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 .
This office action is sent in response to Applicant’s communication received on 01/02/2025 for the application number 19007859. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as explained below.
Claim 1 recites a method for utilizing a plurality of artificial intelligence (AI) modules to determine audience sentiment, the method comprising:
receiving, by a computing device, audio data of a video file associated with a focus group
generating, via a language processing AI module, based on the audio data, transcription information associated with the video file
generating, via a generative AI module, based on the transcription information, insight information associated with the video file
generating, via a predictive AI module, based on the insight information, an audience sentiment report associated with the focus group
and facilitating, by the computing device, transmission of the audience sentiment report.
Step (a) comprises a mental process. This step can be performed by a human as a person
can receive an audio file associated with a focus group by viewing the video.
Step (b) comprises a mental process. This step can be performed by a human as a person can generate transcription information associated with the video file by transcribing the received audio.
Step (c) comprises a mental process. This step can be performed by a human as a person can generate insight information associated with the video file based on the transcription information by reviewing the transcript and identifying themes, observations, or other insights.
Step (d) comprises a mental process. This step can be performed by a human as a person can generate a report based on insight information.
Step (e) comprises a mental process. This step can be performed by a human as a person can facilitate transmission of the audience sentiment report by communicating or providing the report to another person.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any
statutory category. See MPEP 2106.03. The claim recites at least method. Thus, the claim is a process, which is one of the statutory categories of invention. (Step 1: YES). Step 2A, Prong
One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception.
As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the
judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest
reasonable interpretation of steps (a)-(e) recites a mental process.
Specifically, step (a) comprises a mental process. This step can be performed by a human as a person can receive an audio file associated with a focus group by viewing the video.
Step (b) can be performed by a human as a person can generate transcription information associated with the video file by transcribing the received audio.
Step (c) can be performed by a human as a person can generate insight information associated with the video file based on the transcription information by reviewing the transcript and identifying themes, observations, or other insights.
Step (d) can be performed by a human as a person can generate a report based on insight information.
Step (e) can be performed by a human as a person can facilitate transmission of the audience sentiment report by communicating or providing the report to another person.
Hence the claim encompasses mental processes practically performed in the human mind
by observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2), subsection III.
(Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a
whole integrates the recited judicial exception into a practical application of the exception or
whether the claim is “directed to” the judicial exception. This evaluation is performed by (1)
identifying whether there are any additional elements recited in the claim beyond the judicial
exception, and (2) evaluating those additional elements individually and in combination to
determine whether the claim as a whole integrates the exception into a practical application. See
MPEP 2106.04(d).
The claim recited additional elements including a computing device, a plurality of artificial intelligence (AI) modules including a language processing AI module, a generative AI model and a predictive AI model. However, these elements are recited at a high level of generality and perform generic computer functions, such as receiving data, processing data, generating content, and providing output. The use of these elements to receive audio data, generate transcription information, generate insight information, generate an audience sentiment report, and facilitate transmission of the audience sentiment merely automates the mental processes described above using generic computer components. Such implementation does not impose any meaningful limit on the judicial exception. Accordingly, these elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole
amounts to significantly more than the recited exception i.e., whether any additional element, or
combination of additional elements, adds an inventive concept to the claim. As discussed with
respect to Step 2A, Prong Two, a computing device, a plurality of artificial intelligence (AI) modules including a language processing AI module, a generative AI model and a predictive AI model comprise additional elements that perform well-understood, routine, and conventional
activities in the field such as receiving data, processing data, generating content, and providing
output. See MPEP 2106.05(g). As known in the art these elements are well understood, routine,
and conventional functions of a computing device. Even when considered in combination these
additional elements merely implement the abstract idea using generic computer components and
perform insignificant extra - solutional activity, which does not provide an inventive concept.
The claim is not patent eligible.
Claim 2 recites a mental process as a human can transcribe audio into text associated with one or more speakers.
Claim 3 recites a mental process as a human can process language using one or more language processing techniques, including a text-based learning model, a large language model, or a natural language processing model by prompting one of these models.
Claim 4 recites a mental process as a human can extract audio format video and convert the audio from one format to another.
Claim 5 recites a mental process as a human can extract text from audio, determine one or more speakers, associate the speakers with the text, and synchronize the text with corresponding segments of the audio.
