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 .
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 (i.e., changing from AIA to pre-AIA ) 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, 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) 2, 7-9, 14, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2021/0150546 A1, “Zhu”) in view of Cavalin et al. (US 2015/0347392 A1, “Cavalin”).
As to claims 2, 9, 16, Zhu discloses a method of providing sentiment analysis (data mining and sentiment analysis process 200, Fig. 2, para. 0030, 0033), comprising:
aggregating, by a processor, a plurality of comments from one or more data sources (obtaining reviews and/or complaints from a plurality of data sources, 202, 212, 216, para. 0030);
classifying, by the processor, the plurality of comments as being associated with a polarity of sentiment using a machine learning module that is trained to detect sentiment (performing sentiment analysis on the obtained data, para. 0033; sentiment algorithm identifies positive, negative, and neutral sentiments, para. 0041; server uses a BERT classifier to classify each related sentence as highly positive, positive, neutral, negative or highly negative, para. 45); and
providing, by the processor, an output indicative of the polarity of the sentiment (intuitive visual representations to show sentiment feedbacks, para. 0051, Fig. 5B; visual representation of sentiment analysis results, para. 0069, Fig. 5G).
Zhu differs from claims 2, 9, 16 in that it does not disclose:
identifying, by the processor, a trend in the polarity of the sentiment over a period of time;
identifying, by the processor, at least one most common phrase associated with the polarity of the sentiment over the period of time; and
determining, by the processor, a cause of the trend in the polarity of the sentiment based on the at least one most common phrase.
Cavalin teaches using sentiment analysis to identify trends based on most commonly occurring words/phrases/topics within a time period of interest (Abstract; para. 0032-0033). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu with the above teaching of Cavalin in order to make use of other well known trending metrics, such as the most commonly occurring phrases taught by Cavalin.
As to claim 7, Zhu in view of Cavalin discloses: aggregating the plurality of comments comprises receiving the plurality of comments one or both of a website and a database (obtaining various customer reviews via server system 120, para. 0026, 0038-0039, which includes one or more databases, para. 0026, and websites, para. 0024, 0059, 0063).
As to claim 8, Zhu in view of Cavalin discloses: determining that each comment of the plurality of comments received from the one or both of the website and the database has no associated sentiment score (user comments may be text reviews requiring NLP analysis and topic extraction analysis, para. 0030, 0039).
As to claim 14, Zhu in view of Cavalin discloses: each of the plurality of comments is associated with a particular product (customer sentiments from product reviews, Abstract, para. 0005; product-specific data, para. 0047).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, and further in view of Parent et al. (US 10,546,027 B1, “Parent”).
Zhu in view of Cavalin discloses: processing, by the processor, at least some comments of the plurality of comments to generate individual terms (sentiment, attribute and topic words are extracted, para. 0034-0035),
but differs from claim 3 in that it does not specifically disclose: comparing, by the processor, the individual terms to known terms that are each associated with a particular polarity of sentiment.
Parent teaches determining whether a customer review is positive, negative or neutral by comparing identified adjectives with a predefined list of keywords (col. 6, lines 39-53; col. 10, lines 35-43). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin with the above teaching of Parent in order to more accurately determine a sentiment metric.
As to claim 4, Zhu in view of Cavalin and Parent teaches: the polarity of sentiment of the at least some comments is determined based on comparing the individual terms to the known terms (Parent: col. 6, lines 39-53; col. 10, lines 35-43).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, and further in view of Casalino et al. (US 2020/0151777 A1, “Casalino”).
Zhu in view of Cavalin differs from claim 5 in that it does not disclose: at least one comment of the plurality of comments comprises a non-text based message.
Casalino teaches a review sentiment analysis module which determines a sentiment score based on non-text messages (star rating, “thumbs down” symbol, etc.,) as well as words (para. 0021, 0031-0032). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin with the above teaching of Casalino in order to consider the non-text elements which are known to express a customer’s sentiment.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, and further in view of Sicora et al. (US 10,684,738 B1, “Sicora”).
