DETAILED ACTION
Summary and Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Applicant’s reply filed 1/30/2026.
Claims 21-40 are pending.
Claims 21-40 are rejected under 35 U.S.C. 112(b).
Claims 21-40 are rejected under 35 U.S.C. 101.
Claims 21-25, 29-33, and 36-40 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al. (US Patent Pub 2017/0091318), in view of Roitman et al. (US Patent Pub 2021/0397595), further in view of Kephart et al. (US Patent 7,051,277), further in view of Muffat et al. (US Patent Pub 2020/0226154)1.
Claims 26-28, 34, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al. (US Patent Pub 2017/0091318), in view of Roitman et al. (US Patent Pub 2021/0397595), further in view of Kephart et al. (US Patent 7,051,277), , further in view of Muffat et al. (US Patent Pub 2020/0226154), further in view of Lin et al. (“A Chinese text similarity algorithm based on Yake and neural network”).
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 21-40 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 21 recites in the first limitation “… each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables.” The broadest reasonable interpretation of the limitation is that each document comprises either a table or metadata about the table. The claim continues by reciting in the second limitation, “automatically generating a set of labels for the data corpus by, for at least one particular table in the one or more tables”. The limitation merely requires that a set of labels is generated by performing the recited steps “for at least one particular table in the one or more tables” (i.e., one table). The limitation lacks antecedent basis if the first limitation is interpreted to mean that each of the plurality of documents comprises metadata associated with the one or more tables because this means that each document only comprises the metadata and not actual “one or more tables.”
Claims 30 and 37 recite similar limitations as claim 21 and are rejected for the same reasons.
The remaining claims are rejected because they depend on a rejected claim.
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 21-40 are also rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106.
Claim 21
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Claim 21 recites limitations of (1) generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables (2) automatically generating a set of labels for the data corpus by, for at least one particular table in the one or more tables: performing a first keyword extraction procedure upon metadata for the particular table to determine a first set of candidate words and first associated values for the particular table; (3) performing a second keyword extraction procedure upon the metadata for the particular table to determine a second set of candidate words and second associated values for the particular table, wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values; (4) selecting a first top N set of words from the first set of candidate words having the N highest associated values and selecting a second top N set of words from the second set of candidate words having the N highest associated values from the second set of candidate words; (5) labeling the particular table with one or more words appearing in both the first set of candidate words and the second set of candidate words, (6) providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words, and (7) retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user.
Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” The mental process grouping covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. MPEP 2016(a)(2)(III). Limitations can also be deemed insignificant extra-solution activity (IESA). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g).
Limitation (1) is directed to IESA in the form of mere data gathering. Here, the “generating of a data corpus” is interpreted as collecting a “plurality of documents”, which together form a “data corpus”. Limitations (2) and (3) recite steps that can be practically performed in the mind of a person with the aid of pen and paper. The steps of performing first and second keyword extraction procedures are recited at a high level of generality without specifically reciting how the keyword extraction is performed on the metadata. Limitation (4) is directed to a step that can be practically performed in the mind of a person with the aid of pen and paper through observation, evaluation (i.e., of the first and second sets) and determining the top N words from each set of candidate words using associated values. The limitation does not specify how the selection is performed with merely requiring a qualifier of “highest” associated values. Such analysis and comparison can be practically performed by a person in the mind and subsequently allowing the person to select top N words from each set of candidate words. Limitation (5) is directed to a mental step, which can be practically performed by a person in the mind with the aid of pen and paper as it merely requires labeling of a table using a word appearing in both sets of candidate words. A person can analyze both sets of candidate words, make the determination of at least a word that appears in both, and labeling the particular table. Lastly, limitation (6) is directed to a step of IESA and mental step. The limitation requires providing a recommendation, which is merely data output. The determination of what data to output is a mental step practically performed by the user through analysis of both sets of candidate words and determining at least a word that appears in only one of the sets. Limitation (7) is directed to IESA in the form of retrieving information from storage/memory and transmitting/outputting data.
