Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant's arguments filed 12/02/2025 have been fully considered but they are not persuasive. Applicant argues that Aggarwal does not teach the amended limitations reciting generation of first and second sentence fragments, selection of sequence tokens and generation of a third complete sentence using a second algorithm. However, Aggarwal explicitly discloses generating a simplified text and dividing it into multiple sentence -level units (S1, S2…, Sm), computing entailment and hallucination scores for each unit and selectively including those units to construct a final output text (P) (see ¶[0096]-[0100]). Under broadest reasonable interpretation, each sentence or sentence-level unit corresponds to a “sequence of tokens” and a “sentence fragment” is broader than a complete sentence disclosed by Aggarwal.
With respect to the recited first and second algorithm, the claim does not impose any structural or functional distinction between the algorithms beyond their use in generating text. Aggarwal discloses using a text simplification model to generate candidate sentences (i.e., a first algorithm) and a pruning process to construct a final output based on selected candidates. Constructing the modified text P from selected sequences necessarily involves generating output text based on those sequences, which satisfies the recited generation of a third complete sentence “using a second algorithm.”
Altogether, the claim requires selecting a sequence of tokens from a set that includes the first and second sequences and generating output text based on the selected sequence. Aggarwal’s selection and assembly of sentences Sj into the modified text P satisfies this requirement.
Claim Rejections - 35 USC § 102
3. 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)(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.
4. Claims 1-2, 6-7, 9-18, 22-23, 26-27 and 29-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aggarwal (US 2024/0119220).
Regarding Claim 1
Aggarwal discloses an apparatus for natural language processing, the apparatus comprising:
at least one memory (Aggarwal: p[0006] discloses a memory);
and at least one processor coupled to the at least one memory (Aggarwal: p[0006] discloses a memory configured to store instructions that are run on a processor), the at least one processor configured to:
generate, based on input content, a first sequence of tokens including a first sentence fragment and a second sequence of tokens including a second sentence (Aggarwal: ¶[0096] discloses each simplified sentence is a sequence of tokens generated from input content. A sentence fragment is broader than a complete sentence and therefore reads on the discloses sentences);
determine a first confidence level associated with the first sequence of tokens and a second confidence level associated with the second sequence of tokens, wherein the first confidence level is based on respective confidence levels associated with each token in the first sequence of tokens (Aggarwal: ¶[0097] entailment scores quantify confidence/faithfulness of each sentence level token sequence, separate scores are computing for each Sj corresponding to first and second confidence levels);
generate, using a first algorithm, a first complete sentence that includes the first sequence of tokens and a second complete sentence that includes the second sequence of tokens (Aggarwal: ¶[0060]-[0061], ¶[0106] discloses a text simplification model (i.e., a first algorithm) to generate complete sentences S1, S2 that include the respective token sequences);
generate a first natural language inference (NLI) score for the first complete sentence and a second NLI score for the second complete sentence (Aggarwal: ¶[0076] discloses entailment scores are NLI scores generated per sentence. Separate entailment scores are computed S1 and S2), wherein the first NLI score is based on faithfulness of the first complete sentence to the input content (Aggarwal: ¶[0030], ¶[0074] discloses entailment scores to faithfulness between generated text and input content);
adjust the first confidence level for the first sequence of tokens based on the first NLI score for the first complete sentence to generate an updated first confidence level for the first sequence of tokens (Aggarwal: ¶[0098] thresholding and pruning based on entailment modifies whether a sentence is retained, effectively adjusting its confidence for inclusion);
adjust the second confidence level for the second sequence of tokens based on the second NLI score for the second complete sentence to generate an updated second confidence level for the second sequence of tokens (Aggarwal: ¶[0097]-[0098] entailment scores are computed for each sentence, i.e., the same logic is applied independently to each sequence, including the second sequence);
rank the first sequence of tokens and the second sequence of tokens based on the updated first confidence level and the updated second confidence level to select a sequence of tokens from a set that includes the first sequence of tokens and the second sequence of tokens (Aggarwal: ¶[0099]-[0100] discloses multiple confidence related scores are evaluated comparatively across sentences, meaning it is necessarily ranking them for selection through thresholds);
and generate, using a second algorithm and based on the selected sequence of tokens, a third complete sentence that includes the selected sequence of tokens (Aggarwal: ¶[0099]-[0100] discloses constructing the modified text P from selected sequences necessarily involves generating output text based on the selected token sequence, i.e., a second algorithm).
