Detailed Action
Applicant amended claims 1, 6, 10, 17 and 20 and previously canceled claims 3-5 and presented claims 1-2 and 6-23 for reconsideration on 05/18/2026.
Claim Rejections - 35 USC § 103
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.
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.
Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Tarek A.M. ABDUNABI, Pub. No.: US 2021/0209177 A1 (ABDUNABI), in view of Chang et al., “Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals” (Chang) and further in view of Cardona, Patent No.: US 6,385,611 B1 (Cardona).
Claim 17. ABDUNABI teaches:
A method of processing collection of text-based content items to automatically derive therefrom a trend-indicative representation of topical information, the method comprising:
generating, from each text-based content item within the collection of text-based content items, respective candidate keywords based on at least on of title, abstract, and author keywords, the author keywords being associated with an author keywords database used to exclude irrelevant and inappropriate candidate keywords; (ABDUNABI, ¶¶ 113-128, 160-163, wherein “custom pre-processing of the ingested data for use by downstream modules” is performed by an NLP processing for identifying keywords from title, abstract, etc.)
for each of at least one of a plurality of domains, wherein each domain is associated with at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items, pre-processing the candidate keywords in accordance with excess component removal, acronym identification and replacement, chemical recognition and unification, principle term detection and combination, and word stemming; (ABDUNABI, ¶¶ 160-163, wherein “custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords related to a topic: ¶¶ 161-169, “Examples of NLP pre-processing may include, but not limited to, text cleaning, for example, removing stop words, punctuations, etc., tokenization, lemmatization, stemming, n-grams, and/or word2vec embeddings, to be used as input features to train Machine Learning (ML) models…Data processing to be used by ML topic modelling model 304 is Topic Modelling output 302 b: Example data processing, applied to title & Abstract, may include: text cleaning, tokenization, n-grams, stop-words removal, lemmatization, stemming, vector space representation, for example, bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF), Word2vec, Object2vec, etc.), etc….Papers are automatically assigned to topics, and top keywords/phrases per topic are generated. Patents are automatically assigned to topics, and top keywords/phrases per topic are generated. Research grants are automatically assigned to topics, and top keywords/phrases per topic are generated. Papers, patents and research grants are automatically assigned to topics (jointly), and top keywords/phrases per topic are generated”)
automatically selecting domain surrogates from the pre-processed candidate keywords in accordance with each of an overall candidate keyword usage frequency analysis and a keyword co-occurrence analysis across pre-classified text-based content items within the collection of text-based content items; (¶¶ 160-169, wherein a domain surrogate, a topic is selected by a topic modeling using “Term Frequency-Inverse Document Frequency (TF-IDF)”)
identifying, using iterative rules-based classification, major and minor domain surrogates of interest within the collection of text-based content; and (ABDUNABI, ¶¶ 160-169, wherein using ML topic modeling such as Latent Dirichlet Allocation (LDA) suggests that domain surrogates are selected iteratively for grouping/classifying keywords under related topics)
generating an information product depicting the major and minor domains of interest. (ABDUNABI, ¶¶ 190-191, “trends discovery & prediction module” provides “trends discovery/visualization” ; ¶¶ 103, 167, 188-189, 203-204, wherein content items are filtered/clustered to match certain topic of interest)
ABDUNABI did not specifically disclose but Chang discloses a keyword co-occurrence analysis as in p.2, sec. 2: “This study built the vocabulary lists… constructed the DTM, calculated the TF-IDF…weights, performed topic classification and co-word analysis…”.
ABDUNABI and Chang performs text mining using artificial intelligence and natural language processing as in ABDUNABI ¶¶ 163-167 and Chang, Abs and fig.1. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly using an available tool for calculating a keyword co-occurrence analysis for achieving the same predictable result of identifying topics in in ABDUNABI.
