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 .
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 audio/text analysis without significantly more. The claims 1, 7 and 19 recite steps of generating, based at least on audio data representative an utterance, text data corresponding to the utterance (i.e., a data evaluation/analysis step), generating, using one or more first models and based at least on the text data, output data indicating whether each token corresponding to the text data is associated with an end of a sentence within the utterance or an end of the utterance (i.e., a data evaluation/analysis step), determining, based at least on the output data, a first location within the text data that is associated with the end of the sentence and a second location within the text data that is associated with the end of the utterance (i.e., a data evaluation/analysis step) and processing, using one or more second models and based at least on the first location and the second location, a first portion of the text data corresponding to the sentence of the utterance prior to processing a second portion of the text data corresponding to a remainder of the utterance (i.e., a data evaluation/analysis step), corresponding to steps achievable by a human manually/mentally transcribing spoken audio and performing analysis on the obtained transcription, and as such, corresponds to the mental processes category of abstract ideas. This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements, where the generically recited computer elements (system, processor, processing circuitry) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because step “processing, using one or more second models and based at least on the first location and the second location, a first portion of the text data corresponding to the sentence of the utterance prior to processing a second portion of the text data corresponding to a remainder of the utterance” correspond to well-understood, routine, conventional computer functions of collecting information and analyzing it as recognized by the court decisions listed in MPEP § 2106.05, and as provided by cited reference Rangarajan(PTO 892 form).
The dependent claims also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
Claim Objections
Claims 2 and 10 are objected to because of the following informalities: “at least;” as recited in the claims should be “at least:”, i.e., colon instead of semicolon. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
1. Claims 1, 6-9, 11 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rangarajan Sridhar et al - US 2015/0134320 A1 (“Rangarajan”)
Per claim 1, Rangarajan discloses a method comprising:
generating, based at least on audio data representative an utterance, text data corresponding to the utterance (As the system receives the stream of speech 212, the system is performing automatic speech recognition 214 on the stream of speech 212. The automatic speech recognition process 214 produces, via a speech recognizer, partial speech hypotheses 216…, para. [0038]);
generating, using one or more first models and based at least on the text data, output data indicating whether each token corresponding to the text data is associated with an end of a sentence within the utterance or an end of the utterance (fig. 2; para. [0023]; para. [0029]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]);
determining, based at least on the output data, a first location within the text data that is associated with the end of the sentence and a second location within the text data that is associated with the end of the utterance (fig. 2; The system's segmenter 226, instead of finding a conjunction, identifies the end of the speech as a likely end of a sentence, and segments the text hypothesis "la nacion en la lucha contra el narcotrafico" as a second segment 230…., para. [0041]); and
processing, using one or more second models and based at least on the first location and the second location, a first portion of the text data corresponding to the sentence of the utterance prior to processing a second portion of the text data corresponding to a remainder of the utterance (fig. 2; para. [0025]; para. [0039]; Immediately upon segmenting the segment 1 (at time 234), the system begins machine translation 232 on segment 1…., para. [0040]; The system, upon identifying the second segment 230 at time 238, immediately begins machine translation 232 of the second segment 241…., para. [0041]; para. [0047]).
Per claim 6, Rangarajan discloses the method of claim 1, wherein the first portion of the text data is processed using the one or more second models based at least on determining the first location and prior to determining the second location (fig. 2; para. [0025]; para. [0039]; para. [0041]; para. [0047]).
Per claim 7, Rangarajan discloses a system comprising: one or more processors to: determine, using one or more models and based at least on text data associated with one or more words, an output indicating whether the one or more words are associated an end of sentence and whether the one or more words are associated with an end of utterance (fig. 2; para. [0023]; para. [0029]; para. [0031]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]); and
cause, based at least on the output, processing of at least a portion of the text data (fig. 2; para. [0025]; para. [0039]; Immediately upon segmenting the segment 1 (at time 234), the system begins machine translation 232 on segment 1…., para. [0040]; The system, upon identifying the second segment 230 at time 238, immediately begins machine translation 232 of the second segment 241…., para. [0041]; para. [0047]).
Per claim 8, Rangarajan discloses the system of claim 7, wherein the one or more processors are further to: determine, based at least on the output, that a first word of the one or more words is associated with the end of sentence (fig. 2; para. [0023]; para. [0029]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]);
determine the at least the portion of the text data based at least on the first word being associated with the end of sentence (fig. 2; para. [0023]; para. [0029]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]);
determine, based at least on the output, that a second word of the one or more words is associated with the end of utterance (fig. 2; para. [0023]; para. [0029]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]);
determine at least a second portion of the text data based at least on the second word being associated with the end of utterance (fig. 2); and
cause processing of the at least the second portion of the text data (para. [0038]-[0041]).
Per claim 9, Rangarajan discloses the system of claim 8, wherein the at least the portion of the text data is processed prior to the at least the second portion of the text data (para. [0038]-[0041]).