Regarding Claim 2:
Aggarwal further discloses the apparatus of claim 1, wherein, to generate the first sequence of tokens and the second sequence of tokens, the at least one processor is configured to: generate the first sequence of tokens and the second sequence of tokens using the second algorithm (Aggarwal: ¶[0060], ¶[0091] disclose using a text simplification model and then a generation process which uses pruning to generate a set of token, interpreted as a second algorithm).
Regarding Claim 6:
Aggarwal further discloses the apparatus of claim 1, the at least one processor configured to: rank the first sequence of tokens and the second sequence of tokens based on the first confidence level associated with the first sequence of tokens and the second confidence level associated with the second sequence of tokens to generate a first ranking. (Aggarwal: p[0066] discloses the processor compares multiple generated sequences (here, simplified sentences is token sequences) according to numeric scores – functionally a ranking among candidate outputs).
Regarding Claim 7:
Aggarwal further discloses the apparatus of claim 6, wherein, to rank the first sequence of tokens and the second sequence of tokens based on the updated first confidence level and the updated second confidence level, the at least one processor configured to: update the first ranking of the first sequence of tokens and the second sequence of tokens based on the updated first confidence level and the updated second confidence level to generate a second ranking (Aggarwal: p[0050], p[0054] discloses res-scoring candidate sequences using updated confidence type metrics (entailment = faithfulness; hallucination = inverse confidence) these updated scores correspond to updated first and second confidence levels, the comparison among them constitutes re-ranking).
Regarding Claim 9:
Aggarwal further discloses the apparatus of claim 1, wherein the third complete sentence is configured to summarize the at least a portion input content (Aggarwal: p[0028] discloses that the system is designed for producing abstractive summaries of the input).
Regarding Claim 10:
Aggarwal further discloses the apparatus of claim 1, wherein the selected sequence of tokens is the first sequence of tokens based on the first updated confidence level for the first sequence of tokens exceeding the second updated confidence level for the second sequence of tokens (Aggarwal: p[0069] discloses once the modified text is determined, it is presented to the user via a user interface, this corresponds to the generated output text including the highest-ranked sequences).
Regarding Claim 11:
Aggarwal further discloses the apparatus of claim 1, wherein the second NLI score for the second complete sentence is based on faithfulness of the second complete sentence to the input content tokens (Aggarwal: p[0050] discloses computing entailment and hallucination scores which reflect faithfulness to the original input content, it generates this for multiple sentences).
Regarding Claim 12:
Aggarwal further discloses the apparatus of claim 1, wherein the third complete sentence output text is configured to be responsive to summarize the input content as part of a conversation (Aggarwal: p[0028] discloses that the system is designed for producing abstractive summaries of the input).
Regarding Claim 13
Aggarwal further discloses the apparatus of claim 1, wherein the first NLI score identifies that at least a portion of the complete sentence is one of true or false, or neutral (Agarwal: p[0111] discloses the neural network outputs a label, “entailment, contradiction, neutral” which meet this limitation).
Regarding Claim 14
Aggarwal further discloses the apparatus of claim 1, wherein the input content includes input text (Aggarwal: p[0041] the input is a complex text selection from the user).
Regarding Claim 15:
Aggarwal further discloses the apparatus of claim 1, wherein each token of the first sequence of tokens is at least a portion of a respective word (Aggarwal: p[0041] the sentences that are produced contain words).