For at least these reasons, claim 21 is directed to an abstract idea categorized under mental processes.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
In this case, as explained above, claim 21 merely recites an abstract idea categorized under mental processes. As discussed above, limitations (1), (6), and (7) are directed to IESA and therefore cannot integrate the abstract idea into a practical application. Limitations (2) and (3) recite performance of keyword extraction procedures at a high level of generality and amounts to nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the claim recites no details about a particular “first keyword extraction procedure” and “second keyword extraction procedure”. The limitations can also be interpreted as being performed by a person in the mind through observation, evaluation, and analysis of the received metadata. The keyword extraction procedures are used to generally apply the abstract idea (i.e., perform the determination of candidate words for the particular table recited in limitations (2) and (3)) without placing any limitation on how the first and second “keyword extraction procedure(s)” operates to determine a set of candidate words. In addition, the limitation would cover every mode of implementing the recited abstract idea using a “keyword extraction procedure”. The claim omits any details as to how the “keyword extraction procedure” solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Limitation (4) is a step that can be practically performed by a person in the mind through observation, evaluation, and judgment to determine top N words from each set of candidate words. The limitation provides no specifics as to how the selection is performed, which would require steps beyond simple evaluation and comparison. Limitation (5) is also a mental step of observation and analysis to determine a word appearing in both sets of candidate words and using that word as a label for the table. Limitation (6) also includes a mental step of analysis to determine a word that appears in only one of the sets of candidate words. None of limitations (4) through (6) recite specific limitations that would make it impractical for a person to perform in the mind and they do not recite specific steps that require more than simple analysis.
For at least these reasons, claim 21 does not integrate the judicial exception into a practical application.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. In this case, claim 21 does not recite limitations that amount to significantly more than the abstract idea. As discussed above, limitations (1), (6), and (7) are directed to IESA, which fall under well understood, routine, and conventional. See MPEP 2106.05(d), subsection II; MPEP 2106.05(g). Limitations (2) and (3) are directed to limitations that are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept, as explained above. See MPEP 2106.05(f). Limitations (4), (5), and (6) recite steps of observation, evaluation, and judgment, at a high level of generality which is insufficient to amount to significantly more than the abstract idea. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, claim 21 is nonstatutory because they are directed to a judicial exception without significantly more.
Claim 22
Pursuant to step 2A, part 1, claim 22 depends on claim 21 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 22 recites the additional limitations of weighting the first set of candidate words and the second set of candidate words, wherein the first and second associated values are weighted values. These limitations are recited at a high level of generality without providing meaningful limits on the abstract idea. The limitation merely specifies that the associated values are weighted values without further reciting steps that would put meaningful limits to the identified abstract idea. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 23
Pursuant to step 2A, part 1, claim 23 depends on claim 22 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 23 recites the additional limitations of wherein said weighting assigns different weights to different words based on a position of the different words in each table. These limitations further define the weighting without adding meaningful limits to the abstract idea because how the weighting is assigned is not particular recited. Therefore, it covers any manner of performing the assigning of the weighting. Accordingly, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 24
Pursuant to step 2A, part 1, claim 24 depends on claim 23 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 24 recites the additional limitations of wherein said weighting comprises: assigning a first weight to a word when the word is associated with a table name, assigning a second weight less than the first weight to a word when the word is associated with a column name; and assigning a third weight less than the second weight to a word when the word is associated with a description field. These limitations amount to steps of analysis and assigning a weight based on the analysis of determining whether a word is associated with a table name, a column name, or a description field. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 25
Pursuant to step 2A, part 1, claim 25 depends on claim 21 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 25 recites the additional limitations of wherein the first keyword extraction procedure comprises Term Frequency-Inverse Document Frequency (TF-IDF). These additional limitations recite a known keyword procedure at a high level of generality. Therefore, these additional limitations do not recite meaningful limits and do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field, as asserted in the specification.
Claim 26
Pursuant to step 2A, part 1, claim 26 depends on claim 25 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 26 recites the additional limitations of wherein the first keyword extraction procedure comprises Yet Another Keyword Extractor (YAKE). These additional limitations recite a known keyword procedure at a high level of generality. Therefore, these additional limitations do not recite meaningful limits and do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field, as asserted in the specification.
Claim 27
Pursuant to step 2A, part 1, claim 27 depends on claim 26 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 27 recites the additional limitations of wherein the first keyword extraction procedure comprises a word span procedure, wherein the word span is calculated using the following formula. These additional limitations merely recites additional considerations, which amount to “apply it” instructions for performing the keyword extraction procedure without reciting specific steps that would provide meaningful limits on the abstract idea. Using the recited formula is merely a mathematical calculation, which is still a mental step recited at a high level and does not provide meaningful limits on the abstract idea. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 28
Pursuant to step 2A, part 1, claim 28 depends on claim 26 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 28 recites the additional limitations of wherein the first keyword extraction procedure considers one or more of: a capital term, a word position, a word frequency, a context relation, and a word occurrence frequency in sentences. These additional limitations merely recites additional considerations, which amount to “apply it” instructions for performing the keyword extraction procedure without reciting specific steps that would provide meaningful limits on the abstract idea. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 29
Pursuant to step 2A, part 1, claim 29 depends on claim 21 and therefore recites the same abstract idea. Pursuant to step 2A, part 2, claim 29 recites the additional limitations of wherein the first and second keyword extraction procedures are selected from the group comprising: Term Frequency-Inverse Document Frequency (TF-IDF), Yet Another Keyword Extractor (YAKE), Rapid Automatic Keyword Extraction (RAKE), Linear Discriminant Analysis (LDA), KeyBert, or TextRank. These additional limitations merely recite a list of procedures, which will be used to apply the abstract idea. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field.