ABDUNABI as modified did not specifically disclose but Cardona discloses pre-classified text-based content items (8:10-46, wherein, text-based content item is classified by selecting documents related to a particular subject matter to be processed: Cardona, 2:47-56, “The process involves the utilization of a database program and provides specific keywords associated with the investigated topic. The present invention also provides a method for indexing the keywords using a keyword tree structure so the data is in the correct format for analysis. The process also provides a method for analyzing the number of occurrences of keywords along with the analysis of an impact factor associated with the keywords. The formatted data then allows the construction of several charts so a user can easily assess the state and forefront of a specified topic”; 8:10-46, “The process starts at step 301 where the user retrieves the resources of the search…this step includes the identification of keywords related to the user selected topic, selecting journals related to the topic, and combining the system”)
ABDUNABI discloses identifying candidate keywords using custom pre-processing. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for disclosing pre-classified text-based content items because doing so would further provided for a user of ABDUNAB as modified to start the process of trend analysis using documents related to certain topic and get answers for questions such as “What is happening in a specific topic, and what is at the forefront of that topic”.
Claims 1-2, 6-8, 10-12, 14-16, 18 and 20-22 are rejected under 35 U.S.C. 103(a) as being unpatentable over Tarek A.M. ABDUNABI, Pub. No.: US 2021/0209177 A1 (ABDUNABI), in view of Chang et al., “Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals” (Chang) and further in view of Uddin et al., “The impact of author-selected keywords on citation counts” (Uddin) and Cardona, Patent No.: US 6,385,611 B1 (Cardona).
Claim 1. ABDUNABI teaches:
A method of processing a collection of text-based content items to automatically derive therefrom a trend-indicative representation of topical information, the method comprising:
generating, from each text-based content item within the collection of text-based content items, respective candidate keywords based on at least one of available title, abstract, and author keywords, the author keywords being associated with an author keywords database used to exclude irrelevant and inappropriate candidate keywords; (ABDUNABI, ¶¶ 113-128, 160-163, wherein “standard and/or custom pre-processing” is performed on data comprising title, abstract, etc.)
for each of at least one of a plurality of domains, wherein each domain is associated with at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items, pre-processing the candidate keywords in accordance with excess component removal, acronym identification and replacement, chemical recognition and unification, principle term detection and combination, and word stemming; (ABDUNABI, ¶¶ 160-163, wherein “custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords related to a topic: ¶¶ 161-169, “Examples of NLP pre-processing may include, but not limited to, text cleaning, for example, removing stop words, punctuations, etc., tokenization, lemmatization, stemming, n-grams, and/or word2vec embeddings, to be used as input features to train Machine Learning (ML) models…Data processing to be used by ML topic modelling model 304 is Topic Modelling output 302 b: Example data processing, applied to title & Abstract, may include: text cleaning, tokenization, n-grams, stop-words removal, lemmatization, stemming, vector space representation, for example, bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF), Word2vec, Object2vec, etc.), etc….Papers are automatically assigned to topics, and top keywords/phrases per topic are generated. Patents are automatically assigned to topics, and top keywords/phrases per topic are generated. Research grants are automatically assigned to topics, and top keywords/phrases per topic are generated. Papers, patents and research grants are automatically assigned to topics (jointly), and top keywords/phrases per topic are generated”)
automatically selecting domain surrogates from the pre-processed candidate keywords in accordance with each of an overall candidate keyword usage frequency analysis and a keyword co-occurrence analysis across content items within the collection of text-based content items; (¶¶ 160-169, wherein a domain surrogate, a topic is selected by a topic modeling using “Term Frequency-Inverse Document Frequency (TF-IDF)”)
performing a trend analysis of selected domain surrogates for at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items by dividing the collection of text-based content items into at least a variable p part and a variable q part for each of the at least one domain group of text-based content items, and (ABDUNABI, ¶¶ 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” refers to how a topic p changes to a topic q over time)
determining for each keyword a respective normalized cumulative keyword frequency (Fvar), normalized cumulative keyword frequency for variable p (Fvar p), normalized cumulative keyword frequency for variable q (Fvar q); and (ABDUNABI, ¶¶ 163, 167-168, 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” suggests that frequency of a keyword (Fvar) is tracked from the frequency of the keyword for topic p during first time to the frequency of the keyword for topic q during a second time for determining “a sudden increase/decrease”)
generating an information product in accordance with the performed trend analysis. (ABDUNABI, ¶¶ 190-191, “trends discovery & prediction module” provides “trends discovery/visualization”)
ABDUNABI did not specifically disclose but Chang discloses a keyword co-occurrence analysis as in p.2, sec. 2: “This study built the vocabulary lists… constructed the DTM, calculated the TF-IDF…weights, performed topic classification and co-word analysis…”.
