DETAILED ACTION
Notice of Pre-AIA or AIA Status
The Action is res e present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Action is responsive to the Application filed 05/23/2025.
Please note claims 1-19 are pending and claims 1, 9 and 17 are independent.
Information Disclosure Statement
The information disclosure statements filed 08/22/2025 are in compliance with 37 CFR 1.97(c) and herein have been considered. Its corresponding PTO-1449 have been electronically signed as attached.
Claim Interpretation
A “patent is invalid for indefiniteness if its claims, read in light of the specification delineating the patent, and the prosecution history, fail to inform, with reasonable certainty, those skilled in the art about the scope of the invention.” Nautilus, Inc. v. Biosig Instruments, Inc., 134 S.Ct. 2120, 2124, 110 USPQ2d 1688 (2014). The Office does not interpret claims when examining patent applications in the same manner as the courts. The Office construes claims by giving them their broadest reasonable interpretation during prosecution in an effort to establish a clear record of what the applicant intends to claim. See, MPEP 2173.02 (Determining Whether Claim Language is Definite). Such claim construction during prosecution may effectively result in a lower threshold for ambiguity than a court's determination. Id. However, Applicant has the ability to amend the claims during prosecution to ensure that the meaning of the language is clear and definite prior to issuance or provide a persuasive explanation (with evidence as necessary) that a person of ordinary skill in the art would not consider the claim language unclear. Id. (citing In re Buszard, 504 F.3d 1364, 1366 (Fed. Cir. 2007) (claims are given their broadest reasonable interpretation during prosecution “to facilitate sharpening and clarifying the claims at the application.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-6, 8-9, 13-14 and 16-17 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by
SIANEZ; Roy Fugère: “METHODS AND APPARATUS FOR USING MACHINE LEARNING TO SECURELY AND EFFICIENTLY RETRIEVE AND PRESENT SEARCH RESULTS”, (U.S. Patent Application Publication US 20210191925 A1, DATE PUBLISHED 2021-06-24 and DATE FILED 2020-12-18, hereafter “SIANEZ").
As per claim 9, SIANEZ teaches a method comprising:
receiving, by at least one processor, an audio query of a verbal input captured by a microphone of a compact search client (See [0027] and [0064], [0027], the user device 121 can be smartphone. The user device 121 is operatively and/or communicatively coupled to the query answering device 101 via network 150; and receiving a set of natural language queries and a set of terms, the set of terms can include a name of a person, a name of an object, or a title of an entity, or other information about which a user is conducting a search.);
generating, by the at least one processor, a search query based at least in part on output of a speech-to-text machine learning model using the audio query as input (See [0064], preparing training data based on the set of natural language queries and the set of terms, training a machine learning model based on the training data to generate a trained machine learning model and providing an indication of a natural language query to the trained machine learning model to generate a query term.);
generating, by the at least one processor, a search summary based at least in part on search results determined from the search query (See [0069], a set of search engines are executed to receive at least one search result based on the at least one query term and/or the indication of at least one query term.);
converting, by the at least one processor, the search summary into an audio search summary using a text-to-speech machine learning model (See [0053], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message); and
communicating, over a network, the audio search summary to the compact search client to cause the compact search client to output the audio search summary using an audio speaker (See [0054] and [0110], the result formatter 115 can provide a media item or an indication of the media item to a user and the he media item can include an image, a table, a video, a drawing, an animated drawing, an audio file; and the formatted response can be presented to the user on a screen of the user's device, through speakers on the user's device. Here the audio file teaches the output the audio search summary).
As per claim 13, SIANEZ teaches the method of claim 9, wherein generating the search query comprises executing a Large Language Model (LLM) (See [0032], the query term identifier 111 can be configured to generate at least one query term based on at least one query (e.g., at least one natural language query, and/or indication of a natural language query). For example, the at least one query term can be the general topic that the user is asking about in the query. ).