Claim 30 recites essentially the same subject matter as claim 21, in the form of a non-transitory computer readable storage medium. Claim 30 also recites the additional limitations of a “non-transitory computer readable storage medium.” Each of these limitations recite components that are recited at a high level of generality, which do not put meaningful limits on the abstract idea. Thus, claim 30 is rejected for much of the same reasons as explained in regards to claim 21 above.
Claims 31-36 recite essentially the same subject matter as claims 22, 23, 25-27, and 29, respectively. Therefore, they are rejected for the same reasons.
Claim 37 recites essentially the same subject matter as claim 21, in the form of a system. Claim 37 also recites the additional limitations of “one or more processors”. These limitations recite components at a high level of generality, which do not put meaningful limits on the abstract idea. Thus, claim 37 is rejected for much of the same reasons as explained in regards to claim 21 above.
Claims 38-40 recite essentially the same subject matter as one of claims 22, 23, and 27, respectively, in the form of a system, and are rejected for the same reasons.
Claims 21-40 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory) above are further rejected as set forth below in anticipation of applicant amending these claims to overcome the rejection.
Note on Prior Art Rejections
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 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.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-25, 29-33, and 36-40 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al. (US Patent Pub 2017/0091318) (Xue), in view of Roitman et al. (US Patent Pub 2021/0397595) (Roitman), further in view of Kephart et al. (US Patent 7,051,277) (Kephart), further in view of Muffat et al. (US Patent Pub 2020/0226154) (Muffat).
In regards to claim 21, Xue discloses a method comprising:
b. automatically generating a set of labels for a document (Xue at paras. 0022-23)2:
i. performing a first keyword extraction procedure upon metadata for the particular document to determine a first set of candidate words and first associated values for document (Xue at para. 0036)3;
ii. performing a second keyword extraction procedure upon the metadata for the document to determine a second set of candidate words and second associated values for the document (Xue at para. 0036)4;
iii. selecting a first top N set of words from the first set of candidate words having the N highest associated values and selecting a second top N set of words from the second set of candidate words having the N highest associated values from the second set of candidate words (Xue at para. 0040-41, 0045)5;
iv. labeling the document with one or more words appearing in both the first set of candidate words and the second set of candidate words (Xue at paras. 0046-48)6; and
v. providing a keyword appearing in only one of the first set of candidate words or the second set of candidate words (Xue at paras. 0045-48.7);
Xue does not expressly disclose generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables and the generating a set of labels for the data corpus is for at least one particular table in the one or more tables. As noted in the rejection above, Xue discloses a method that is used on a single document of a corpus and generating a label for a document. However, Xue does not expressly disclose generation of the corpus and that the documents comprising one or more tables or metadata associated with the one or more tables.
Roitman discloses a system and method for table indexing and extracting table data from tables within documents and performing TF-IDF (i.e., keyword extraction procedure) on the extracted data (i.e., metadata associated with the one or more tables). The method includes adding weights to table modalities (i.e., associated values). Roitman at paras. 0007, 0012, 0025, 0047. The method is performed on a corpus of documents containing one or more tables or metadata associated with the one or more tables. Roitman at para. 0033.
Xue and Roitman are analogous art because they are directed to the same field of endeavor of text analysis and extraction.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue by adding the features of generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables and the generating a set of labels for the data corpus is for at least one particular table in the one or more tables, as disclosed by Roitman.
The motivation for doing so would have been to provide the ability to extract table data from documents, so they can be indexed and searched. Roitman at para. 0003.
Xue in view of Roitman does not expressly disclose providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words. As noted above, Xue discloses including keywords that only appear in one of the candidate sets in the final keyword set to label the document. Xue at paras. 0045-48. What Xue in view of Roitman does not expressly disclose is the step of providing a recommendation, which is interpreted as asking a user for approval of the keyword as a label.
Kephart discloses a system and method for classifying documents based on document analysis to identify most likely labels, which are presented to the user for approval (i.e., providing a recommendation …). Kephart at col. 4, lines 51-58.
Xue, Roitman, and Kephart are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman by adding the features of providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words, as disclosed by Kephart.
The motivation for doing so would have been to assist the user in organizing their documents while improving the system through user feedback. Kephart at col. 4, lines 51-64.
