suggests wherein causing the first generative language model to generate the first dialog summary comprises formulating a first dialog summarization instruction and providing the first dialog summarization instruction to the first generative language model [Thomson: Paragraph 113 (“the transcription system 108 may be configured to generate or direct generation of the transcription of audio using one or more automatic speech recognition (ASR) systems”, i.e., using one or more language models)].
Claim 5:
Thomson suggests wherein causing the second generative language model to generate and output the response comprises formulating a second dialog summarization instruction and providing the second dialog summarization instruction to the second generative language model [Thomson: Paragraph 113 (“the transcription system 108 may be configured to generate or direct generation of the transcription of audio using one or more automatic speech recognition (ASR) systems”, i.e., using one or more language models)].
Claim 6:
Thomson suggests wherein causing the first generative language model to generate the search query based on the first dialog summary comprises formulating a search prompt and providing the search prompt to the first generative language model [Thomson: Paragraph 113 (“the transcription system 108 may be configured to generate or direct generation of the transcription of audio using one or more automatic speech recognition (ASR) systems”, i.e., using one or more language models)] [Thomson: Paragraph 171 (“connect to an IVR system may prompt the detection process to look for familiar audio patterns belonging to the IVR system prompts. … The ASR system 120 may use a grammar derived from the candidate transcription or previous communication session as a language model.”)].
Claim 13:
Claim 13 is essentially the same as claim 1 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 19:
Claim 19 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Allowable Subject Matter
10. Claims 7-12, 14-18 and 20 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.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
02/19/2026
/HUNG D LE/Primary Examiner, Art Unit 2161