DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/22/2024 is being considered by the examiner.
Response to Amendment
The Amendment filed on 02/18/2026 has been entered. The Drawings Objection has been withdrawn. Claims 7 and 18 have been cancelled. Therefore, claims 1-6, 8-17, and 19-20 remain pending in this application.
Response to Arguments
Applicant’s arguments filed on 02/18/2026 have been fully considered. With respect to the 35 U.S.C. 101 rejection, on pages 10-11, the arguments have been fully considered and the rejection is withdrawn. With respect to the 35 U.S.C. 112 rejection, on page 11, the arguments have been fully considered but are not persuasive. With respect to the 35 U.S.C. 103 rejection, on pages 11-13, the arguments have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 112 rejection, on page 11, of claims 8-10 and claims 17-19, the rejection in concern to claims 8-10 has been withdrawn. Concerning claims 17 and 19, the rejection stands, as claim 17 still recites “The computing system of claim 13”. Claim 13 still recites a method, not a computing system. Claim 19 still recites “The computing system of claim 12”. Claim 12 still recites a method, not a computing system. This is inconsistent terminology with respect to the structural relationships between the method and respective computing system.
With respect to the 35 U.S.C. 103 rejection, on pages 11-13, of claims 1-5, 11-12, 14-16, and 20 under Rathod et al. (US Patent No. 8,115,869), hereinafter referred to as Rathod, in view of Gupta et al. (US Patent Application Publication No. 2008/0097951), hereinafter referred to as Gupta, and claims 6-10, 13, and 17-19 under Rathod, in view of Gupta, and further in view of Haraguchi et al. (US Patent Application Publication No. 2005/0160086), hereinafter referred to as Haraguchi, the Applicant asserts that the applied references, alone or in any combination, fail to disclose or suggest the features define by the claims, as amended, and there would have been no apparent reason that would have caused one of ordinary skill in the art to modify the applied references to arrive at the claimed features. Specifically, the Applicant asserts that Gupta does not disclose claim 7, which recites “wherein processing the first document using the anchor rule further comprises processing the first document using a machine learning (ML) model, and further comprising training, by the computing system, the ML model based on the updated rule set”. Claim 7 has been cancelled, and these limitations have since been moved into independent claim 1, which now recites “training, by the computing system, a machine learning (ML) model based on the one or more other rules within the rule set”. The Applicant also asserts that they disagree with Haraguchi’s disclosure of “based on the updated rule set” of claim 7. Haraguchi is not used in the following rejection of amended claim 1, therefore this argument is moot. The Applicant asserts that, as a whole, Rathod, nor Gupta, nor Haraguchi disclose or suggest the amended limitations of “updating, by the computing system and based on the word list, one or more other rules within a rule set that includes the anchor rule; training, by the computing system, a machine learning (ML) model based on the one or more other rules within the rule set; processing, by the computing system using the ML model and based on the word list, a second document to extract one or more points of information from the second document”.
In response to the argument that Gupta does not disclose “training, by the computing system, a machine learning (ML) model based on the one or more other rules within the rule set”, Gupta Fig. 1 reference character 142 shows a classifier, which is a machine learning model, and Gupta Fig. 2 reference character 210 shows training the classifier. Further, Gupta para [0029] states “For example, the classifier 142 extracts features such as word part of speech, parsing and semantic roles of words from training data identified as including a causal relationship or other specified form. Features of subsequently received text data are compared to the features extracted from the classified data and compared with features from subsequently received text data. Hence, the initial training allows the classifier 142 to more accurately identify features of text including a causal relationship or other specified form,” this directly shows that the model is being trained on causal relationships of words and phrases (i.e. rules). Regardless of whether or not this training is supervised, as its broadest reasonable interpretation, Gupta still discloses the machine learning model being trained on rules in a set.