Xue in view of Roitman and Kephart does not expressly disclose wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user. It is noted that Xue does disclose using two keyword extraction procedures and that each procedure can be one of the listed common types. Xue at para. 0036. However, it is not expressly disclosed that they are different even though they could potentially be because Xue discloses different types of extraction procedures. It is also noted that Xue in view of Roitman and Kephart results in a labeled tables by extracting keywords from the table information of Roitman. What is not expressly disclosed is providing at least one label of the results to a user.
Muffat discloses a system and method for keyword extraction of texts and autolabeling of the texts. Muffat at abstract. The method includes utilizing a combination of different keyword extraction techniques including tf-idf, DRAKE, and embedDocRank. The results of each of the techniques are utilized on the texts clusters to form a set of potential candidate categories, which are used for the texts (i.e., autolabeling). Muffat at paras. 0019-20, 0022, 0030-32. The final result of candidate categories (i.e., labeled texts) are returned to the Oracle (i.e., user). Muffat at Fig. 1; para. 0027.
Xue, Roitman, Kephart, and Muffat are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman and Kephart by adding the features of wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user, as disclosed by Muffat.
The motivation for doing so would have been because building efficient word vocabularies would enable integration and upgrading of existing tools and improve the quality of processes. Muffat at paras. 0030-32.
In regards to claim 22, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 21 further comprising weighting the first set of candidate words and the second set of candidate words, wherein the first and second associated values are weighted values. Xue at paras. 0036.8
In regards to claim 23, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 22 wherein said weighting assigns different weights to different words based on a position of the different words in each table. Roitman discloses every table has multiple modalities, such as a title, a caption, column labels, and tabular data. Roitman at para. 0012. Roitman further discloses that individual, differing weights can be assigned to these modalities. Roitman at para. 0047. Therefore, the combination of Xue in view of Roitman, Kephart, and Muffat discloses the limitation of weighting words based on their position in the table (i.e., based on which modality they are located in).
In regards to claim 24, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 23 wherein said weighting comprises:
a. assigning a first weight to a word when the word is associated with a table name (Roitman at paras. 0012, 0047),
b. assigning a second weight less than the first weight to a word when the word is associated with a column name (Roitman at paras. 0012, 0047); and
c. assigning a third weight less than the second weight to a word when the word is associated with a description field. (Roitman at paras. 0012, 0047)9
Roitman discloses assigning differing weights to modalities of a table (i.e., table name, column name, description). Since they are differing weights, than they would have some type of relationship with respect to each other, such as greater than or less than. Therefore, it would have been obvious to one of ordinary skill in the art, at a time before the effective filing date of the instant application, to design the weight of a table name to be greater than the weight of a column name, which is greater than the weight of a description field. The motivation for doing so would have been to provide better retrieval. Roitman at para. 0047.
In regards to claim 25, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 21 wherein the first keyword extraction procedure comprises Term Frequency-Inverse Document Frequency (TF-IDF). Xue at paras. 0036.10
In regards to claim 29, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 21 wherein the first and second keyword extraction procedures are selected from the group comprising: Term Frequency-Inverse Document Frequency (TF-IDF), Yet Another Keyword Extractor (YAKE), Rapid Automatic Keyword Extraction (RAKE), Linear Discriminant Analysis (LDA), KeyBert, or TextRank. Xue at paras. 0036.11
In regards to claim 30, Xue discloses a computer program for performing a method, said method comprising:
b. automatically generating a set of labels for a document (Xue at paras. 0022-23)12:
i. performing a first keyword extraction procedure upon metadata for the particular document to determine a first set of candidate words and first associated values for the particular table (Xue at para. 0036)13;
ii. performing a second keyword extraction procedure upon the metadata for the particular table to determine a second set of candidate words and second associated values for the particular document (Xue at para. 0036)14, wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values (Xue at para. 0036)15;
iii. selecting a first top N set of words from the first set of candidate words having the N highest associated values and selecting a second top N set of words from the second set of candidate words having the N highest associated values from the second set of candidate words (Xue at para. 0040-41, 0045)16;
iv. labeling the document with one or more words appearing in both the first set of candidate words and the second set of candidate words (Xue at paras. 0046-48)17; and
v. providing a keyword appearing in only one of the first set of candidate words or the second set of candidate words (Xue at paras. 0045-48.18);
Xue does not expressly disclose generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables, the generating a set of labels for the data corpus is for at least one particular table in the one or more tables in a document, and a non-transitory computer readable storage medium. As noted in the rejection above, Xue discloses a method that is used on a single document of a corpus and generating a label for a document. However, Xue does not expressly disclose generation of the corpus and that the documents comprising one or more tables or metadata associated with the one or more tables.
