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 .
Priority
Receipt is acknowledged or paper submitted under 35 U.S.C. 119(a)-(d), which papers have been places of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/21/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were submitted on 10/21/2024. These drawings are reviewed and accepted by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Leonard (US 20160300023 A1).
Regarding claims 1 and 11, Leonard teaches:
“a crawling module, coupled to a network database and configured to search for a plurality of user comments from the network database and store the user comments into a database” (par. 0015; ‘The collection processor 102 also collects user sentiments.’ ‘This information can come from public websites and is gathered through direct links with public websites and/or using a web crawler to gather information.’; See also par. 0027);
“a text detection module, coupled to the crawling module and comprising a font library, wherein the font library has a plurality of preset word strings, the text detection module is configured to collect at least one of the user comments that comprises at least one of the word strings” (par. 0024; ‘For example, “the doctor correctly diagnosed my problem but he was rude and dismissive” conveys an opinion about two different aspects of the entity. These are understood independently. The NLP system is trained with words and phrases that are relevant in order to make it accurate for use.’)
“an analysis module, coupled to the text detection module and configured to analyze the at least one of the user comments” (par. 0025; ‘There are multiple approaches that can be used for sentiment analysis. For example, a scoring system could include both objective and emotional scoring of words.’); and
“a determination module, coupled to the analysis module and configured to determine whether the word strings have constructive significance in the at least one of the user comments according to an analysis result of the at least one of the user comments” (par. 0027; ‘Each sentence is individually analyzed. The first sentence, “Dr. Ng is awesome!” is placed into a tree 302.’ ‘Node 310 receives a score of 2 or ++ because the word “awesome” indicates positive emotion. Node 316 is the average of nodes 308 and 310 and is 1 or +. Finally, node 312 receives a score of 2 or ++ because the exclamation point is positive emotion. The score for Nodes 314, 316 and 312 is averaged. The final score for the sentence “Dr. Ng is awesome!” receives a score of 1 or + based on the average of nodes 314, 316 and 312.’).
Regarding claims 2 (dep. on claim 1) and 12 (dep. on claim 11), Leonard further teaches:
“wherein the crawling module obtains link information according to a first schedule, and the crawling module stores the link information into the database” (par. 0015; ‘This information can come from public websites and is gathered through direct links with public websites and/or using a web crawler to gather information.’);
“wherein the crawling module selects the link information according to a second schedule, the crawling module crawls the user comments according to the link information, and the crawling module stores the user comments into the database” (par. 0015; ‘This information can come from public websites and is gathered through direct links with public websites and/or using a web crawler to gather information.’).
Regarding claims 3 (dep. on claim 1) and 13 (dep. on claim 11), Leonard further teaches:
“wherein the crawling module crawls the user comments, and records crawling time of a last user comment of the user comments to identify a starting comment for a next crawl” (par. 0018; ‘Alternatively, the collection processor can use a web crawler or web scraper to collect comments, reviews and other information posted by consumers on the websites in the list of public sources of information 202. For some websites the collection processor may use an API and use a web crawler or web scraper for other sites.’).
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) 4-5, 6, 8, 14-15, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Leonard in view of Cardona De Leon et al. (US 20240161123 A1).
Regarding claims 4 (dep. on claim 1) and 14 (dep. on claim 11), Leonard does not expressly teach:
“wherein the word strings are a plurality of defective code sentences.”
Cardona De Leon teaches:
“wherein the word strings are a plurality of defective code sentences” (par. 0058; ‘In some embodiments, user feedback auditor 300 may be used to analyze and classify the user communications received by the service provider through various communication channels, as described above. Such communications may include, for example, conversations between users and customer service agents of the service provider.’ ‘Each transaction claim may include a user's comment (e.g., text-based input, text-based electronic data) or reason for submitting the claim/dispute. For example, the comment may include text describing that items purchased from a merchant were never delivered, the items are defective, the items are substantially different than the merchant's description, the items were perished upon delivery, the items were not delivered on time, etc.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Leonard’s NLP system trained on words and phrases by incorporating Cardona De Leon’s user feedback auditor in order to analyze and classify user communications (e.g., user’s comments that items are defective) received by the service provider through various communication channels. The combination would produce feedback categories representing the most common customer issues. (Cardona: par. 0058)
Regarding claims 5 (dep. on claim 4) and 15 (dep. on claim 14), the combination of Leonard in view of Cardona De Leon further teaches:
“wherein the text detection module performs language conversion on the user comments, and searches for at least one of the language-converted user comments having at least one of the defective code sentences according to the defective code sentences” (Leonard: par. 0032; ‘Next, at step 510 a natural language processor analyzes the normalized user comments using sentiment analysis to determine a sentiment analysis provider rating.’; Cardona: par. 0058: ‘Each transaction claim may include a user's comment (e.g., text-based input, text-based electronic data) or reason for submitting the claim/dispute. For example, the comment may include text describing that items purchased from a merchant were never delivered, the items are defective, the items are substantially different than the merchant's description, the items were perished upon delivery, the items were not delivered on time, etc.’).
Although Leonard in view of Cardona De Leon teach natural language processing, the combination does not explicitly recite language conversion. However, language conversion is well-known in the art as evident by Boone (US 20020078152 A1) (par. 0029; ‘In another embodiment, the predefined comment may be translated into any number of different languages, and depending on an indication of a default or preferred language of a user viewing his comments or those of another user, the comments may be provided in the default or preferred language.’). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date to perform language conversion, as taught by Boone, on the user comments, as taught by Leonard in view of Cardona De Leon, using techniques well-known in the art.