In response to the argument that neither Rathod, nor Gupta, nor Haraguchi discloses the rest of the amended limitations, “updating, by the computing system and based on the word list, one or more other rules within a rule set that includes the anchor rule; processing, by the computing system using the ML model and based on the word list, a second document to extract one or more points of information from the second document”, Rathod col. 10 lines 48-52 states “As noted, the rule library 616 maintains a list of rules that can be used to extract different types of keywords. Rules can either be added to the library 616 manually, be pre-learned or learned over time. Each rule is a regular expression that the rule engine 612 understands," and Rathod col. 11 lines 49-57 further states “The above process for creating a mapping can be learned over time as well. In step (a) whenever the user is using the extractor 600 and is presented with some keywords, if the user clicks one of them (indicating that the user finds the keyword useful), it is treated as a keyword marked by a user. The rest of the process is same as the steps (b)-(f), above. If the final rule set already contains this newly created rule, it is discarded. The mapping in the rule selector 614 can include other mappings in addition to the genre mapping.” These two citations disclose that the word list output to the user can be edited or interacted with to affect the rule library, per Rathod. The user is able to interact with the keywords within the list to further change keyword rules. This directly discloses that rules are being updated based on the word list. Gupta paragraph [0006] states "The trained classifier is then applied to a second text corpus to identify components of the text corpus, such as sentences, having similar characteristics to the training corpus.” This directly discloses that the machine learning model is being used to process a second document to extract more information, as shown within the amended limitation. Hence, the Applicant’s arguments are not persuasive.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 17 and 19 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
Regarding claim 17, claim 17 recites “the computing system of claim 13”. Claim 13 recites a method, not a computing system. This is inconsistent terminology with respect to the structural relationships between the method and respective computing system.
Regarding claim 19, claim 19 recites “the computing system of claim 12”. Claim 12 recites a method, not a computing device. This is inconsistent terminology with respect to the structural relationships between the method and respective computing system.
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.
Claim(s) 1-5, 11-12, 14-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rathod et al. (US Patent Number 8,115,869), hereinafter referred to as Rathod, in view of Gupta et al. (US Patent Application Publication No. 2008/0097951), hereinafter referred to as Gupta.
Regarding claim 1, Rathod discloses a method, comprising: processing, by a computing system, a first document using an anchor rule of a rules-based information extractor, wherein the anchor rule identifies tokens for a domain ("The extraction rules define the kinds of sequences of such tags that are important," Rathod col. 7 lines 37-38 and Rathod Fig. 5 reference character 456);
identifying, by the computing system and using the anchor rule, a first set of phrases from the first document that match the tokens (Rathod Fig. 5 reference character 458); and
determining, by the computing system and based on the first selection, a word list (Rathod Fig. 3B reference character 314D), wherein the word list is a list of words ranked by a rate of appearance in the first document (Rathod Fig. 3B reference character 314B and "Further, each word is ranked based on its frequency or another ranking mechanism," Rathod col. 5 lines 58-59);
updating, by the computing system and based on the word list, one or more other rules within a rule set that includes the anchor rule ("As noted, the rule library 616 maintains a list of rules that can be used to extract different types of keywords. Rules can either be added to the library 616 manually, be pre-learned or learned over time. Each rule is a regular expression that the rule engine 612 understands," Rathod col. 10 lines 48-52 and “The above process for creating a mapping can be learned over time as well. In step (a) whenever the user is using the extractor 600 and is presented with some keywords, if the user clicks one of them (indicating that the user finds the keyword useful), it is treated as a keyword marked by a user. The rest of the process is same as the steps (b)-(f), above. If the final rule set already contains this newly created rule, it is discarded. The mapping in the rule selector 614 can include other mappings in addition to the genre mapping,” Rathod col. 11 lines 49-57).
However, Rathod fails to disclose receiving, by the computing system, a first selection from a first subset of the first set of phrases; training, by the computing system, a machine learning (ML) model based on the one or more other rules within the rule set; and processing, by the computing system using the ML model and based on the word list, a second document to extract one or more points of information from the second document. Gupta teaches a method for extracting relationships between words in textual data.