Roitman discloses a system and method for table indexing and extracting table data from tables within documents and performing TF-IDF (i.e., keyword extraction procedure) on the extracted data (i.e., metadata associated with the one or more tables). The method includes adding weights to table modalities (i.e., associated values). Roitman at paras. 0007, 0012, 0025, 0047. The method is performed on a corpus of documents containing one or more tables or metadata associated with the one or more tables. Roitman at para. 0033. Roitman also discloses a computer readable storage medium for storing instructions, which when executed, perform a method. Roitman at paras. 0091-92.
Xue and Roitman are analogous art because they are directed to the same field of endeavor of text analysis and extraction.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue by adding the features of generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables, the generating a set of labels for the data corpus is for at least one particular table in the one or more tables in a document, and a non-transitory computer readable storage medium, as disclosed by Roitman.
The motivation for doing so would have been to provide the ability to extract table data from documents, so they can be indexed and searched. Roitman at para. 0003.
Xue in view of Roitman does not expressly disclose providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words. As noted above, Xue discloses including keywords that only appear in one of the candidate sets in the final keyword set to label the document. Xue at paras. 0045-48. What Xue does not expressly disclose is the step of providing a recommendation, which is interpreted as asking a user for approval of the keyword as a label.
Kephart discloses a system and method for classifying documents based on document analysis to identify most likely labels, which are presented to the user for approval (i.e., providing a recommendation …). Kephart at col. 4, lines 51-58.
Xue, Roitman, and Kephart are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman by adding the features of providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words, as disclosed by Kephart.
The motivation for doing so would have been to assist the user in organizing their documents while improving the system through user feedback. Kephart at col. 4, lines 51-64.
Xue in view of Roitman and Kephart does not expressly disclose wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user. It is noted that Xue does disclose using two keyword extraction procedures and that each procedure can be one of the listed common types. Xue at para. 0036. However, it is not expressly disclosed that they are different even though they could potentially be because Xue discloses different types of extraction procedures. It is also noted that Xue in view of Roitman and Kephart results in a labeled tables by extracting keywords from the table information of Roitman. What is not expressly disclosed is providing the results to a user.
Muffat discloses a system and method for keyword extraction of texts and autolabeling of the texts. Muffat at abstract. The method includes utilizing a combination of different keyword extraction techniques including tf-idf, DRAKE, and embedDocRank. The results of each of the techniques are utilized on the texts clusters to form a set of potential candidate categories, which are used for the texts (i.e., autolabeling). Muffat at paras. 0019-20, 0022, 0030-32. The final result of candidate categories (i.e., labeled texts) are returned to the Oracle (i.e., user). Muffat at Fig. 1; para. 0027.
Xue, Roitman, Kephart, and Muffat are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman and Kephart by adding the features of wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user, as disclosed by Muffat.
The motivation for doing so would have been because building efficient word vocabularies would enable integration and upgrading of existing tools and improve the quality of processes. Muffat at paras. 0030-32.
Claims 31-33 and 36 are essentially the same as claims 22, 23, 25, and 29, respectively, in the form of a non-transitory computer readable storage medium. Therefore, they are rejected for the same reasons.
In regards to claim 37, Xue discloses a system performing a method comprising:
ii. automatically generating a set of labels for a document (Xue at paras. 0022-23)19:
(A) performing a first keyword extraction procedure upon metadata for the particular document to determine a first set of candidate words and first associated values for the particular document (Xue at para. 0036)20;
(B) performing a second keyword extraction procedure upon the metadata for the particular document to determine a second set of candidate words and second associated values for the particular document (Xue at para. 0036)21, wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values (Xue at para. 0036)22;
(C) selecting a first top N set of words from the first set of candidate words having the N highest associated values and selecting a second top N set of words from the second set of candidate words having the N highest associated values from the second set of candidate words (Xue at para. 0040-41, 0045)23;
(D) labeling the particular table with one or more words appearing in both the first set of candidate words and the second set of candidate words (Xue at paras. 0046-48)24; and
(E) providing a keyword appearing in only one of the first set of candidate words or the second set of candidate words (Xue at paras. 0045-48.25); and
Xue does not expressly disclose a computing system with one or more processors and a software program, executable thereon, configured to cause the one or more processors to perform a method, generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables, the generating a set of labels for the data corpus is for at least one particular table in the one or more tables in a document. As noted in the rejection above, Xue discloses a method that is used on a single document of a corpus and generating a label for a document. However, Xue does not expressly disclose generation of the corpus and that the documents comprising one or more tables or metadata associated with the one or more tables..
Roitman discloses a system and method for table indexing and extracting table data from tables within documents and performing TF-IDF (i.e., keyword extraction procedure) on the extracted data (i.e., metadata associated with the one or more tables). The method includes adding weights to table modalities (i.e., associated values). Roitman at paras. 0007, 0012, 0025, 0047. The method is performed on a corpus of documents containing one or more tables or metadata associated with the one or more tables. Roitman at para. 0033. Roitman further discloses processors and a computer readable storage medium for storing instructions, which when executed, perform a method. Roitman at paras. 0091-92.