Regarding claims 6 (dep. on claim 5) and 16 (dep. on claim 15), the combination of Leonard in view of Cardona De Leon as evident by Boone further teaches:
“wherein the text detection module determines whether the at least one of the language-converted user comments having at least one of the defective code sentences satisfies a restrictive form, so as to determine whether to perform sentiment analysis” (Leonard: par. 0024; ‘Sentence-level analysis is used to determine whether an individual sentence has different sentiment from the overall review or comment. For example, a patient may have an overall favorable view of a doctor but in a particular sentence may have a negative comment about something specific, such as waiting too long to be seen. The sentence may be considered independent of the document as long as the relationship with the entity (in this case the healthcare provider) is maintained.’).
Regarding claims 8 (dep. on claim 1) and 18 (dep. on claim 11), the combination of Leonard in view of Cardona De Leon further teaches:
“wherein the text detection module detects the user comments according to a defective code detection table to identify a plurality of capture frame word strings, and the analysis module analyzes the capture frame word strings, the detection module generates a determination result according to an analysis result” (Cardona: par. 0058; ‘For example, the comment may include text describing that items purchased from a merchant were never delivered, the items are defective, the items are substantially different than the merchant's description, the items were perished upon delivery, the items were not delivered on time, etc.’).
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Leonard in view of Cardona De Leon, further in view of Farivar et al. (US 20210318865 A1).
Regarding claims 7 (dep. on claim 1) and 17 (dep. on claim 11), Leonard in view of Cardona De Leon teaches a defective code rule (Cardona: par. 0058, items are defective).
However, Leonard in view of Cardona De Leon does not expressly teach:
“wherein the analysis module performs a full comment sentiment analysis and a partial comment sentiment analysis on the at least one of the user comments, and the determination module determines whether a defective code rule is met according to a full comment sentiment analysis result and a partial comment sentiment analysis result.” (emphasis added)
Farivar teaches:
“wherein the analysis module performs a full comment sentiment analysis and a partial comment sentiment analysis on the at least one of the user comments, and the determination module determines whether a defective code rule is met according to a full comment sentiment analysis result and a partial comment sentiment analysis result” (par. 0016; ‘The system may present options to a user for new comments, and, based on receiving user feedback, replace the obsolete comments with updated comments.’; par. 0048; ‘Alternatively, or additionally, a request may comprise an inferred instruction. For example, settings for a comment manager 204 may instruct partial or full analysis of comments to be performed based on a trigger at the user interface or in a file 201-n.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Leonard’s (in view of Cardona De Leon) sentiment analysis and defective code rule by incorporating Farivar’s partial or full analysis of comments in order to determine whether a defective code rule is met according to a full comment sentiment analysis result and a partial comment sentiment analysis result.
Claim(s) 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Leonard in view of Cardona De Leon, and further in view of Wadhwa et al. (US 20190385242 A1).
Regarding claims 9 (dep. on claim 1) and 19 (dep. on claim 11), Leonard in view of Cardona De Leon teaches:
“wherein the user comments correspond to a specific defective code, [[and the determination module generates a statistical control graph according to the user comments corresponding to the specific defective code]]” (Cardona: par. 0058; ‘For example, the comment may include text describing that items purchased from a merchant were never delivered, the items are defective, the items are substantially different than the merchant's description, the items were perished upon delivery, the items were not delivered on time, etc.’).
However, Leonard in view of Cardona De Leon does not explicitly teach:
“and the determination module generates a statistical control graph according to the user comments corresponding to the specific defective code.”
Wadhwa teaches:
“and the determination module generates a statistical control graph according to the user comments corresponding to the specific defective code” (par. 0068; ‘Based on the analysis of user comment 330, text analyzer module 332 generates inferred context 334. Inferred context 334 represents the context of user comment 330 as it relates to data context 326 of data 312 shown in visualization 322.’; par. 0069; ‘Text analyzer module 332 sends inferred context 334 to visual action module 336 of dashboard application 320. Visual action module 336 utilizes inferred context 334 of user comment 330 to determine and generate the appropriate visual treatment 338 to apply to graph 324 in visualization 322 of data 312.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Leonard in view of Cardona De Leon’s specific defective code by incorporating Wadhwa’s Visual action module in order to generate a statistical control graph according to user comments corresponding to a specific defective code. The combination would provide additional comment-related insights into data of a dashboard visualization. (Wadhwa: par. 0001).
Regarding claims 10 (dep. on claim 9) and 20 (dep. on claim 19), the combination of Leonard in view of Cardona De Leon and Wadhwa further teaches:
“wherein in response to a trend line in the statistical control graph exceeding an upper control boundary in the statistical control graph, or being lower than a lower control boundary in the statistical control graph, the determination module generates a prompt signal” (Wadhwa: par. 0079; ‘Data visualizations 1100 again emphasize orders trend data visualization 1102 and inventory data visualization 1104 similar to data visualizations 900 in FIGS. 9A-9B. Emphasized orders trend data visualization 1102 and inventory data visualization 1104 relate to relevant comment chain 1106.’).
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658
Prosecution Insights