ABDUNABI and Chang performs text mining using artificial intelligence and natural language processing as in ABDUNABI ¶¶ 163-167 and Chang, Abs and fig.1. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly using an available tool for calculating a keyword co-occurrence analysis for achieving the same predictable result of identifying topics in in ABDUNABI.
ABDUNABI as modified did not specifically disclose but Uddin discloses a trend factor/keyword growth by slicing the total time considered for data collection into small time windows to calculate the growth of a keyword and detecting “sudden bursts or declines” of keywords in sec. 4.2.2.
ABDUNABI ¶ 188 discloses “a trained model detects a sudden increase/decrease in the number of publications of a specific research keywords/topic. For example, statistical approaches, such as calculating moving average and standard deviation over specified timeframes (last month, 1 year, 5 years, etc.) are used, followed by marking the data points that are outside those limits as anomalous. Alternatively, or additionally, advanced unsupervised anomaly detection algorithms, such as Isolation Forest, Self-Organizing Maps (SOM) neural network, may be also utilized”. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly disclosing a trend factor by a measure representing relative increase or decrease of keywords over a certain period of time for achieving the same predictable result of identifying trends of a topic in ABDUNABI.
ABDUNABI as modified did not specifically disclose but Cardona discloses pre-classified text-based content items (8:10-46, wherein, text-based content item is classified by selecting documents related to a particular subject matter to be processed: Cardona, 2:47-56, “The process involves the utilization of a database program and provides specific keywords associated with the investigated topic. The present invention also provides a method for indexing the keywords using a keyword tree structure so the data is in the correct format for analysis. The process also provides a method for analyzing the number of occurrences of keywords along with the analysis of an impact factor associated with the keywords. The formatted data then allows the construction of several charts so a user can easily assess the state and forefront of a specified topic”; 8:10-46, “The process starts at step 301 where the user retrieves the resources of the search…this step includes the identification of keywords related to the user selected topic, selecting journals related to the topic, and combining the system”)
ABDUNABI discloses identifying candidate keywords using custom pre-processing. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for disclosing pre-classified text-based content items because doing so would further provided for a user of ABDUNAB as modified to start the process of trend analysis using documents related to certain topic and get answers for questions such as “What is happening in a specific topic, and what is at the forefront of that topic”.
Claim 20. ABDUNABI teaches:
An apparatus, comprising processing resources and non-transitory memory resources, the processing resources configured to execute software instructions stored in the non-transitory memory resources to provide thereby a network function (NF), the core network function configured to perform a method of processing collection of text-based content items to automatically derive therefrom a trend-indicative representation of topical information, the method comprising:
generating, from each text-based content item within the collection of text-based content items, respective candidate keywords based on at least one of title, abstract, and author keywords, the author keywords being associated with an author keywords database used to exclude irrelevant and inappropriate candidate keywords; (ABDUNABI, ¶¶ 113-128, 160-163, wherein “standard and/or custom pre-processing” is performed on data comprising title, abstract, etc.)