Gupta teaches receiving, by the computing system, a first selection from a first subset of the first set of phrases ("In one embodiment, conditional probabilities of the stored text data are examined to identify stored text data most likely to include a causal relationship and the stored text data most likely to include a causal relationship is selected 250. For example, 400 sentences with the highest conditional probability of including a causal relationship are selected 250 from the stored text data," Gupta para [0040]);
training, by the computing system, a machine learning (ML) model based on the one or more other rules within the rule set (Gupta Fig. 1 reference character 142 shows a classifier and Gupta Fig. 2 reference character 210 shows training the classifier);
processing, by the computing system using the ML model and based on the word list, a second document to extract one or more points of information from the second document ("The trained classifier is then applied to a second text corpus to identify components of the text corpus, such as sentences, having similar characteristics to the training corpus," Gupta para [0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of receiving a set of phrases and processing a second document during information extraction, as well as Gupta’s method of using machine learning for information extraction. Only selecting those phrases with the highest frequency, score, or probability would decrease the necessary computational time and storage capabilities required to extraction information in this regard, while also increasing accuracy and efficacy. Machine learning would improve speed, efficiency, accuracy, all while lowering operation costs and time necessary for information extraction to be done by humans. Repetitive tasks, such as information extraction, are very easily performed by machine learning models. Both of these inclusions would lessen the time and cost of implementing a manual method and associated system of information extraction. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 2, Rathod, in view of Gupta, discloses all of the limitations of claim 1. Rathod further discloses receiving, by the computing system, a user selection of one or more matching phrases from the first subset of the first set of phrases from a displayed user interface ("In step (a) whenever the user is using the extractor 600 and is presented with some keywords, if the user clicks one of them (indicating that the user finds the keyword useful), it is treated as a keyword marked by a user," Rathod col. 11 lines 50-53 and Rathod Fig. 7 reference character 602);
and outputting an indication of one or more candidate words for [[a]] the word list ("The keywords may be suggested to the user," Rathod col. 4 lines 8-9).
Regarding claim 3, Rathod, in view of Gupta, discloses all of the limitations of claim 2. Rathod further discloses selecting, by the computing system, the first subset of the first set of phrases using at least one of: a frequency of occurrence of one or more phrases within the first set of phrases, corpus analysis of the first document, or one or more embeddings ("The remaining words, and the number of their occurrences (frequency), is recorded by the indexer 208. More frequent words are important words, from which keywords will be selected," Rathod col. 5 lines 13-16).
Regarding claim 4, Rathod, in view of Gupta, discloses all of the limitations of claim 2. Rathod further discloses receiving, by the computing system, a user selection of two or more words from the first subset of the first of phrases ("In step (a) whenever the user is using the extractor 600 and is presented with some keywords, if the user clicks one of them (indicating that the user finds the keyword useful), it is treated as a keyword marked by a user," Rathod col. 11 lines 50-53) from the displayed user interface (Rathod Fig. 1 reference character 40).
However, Rathod fails to disclose and displaying, by the computing system, grammatical relationships among the two or more words.
Gupta teaches and displaying, by the computing system, grammatical relationships among the two or more words ("For example, the classifier 142 identifies text describing a causal relationship, such as text having a condition-action format or a situation-response format, based on the initial training results. For example, verb-argument structures identified by the classifier 142 are compared with the verb-argument structures stored from training 210 to identify the situation and response or cause and effect components from the text data," Gupta para [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of showing grammatical relationships between the words extracted. Including grammatical relationships in information extraction from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled grammatical relationship data is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 5, Rathod, in view of Gupta, discloses all of the limitations of claim 2. Rathod fails to disclose comprising based on receiving a user selection, displaying, by the computing system, one or more grammatical relationships.
Gupta teaches comprising based on receiving [[a]] the user selection, displaying, by the computing system, one or more grammatical relationships between the one or more matching phrases ("For example, the classifier 142 identifies text describing a causal relationship, such as text having a condition-action format or a situation-response format, based on the initial training results. For example, verb-argument structures identified by the classifier 142 are compared with the verb-argument structures stored from training 210 to identify the situation and response or cause and effect components from the text data," Gupta para [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of showing grammatical relationships between the words extracted. Including grammatical relationships in information extraction from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled grammatical relationship data is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 11, Rathod, in view of Gupta, discloses all of the limitations of claim 1. Rathod fails to disclose wherein extracting the one or more points of information further comprises determining one or more relationships between words of the second document.
Gupta teaches extracting the one or more points of information further comprises determining one or more relationships between words of the second document ("The trained classifier is then applied to a second text corpus to identify components of the text corpus, such as sentences, having similar characteristics to the training corpus," Gupta para [0006] and "Data from the text corpus having the specific form is then stored," Gupta para [0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of extracting relationships between the words. Including word and textual relationships in information extraction from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled word relationship data is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 12, Rathod, in view of Gupta, discloses all of the limitations of claim 1. Rathod further discloses processing, by the computing system, multiple additional documents in addition to the first document, using the anchor rule (Rathod Fig. 6 reference character 600 Keyword Extractor can be used repeatedly).
