DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on May 20, 2026. Claims 1-24 are pending and have been examined. Hence, this action has been made FINAL.
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 .
Priority
Applicant claims the benefit of US Provisional Application No. 63/486,746, filed February 24, 2023. Claims 1-24 have been afforded the benefit of this filing date.
Response to Arguments
The reply filed on May 20, 2026 has been entered. Applicant’s arguments with respect to claims 1-24 have been considered but are moot in view of new ground(s) of rejection caused by the amendments.
With respect to the applicant’s arguments to claim rejections under 35 U.S.C § 103, the applicant’s arguments with respect to claims 1-24 have been considered but are moot in view of new ground(s) of rejection caused by the amendments.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
Noisy text data: ¶ [0024], “travel data may include data in a conversational format ( e.g., grammatical errors, punctuation or capitalization errors, spelling errors, formatting errors, etc.). Such conversational data is sometimes referred to herein as "noisy" data.”
Thus, noisy text data may include conversational or error-prone text. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 14, should be “in response to determining that the additional text data has been generated and is accessible”
Appropriate correction is required.
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.
Claims 1-4, 7-8, 12-15, 18-19, and 23 are rejected under 35 U.S.C. 103 as obvious over "Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation" (Zhu et al.) in view of US Patent Publication 20220366894 A1 (Lim et al.) in view of "Parameter-Efficient Transfer Learning for NLP" (Houlsby et al.).
Claim 1
Regarding claim 1, Zhu et al. disclose a method, comprising:
maintaining, by one or more processors (Zhu et al. pg. 12, Appendix A.2, Paragraph 1, "These experiments are run on an Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz (Mem: 330G) with 8 Tesla V100s (32G) and an Intel(R) Xeon(R) CPU E5-2680 v4@ 2.40GHz (Mem: 256) with 8 RTX 2080Tis (11G)."), a first dataset having a standardized text format (Zhu et al. pg. 11, Appendix, Table 3, "[TravelQA] is a travel domain question answering dataset. It is created based on ctrip.com" A QA dataset is considered analogous to a standardized text format) and a second dataset having a noisy text format (Zhu et al. pg. 11, Appendix, Table 3, "[KdConv] is developed by (Zhou et al., 2020) through crowdsourcing to generate multi-turn conversations related to the domain-specific knowledge graph. ... Considering the scope of this paper, we only use the travel domain dataset for evaluation."), each of the first dataset and the second dataset corresponding to a travel-specific lexicon (Zhu et al. pg. 2, Section 1, Paragraph 6, "The main contributions of this paper are as follows: ... We construct 4 datasets for evaluating downstream tourism NLP tasks." See table 5 for the list of travel-specific datasets);
generating, by the one or more processors, training data corresponding to a first training objective and a second training objective (Zhu et al. pg. 2, Section 1, Paragraph 5, "We combine three objective functions to jointly pre-train the multi-format text. For unstructured text, we adopt the masked language model (MLM) objective to train the domain adaption model. For semi-structured text, we propose title matching training (TMT) to classify whether the title matches the paragraph. For well-structured text, we propose a triple classification (TC) task to classify whether the knowledge triple is modified.") based on the first dataset and the second dataset (Zhu et al. pg. 2, Section 1, Paragraph 5, "To obtain the aforementioned multi-format text, we construct a corpus in the tourism domain and pre-train our TravelBERT." See Table 5 for tourism corpora);
training, by the one or more processors, a first set of parameters of a travel-specific natural language machine-learning model using the training data according to the first and second training objectives (Zhu et al. pg. 5, Section 2.3, Paragraph 1, "For the three tasks, we can optimize the combined objective function
min
θ
L
=
∑
i
=
1
|
D
|
(
L
i
m
l
m
+
λ
L
i
t
c
+
μ
L
i
t
m
t
)
where
L
i
m
l
m
,
L
i
t
c
, and
L
i
t
m
t
are the objectives of three tasks respectively. ...