for each of at least one of a plurality of domains, wherein each domain is associated with at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items, pre-processing the candidate keywords in accordance with excess component removal, acronym identification and replacement, chemical recognition and unification, principle term detection and combination, and word stemming; (ABDUNABI, ¶¶ 160-163, wherein “custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords related to a topic: ¶¶ 161-169, “Examples of NLP pre-processing may include, but not limited to, text cleaning, for example, removing stop words, punctuations, etc., tokenization, lemmatization, stemming, n-grams, and/or word2vec embeddings, to be used as input features to train Machine Learning (ML) models…Data processing to be used by ML topic modelling model 304 is Topic Modelling output 302 b: Example data processing, applied to title & Abstract, may include: text cleaning, tokenization, n-grams, stop-words removal, lemmatization, stemming, vector space representation, for example, bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF), Word2vec, Object2vec, etc.), etc….Papers are automatically assigned to topics, and top keywords/phrases per topic are generated. Patents are automatically assigned to topics, and top keywords/phrases per topic are generated. Research grants are automatically assigned to topics, and top keywords/phrases per topic are generated. Papers, patents and research grants are automatically assigned to topics (jointly), and top keywords/phrases per topic are generated”)
automatically selecting domain surrogates from the pre-processed candidate keywords in accordance with each of an overall candidate keyword usage frequency analysis and a keyword co-occurrence analysis across content items within the collection of text-based content items; (¶¶ 160-169, wherein a domain surrogate, a topic is selected by a topic modeling using “Term Frequency-Inverse Document Frequency (TF-IDF)”)
performing a trend analysis of selected domain surrogates for at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items by dividing the collection of text-based content items into at least a variable p part and a variable q part for each of the at least one domain group of text-based content items, and (ABDUNABI, ¶¶ 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” refers to how a topic p changes to a topic q over time)
determining for each keyword a respective normalized cumulative keyword frequency (Fvar), normalized cumulative keyword frequency for variable p (Fvar p), normalized cumulative keyword frequency for variable q (Fvar q); and (ABDUNABI, ¶¶ 163, 167-168, 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” suggests that frequency of a keyword (Fvar) is tracked from the frequency of the keyword for topic p during first time to the frequency of the keyword for topic q during a second time for determining “a sudden increase/decrease”)
generating an information product in accordance with the performed trend analysis. (ABDUNABI, ¶¶ 190-191, “trends discovery & prediction module” provides “trends discovery/visualization”)
ABDUNABI did not specifically disclose but Chang discloses a keyword co-occurrence analysis as in p.2, sec. 2: “This study built the vocabulary lists… constructed the DTM, calculated the TF-IDF…weights, performed topic classification and co-word analysis…”.
ABDUNABI and Chang performs text mining using artificial intelligence and natural language processing as in ABDUNABI ¶¶ 163-167 and Chang, Abs and fig.1. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly using an available tool for calculating a keyword co-occurrence analysis for achieving the same predictable result of identifying topics in in ABDUNABI.
ABDUNABI as modified did not specifically disclose but Uddin discloses a trend factor/keyword growth by slicing the total time considered for data collection into small time windows to calculate the growth of a keyword and detecting “sudden bursts or declines” of keywords in sec. 4.2.2.
ABDUNABI ¶ 188 discloses “a trained model detects a sudden increase/decrease in the number of publications of a specific research keywords/topic. For example, statistical approaches, such as calculating moving average and standard deviation over specified timeframes (last month, 1 year, 5 years, etc.) are used, followed by marking the data points that are outside those limits as anomalous. Alternatively, or additionally, advanced unsupervised anomaly detection algorithms, such as Isolation Forest, Self-Organizing Maps (SOM) neural network, may be also utilized”. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly disclosing a trend factor by a measure representing relative increase or decrease of keywords over a certain period of time for achieving the same predictable result of identifying trends of a topic in ABDUNABI.
ABDUNABI as modified did not specifically disclose but Cardona discloses pre-classified text-based content items (8:10-46, wherein, text-based content item is classified by selecting documents related to a particular subject matter to be processed: Cardona, 2:47-56, “The process involves the utilization of a database program and provides specific keywords associated with the investigated topic. The present invention also provides a method for indexing the keywords using a keyword tree structure so the data is in the correct format for analysis. The process also provides a method for analyzing the number of occurrences of keywords along with the analysis of an impact factor associated with the keywords. The formatted data then allows the construction of several charts so a user can easily assess the state and forefront of a specified topic”; 8:10-46, “The process starts at step 301 where the user retrieves the resources of the search…this step includes the identification of keywords related to the user selected topic, selecting journals related to the topic, and combining the system”)
ABDUNABI discloses identifying candidate keywords using custom pre-processing. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for disclosing pre-classified text-based content items because doing so would further provided for a user of ABDUNAB as modified to start the process of trend analysis using documents related to certain topic and get answers for questions such as “What is happening in a specific topic, and what is at the forefront of that topic”.