As to claim 14, system claim 14 and method claim 1 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 15, system claim 15 and method claim 2 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 16, system claim 16 and method claim 3 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 20, computer-readable medium (CRM) claim 20 and method claim 1 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to the method claim.
Claim(s) 6, 8-10, 13, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rathod, in view of Gupta, and further in view of Haraguchi et al. (US Patent Application No. 2005/0160086), hereinafter referred to as Haraguchi.
Regarding claim 6, Rathod, in view of Gupta, discloses all of the limitations of claim 2. Rathod further discloses wherein updating the one or more other rules comprises responsive to the selection of the one or more matching phrases, ("The rule engine 612 comprises a conventional pattern matching program, taking in text input and one or more patterns (rules), and extracting all keywords that match one or more of the patterns," Rathod col. 11 lines 60-63 and "As noted, the rule library 616 maintains a list of rules that can be used to extract different types of keywords. Rules can either be added to the library 616 manually, be pre-learned or learned over time. Each rule is a regular expression that the rule engine 612 understands," Rathod col. 10 lines 48-52).
However, Rathod fails to disclose updating, by the computing system, the one or more other rules within [[a]] the rule set, processing, by the computing system, the first document using the updated rule set; and identifying, by the computing system and using the updated rule set, a second set of phrases from the first document that match the tokens.
Gupta teaches processing, by the computing system, the first document using the updated rule set (Gupta Fig. 2 shows an arrow from reference character 260 to reference character 220 showing to run the information extraction again);
and identifying, by the computing system and using the updated rule set, a second set of phrases from the first document that match the tokens (Gupta Fig. 2 shows an arrow from reference character 260 to reference character 220 showing to run the information extraction again).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of extracting another set of relationships between the words. Including word and textual relationships in information extraction from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled word relationship data is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. This inclusion would have been obvious to one of ordinary skill in the art.
Haraguchi teaches a rule-based information extraction method.
Haraguchi teaches updating, by the computing system, the one or more other rules within [[a]] the rule set (Haraguchi Fig. 12 reference character 811 shows supplementing, or updating, the extraction rule).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata and Gupta’s method of extracting another set of relationships between the words by including Haraguchi’s method of updating an extraction rule. The ability to update an information extraction rule would improve accuracy, efficiency, and adaptability. A static system can quickly become outdated, or it can begin to return erroneous data that doesn’t match with the static information extraction rule. Changing the rule and its format can facilitate extracting different variations of the same item, thus increasing efficiency and lowering the time it takes to instead use multiple static extraction methods. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 8, Rathod, in view of Gupta, discloses all of the limitations of claim 1. Rathod fails to disclose wherein the anchor rule includes entity expressions used in assertions regarding at least one of quantitative claims or observations in the scientific literature; relationships among people and organizations reported in the news; the parties and findings in legal documents involved in litigation; products, business entities, prices, in documents detailing business transactions; or other domains involving textual communication.
Haraguchi teaches wherein the anchor rule includes entity expressions used in assertions regarding at least one of quantitative claims or observations in [[the]] scientific literature; relationships among people and organizations reported in the news; the parties and findings in legal documents involved in litigation; products, business entities, prices, in documents detailing business transactions; or other domains involving textual communication ("The present invention relates to an information extraction apparatus and method for extracting information from messages exchanged and stored through a computer network," Haraguchi para [0002]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Haraguchi’s method of extracting information from a textual communication. Extracting information from textual communications can benefit users and members of the textual communication by helping users retrieve information that they overlooked as non-important, as well as helping users to understand the flow of a discussion that has occurred over a plethora of messages. It can also help to obtain more specific and direct items if the user knows what they are searching for in particular. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 9, Rathod, in view of Gupta, and further in view of Haraguchi, discloses all of the limitations of claim 8. Rathod fails to disclose wherein the claim expressions include measurement expressions and metric expressions, wherein the measurement expressions are quantitative claims or observations, and wherein the metric expressions correspond to the measurement expressions and are quantitative values of the quantitative expressions.