θ
is the model parameter."); …
retrieving, by the one or more processors, [the additional] text data from one or more data sources [in response to determining that the additional text data has been generated and is accessible], the [additional] text data corresponding to the travel-specific lexicon (Zhu et al. pg. 11, Appendix, Table 5, "[KdConv] is developed by (Zhou et al., 2020) through crowdsourcing to generate multi-turn conversations related to the domain-specific knowledge graph. ... Considering the scope of this paper, we only use the travel domain dataset for evaluation."); … and
tuning, by the one or more processors, a second set of parameters of [at least one adapter layer of] the travel-specific natural language machine-learning model using the [updated] training data (Zhu et al. pg. 5, Section 2.4, Paragraph 1, "When fine-tuning downstream tasks, our model does not need to change the input text because the model can learn heterogeneous knowledge in the pretraining stage and learn better parameters, like GPT-3 (Brown et al., 2020) and WKLM (Xiong et al., 2020). Then the model uses the learned knowledge (parameters) to better solve downstream tasks." Learned parameters are considered analogous to a second set of parameters).
Zhu et al. do not explicitly disclose all of updating datasets.
However, Lim et al. disclose generating, by the one or more processors, training data [corresponding to a first training objective and a second training objective] based on a first dataset (Lim et al. ¶ [0024]-[0029], "Referring to FIGS. 1 and 2, in operation S100, an initial text (also, referable to as first text data) of a mono corpus is converted to speech data. ... In operation S110, noise-speech data may be acquired by adding noise to each piece of the converted speech data. ... In operation S120, the noise-speech data may be converted to a final text. The final text may be referred to as second text data and the second text data may include a plurality of sentences corresponding to the plurality of sentences included in the first text data, respectively.") [and the second dataset]; …
determining, by the one or more processors, that additional text data having noisy text format has been generated and is accessible (Lim et al. ¶ [0025], "In operation S110, noise-speech data may be acquired by adding noise to each piece of the converted speech data. The noise-speech data refers to speech data acquired by adding noise to the first speech data and may be referred to as noise-added speech data or second speech data." Acquiring noise-speech data is considered analogous to determining that additional text data having a noisy text format has been generated and is accessible) [from one or more data sources];
retrieving, by the one or more processors, the additional text data from one or more data sources in response to determining that the additional text data has been generated and is accessible (Lim et al. ¶ [0030], "The initial text (e.g., the first text data) and the final text (e.g., the second text data) constitute a parallel corpus. That is, the second text data is text data corresponding to the second speech data and the first text data is text data that becomes a basis of the first speech data. Therefore, in the case of training a predetermined model using the first text data and the second text data (i.e., a parallel corpus), a speech recognition postprocessing model capable of correcting typos and errors in text generated as a result of an acoustic model may be generated." Training a speech recognition postprocessing model using a generated parallel corpus implies retrieving the additional text data in response to determining that the additional text data has been generated and is accessible), [the additional text data corresponding to the travel-specific lexicon]; and
updating, by the one or more processors, the training data in real-time or near real-time using the additional text data (Lim et al. ¶ [0031], "As described above, it is possible to infinitely generate training data by converting a text to speech data, by adding noise to the speech data, and by converting the speech data to the text." See Figure 2, which illustrates constructing a parallel corpus using the additional text data (e.g. the second "Text" after "Speech to Text" and "Noise Controller"))….
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al.’s travel-specific natural language processing system to include Lim et al.’s dataset updating because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zhu et al.’s static datasets as modified by Lim et al.’s dataset updating can yield a predictable result of improving model performance since a model could be iteratively trained on new noisy data generated overtime instead of only on static datasets. Thus, a person of ordinary skill would have appreciated including in Zhu et al.’s travel-specific natural language processing system the ability to do Lim et al.’s dataset updating since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Zhu et al. in view of Lim et al. do not disclose all of tuning an adapter layer.