Claim 2. The method of claim 1, further comprising identifying, using rules-based classification, major and minor domains of interest within the collection of the text based content items. (ABDUNABI ¶¶ 103, 167, 188-189, 203-204, wherein content items are filtered/clustered to match certain topic of interest; Chang, sec. 3.2)
Claim 21 is rejected under the same above rationale.
Claims 3-5. (Canceled)
Claim 6. The method of claim 1, wherein pre-processing of candidate keywords further comprises lemmatization. (ABDUNABI, ¶ 161, text cleaning includes lemmatization)
Claim 7. The method of claim 1, further comprising automatically selecting keywords in accordance with a user defined variance analysis of the content items within the collection of content items. (ABDUNABI ¶¶ 201, 222, wherein content items are provided to match user search query)
Claim 8. The method of claim 1, further comprising automatically selecting keywords in accordance with one or more target domain classifications. (ABDUNABI ¶¶ 201, 222, wherein content items are provided to match user search query)
Claim 10. The method of claim 1, wherein the collection of text-based content items is identified via a customer request, the method further comprising:
responsive to the customer request, automatically gathering each of the content items within the collection of text-based content items. (ABDUNABI ¶¶ 201, 204, 222, wherein content items are provided to match user search query)
Claim 11. The method of claim 1, wherein the information product comprises a visual representation of groups of structured text-based content items. (ABDUNABI ¶¶ 190-191, 201, “trends discovery & prediction module” provides “trends discovery/visualization”)
Claim 12. The method of claim 1, wherein the text comprises at least one of a title, abstract, one or more keywords, and deep text of at least one text-based content item. (ABDUNABI, ¶¶ 113-128, 160-163, wherein “standard and/or custom pre-processing” is performed on data comprising title, abstract, etc.)
Claim 14. The method of claim 2, wherein the rule-based classification scheme comprises an iterative selection of domain surrogates until a desired classification accuracy is achieved. (ABDUNABI, ¶¶ 167-168, wherein using ML topic modeling such as Latent Dirichlet Allocation (LDA) suggests that domain surrogates are selected iteratively for identifying a topic; Chang, p.2, sec. 2, pp. 15-16, sec. 3.4)
Claim 15. The method of claim 1, wherein co-occurrence analysis comprises frequency analysis of co-occurring items in the same content item. (Chang, p.2, sec. 2, pp. 15-16, sec. 3.4: “Co-word analysis uses the co-occurrence feature of vocabulary to divide the specific topic into several subclusters”)
Claim 16. The method of claim 1, wherein said automatically selecting keywords is further performed in accordance with an analysis of keyword association among different content items based on the same keyword. (ABDUNABI, ¶ 103, “a cloud of most frequent terms”; Chang, sec.2, “TF indicates the occurrence frequency of a specific word in all documents”)
Claim 18. ABDUNABI as modified by Chang teaches:
The method of claim 17, further comprising:
performing a trend analysis of keywords for at least one of spatial, topical, geographical, and demographical domain groups within the collection of text-based content items by dividing the collection of the text-based content items into at least a variable p part and a variable q part for each of the at least one group of text-based content items, and (ABDUNABI, ¶¶ 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” refers to how a topic p changes to a topic q over time)
determining for each keyword a respective normalized cumulative keyword frequency (Fvar), normalized cumulative keyword frequency for variable p (Fvar p), normalized cumulative keyword frequency for variable q (Fvar q). (ABDUNABI, ¶¶ 163, 167-168, 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” suggests that frequency of a keyword (Fvar) is tracked from the frequency of the keyword for topic p during first time to the frequency of the keyword for topic q during a second time for determining “a sudden increase/decrease”)
ABDUNABI as modified did not specifically disclose but Uddin discloses a trend factor/keyword growth by slicing the total time considered for data collection into small time windows to calculate the growth of a keyword and detecting “sudden bursts or declines” of keywords in sec. 4.2.2.
ABDUNABI ¶ 188 discloses “a trained model detects a sudden increase/decrease in the number of publications of a specific research keywords/topic. For example, statistical approaches, such as calculating moving average and standard deviation over specified timeframes (last month, 1 year, 5 years, etc.) are used, followed by marking the data points that are outside those limits as anomalous. Alternatively, or additionally, advanced unsupervised anomaly detection algorithms, such as Isolation Forest, Self-Organizing Maps (SOM) neural network, may be also utilized”. It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for explicitly disclosing a trend factor by a measure representing relative increase or decrease of keywords over a certain period of time for achieving the same predictable result of identifying trends of a topic in ABDUNABI.