Regarding Claim 16:
Aggarwal further discloses the apparatus of claim 1, wherein the first sequence of tokens is configured to follow after a previously-determined sequence of tokens in the first complete sentence, wherein the first complete sentence includes the previously-determined sequence of tokens, the first sequence of tokens, and at least one additional token (Aggarwal: p[0048] discloses a sequence to sequence model that is auto regressive and uses an encoder decoder architecture, the process continues one token at a time until a "stop token" is predicted, signaling the end of the sentence or text generation. Like all transformer architecture this process is fundamental).
Regarding Claim 17:
Aggarwal further discloses the apparatus of claim 1, wherein the second algorithm is more computationally resource-intensive than the first algorithm (¶[0063]-[0066] the text simplification algorithm performs a single forward generative pass, this corresponds to standard autoregressive decoding, no loops, logic controls or thresholds. The second algorithm discloses in ¶[0097] discloses finding entailment scores for each pieces of simplified text sentence, computing scores and using a bi-directional encoder from transformers architecture (BERT). This is not generation, it requires a BERT based-based NLI inference, a plurality of sentence comparisons, each Sj triggers multiple forward passes through the second network which is an explicit and clear increase in computation compared to the previous generation algorithm. It also performs conditional logic and score aggregation altogether making this algorithm more resource-intensive by definition).
Regarding Claim 18:
Aggarwal further discloses the apparatus of claim 1, wherein the at least one processor is configured to: output the third complete sentence (Aggarwal: p[0041] the output contains the sequence of tokens that were selected).
Regarding Claim 22:
Claim 22 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 23:
Claim 23 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 26:
Claim 26 has been analyzed with regard to claim 6 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 27:
Claim 27 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 29:
Claim 28 has been analyzed with regard to claim 17 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 30:
Claim 30 has been analyzed with regard to claim 18 (see rejection above) and is rejected for the same reasons of anticipation used above.
Claim Rejections - 35 USC § 103
5. 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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
6. Claims 3 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (US 2024/0119220) in view of Rush et al. “A Neural Attention Model for Abstractive Sentence Summarization” herein Rush.
Regarding Claim 3:
Aggarwal further discloses the apparatus of claim 1, except wherein the first algorithm is, a greedy search. However, Rush discloses this limitation (Rush: Section 4 discloses using a greedy search as a baseline decoding method).
It would have been obvious to combine Li and Rush in order to obtain the claimed invention. Li discloses generating a complete sentence using a generative transformer model but does not teach greedy decoding. Rush teaches greedy decoding as a baseline method for generating output sequences in a neural summarization system. The motivation to combine Li and Rush would be to substitute greedy decoding in Aggarwal’s framework as a well-known, simpler alternative to beam search commonly used for faster inference with minimal implementation changes. This substitution would produce predictable results and not require any undue experimentation.
Regarding Claim 24:
Claim 24 has been analyzed with regard to claim 3 (see rejection above) and is rejected for the same reasons of obviousness used above.
7. Claims 8 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Li et al “Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization” herein Li.
Regarding Claim 8:
Aggarwal further discloses the apparatus of claim 1, except wherein the second algorithm is a beam search. However, Li discloses wherein the second algorithm is a beam search (Li: Section 6.1 discloses beam search used for sequence decoding).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose generation of multiple candidate sequences using Li’s beam search strategy. Both references are directed to sequence generation models in natural language generation tasks, and specifically mitigating hallucinations and producing faithful output. Beam search is a well-known, standard technique for efficiently producing multiple candidate sequences with varying likelihoods from an encoder-decoder model. One of ordinary skill in the art would recognize that substituting Li’s beam search for Aggarwal’s unspecified generation step would predictably yield improved diversity and faithfulness in the candidate outputs, without changing the underlying operation of Aggarwal’s system.
Regarding Claim 28:
Claim 28 has been analyzed with regard to claim 8 (see rejection above) and is rejected for the same reasons of obviousness used above.