Claim 6 recites a mental process as a human can generate one or more of a text summary, one or more questions, or one or more topics associated with a video based on transcription information.
Claim 7 recites a mental process as a human can determine audience sentiment associated with one or more topics.
Claim 8 recites a mental process as a human can produce output based on an audience sentiment report.
Claim 9 recites a mental process as a human can generate one or more outputs based on one or more queries associated with an audience sentiment report.
Claim 10 recites a mental process as a human can generate information associated with speakers, topics, questions, and reasoning based on an audience sentiment report.
Claims 11-20 recite substantially the same limitations as claims 1-10, but in a different statutory category (machine vs process). As such they are directed to the same abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 6-12, 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hilmarsson (US 20240395259).
Regarding claim 1, Hilmarsson teaches a method for utilizing a plurality of artificial intelligence (AI) modules (Para 0012, “The recognized speech is then saved to a transcript, which may then be provided to one or more AI models to analyze the transcript.”) to determine audience sentiment, the method comprising (Fig. 6, “Teaches the whole system including determining metainformation which includes sentiment”): receiving, by a computing device, audio data of a video file associated with a focus group (Abstract, “receiving, by a virtual conference provider, audio streams from a plurality of participants during a virtual conference”); generating, via a language processing AI module, based on the audio data, transcription information associated with the video file. (Para 0012, “the virtual conference provider performs automatic speech recognition (“ASR”) on audio streams received during a virtual conference. The recognized speech is then saved to a transcript”), generating, via a generative AI module, based on the transcription information, insight information associated with the video file (Para 0013, “After identifying the topics, sub-topics, and utterances…”, where the topics comprise the insight information) generating, via a predictive AI module, based on the insight information, an audience sentiment report associated with the focus group (Para 0013, “After identifying the topics, sub-topics, and utterances, … can use an AI model to analyze contextual information, such as … sentiment”); And facilitating, by the computing device, transmission of the audience sentiment report. (Para 0095, “The follow-up suggestions 380 may be provided in the form of one or more topics to discuss, information indicating the user's sentiment” where “The follow-up suggestions 380 may be provided in a GUI presented to the user, sent in an email or chat message, sent via text messaging”).
Regarding claim 2, Hilmarsson teaches wherein the transcription information comprises text data associated with audio data associated with one or more speakers. (Para 0012, “performs automatic speech recognition (“ASR”) on audio streams received during a virtual conference. The recognized speech is then saved to a transcript,” where a recording of a specific person is associated with a speaker).
Regarding claim 6, Hilmarsson teaches wherein the insight information comprises one or more of a text summary associated with the video file, one or more questions associated with the video file, or one or more topics associated with the video file. (Para 0013, “After identifying the topics, sub-topics, and utterances…”, where the topics comprise the insight information).
Regarding claim 7, Hilmarsson teaches wherein the audience sentiment report comprises information indicative of audience sentiment associated with one or more topics associated with the focus group. (Para 0064, “can use an AI model to analyze contextual information, such as a level of engagement, sentiment, or level of interest for a sub-topic or topic.”).
Regarding claim 8, Hilmarsson teaches causing, based on the audience sentiment report, output of audience sentiment data. (Para 0095, “The follow-up suggestions 380 may be provided in the form of one or more topics to discuss, information indicating the user's sentiment” where “The follow-up suggestions 380 may be provided in a GUI presented to the user, sent in an email or chat message, sent via text messaging”).
Regarding claim 9, Hilmarsson teaches generating, via a second generative AI module, based on one or more queries associated with the audience sentiment report, one or more outputs. (Para 0013, “Thus, the AI model(s) used by the virtual conference provider can create a systematic analysis of the conversation held during the conference, including identifying contextual information associated with the topics discussed.” Where the use of an AI a second time fulfills the same purpose as a second generative AI module).
Regarding claim 10, Hilmarsson teaches wherein the one or more outputs comprise one or more of sentiment data associated with each speaker of one or more speakers, one or more topics associated with each speaker, sentiment data associated with each topic of one or more topics associated with the focus group( Para 0073- after identifying the utterances and generating the sub-topics and topics, the transcript analysis system 360 performs feature analysis 367 to identify metainformation for the utterances, sub-topics, and topics. In this example, feature analysis 367 determines sentiments associated with utterances, sub-topics, and topics. Sentiments relate to the speaker's attitude, such as positive, negative, or neutral), sentiment data and one or more topics associated with one or more questions, or one or more questions and reasoning associated with each question of the one or more questions.