Claim 22. The method of claim 1, further comprising:
performing a trend analysis of keywords for a temporal group within the collection of text-based content items by dividing the collection of the text-based content items into at least a variable p part and a variable q part for each of the at least one group of structured text-based content items, and (ABDUNABI, ¶¶ 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” refers to how a topic p changes to a topic q over time)
determining for each keyword a respective normalized cumulative keyword frequency (Fvar), normalized cumulative keyword frequency for variable p (Fvar p), normalized cumulative keyword frequency for variable q (Fvar q); and trend factor; (ABDUNABI, ¶¶ 188-189, wherein “a sudden increase/decrease in the number of publications of a specific research keywords/topic” refers to how a topic p changes to a topic q over time; Uddin, sec. 4.2.2, wherein a trend factor/keyword/topic growth disclosed by slicing the total time considered for data collection into small time windows to calculate the growth of a keyword and detecting “sudden bursts or declines” in keywords)
wherein for the temporal group the variable p part comprises text-based content items associated with a first time period and the variable q part comprises text-based content items associated with a second time period; (see above for explanation)
wherein differences in keyword frequency between variable p part and variable q part being indicative of keyword trend. (see above for explanation)
Claims 9, 13, 19 and 23 are rejected under 35 U.S.C. 103(a) as being unpatentable over ABDUNABI, Chang and Cardona as applied to claim 17 above, and ABDUNABI, Chang, Uddin and Cardona as applied to claim 1 and 18 above in view of Examiner's Official Notice.
Claim 9. ABDUNABI as modified taught the method of claim 1 without disclosing the following mathematical equations:
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200
400
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wherein Fvar denotes keyword frequency and Nvar denotes number of papers.
The Examiner takes Official Notice that the usage of mathematical symbols as a tool for altering the representation of the obtained values to fit the need of an application is very well- known in the art and it would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to represent the obtained values according to the teaching of ABDUNABI as modified differently using mathematical symbols as noted by the Examiner's Official Notice to achieve the same predictable result of tracking changes to usage of a keyword over time.
Claims 19 and 23 are rejected under the same rationale.
Claim 13. ABDUNABI as modified taught a trend factor as shown in claim 1 above; ABDUNABI as modified did not disclose wherein the trend factor comprises a logarithm value of the ratio of current normalized cumulative keyword frequencies to past normalized cumulative keyword frequencies.
The Examiner takes Official Notice that the usage of mathematical symbols as a tool for altering the representation of the obtained values to fit the need of an application is very well- known in the art and it would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to represent the obtained values according to the teaching of ABDUNABI as modified, in particular the measure representing keyword growth as taught by Uddin, differently using mathematical symbols as noted by the Examiner's Official Notice to achieve the same predictable result of tracking changes to usage of a keyword over time.
Response to Amendment and Arguments
In light of amendments, claim objections and 112(b) rejections are withdrawn.
Applicant’s arguments with respect to amended claims have been fully considered but are moot in view of the new grounds of rejections as provided above.
Applicant’s argument with respect to the applied references have been considered but are not persuasive for at least the following reason.
Applicant argues that “ABDUNABI is directed in relevant parts to processing at a standard (i.e., domain agnostic) word treatment level (e.g., stop words, punctuations, stemming, etc.) during preprocessing. By contrast, the claimed invention is directed to a domain-based comprehensive preprocessing method, including excess component removal, acronym identification and replacement, chemical recognition and unification, principle term detection and combination, etc.”
In response, ABDUNABI clearly discloses using standard as well as custom pre-processing of the ingested data. ¶ 160, “The processing component 106 includes an NLP (Natural Language Processing) processing module 302 responsible for performing standard and/or custom pre-processing of the ingested data for use by downstream modules”.
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 Mohsen Almani whose telephone number is (571)270-7722. The examiner can normally be reached on M-F, 9 AM-5 PM, ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached on 571-272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHSEN ALMANI/Primary Examiner, Art Unit 2159
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