As per claim 14, SIANEZ teaches the method of claim 9, wherein generating the search summary comprises executing a Large Language Model (LLM) (See [0032], the query term identifier 111 can be configured to generate at least one query term based on at least one query (e.g., at least one natural language query, and/or indication of a natural language query). For example, the at least one query term can be the general topic that the user is asking about in the query. The at least one query term can be generated to interface with (e.g., be compatible with) a search engine or set of search engines to generate a set of search results).
As per claim 16, SIANEZ teaches the method of claim 9, wherein generating the search summary comprises determining the search results at least in part by querying an external search engine and receiving output from the external search engine (See [0045], appending the first term to a predetermined (e.g., pre-identified) search engine accessed by using a uniform resource locator (URL), to generate an updated search engine URL. The updated search engine URL produces and/or links to a webpage and/or other resources that the query answering device 101 can download using the communicator 103 and via the network 150.).
As per claim 17, SIANEZ teaches a method comprising:
capturing a verbal query for a search using a device such as microphone (See [0027], the user device 121 can be smartphone. The user device 121 is operatively and/or communicatively coupled to the query answering device 101 via network 150.);
communicating audio data of the verbal query over a network to an augmented search engine to cause the augmented search engine to generate a search query using a speech-to-text machine learning model (See [0031], the processor 104 can include machine learning models, such as text-to-speech models and the models for query answering, data processing and analysis);
receiving, from the augmented search engine, an audio search summary, the audio search summary generated using a search summary and a text-to-speech machine learning model, the search summary generated using search results determined by the search query (See [0053] and [0064], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message; and preparing training data based on the set of natural language queries and the set of terms, training a machine learning model based on the training data to generate a trained machine learning model and providing an indication of a natural language query to the trained machine learning model to generate a query term); and
generating an audio signal for the audio search summary using an audio speaker (See [0053], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message).
As per claims 1, 5-6 and 8, the claims recite a system comprising a memory and at least one processor (See SIANEZ: [0018], a non-transitory processor-readable medium storing code representing instructions to be executed by a processor) configured to perform operations recited as the steps of the methods of claims 9, 13-14 and 16 above, respectively, and as rejected under 35 U.S.C. § 102(a)(2) as being anticipated by SIANEZ.
Accordingly, claims 1, 5-6 and 8 are rejected along the same rationale that rejected claims 9, 13-14 and 16, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
For application naming joint inventors, in considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-4, 10-12 and 18-19, are rejected under 35 U.S.C. § 103 as being unpatentable over
SIANEZ, applied to claims 9, 1 and 17 above and in view of
ARSLAN et al.: “SYSTEMS AND METHODS FOR GENERATING SYNTHETIC DATA, AND TRAINING AND TESTING CONVERSATIONAL ARTIFICIAL INTELLIGENCE PLATFORMS”, (U.S. Patent Application Publication US 20250342820 A1, DATE PUBLISHED 2025-11-06 and DATE FILED 2024-05-03, hereafter “ARSLAN").
As per claim 10, SIANEZ teaches the method of claim 9, further comprising:
storing the search query in a search state database (See [0091], interrogating a database of transformed query terms for possible transformations. a database of transformed query terms reads on search query stored in the database); and
determining, using the search state database, that additional user input is needed to determine the search results (See [0009], identifying relevant parts in each search result from the set of search results based on the plurality of query terms by assigning an indication of likelihood of relevance of each search result to the specific term based on the relevant parts in that search result and selecting a search result from the set of search results based on the indication of likelihood of relevance of the search result.).
SIANEZ does not explicitly teach generating an audio prompt.