Xue and Roitman are analogous art because they are directed to the same field of endeavor of text analysis and extraction.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue by adding the features of a computing system with one or more processors and a software program, executable thereon, configured to cause the one or more processors to perform a method, generating a data corpus from a plurality of documents, each of the plurality of documents comprising at least one of: one or more tables or metadata associated with the one or more tables, the generating a set of labels for the data corpus is for at least one particular table in the one or more tables in a document, as disclosed by Roitman.
The motivation for doing so would have been to provide the ability to extract table data from documents, so they can be indexed and searched. Roitman at para. 0003.
Xue in view of Roitman does not expressly disclose providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words. As noted above, Xue discloses including keywords that only appear in one of the candidate sets in the final keyword set to label the document. Xue at paras. 0045-48. What Xue does not expressly disclose is the step of providing a recommendation, which is interpreted as asking a user for approval of the keyword as a label.
Kephart discloses a system and method for classifying documents based on document analysis to identify most likely labels, which are presented to the user for approval (i.e., providing a recommendation …). Kephart at col. 4, lines 51-58.
Xue, Roitman, and Kephart are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman by adding the features of providing a recommendation to label the particular table with a keyword appearing in only one of the first set of candidate words or the second set of candidate words, as disclosed by Kephart.
The motivation for doing so would have been to assist the user in organizing their documents while improving the system through user feedback. Kephart at col. 4, lines 51-64.
Xue in view of Roitman and Kephart does not expressly disclose wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user. It is noted that Xue does disclose using two keyword extraction procedures and that each procedure can be one of the listed common types. Xue at para. 0036. However, it is not expressly disclosed that they are different even though they could potentially be because Xue discloses different types of extraction procedures. It is also noted that Xue in view of Roitman and Kephart results in a labeled tables by extracting keywords from the table information of Roitman. What is not expressly disclosed is providing the results to a user.
Muffat discloses a system and method for keyword extraction of texts and autolabeling of the texts. Muffat at abstract. The method includes utilizing a combination of different keyword extraction techniques including tf-idf, DRAKE, and embedDocRank. The results of each of the techniques are utilized on the texts clusters to form a set of potential candidate categories, which are used for the texts (i.e., autolabeling). Muffat at paras. 0019-20, 0022, 0030-32. The final result of candidate categories (i.e., labeled texts) are returned to the Oracle (i.e., user). Muffat at Fig. 1; para. 0027.
Xue, Roitman, Kephart, and Muffat are analogous art because they are directed to the same field of endeavor of text analysis and document labeling.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman and Kephart by adding the features of wherein the first keyword extraction procedure and the second keyword extraction procedure are different keyword extraction procedure types that generate different sets of candidate words having different associated values and retrieving at least one label from the set of labels automatically generated for the data corpus and communicating the at least one label to a user, as disclosed by Muffat.
The motivation for doing so would have been because building efficient word vocabularies would enable integration and upgrading of existing tools and improve the quality of processes. Muffat at paras. 0030-32.
Claims 38-40 are essentially the same as claims 22, 23, and 29, respectively, in the form of a system. Therefore, they are rejected for the same reasons.
Claims 26-28, 34, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al. (US Patent Pub 2017/0091318) (Xue), in view of Roitman et al. (US Patent Pub 2021/0397595) (Roitman), further in view of Kephart et al. (US Patent 7,051,277) (Kephart), further in view of Lin et al. (“A Chinese text similarity algorithm based on Yake and neural network”) (Lin).
In regards to claim 26, Xue in view of Roitman, Kephart, and Muffat discloses the method as in claim 25 but does not expressly disclose wherein the first keyword extraction procedure comprises Yet Another Keyword Extractor (YAKE). Xue does disclose the ability to use any common keyword extraction procedures. Xue at para. 0036.
Lin discloses a system and method of determining text similarity by utilizing a modified YAKE procedure. Lin at abstract; sections I and II.
Xue, Roitman, Kephart, Muffat and Lin are analogous art because they are directed to the same field of endeavor of text analysis and extraction.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Xue in view of Roitman, Kephart, and Muffat by adding the features of wherein the first keyword extraction procedure comprises Yet Another Keyword Extractor (YAKE), as disclosed by Lin.
The motivation for doing so would have been because YAKE is superior to other keyword extraction techniques. Lin at section I.