8. Claims 4-5 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Yang et al. “Saliency Detection via Graph-Based Manifold Ranking” herein Yang.
Regarding Claim 4:
Aggarwal discloses the apparatus of claim 1, except the at least one processor configured to: restrict candidate tokens for use in generating the first complete sentence and the second complete sentence based on respective saliency values for the candidate tokens and a saliency threshold. However, Yang discloses this limitation (Yang: At least Section 2.2 discloses computing saliency values and filtering based on a threshold to select only salient components. This satisfies the restriction of candidate tokens based on saliency exceeding a threshold).
It would have been obvious to combine Aggarwal and Yang in order to obtain the claimed invention. Aggarwal discloses generating sequences using a decoder but does not filter candidate tokens based on saliency. Yang discloses restriction based on saliency values indicating content importance. Yang’s solution is reasonably pertinent to the problem faced. The motivation to combine Aggarwal and Yang would be to improve the quality, relevance and faithfulness of the generated output by pruning unimportant tokens, a common technique that could improve the overall accuracy of the model.
Regarding Claim 5:
Aggarwal and Yang further discloses the apparatus of claim 4, wherein the saliency threshold is based on an average of the respective saliency values for the candidate tokens (Yang: Section 5.1 uses a threshold set to twice the mean saliency of the image).
Regarding Claim 25
Claim 25 has been analyzed with regard to claim 4 (see rejection above) and is rejected for the same reasons of obviousness used above.
9. Claims 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Tucker (US 11,893,994).
Regarding Claim 19:
Aggarwal further discloses the apparatus of claim 1, except wherein the at least one processor is configured to: cause a display to display the third complete sentence. However, Tucker discloses this limitation: (Tucker: Fig. 5a discloses a display component for displaying the output sequence).
Aggarwal teaches generating natural language output using a generative inference model, but does not disclose displaying that output via a physical display. Tucker discloses an apparatus comprising a display configured to present text-based responses generated by a conversational/inference model, Tuckers display renders outputs in real time. It would have been obvious to one of ordinary skill in the art to combine Tucker’s system into Aggarwal in order to render generated summaries or other natural language output for human users, particularly in application involving interactive summarization, accessibility or assistive interfaces. Displaying model output is a standard feature in modern machine learning applications and would involve only routine design choices
Regarding Claim 20
Aggarwal further discloses the apparatus of claim 1, except further comprising: a communication interface configured to transmit the third complete sentence to a recipient device. However, Tucker discloses this limitation (Tucker: Fig. 2 and Col 7 lines 30-40 disclose that the implementation may be triggered to run a skill in response to a third party calling the system via the internet (i.e., there is a recipient device)).
Aggarwal teaches generating natural language output using a generative inference model, but does not disclose displaying that output via a physical display. Tucker discloses an apparatus that has the ability to send results to a requesting recipient device, Tuckers system can send and receive requests in real time. It would have been obvious to one of ordinary skill in the art to combine Tucker’s system into Aggarwal in order to transmit generated output to client devices or user-facing systems. This would be especially useful in distributed or client-server NLP system where the inference model runs on a backend or edge device, and the output is delivered elsewhere. This is a common application that requires not undue experimentation.
Regarding Claim 21:
Aggarwal further discloses the apparatus of claim 1, except wherein the apparatus includes at least one of a head-mounted display (HMD), a mobile handset, or a wireless communication device. However, Tucker discloses this limitation: (Tucker: Col 20 lines 60-67 wireless communication module)).
Aggarwal teaches generating natural language output using a generative inference model, but does not disclose displaying that output via a physical display. Tucker discloses an apparatus contains wireless capabilities. It would have been obvious to one of ordinary skill in the art to combine Tucker’s system into Aggarwal in with the use of a mobile handset or wireless headset as taught by Tucker, in order to enhance portability, accessibility, and real-time interaction. These are common platforms for natural language systems and their use involves only routine engineering implementation.
Conclusion
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654