Claims 11, 12, 16-20 are analogous to claim 1, 2, 6-10 in that they recite substantially the same limitations. They are therefore rejected for the same reasons set forth above.
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, 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.
The factual inquiries 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 nonobviousness.
Claims 3, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hilmarsson (US 20240395259) in view of Suto (US 20250013692 A1).
Regarding claim 3, Hilmarsson does not teach wherein the language processing AI module comprises one or more of a text-based learning model, a large language model, or a natural language processing application.
However, Suto teaches wherein the language processing AI module comprises one or more of a text-based learning model, a large language model, or a natural language processing application. (Para 0052, “may execute a natural language processing (NLP) model 222”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Hilmarsson in order to incorporate the teachings of Suto in order to improve the language processing capabilities of Hilmarsson by utilizing a natural language processing model capable or more effectively processing and analyzing text generated text (Para 0052).
Claim 13 is analogous to claim 3 in that it recites substantially the same limitations. It is therefore rejected for the same reasons set forth above.
Claims 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hilmarsson (US 20240395259) and Suto (US 20250013692 A1).as above in claims 1-3, 6-13, 16-20 and further in view of Kiyooka (US 20210176532 A1).
Regarding claim 4, Hilmarsson modified by Suto does not teach wherein receiving the audio data of the video file comprises: extracting the audio data from the video file; and converting the audio data from a first format to a second format.
However, Kiyooka teaches wherein receiving the audio data of the video file comprises: extracting the audio data from the video file (Para 0195, “The video audio module 203 extracts the audio data from the video data”); and converting the audio data from a first format to a second format. (Para 0195, “creates a file by converting (encoding, transcoding) and compressing the data to an optimal audio file format, such as MP3, FLAC, Vorbis, WAV, AAC, or the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Hilmarsson to incorporate the teachings of Kiyooka in order to improve preprocessing of audio data by converting audio into a suitable format for subsequent processing. (Para 0195).
Claim 14 is analogous to claim 4 in that it recites substantially the same limitations. It is therefore rejected for the same reasons set forth above.
Claims 5, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hilmarsson (US 20240395259) and Suto (US 20250013692 A1).as above in claims 1-3, 6-13, 16-20 and further in view of Su (US 20250174249 A1).
Regarding claim 5, Hilmarsson modified by Suto teaches extracting, via the language processing AI module, text data from the audio data (Abstract, “performing speech recognition, by the virtual conference provider, on the received audio streams to generate a transcript”);
Hilmarsson modified by Suto does not teach determining, via the language processing AI module, one or more speakers associated with the audio data; associating, via the language processing AI module, the one or more speakers with the text data; and synchronizing, via the language processing AI module, the text data associated with the one or more speakers with one or more segments of the audio data.
However, Su teaches determining, via the language processing AI module, one or more speakers associated with the audio data (Para 0062, “saving, tracking, and updating data, such as, but not limited to, a video ID, an audio segment ID, an audio speaker ID”); associating, via the language processing AI module, the one or more speakers with the text data (Para 0062, “segment media content according to speaker/role or by sentence, parse a segment into basic elements (e.g., word phrase and associated phonemes)” where in phrases and phonemes can comprise text data); and synchronizing, via the language processing AI module, the text data associated with the one or more speakers with one or more segments of the audio data. (Para 0043, “synchronize the corrected lip animation of speakers in the video according to the synthesized corrected audio segments”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Hilmarsson to incorporate the teachings of Su in order to improve transcript clarity by identifying speakers, associating transcript data with speakers, and synchronizing the audio with corresponding speakers.
Claim 15 is analogous to claim 5 in that it recites substantially the same limitations. It is therefore rejected for the same reasons set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ALAN FOSTER JR. whose telephone number is (571)272-8874. The examiner can normally be reached M - F 8:00am - 6:00pm.
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, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL A FOSTER JR/ Examiner, Art Unit 2654
/HAI PHAN/ Supervisory Patent Examiner, Art Unit 2654
Prosecution Insights