However, ARSLAN teaches generating an audio prompt (See [0098], dialog flows outline the structure and logic of a generated conversation, including the orchestration of the language services (e.g., LLM, TTS, Voice Cloning) to generate dialog prompts and the subsequent actions taken based on those responses).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of ARSLAN with SIANEZ reference because SIANEZ is dedicated to using machine learning for information retrieval and content presentation to a user based on a search query, and ARSLAN is dedicated to generating synthetic data to train and test conversational artificial intelligence platforms, and the combined teaching would have enabled SIANEZ to enhance a crucial voice-based interactions feature to significantly expands the scope of potential applications.
SIANEZ in view of ARSLAN further teaches the following:
communicating, over the network, the audio prompt to the compact search client to cause the compact search client to output the audio prompt using the audio speaker (See ARSLAN: [0034], Text A represents the content of the dialogue generated by the LLM service 128 as Speaker-2 to convey to Speaker-1. Text B denotes a prompt text generated by the LLM Service 126 as the Speaker-1 side of the conversation, which will be delivered back to the Bot Builder Service 118 and further passed on to the Speaker-2 Scenario 116, thereby keeping the conversation going.);
receiving an audio response generated by the compact search client based at least in part on a verbal response captured by a microphone after the compact search client output the audio prompt using the audio speaker (See ARSLAN: [0034], Text A represents the content of the dialogue generated by the LLM service 128 as Speaker-2 to convey to Speaker-1. Text B denotes a prompt text generated by the LLM Service 126 as the Speaker-1 side of the conversation, which will be delivered back to the Bot Builder Service 118 and further passed on to the Speaker-2 Scenario 116, thereby keeping the conversation going); and
wherein generating the search query is further based at least in part on output of the speech-to-text machine learning model using the audio query and the audio response as input (See ARSLAN: [0062], the result formatter 215 can use the text-to-speech model 216 to present the most relevant parts as a natural language response to the user's query, and may be expressed visually (e.g., on a screen of the user device 221), audibly (e.g., as an audio signal), or as any other suitable medium of communication to the user.).
As per claim 11, SIANEZ in view of ARSLAN teaches the method of claim 10, wherein determining that additional user input is needed to determine the search results comprises executing a search state classification model (See ARSLAN: [0060] and [0092], The set of query terms can be configured to be input into a set of search engines to generate a set of search results (e.g., the query terms can interrogate the search engine to produce search results). In some implementations, the set of search engines can be accessed via the network 250, or a local search engine within the user device 221 and executed via the processer 224 and memory 222; and a query can be classified using a set of machine learning models such as a word tagging model (e.g., a word tagging model similar to the word tagging model 112 shown and described with respect to FIG. 1) or a text classification model.).
As per claim 12, SIANEZ in view of ARSLAN teaches the method of claim 10, wherein generating the audio prompt comprises executing a Large Language Model (LLM) (See ARSLAN: [0060], the systems described herein employ a Bot Builder System to design and orchestrate LLM speakers' dialog flows, a triggering service to initiate the system, a dialog channel to perform the dialog scenario, a large language model service to generate prompts, a TTS service to convert textual prompts into speech, a Voice Cloning Service to convert the TTS audio into a more suitable voice, a Voice Cloning Database to store reference voice recordings to be cloned in the converted TTS audio, and a data storing unit to store the generated conversational data.).
As per claim 18, SIANEZ does not explicitly teach the method of claim 17 further comprising:
receiving, from the augmented search engine, an audio prompt requesting additional user input, the audio prompt generated for the user using a search state database including the search query.
However, ARSLAN teaches the method of claim 17 further comprising:
receiving, from the augmented search engine, an audio prompt requesting additional user input, the audio prompt generated for the user using a search state database including the search query (See [0098], dialog flows outline the structure and logic of a generated conversation, including the orchestration of the language services (e.g., LLM, TTS, Voice Cloning) to generate dialog prompts and the subsequent actions taken based on those responses,).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of ARSLAN with SIANEZ reference because SIANEZ is dedicated to using machine learning for information retrieval and content presentation to a user based on a search query, and ARSLAN is dedicated to generating synthetic data to train and test conversational artificial intelligence platforms, and the combined teaching would have enabled SIANEZ to enhance a crucial voice-based interactions feature to significantly expands the scope of potential applications.
