Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/14/2025 has been entered.
Detailed Action
Applicant amended claims 1, 2, 6, 10-11, 17-18, and 20-22, canceled claim 3 and presented claims 1-2 and 6-23 for reconsideration on 11/14/2025.
Claim Objections
Claim 6 is objected to for the following informalities.
Claim 6 recites “wherein the text-norming further comprises lemmatization”. There is insufficient antecedent basis for “the text-norming” in the claim.
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.
Claims 1, 17 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The claims recite “generating, from each text-based content item within the collection of text-based content items, respective candidate keywords based on available title, abstract, and author keywords, the author keywords being associated with an author keywords database used to exclude irrelevant and inappropriate candidate keywords”. It means the claims are functional only if “title, abstract, and author keywords” are available. It is not clear how the claims function if “title, abstract, and author keywords” are not available.
In order to move the prosecution forward, it is assumed that at least one of “title, abstract, and author keywords” is available.
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).
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 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.)
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 “standard and/or custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords)
automatically selecting keywords in accordance with each of a keyword usage frequency analysis across the collection of the text-based content items; (¶¶ 160-163, 167-168, wherein “Term Frequency-Inverse Document Frequency (TF-IDF)” provides for selecting keywords based on frequency of keywords in a document and collection of documents)
identifying, using iterative rules-based classification, major and minor domain surrogates of interest within the collection of text-based content; and (ABDUNABI, ¶¶ 167-168, 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.
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).
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 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.)
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 “standard and/or custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords)
automatically selecting keywords in accordance with each of a keyword usage frequency analysis across the collection of the text-based content items; (ABDUNABI, ¶¶ 160-163, 167-168, wherein “Term Frequency-Inverse Document Frequency (TF-IDF)” provides for selecting keywords based on frequency of keywords in a document and collection of documents)
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 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.
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 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.)
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 “standard and/or custom pre-processing” provides for operations such as acronym identification and replacement, chemical recognition and unification, etc., as desired for selecting keywords)
automatically selecting keywords in accordance with each of a keyword usage frequency analysis across the collection of the text-based content items; (ABDUNABI, ¶¶ 160-163, 167-168, wherein “Term Frequency-Inverse Document Frequency (TF-IDF)” provides for selecting keywords based on frequency of keywords in a document and collection of documents)
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 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.
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 the text-norming 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 as applied to claim 17 above, and ABDUNABI, Chang and Uddin as applied to claim 1 and 18 above in view of Examiner's Official Notice.
Claim 9. ABDUNABI as modified by and Chang and Uddin taught the method of claim 1 without disclosing the following mathematical equations:
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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
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.
Conclusion
The prior arts made of record in PTO-326 and not relied upon are considered pertinent to applicant's disclosure.
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/MOHSEN ALMANI/Primary Examiner, Art Unit 2159