In regards to claim 27, Xue in view of Roitman, Kephart, Muffat, and Lin discloses the method as in claim 26 wherein the first keyword extraction procedure comprises a word span procedure, wherein the word span is calculated using the following formula:
lasth - firsti + 1 span =sum wherein spani is the word span, lasth denotes a last occurrence of a word, first denotes a first occurrence of the word, and sum denotes the total number of words. Lin at section III.B26.
In regards to claim 28, Xue in view of Roitman, Kephart, Muffat, and Lin discloses the method as in claim 26 wherein the first keyword extraction procedure considers one or more of: a capital term, a word position, a word frequency, a context relation, and a word occurrence frequency in sentences (Xue at para. 0036; Lin at section II; section III.B).
Claims 34 and 35 are essentially the same as claims 26 and 27, respectively, in the form of a non-transitory computer readable storage medium. Therefore, they are rejected for the same reasons.
Response to Arguments
Rejection of claims 21-40 under 35 U.S.C. 101
Applicant’s arguments in regards to the rejection of claims 21-40 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant alleges the claims do not recite any process that can be practically performed within the human mind because the human mind is not equipped for “generating a data corpus from a plurality of documents …” nor is it equipped for “automatically generating a set of labels for the data corpus” as recited in claim 21. Remarks at 10. Examiner respectfully disagrees.
Examiner is required to give claim limitations their broadest reasonable interpretation in light of the specification. However, limitations from the specification are not read into the claim. MPEP 2111.
As discussed in the rejection above, the limitation of “generating a data corpus” is essentially insignificant extra solution activity because it amounts to a step of gathering information or data, which here is in the form of two or more documents, each document having a table or metadata about the table. The limitation does not recite “generating a data corpus” in a specific manner that provides meaningful limitations to the invention and is merely collecting documents, which together form a “data corpus”. Accordingly, the broadest reasonable interpretation of the limitation is insignificant extra solution activity in the form of gathering information/data, as set forth in the rejection above. In regards to the second limitation of “automatically generating a set of labels for the data corpus,” this step is accomplished by performing the other recited steps of the claim, which are identified and explained above as being mental processes that require evaluation and judgment of table or metadata associated with a table within a document of the corpus. In doing so, a person can use their judgment to determine a set of labels for a particular table of the document. The recitation of “automatically generating” does not mean that the steps cannot be performed by a person in the mind and amounts to merely application of mental instructions to a computer for execution, which is insufficient to show an improvement in computer functionality, as discussed further below. Applicant does not present arguments with regards to the remaining limitations. Therefore, Examiner asserts the limitations of claim 21 are correctly categorized under mental processes as set forth in the rejection above.
Applicant also alleges the claims improve a technical field of artificial intelligence by addressing a technical problem relating to keyword extraction, unsupervised learning, and other natural language processing techniques. Remarks at 10. Examiner respectfully disagrees. Applicant has not explained how the claims or particular limitations, either alone or in combination, integrate the abstract idea into a technical application. While the specification may describe a technical problem and an improvement to the relevant technology, the claims must recite additional elements that demonstrate the asserted improvement. MPEP 2106.04(d)(1). Here, the claims limitations are recited in a manner that lacks the specificity to sufficiently demonstrate any asserted improvement, as explained in the rejection above. Applicant has not explained how the claims demonstrate the improvement asserted in the cited portions of the specification. For at least these reasons, Examiner asserts the claims do not recite additional elements that integrate the abstract idea into a practical application.
Applicant does not present arguments in regards to step 2B. Therefore, Examiner asserts the claims also do not recite limitations that amount to significantly more than the abstract idea.
For at least the reasons, claims 21-40 remain rejected under 35 U.S.C. 101.
Rejection of claims 21-40 under 35 U.S.C. 103
Applicant’s arguments in regards to the rejection of claims 21-40 under 35 U.S.C. 103 have been fully considered and they are not persuasive. Applicant’s argument that neither Xue nor Roitman discloses extracting labels from table metadata because Xue only discloses sentences and makes no mention of metadata at all and Roitman merely provides a method of scoring and ranking tables. Applicant argues Xue and Roitman would not have suggested to a person of ordinary skill in the art that using table metadata in the manner of claim 21 would provide an improved method of label generation and the rejection is therefore, completely based on hindsight rather than in light of the references or the understanding of a person of ordinary skill at the time of filing. Remarks at 15. Examiner respectfully disagrees.
Examiner is required to give claim limitations their broadest reasonable interpretation in light of the specification. However, limitations from the specification are not read into the claims. MPEP 2111.