However, Houlsby et al. disclose training, by the one or more processors, a first set of parameters of a travel-specific natural language machine-learning model (Houlsby et al. pg. 3, Section 3.1, Paragraph 2, "Our training procedure also follows Devlin et al. (2018). We optimize using Adam (Kingma & Ba, 2014), whose learning rate is increased linearly over the first 10% of the steps, and then decayed linearly to zero. All runs are trained on 4 Google Cloud TPUs with a batch size of 32.") [using the training data according to the first and second training objectives];
determining, by the one or more processors, that additional text data [having the noisy text format has been generated and] is accessible from one or more data sources (Houlsby et al. pg. 4, Section 3.3, Paragraph 1, "To further validate that adapters yields compact, performant, models, we test on additional, publicly available, text classification tasks.");
retrieving, by the one or more processors, the additional text data from one or more data sources (Houlsby et al. pg. 4, Section 3.3, Paragraph 1, "To further validate that adapters yields compact, performant, models, we test on additional, publicly available, text classification tasks." Testing on additional text classification tasks implies retrieving the additional text data from one or more data sources) [in response to determining that the additional text data has been generated and is accessible], the additional text data corresponding to the travel-specific lexicon (Houlsby et al. pg. 5, Table 2. The dataset "Crowdflower airline" comprises tweets about airlines. Thus, dataset "Crowdflower airline" is considered analogous to additional text data corresponding to travel-specific lexicon);
updating, by the one or more processors, the training data in real-time or near real-time using the additional text data (Houlsby et al. pg. 4, Section 3.3, Paragraph 1, "To further validate that adapters yields compact, performant, models, we test on additional, publicly available, text classification tasks." Pulling additional classification tasks in addition to the GLUE benchmark (see Section 3.2) is considered analogous to updating training data); and
tuning, by the one or more processors, a second set of parameters of at least one adapter layer of the [travel-specific] natural language machine-learning model using the updated training data (Houlsby et al. pg. 2, Section 2, Paragraph 2, "Adapter modules perform more general architectural modifications to re-purpose a pretrained network for a downstream task. In particular, the adapter tuning strategy involves injecting new layers into the original network. The weights of the original network are untouched, whilst the new adapter layers are initialized at random." pg. 3, Figure 2, "During adapter tuning, the green layers are trained on the downstream data, this includes the adapter, the layer normalization parameters, and the final classification layer (not shown in the figure)." See Figure 2 for adapter layer architecture).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al. in view of Lim et al. to incorporate Houlsby et al.’s adapter layer.
The suggestion/motivation for doing so would have been that, “Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task,” as noted by Houlsby et al. in the abstract.
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated.
Zhu et al. further disclose tuning, by the one or more processors, the travel-specific natural language machine-learning model using an additional training set according to a third training objective (Zhu et al. pg. 2, Section 1, Paragraph 5, "We combine three objective functions to jointly pre-train the multi-format text. For unstructured text, we adopt the masked language model (MLM) objective to train the domain adaption model. For semi-structured text, we propose title matching training (TMT) to classify whether the title matches the paragraph. For well-structured text, we propose a triple classification (TC) task to classify whether the knowledge triple is modified." Well-structured text is considered analogous to the additional training set. The triple classification (TC) task is considered analogous to a third training objective.).
Claim 3
Regarding claim 3, the rejection of claim 1 is incorporated.
Zhu et al. further disclose wherein maintaining the first dataset comprises scraping, by the one or more processors, a plurality of webpages hosted by one or more second data sources to retrieve data having the standardized text format (Zhu et al. pg. 7, Section 4.1.2, Paragraph 3, "For the TravelQA dataset, we use a crawler to get user questions from the travel guide channel of ctrip.com. We select the answer adopted by the questioner as the gold answer from the replies, and the negative answers come from replies to other questions." A Q&A structure is considered analogous to a standardized text format.).
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated.
Zhu et al. further disclose wherein maintaining the second dataset comprises accessing, by the one or more processors, one or more chat logs, social media sources, or peer-to-peer communications maintained by the one or more data sources to retrieve the additional text data having the noisy text format (Zhu et al. pg. 6, Section 3.1, Paragraph 2, “For downstream tourism NLP tasks, due to the lack of sufficient evaluation datasets in the tourism domain, we adopt a well-known dialogue dataset KdConv (Zhou et al., 2020) and construct 4 tourism NLP datasets.”; pg. 11, Section A, Table 5, “[KdConv] is developed by (Zhou et al., 2020) through crowdsourcing to generate multi-turn conversations related to the domain-specific knowledge graph.” Crowdsourced Multi-turn conversations are considered analogous to peer-to-peer communication. Conversation data is considered analogous to noisy text, as per ¶ [0024]-[0027] in the specification of the instant application).
