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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Jul. 23, 2024.
Claims 1-10 are pending in the case. Claims 1, 9 and 10 are independent claims.
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 1-6, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang) U.S. Patent Publication No. 2022/0050964 in view of Sewak et al. (hereinafter Sewak) U.S. Patent Publication No. 2022/0414137.
With respect to independent 1, Wang teaches an inference apparatus comprising at least one processor (see e.g., Para [16][17] – “As shown in FIG. 1, processor 110 is coupled to memory 120. Operation of computing device 100 is controlled by processor 110. And although computing device 100 is shown with only one processor 110, it is understood that processor 110 may be representative of one or more central processing units (CPUs), multi-core processors, microprocessors, microcontrollers, and/or the like in computing device 100. ”), the processor carrying out:
a data converting process of converting target data subject to label assignment into text (see e.g., Para [12]-[21]-“data-to-text generation systems that may generate a textual description from structured input data. More specifically, given a structured input data, such as a set of resource description framework (RDF) triples or a Wikipedia infobox in the form of trees or graphs, the embodiments may generate corresponding text descriptions … receive input 140, which may be structured data, such as an RDF graph. Input 140 may be provided to the data-to-text generation system 130. The data-to-text generation system 130 operates on the input 140 to generate an output 150. Output 150 may be a textual description of the input 140, e.g. textual description of the RDF graph.”).
Wang does not expressly show the features discussed below. However, Sewak teaches a label inferring process of inferring a label to be assigned to the target data (see e.g., Para [29][62]-“determines whether a candidate text is in a requested class. The technology may perform this classification without any prior training data or model trained on the requested class. …The requested class may be described herein as a label.”), in accordance with a label inference model for inferring a label to be assigned to text and the text obtained by the converting carried out in the data converting process (see e.g., Para [32] and Claim 1 -“The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text.” ”determining a label probability estimate based on the generated text; and outputting an indication whether the candidate text corresponds to the label description based on the label probability estimate.”).Both Wang and Sewak are directed to AI assisted text data processing. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Wang and Sewak in front of them to modify the system of Wang to include the above feature. The motivation to combine Wang and Sewak comes from Sewak. Sewak discloses the motivation to infer label for machine learning process (see e.g. para [29]-[32][64]-[67]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent 2, the modified Wang teaches in a case where the target data is semi-structured data, in the data converting process, the processor converts the target data into text with use of a predetermined conversion rule for semi-structured data (see e.g., Para [12][21]-“ given a structured input data, such as a set of resource description framework (RDF) triples or a Wikipedia infobox in the form of trees or graphs, the embodiments may generate corresponding text descriptions” “Although the description below is discussed in terms of RDF, the embodiments equally apply to other types of structured data.” – It would have been obvious that the input data can be semi-structured based on the teachings of Wang.).
With respect to dependent 3, the modified Wang teaches in a case where the target data is data in a predetermined form, in the data converting process, the processor converts the target data into text with use of a conversion model constructed by learning of a correspondence relationship between data in the predetermined form and text which indicates content of the data in the predetermined form (see e.g., Para [25][30] - “training module 160 may train data-to-text module 170 using known structured datasets, such as WebNLG dataset or a Wikipedia Corpus. Unlike conventional language models, training module 160 may also train data-to-text module 170 to generate and process different types of embeddings, including position aware embeddings.” ”The wikidata-description pairs may be RDF graph-description pairs. To pretrain the data-to-text module 170, training module 160 may create different categories in the dataset and select the graph-description pairs for a particular category. Training module 160 may then linearize the dataset in the particular category by prepending tokens such as [CLS], S|, P|, and O| to the dataset and pass the dataset through data-to-text module 170 that is trained to recognize the token embeddings, position embeddings, triple role embeddings, and tree-level embeddings. Once data-to-text module 170 is trained, data-to-text module 170 may be used to generate text description for an RDF graph.”).
With respect to dependent 4, the modified Wang teaches in the data converting process, the processor can convert the target data into text with respect to a plurality of forms taken by the target data (see e.g., Para [12][21]-“a set of resource description framework (RDF) triples or a Wikipedia infobox in the form of trees or graphs”” Although the description below is discussed in terms of RDF, the embodiments equally apply to other types of structured data.”).
With respect to dependent 5, the modified Wang teaches in a case where the data converting process converts a piece of target data into a plurality of pieces of text, the piece of target data being subject to label assignment, in the label inferring process, the processor infers the label to be assigned to the piece of target data, in accordance with respective output values obtained by inputting the plurality of pieces of text into the label inference model (see e.g., Sewak Claim 6 – “using a first weight applied to a first label score that is based on the generated text and a second weight applied to a second label score that is based on a second generated text received from a second generative model when the candidate text is input to the second generative model.” and Para [32]-“The success rate of the classification can be improved, while maintaining this improved efficiency, by obtaining a second generative result from a generative model and estimating label probability using the second generative result.”).
With respect to dependent 6, the modified Wang teaches in the data converting process, the processor determines, from among character strings contained in the target data, one or more character strings related to the label, and converts the target data into text which contains the one or more character strings determined (see e.g., Sewak Para [34][66] -“A set of candidate text priority keywords are obtained from candidate text. A set of label priority keywords are obtained from the label. Priority keywords are assigned embedding vectors using a transformer-based model. Context-aware keywords are determined by similarity of the priority keywords based on the embedding vectors to obtain a set of context-aware keywords. ” ”Display area 204 shows a set of context aware keywords that represent candidate text, as determined by labeling service 142.”).