SIANEZ in view of ARSLAN further teaches the following:
generating an audio signal for the audio prompt using the audio speaker (See SIANEZ: [0053], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message);
capturing a verbal response using the microphone (See SIANEZ: [0027] and [0064], [0027], the user device 121 can be smartphone. The user device 121 is operatively and/or communicatively coupled to the query answering device 101 via network 150; and receiving a set of natural language queries and a set of terms, the set of terms can include a name of a person, a name of an object, or a title of an entity, or other information about which a user is conducting a search.); and
communicating an audio response of the verbal response, to the augmented search engine over the network (See SIANEZ: [0027] and [0031], the user device 121 can be smartphone. The user device 121 is operatively and/or communicatively coupled to the query answering device 101 via network 150.; and the processor 104 can include machine learning models, such as text-to-speech models and the models for query answering, data processing and analysis).
As per claim 19, SIANEZ in view of ARSLAN teaches the method of claim 18,
wherein communicating the audio response occurs before receiving the audio search summary (See SIANEZ: [0053] and [0064], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message; and preparing training data based on the set of natural language queries and the set of terms, training a machine learning model based on the training data to generate a trained machine learning model and providing an indication of a natural language query to the trained machine learning model to generate a query term), and
wherein the search query is generated based at least in part on the audio response (See SIANEZ: [0053], the result formatter receives the subset of the most relevant parts to generate response to the query, which can be expressed as a natural language response as a formatted search result and the formatted search result with portions converted to an audio message).
As per claims 2-4, the claims recite a system comprising a memory and at least one processor (See SIANEZ: [0018], a non-transitory processor-readable medium storing code representing instructions to be executed by a processor) configured to perform operations recited as the steps of the methods of claims 10-12 above, respectively, and as rejected under 35 U.S.C. § 103 as being rejected under 35 U.S.C. § 103 as being unpatentable over SIANEZ in view of ARSLAN.
Accordingly, claims 1, 5-6 and 8 are rejected along the same rationale that rejected claims 9, 13-14 and 16, respectively.
Claims 15 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over
SIANEZ, as anticipated and as applied to claims 9, 1 and 17 above and in view of
Zang et al.: “KNOWLEDGE BASE QUESTION-ANSWERING SYSTEM UTILIZING A LARGE LANGUAGE MODEL OUTPUT IN RE-RANKING”, (U.S. Patent Application Publication US 20250147991 A1, DATE PUBLISHED 2025-11-06 and DATE FILED 2024-05-03, hereafter “Zang").
As per claim 15, SIANEZ does not explicitly teach the method of claim 9, wherein generating the search summary comprises determining the search results using an internal index.
However, Zang teaches the method of claim 9, wherein generating the search summary comprises determining the search results using an internal index (See [0015], retrieving a first answer including a list of a first number of documents relevant to the user input by searching indexed documents in a knowledge base).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of Zang with SIANEZ reference because SIANEZ is dedicated to using machine learning for information retrieval and content presentation to a user based on a search query, and Zang is dedicated to the knowledge based question-answering systems and utilizing output of large language models (LLMs) in a re-ranking process of retrieved answers, and the combined teaching would have enabled SIANEZ to retrieve information of more relevant to the context or intent of the user query without matching phrases.
As per claim 7, the claim recites a system comprising a memory and at least one processor (See SIANEZ: [0018], a non-transitory processor-readable medium storing code representing instructions to be executed by a processor) configured to perform operations recited as the steps of the method of claim 15 above, respectively, and as rejected under 35 U.S.C. § 103 as being rejected under 35 U.S.C. § 103 as being unpatentable over SIANEZ in view of ARSLAN.
Accordingly, claim 7 is rejected along the same rationale that rejected claim 15.
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
June 17, 2026
Prosecution Insights