Claim 7
Regarding claim 7, the rejection of claim 1 is incorporated.
Zhu et al. further disclose wherein training the travel-specific natural language machine- learning model comprises:
training, by the one or more processors, using the training data, the travel-specific natural language machine-learning model according to the first training objective and the second training objective concurrently (Zhu et al. pg. 6, Section 3.3, Paragraph 1, "For the three tasks, we can optimize the combined objective function:
m
i
n
θ
L
=
∑
i
=
1
|
D
|
(
L
i
m
l
m
+
L
i
t
c
+
L
i
(
t
m
t
)
)
").
Claim 8
Regarding claim 8, the rejection of claim 1 is incorporated.
Zhu et al. further disclose wherein the travel-specific natural language machine-learning model comprises a deep learning model (Zhu et al. pg. 4, Section 3.1, Paragraph 2, "Given that BERT is a representative PLM, all studies in this paper use BERT as the backbone." BERT is considered analogous to a deep learning model).
Claim 12
Regarding claim 12, Zhu et al. disclose a system, comprising:
one or more processors coupled to non-transitory memory (Zhu et al. pg. 12, Appendix A.2, Paragraph 1, "These experiments are run on an Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz (Mem: 330G) with 8 Tesla V100s (32G) and an Intel(R) Xeon(R) CPU E5-2680 v4@ 2.40GHz (Mem: 256) with 8 RTX 2080Tis (11G).").
The remaining limitations of claim 12 are similar in scope to that of claim 1 and therefore are rejected for similar reasons as described above.
Claim 13
Regarding claim 13, the rejection of claim 12 is incorporated. The limitations of claim 13 are similar in scope to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 14
Regarding claim 14, the rejection of claim 12 is incorporated. The limitations of claim 14 are similar in scope to that of claim 3 and therefore are rejected for similar reasons as described above.
Claim 15
Regarding claim 15, the rejection of claim 12 is incorporated. The limitations of claim 15 are similar in scope to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 18
Regarding claim 18, the rejection of claim 12 is incorporated. The limitations of claim 18 are similar in scope to that of claim 7 and therefore are rejected for similar reasons as described above.
Claim 19
Regarding claim 19, the rejection of claim 12 is incorporated. The limitations of claim 19 are similar in scope to that of claim 8 and therefore are rejected for similar reasons as described above.
Claim 23
Regarding claim 23, Zhu et al. disclose a non-transitory computer-readable medium with instructions embodied thereon (Zhu et al. pg. 12, Appendix A.2, Paragraph 1, "These experiments are run on an Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz (Mem: 330G) with 8 Tesla V100s (32G) and an Intel(R) Xeon(R) CPU E5-2680 v4@ 2.40GHz (Mem: 256) with 8 RTX 2080Tis (11G).").
The remaining limitations of claim 23 are similar in scope to that of claim 1 and claim 2, and therefore are rejected for similar reasons as described above.
Claims 5-6 and 16-17 are rejected under 35 U.S.C. 103 as obvious over Zhu et al. in view of Lim et al. in view of Houlsby et al. as applied to claims 1 and 12 above, and further in view of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (Devlin et al.).
Claim 5
Regarding claim 5, the rejection of claim 1 is incorporated.
Zhu et al. in view of Lim et al. in view of Houlsby et al. do not explicitly disclose all of the first and second training objectives being MLM and NSP respectively.
However, Devlin et al. disclose wherein a first training objective is masked-language modeling (MLM) and a second training objective is next sentence prediction (NSP) (Devlin et al. pg. 4, Section 3.1, Paragraph 1, "we pre-train BERT using two unsupervised tasks, described in this section. This step is presented in the left part of Figure 1." See Figure 1, which illustrates MLM training followed by NSP training).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al. in view of Lim et al. in view of Houlsby et al. to incorporate Devlin et al.’s training objectives because such a modification is the result of simple substitution of one known element for another producing a predictable result. More specifically, Zhu et al.’s training objectives and Devlin et al.’s training objectives perform the same general and predictable function, the predictable function being training a language model for downstream task processing. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of Zhu et al.’s training objectives by replacing it with Devlin et al.’s training objectives. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated.