The claims are rejected as a combination of prior art. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In regards to Applicant’s allegations, while Xue does not expressly disclose analyzing tables or tabular data, this deficiency is resolved by Roitman, which discloses extracting table data from tables within documents in order to analyze the data, using different types of extraction procedures, in order to determine tables that most closely match a provided query. Roitman at paras. 0007, 0012, 0025, 0047. Roitman specifically states that the tables and its multiple modalities (i.e., table metadata) are represented as documents. Roitman at para. 0012. Roitman further discloses performing extraction procedures, such as TF-IDF on passages of a table (i.e., metadata associated with a table). Roitman at para. 0056. Thus, given Roitman’s use of an extraction procedure (e.g., TF-IDF), which is also disclosed in Xue, on metadata associated with a table (note, metadata associated with a table does not mean that the metadata describes the table, but is merely associated with it), one of ordinary skill in the art would have been motivated to utilize the keyword extraction method of Xue on metadata associated with a table, as disclosed by Roitman. Applicant does not present arguments in regards to the remaining limitations other than a mere allegation of patentability. Therefore, in regards to those other limitations, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. For at least these reasons, Examiner asserts Xue in view of Roitman, Kephart, and Muffat discloses the limitations of claim 21.
Applicant does not present additional arguments in regards to the remaining claims. Therefore, they remain rejected for at least the same reasons.
Additional Prior Art
Additional relevant prior art are listed on the attached PTO-892 form. Some examples are:
Filoti et al. (US Patent Pub 2020/0104414) discloses a system and method for analyzing table data by question answering systems.
Chatzistamatiou et al. (US Patent Pub 2023/0410543) discloses a system and method for list and tabular data extraction.
Chan et al. (US Patent Pub 2021/0406266) discloses a system and method for information extraction from tables.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Michael Le whose telephone number is 571-272-7970 and fax number is 571-273-7970. The examiner can normally be reached Mon-Fri 9:30 AM – 6 PM.
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, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL LE/Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
1 Previously provided on the PTO-892 form mailed 12/3/2024.
2 The limitation requires generating a set of labels by “for at least one particular table in the one or more tables,” which under the broadest reasonable interpretation requires generating one label for one table, which is essentially one document.
3 A first keyword extraction algorithm is performed on the first key sentence set (i.e., metadata) to get a first candidate keyword set (i.e., set of candidate words).
4 A second keyword extraction algorithm is performed on a key sentence set to get a second candidate keyword set.
5 Candidate keywords are sorted by assigned weights and the top N candidate keywords are extracted from the set (i.e., selecting first top N…).
6 The top N words from the first set are removed from the second and third set. In other words, the top N words appear in both the first and second candidate keyword sets. The resulting set is the final target keyword set (i.e., labeling the document…).
7 The final keyword set includes keywords that only appear in one of the candidate sets.
8 One of the algorithms is TFIDF, which performs weighted extraction of keywords. Another procedure is textrank, which also utilizes weighted values.
9 Roitman discloses assigning differing weights to
10 One of the algorithms is TFIDF, which performs weighted extraction of keywords.
11 There are a few different procedures used, including TF-IDF and TextRank.
12 The limitation requires generating a set of labels by “for at least one particular table in the one or more tables,” which under the broadest reasonable interpretation requires generating one label for one table, which is essentially one document.
13 A first keyword extraction algorithm is performed on the first key sentence set (i.e., metadata) to get a first candidate keyword set (i.e., set of candidate words).
14 A second keyword extraction algorithm is performed on a key sentence set to get a second candidate keyword set.
15 A second keyword extraction algorithm is performed on a key sentence set to get a second candidate keyword set. The second algorithm can be textRank (i.e., different algorithm).
16 Candidate keywords are sorted by assigned weights and the top N candidate keywords are extracted from the set (i.e., selecting first top N…).
17 The top N words from the first set are removed from the second and third set. In other words, the top N words appear in both the first and second candidate keyword sets. The resulting set is the final target keyword set (i.e., labeling the document…).
18 The final keyword set includes keywords that only appear in one of the candidate sets.
19 The limitation requires generating a set of labels by “for at least one particular table in the one or more tables,” which under the broadest reasonable interpretation requires generating one label for one table, which is essentially one document.
20 A first keyword extraction algorithm is performed on the first key sentence set (i.e., metadata) to get a first candidate keyword set (i.e., set of candidate words).
21 A second keyword extraction algorithm is performed on a key sentence set to get a second candidate keyword set.
22 A second keyword extraction algorithm is performed on a key sentence set to get a second candidate keyword set. The second algorithm can be textRank (i.e., different algorithm).
23 Candidate keywords are sorted by assigned weights and the top N candidate keywords are extracted from the set (i.e., selecting first top N…).
24 The top N words from the first set are removed from the second and third set. In other words, the top N words appear in both the first and second candidate keyword sets. The resulting set is the final target keyword set (i.e., labeling the document…).
25 The final keyword set includes keywords that only appear in one of the candidate sets.
26 Subsection e describes use of word span.
Prosecution Insights