Per claim 11, Rangarajan discloses system of claim 7, wherein the one or more words include a plurality of words, and wherein the output represents at least: one or more first indicators that one or more first words from the plurality of words are associated with the end of sentence (fig. 2; para. [0038]-[0039]; para. [0041]); and
one or more second indicators that one or more second words from the plurality of words are associated with the end of utterance (fig. 2; para. [0038]-[0039]).
Per claim 17, Rangarajan discloses the system of claim 7, wherein: the text data represents one or more tokens associated with the one or more words (para. [0038]-[0039]); and
the output indicates whether the one or more tokens are associated with the end of sentence or whether the one or more tokens are associated with the end of utterance (para. [0046]; para. [0054]; para. [0057]).
Per claim 18, Rangarajan discloses the system of claim 7, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs); a system for performing operations using one or more visual language models (VLMs); a system for performing one or more conversational AI operations; a system for generating synthetic data (para. [0042]; para. [0044]; para. [0047]); a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
Per claim 19, Rangarajan discloses one or more processors comprising:
processing circuitry to process at least a first portion of text data at a first instance and a second portion of the text data at a second instance based at least on an output indicating that the first portion of the text data is associated with an end of a sentence and the second portion of the text data is associated with an end of an utterance that includes the sentence, wherein the output is generated based at least on one or more models processing the text data (fig. 2; para. [0023]; para. [0029]; para. [0031]; The segment consisting of the beginning of the sentence to the conjunction, is referred to as segment 1 228, and includes text associated with the speech originally received, beginning with "La" and going to "y.", para. [0039]; para. [0041]).
Per claim 20, Rangarajan discloses the one or more processors of claim 19, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative AI operations; a system for performing operations using one or more large language models (LLMs); a system for performing operations using one or more visual language models (VLMs); a system for performing one or more conversational AI operations; a system for generating synthetic data (para. [0042]; para. [0044]; para. [0047]); a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
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.
2. Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Rangarajan in view of Pathak et al - US 2025/0054491 A1 (“Pathak”)
Per claim 2, Rangarajan discloses the method of claim 1,
Rangarajan does not explicitly disclose wherein the output data represents at least; first probabilities indicating whether each token is associated with the end of the sentence, second probabilities indicating whether each token is associated with the end of the utterance or third probabilities indicating whether each token is not associated with the end of the sentence and the end of the utterance
However, these features are taught by Pathak:
wherein the output data represents at least; first probabilities indicating whether each token is associated with the end of the sentence (para. [0060]-[0061]);
second probabilities indicating whether each token is associated with the end of the utterance (para. [0060]-[0061]); and
third probabilities indicating whether each token is not associated with the end of the sentence and the end of the utterance (Abstract; para. [0060]-[0061], low scores as implying limitation)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Pathak with the method of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in improving the quality of speech recognition and machine translation (Pathak, para. [0049])
Per claim 10, Rangarajan discloses the system of claim 7,
Rangarajan does not explicitly disclose wherein the output represents at least; one or more first probabilities indicating whether the one or more words are associated with the end of sentence or one or more second probabilities indicating whether the one or more words are associated with the end of utterance
However, these features are taught by Pathak:
wherein the output represents at least; one or more first probabilities indicating whether the one or more words are associated with the end of sentence (para. [0060]-[0061]); and
one or more second probabilities indicating whether the one or more words are associated with the end of utterance (para. [0060]-[0061])
It would have been obvious to one of ordinary skill in the art to combine the teachings of Pathak with the system of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in improving the quality of speech recognition and machine translation (Pathak, para. [0049]).
3. Claims 3, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Rangarajan in view of Bijwadia et al - “Text Injection for Capitalization and Turn-Taking Prediction in Speech Models” (“Bijwadia”)
Per claim 3, Rangarajan discloses the method of claim 1,
Rangarajan does not explicitly disclose generating, using one or more second models and based at least on the text data, second output data representative of whether each token is associated with a lowercase word or an uppercase word, wherein the determining the first location and the second location is further based at least on the second output data
However, this feature is taught by Bijwadia (sec. 3.2; sec. 4.1; The capitalization sequence is defined as follows: each token is either ⟨cap⟩ (capitalized) or ⟨non-cap) (not capitalized), based on the corresponding wordpiece in the ASR transcript …, sec. 5.3)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Bijwadia with the method of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in restoring the correct case of noisy text (Bijwadia, sec. 3.1)
Per claim 12, Rangarajan discloses the system of claim 7,
Rangarajan does not explicitly disclose wherein the one or more processors are further to: determine, using one or more second models and based at least on the text data, a second output indicating whether the one or more words are at least one of lowercase or uppercase, wherein the processing of the at least the portion of the text data is further caused based at least on the second output.