Zhu et al. in view of Lim et al. in view of Houlsby et al. do not explicitly disclose all of training a model by applying training objectives in sequence.
However, Devlin et al. disclose wherein training the travel-specific natural language machine-learning model comprises:
training, by the one or more processors, the travel-specific natural language machine- learning model using a first training set according to the first training objective (Devlin et al. pg. 4, Section 3.1, Paragraph 2, "we pre-train BERT using two unsupervised tasks ... In order to train a deep bidirectional representation, we simply mask some percentage of the input tokens at random, and then predict those masked tokens. We refer to this procedure as a “masked LM” (MLM)," Masked LM (MLM) is considered analogous to the first training objective. Input tokens to BERT are considered analogous to a first training set); and
subsequently training, by the one or more processors, the travel-specific natural language machine-learning model using a second training set according to the second training objective (Devlin et al. pg. 4, Section 3.1, Paragraph 2, "In order to train a model that understands sentence relationships, we pre-train for a binarized next sentence prediction task that can be trivially generated from any monolingual corpus." See Figure 1 and Appendix A.1, which both display NSP as a subsequent step to MLM. The output of the first task (MLM) is therefore considered analogous to a second training set).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al. in view of Lim et al. in view of Houlsby et al. to incorporate Devlin et al.’s training objectives.
The suggestion/motivation for doing so is similar to the suggestion/motivation described above with respect to claim 5.
Claim 16
Regarding claim 16, the rejection of claim 12 is incorporated. The limitations of claim 16 are similar in scope to that of claim 5 and therefore are rejected for similar reasons as described above.
Claim 17
Regarding claim 17, the rejection of claim 12 is incorporated. The limitations of claim 17 are similar in scope to that of claim 6 and therefore are rejected for similar reasons as described above.
Claims 9-10, 20-21, and 24 are rejected under 35 U.S.C. 103 as obvious over Zhu et al. in view of Lim et al. in view of Houlsby et al. as applied to claims 1 and 12 above, and further in view of US Patent Publication 20190065462 A1 (Salloum et al.).
Claim 9
Regarding claim 9, the rejection of claim 1 is incorporated.
Zhu et al. in view of Lim et al. in view of Houlsby et al. do not explicitly disclose all of sentence pairs.
However, Salloum et al. disclose wherein generating the training data comprises generating, by the one or more processors, a plurality of sentence pairs using text data from the first dataset and the second dataset (Salloum et al. ¶ [0031]-[0032], "FIG. 1 is a flowchart depicting contemplated steps in preferred methods of generating bitexts for training a SMT system. In Step 101, a corpus comprising automated speech recognition (ASR) dictations and formatted reports is obtained. ... SMT requires sentence-aligned data, or bitexts: parallel pairs of translational equivalent sentences in source and target languages. The source language is the output from a speech recognition system, usually unformatted text transcripts (i.e., hypotheses) ... The target language (output of the machine translation postprocessor) is a fully formatted report" Fully formatted reports are considered analogous to the first dataset. Retrieved ASR output is considered analogous to the second dataset).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al. in view of Lim et al. in view of Houlsby et al. to include Salloum et al.’s sentence pairs because such a modification is the result of simple substitution of one known element for another producing a predictable result. More specifically, Zhu et al.’s travel-specific datasets and Salloum et al.’s sentence pairs perform the same general and predictable function, the predictable function being providing training data for training a natural language machine-learning model. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of Zhu et al.’s travel-specific datasets by replacing it with Salloum et al.’s sentence pairs. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 10
Regarding claim 10, the rejection of claim 9 is incorporated.