With respect to dependent 9, the modified Wang teaches an inference method comprising:
at least one processor converting target data subject to label assignment into text (see e.g., Para [12]-[21] - “data-to-text generation systems that may generate a textual description from structured input data. More specifically, given a structured input data, such as a set of resource description framework (RDF) triples or a Wikipedia infobox in the form of trees or graphs, the embodiments may generate corresponding text descriptions … receive input 140, which may be structured data, such as an RDF graph. Input 140 may be provided to the data-to-text generation system 130. The data-to-text generation system 130 operates on the input 140 to generate an output 150. Output 150 may be a textual description of the input 140, e.g. textual description of the RDF graph.”); and
the at least one processor inferring a label to be assigned to the target data (see e.g., Sewak Para [29][62]-“determines whether a candidate text is in a requested class. The technology may perform this classification without any prior training data or model trained on the requested class. …The requested class may be described herein as a label.” The motivation to combine is discussed above with respect to claim 1), in accordance with a label inference model for inferring a label to be assigned to text and the text obtained by the converting (see e.g., Sewak Para [32] and Claim 1 -“The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text.””determining a label probability estimate based on the generated text; and outputting an indication whether the candidate text corresponds to the label description based on the label probability estimate.” The motivation to combine is discussed above with respect to claim 1).
With respect to dependent 10, the modified Wang teaches a non-transitory storage medium storing an inference program for causing a computer to carry out: a data converting process of converting target data subject to label assignment into text (see e.g., Para [12]-[21] - “data-to-text generation systems that may generate a textual description from structured input data. More specifically, given a structured input data, such as a set of resource description framework (RDF) triples or a Wikipedia infobox in the form of trees or graphs, the embodiments may generate corresponding text descriptions … receive input 140, which may be structured data, such as an RDF graph. Input 140 may be provided to the data-to-text generation system 130. The data-to-text generation system 130 operates on the input 140 to generate an output 150. Output 150 may be a textual description of the input 140, e.g. textual description of the RDF graph.”); and a label inferring process of inferring a label to be assigned to the target data, in accordance with a label inference model for inferring a label to be assigned to text (see e.g., Sewak Para [29][62]-“determines whether a candidate text is in a requested class. The technology may perform this classification without any prior training data or model trained on the requested class. …The requested class may be described herein as a label.” The motivation to combine is discussed above with respect to claim 1)and the text obtained by the converting carried out in the data converting process (see e.g., Sewak Para [32] and Claim 1 -“The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text.””determining a label probability estimate based on the generated text; and outputting an indication whether the candidate text corresponds to the label description based on the label probability estimate.” The motivation to combine is discussed above with respect to claim 1).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Sewak and further in view of Rajani et al. (hereinafter Rajani) U.S. Patent Publication No. 2021/0374488.
With respect to dependent 7, Wang does not expressly show the features disclosed below. However, Rajani teaches in the label inferring process, the processor infers the label to be assigned to the target data, in accordance with a numerical value calculated with use of a language understanding model constructed by learning of whether a premise sentence entails a hypothesis sentence (see e.g., Para [22][23]-“ a specific example of employing kNN on the natural language inference (NLI) tasks via a BERT model 110. “), the numerical value indicating a degree to which the text entails a hypothesis sentence related to the label (see e.g., Para [22]-[24] – “During inference, a confidence score is also generated by the BERT model 110. For example, the confidence score corresponds to the output probability of the predicted class, ranging from 0.0 to 1.0. When the confidence score is low at 119 (e.g., lower than a threshold), the stored hidden states encodings 125 may be retrieved from cache memory, and the k-nearest neighbors to the hidden states 115 may be selected from the stored hidden states 125. Then selected kNN may be used to compute a kNN probability 126.”). Both Wang and Rajani are directed to AI assisted text data processing. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Wang and Rajani in front of them to further modify the modified system of Wang to include the above feature. The motivation to combine Wang and Rajani comes from Rajani. Rajani discloses the motivation to use NLI classifier as a scoring mechanism to improve the system performance (see e.g. para [15][16]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Sewak and further in view of Goyal et al. (hereinafter Goyal) U.S. Patent Publication No. 2021/0279525.
With respect to dependent 8, Wang does not expressly show the features disclosed below. However, Goyal teaches in the label inferring process, the processor infers the label to be assigned to the target data (see e.g., Para [30][31]-“the programming analytics system may train, by a machine-learning model, a classification model for classifying content objects within the set of content objects.”), in accordance with an output value obtained for each of labels contained in a label group having a hierarchical structure (see e.g., Para [3][11][30]-“ accessing a set of content objects, wherein each content object of the set of content objects is pre-labeled with one or more concepts of a plurality of concepts, and in which the plurality of concepts are organized according to a hierarchical relationship. ”), by inputting, to the label inference model, the text obtained by the converting carried out in the data converting process (see e.g., Para [30]-[32]). Both Wang and Goyal are directed to machine learning classification. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Wang and Goyal in front of them to further modify the modified system of Wang to include the above feature. The motivation to combine Wang and Goyal comes from Goyal. Goyal discloses the motivation to incorporate hierarchical multi-label classification to improve prediction (see e.g. Abstract and para [3][30]-[32]).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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