However, this feature is taught by Bijwadia (sec. 3.2; sec. 4.1; The capitalization sequence is defined as follows: each token is either ⟨cap⟩ (capitalized) or ⟨non-cap) (not capitalized), based on the corresponding wordpiece in the ASR transcript …, sec. 5.3)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Bijwadia with the system of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in restoring the correct case of noisy text (Bijwadia, sec. 3.1)
Per claim 13, Rangarajan in view of Bijwadia discloses the system of claim 12,
Bijwadia discloses wherein the second output represents at least: one or more first probabilities indicating whether the one or more words are lowercase (sec. 4.1; sec. 5.2; sec. 5.3); and one or more second probabilities indicating whether the one or more words are uppercase (sec. 4.1; sec. 5.2; sec. 5.3).
4. Claims 4, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Rangarajan in view of Hassid et al - US 2023/0335111 A1 (“Hassid”)
Per claim 4, Rangarajan discloses the method of claim 1,
Rangarajan does not explicitly disclose generating, using the one or more second models and based at least on the text data, second output data representative of whether each token is associated with one or more types of punctuation marks, wherein the determining the first location and the second location is further based at least on the second output data
However, this feature is taught by Hassid (para. [0092])
It would have been obvious to one of ordinary skill in the art to combine the teachings of Hassid with the method of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in detecting the end of an input string (Hassid, para. [0097]).
Per claim 14, Rangarajan discloses the system of claim 7,
Rangarajan does not explicitly disclose wherein the one or more processors are further to: determine, using one or more second models and based at least on the text data, a second output indicating whether the one or more words are associated with one or more types of punctuation marks, wherein the processing of the at least the portion of the text data is further caused based at least on the second output
However, this feature is taught by Hassid (para. [0092])
It would have been obvious to one of ordinary skill in the art to combine the teachings of Hassid with the system of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in in detecting the end of an input string (Hassid, para. [0097]).
Per claim 15, Rangarajan in view of Hassid discloses the system of claim 14,
Hassid discloses wherein the second output represents at least: one or more first probabilities indicating whether the one or more words are associated with one or more first types of punctuation marks (para. [0092]; para. [0097]); and
one or more second probabilities indicating whether the one or more words are associated with one or more second types of punctuation marks (para. [0092]; para. [0097]).
5. Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rangarajan in view of Lineback et al - US 2025/0330665 a1 (“Lineback”)
Per claim 5, Rangarajan discloses method of claim 1, further comprising:
Rangarajan does not explicitly disclose generating, using one or more first encoders and based at least on the audio data, one or more first embeddings, generating, using one or more second encoders and based at least on the text data, one or more second embeddings or generating input data based at least on the one or more first embeddings and the one or more second embeddings, wherein the generating the output data uses the one or more models and is based at least on the input data
However, these features are taught by Lineback:
generating, using one or more first encoders and based at least on the audio data, one or more first embeddings (fig. 9A; para. [0128]; the neural network 908 can use an audio signal (e.g., audio data) in the one or more media content items 906 to generate an embedding 910B …, para. [0129]);
generating, using one or more second encoders and based at least on the text data, one or more second embeddings (fig. 9A; para. [0128]; The neural network 908 can use a text signal (e.g., closed caption data, metadata, etc.) in the one or more media content items 906 to generate an embedding 910N representing and/or encoding information from the text signal …, para. [0129]); and
generating input data based at least on the one or more first embeddings and the one or more second embeddings, wherein the generating the output data uses the one or more models and is based at least on the input data (fig. 9A; para. [0129]).
It would have been obvious to one of ordinary skill in the art to combine the teachings of Lineback with the method of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in determining one or more segment categories for media content (Lineback, para. [0135]).
Per claim 16, Rangarajan discloses system of claim 7,
Rangarajan does not explicitly disclose wherein the one or more processors are further to: generate, using one or more encoders and based at least on audio data, one or more first embeddings, generate, using the one or more encoders and based at least on the text data, one or more second embeddings or generate input data based at least on the one or more first embeddings and the one or more second embeddings, wherein the determination of the output is based at least on the input data
However, these features are taught by Lineback:
wherein the one or more processors are further to: generate, using one or more encoders and based at least on audio data, one or more first embeddings (fig. 9A; para. [0128]; the neural network 908 can use an audio signal (e.g., audio data) in the one or more media content items 906 to generate an embedding 910B …, para. [0129]);
generate, using the one or more encoders and based at least on the text data, one or more second embeddings (fig. 9A; para. [0128]; The neural network 908 can use a text signal (e.g., closed caption data, metadata, etc.) in the one or more media content items 906 to generate an embedding 910N representing and/or encoding information from the text signal …, para. [0129]); and
generate input data based at least on the one or more first embeddings and the one or more second embeddings, wherein the determination of the output is based at least on the input data (fig. 9A; para. [0129]).
It would have been obvious to one of ordinary skill in the art to combine the teachings of Lineback with the system of Rangarajan in arriving at the missing features of Rangarajan, because such combination would have resulted in determining one or more segment categories for media content (Lineback, para. [0135]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUJIMI A ADESANYA whose telephone number is (571)270-3307. The examiner can normally be reached Monday-Friday 8:30-5:00pm.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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