Salloum et al. further disclose wherein the plurality of sentence pairs comprise a first pair having a first sentence in the standardized text format and a second sentence in the noisy text format (Salloum et al. ¶ [0031]-[0032], "FIG. 1 is a flowchart depicting contemplated steps in preferred methods of generating bitexts for training a SMT system. In Step 101, a corpus comprising automated speech recognition (ASR) dictations and formatted reports is obtained. ... SMT requires sentence-aligned data, or bitexts: parallel pairs of translational equivalent sentences in source and target languages. The source language is the output from a speech recognition system, usually unformatted text transcripts (i.e., hypotheses) ... The target language (output of the machine translation postprocessor) is a fully formatted report" Fully formatted reports are considered analogous to a standardized text format. Retrieved ASR output is considered analogous to noisy data, since ¶ [0024] of the instant application states that "noisy" data can include conversational data.).
Claim 20
Regarding claim 20, the rejection of claim 12 is incorporated. The limitations of claim 20 are similar in scope to that of claim 9 and therefore are rejected for similar reasons as described above.
Claim 21
Regarding claim 21, the rejection of claim 20 is incorporated. The limitations of claim 21 are similar in scope to that of claim 10 and therefore are rejected for similar reasons as described above.
Claim 24
Regarding claim 24, the rejection of claim 23 is incorporated. The limitations of claim 24 are similar in scope to that of claim 9 and claim 10, and therefore are rejected for similar reasons as described above.
Claims 11 and 22 are rejected under 35 U.S.C. 103 as obvious over Zhu et al. in view of Lim et al. in view of Houlsby et al. in view of Salloum et al. as applied to claims 9 and 20 above, and further in view of "Deep Sentence Denoising beyond Grammatical Error Correction" (Liang et al.).
Claim 11
Regarding claim 11, the rejection of claim 9 is incorporated.
Salloum et al. further disclose wherein the plurality of sentence pairs comprise a first [predetermined] number of sentence pairs having at least one sentence in the noisy text format (Salloum et al. ¶ [0032], “SMT requires sentence-aligned data, or bitexts: parallel pairs of translational equivalent sentences in source and target languages. The source language is the output from a speech recognition system, usually unformatted text transcripts (i.e., hypotheses) … Hypotheses and reports cannot be naïvely used as translational equivalents because there are too many discontinuities between the two. For example: … errors and corrections spoken in the dictation (“scratch that,” e.g.) are not present in the report" ASR dictations that contain speech errors (“scratch that,” e.g.) are considered analogous to noisy text; see ¶ [0024] of the instant application).
Zhu et al. in view of Lim et al. in view of Houlsby et al. in view of Salloum et al. do not explicitly disclose all of generating a predetermined number of sentence pairs.
However, Liang et al. disclose wherein the plurality of sentence pairs comprise a first predetermined number of sentence pairs (Liang et al. pg. 3, Section 3B, Paragraph 1, "We use SimpleWiki as the seed corpus for verbosity noise, misordering noise, and synonym noise. A cleaning process is performed and 680k sentences are kept, which are further split into training/validation/test sets by 90:5:5. Noise generation scripts are applied after the data split. As a result, verbosity noise, misordering noise, and synonym noise will share the same seed corpus for train/validation/test sets, even though they are noised in different ways." Splitting up a set number of sentence pairs using a predetermined ratio is analogous to generating a predetermined number of sentence pairs) having at least one sentence in the noisy text format (Liang et al. pg. 3, Section 3A, Paragraph 1, "Every dataset consists of pairs of two sentences: (noisy sentence, noise-free sentence)").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhu et al. in view of Lim et al. in view of Houlsby et al. in view of Salloum et al. to include Liang et al.’s predetermined number of sentence pairs because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zhu et al.’s travel-specific NLP model as modified by Liang et al.’s predetermined number of sentence pairs can yield a predictable result of reducing model variability since using a constant number of inputs would make the input to the machine learning model more consistent over multiple tests. Thus, a person of ordinary skill would have appreciated including in Zhu et al.’s travel-specific NLP model the ability to do Liang et al.’s predetermined generation since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 22
Regarding claim 22, the rejection of claim 20 is incorporated. The limitations of claim 22 are similar in scope to that of claim 9 and therefore are rejected for similar reasons as described above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB B VOGT whose telephone number is (571)272-7028. The examiner can normally be reached Monday - Friday, 11am - 8pm EST.
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, PARAS D SHAH can be reached at (571)270-1650. 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.
/JACOB B VOGT/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
09/01/2026
Prosecution Insights