Gupta teaches wherein the claim expressions include measurement expressions and metric expressions, wherein the measurement expressions are quantitative claims or observations, and wherein the metric expressions correspond to the measurement expressions and are quantitative values of the quantitative expressions ("This training 210 allows words which frequently occur in text data including a causal relationship or having another specified format are recognized. In one embodiment, chunk structures, dependency patterns and/or verb-argument structure are also extracted and captured, allowing identification of the sentence structure of text data including causal relationships or another specified format," Gupta para [0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of extracting phrases of a certain format. Extracting word and textual relationships of a certain format from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled word relationship data of a certain format is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. It would also facilitate the information retrieval of very specific data if the user knew what to search for. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 10, Rathod, in view of Gupta, and further in view of Haraguchi, discloses all of the limitations of claim 9. Rathod fails to disclose determining, by the computing system and based on the claim expressions, one or more relationships among the claim expressions.
Gupta teaches determining, by the computing system and based on the claim expressions, one or more relationships among the claim expressions ("This training 210 allows words which frequently occur in text data including a causal relationship or having another specified format are recognized. In one embodiment, chunk structures, dependency patterns and/or verb-argument structure are also extracted and captured, allowing identification of the sentence structure of text data including causal relationships or another specified format," Gupta para [0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of extracting phrases of a certain format. Extracting word and textual relationships of a certain format from text would facilitate the creation of structured data for knowledge graphs, and it would also help to improve information retrieval systems. Labeled word relationship data of a certain format is limited, and being able to extract this information from text automatically would help to create more data in the field in order to teach further information extraction and retrieval systems. It would also facilitate the information retrieval of very specific data if the user knew what to search for. This inclusion would have been obvious to one of ordinary skill in the art.
Regarding claim 13, Rathod, in view of Gupta, discloses all of the limitations of claim 1. Rathod further discloses wherein the anchor rule is a first anchor rule, wherein the word list is a first word list, and further comprising responsive to determining the first word list: processing, by the computing system, the first document using a second anchor rule ("The extraction rules define the kinds of sequences of such tags that are important," Rathod col. 7 lines 37-38 and Rathod Fig. 5 reference character 456 and Rathod Fig. 6 reference character 514 shows selecting another rule),
identifying, by the computing system and using the second anchor rule, a second set of phrases from the first document that match the tokens (Rathod Fig. 5 reference character 458);
determining, by the computing system and based on the second selection, a second word list (Rathod Fig. 3B reference character 314D).
Rathod fails to disclose wherein the second anchor rule is based on the first word list; receiving, by the computing system, a second selection from the first subset of the first set of phrases; and processing, by the computing system and based on the second word list, the second document to extract one or more points of information from the second document.
Gupta teaches receiving, by the computing system, a second selection from the first subset of the first set of phrases ("In one embodiment, conditional probabilities of the stored text data are examined to identify stored text data most likely to include a causal relationship and the stored text data most likely to include a causal relationship is selected 250. For example, 400 sentences with the highest conditional probability of including a causal relationship are selected 250 from the stored text data," Gupta para [0040]);
and processing, by the computing system and based on the second word list, the second document to extract one or more points of information from the second document ("The trained classifier is then applied to a second text corpus to identify components of the text corpus, such as sentences, having similar characteristics to the training corpus," Gupta para [0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata by including Gupta’s method of receiving a set of phrases and processing a second document during information extraction. Only selecting those phrases with the highest frequency, score, or probability would decrease the necessary computational time and storage capabilities required to extraction information in this regard, while also increasing accuracy and efficacy. This would lessen the time and cost of implementing a method and associated system of information extraction. This inclusion would have been obvious to one of ordinary skill in the art.
Haraguchi teaches wherein the second anchor rule is based on the first word list (Haraguchi Fig. 12 reference character 811 shows supplementing, or updating, the extraction rule).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Rathod’s method of extracting relevant data from content metadata and Gupta’s method of using machine learning for information extraction by including Haraguchi’s method of updating an extraction rule. The ability to update an information extraction rule would improve accuracy, efficiency, and adaptability. A static system can quickly become outdated, or it can begin to return erroneous data that doesn’t match with the static information extraction rule. Changing the rule and its format can facilitate extracting different variations of the same item, thus increasing efficiency and lowering the time it takes to instead use multiple static extraction methods. This inclusion would have been obvious to one of ordinary skill in the art.
As to claim 17, system claim 17 and method claim 6 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 19, system claim 19 and method claim 8 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to the method claim.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 ADAM MICHAEL WEAVER whose telephone number is (571)272-7062. The examiner can normally be reached Monday-Friday, 8AM-5PM EST.
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/ADAM MICHAEL WEAVER/ Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658
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