Zhu in view of Cavalin differs from claim 6 in that it does not disclose:
each non-text based message comprises one or both of an emoji and a character string representing an emoji;
the method further comprises comparing each non-text based message to one or both of known emoji and known character strings; and
the polarity of the sentiment of each non-text based message is determined based on comparing the non-text based message to the one or both of known emoji and known character strings.
Sicora teaches analyzing user comments in social media posts regarding products, the comments including emojis (col. 10, line 27 – col. 12, line 11). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin with the above teaching of Sicora in order to provide more insight into how users view a particular product (Sicora: col. 147, lines 21-28).
Claim(s) 10-11, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, and further in view of Zhang et al. (US 2021/0216723 A1, “Zhang”).
Zhu in view of Cavalin differs from claim 10 in that it does not disclose: determine that the polarity of the sentiment of at least one comment of the plurality of comments is unrecognized; and provide an output indicating that the polarity of sentiment of the at least one comment cannot be determined.
Zhang teaches the output of information that a sentiment class of a text cannot be determined (para. 0156). It would have been obvious to one of ordinary skill in in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin with the above teaching of Zhang in order to in order to inform a user of an undetermined sentiment, thus providing clear feedback.
As to claim 11, Zhu in view of Cavalin and Zhang teaches: determining that the polarity of the sentiment of at least one comment of the plurality of comments is unrecognized comprises determining that a particular phrase within the at least one comment does not match and is not similar to at least one of a known term, a known emoji, or a known character string (Zhang: word or phrase, para. 0070; emoji, para. 0109).
As to claim 17, Zhu in view of Cavalin and Zhang teaches: train the machine learning module by: aggregating a plurality of training comments; and classifying each of the plurality of training comments as having a particular polarity of sentiment (Zhang: sentiment classification model is trained using collected sample sets, Abstract).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, as applied to claim 9 above, and further in view of Sicora and Al-Halah et al. (US 2020/0073485 A1, “Al-Halah”).
Zhu in view of Cavalin differs from claim 12 in that it does not disclose:
at least some of the plurality of comments comprises a non-text based message;
the instructions further cause the one or more processors to compare each non-text based message to one or both of known emoji and known character strings, wherein at least some of the one or both of known emoji and known character strings are associated with human-assigned polarity scores that are indicative of a particular polarity of sentiment; and
the polarity of the sentiment of each non-text based message is determined based on comparing the non-text based message to the one or both of known emoji and known character strings.
Sicora teaches analyzing user comments in social media posts regarding products, the comments including emojis (col. 10, line 27 – col. 12, line 11). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin with the above teaching of Sicora in order to provide more insight into how users view a particular product (Sicora: col. 147, lines 21-28).
Zhu in view of Cavalin and Sicora further differs from claim 12 in that it does not teach: wherein at least some of the one or both of known emoji and known character strings are associated with human-assigned polarity scores that are indicative of a particular polarity of sentiment.
Al-Halah teaches understanding sentiment in customer reviews (para. 0024) and using human annotators to label each emoji with a positive or negative sentiment in order to generate a trained model (para. 0041, 0069-0070). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin and Sicora with the above teaching of Al-Halah in order to provide improved sentiment analysis.
As to claim 13, Zhu in view of Cavalin, Sicora and Al-Halah teaches: the human-assigned polarity scores are used to train the machine learning module (Al-Halah: emoji-to-sentiment model may be trained using images with sentiments labeled by human reviewers, para. 0076).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, as applied to claim 14 above, and further in view of Gugnani et al. (US 2021/0192552 A1, “Gugnani”).
Zhu differs from claim 15 in that it does not disclose: generate a prediction of potential end users of the particular product based at least in part on a sentiment trend of reviewers in a given geographic region and demographic information of the reviewers.
Gugnani teaches analyzing a sentiment trend of reviewers from a plurality of information sources and according to geographical regions, and utilizing the location-dependent attributes and user sentiment to make recommendations regarding what would be well received within the geographical region (para. 0020-0022, 0036-0037). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu with the above teaching of Gugnani in order to target products to people of a geographical regions more likely to result in a sale, as taught by Gugnani (para. 0022).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin and Zhang, as applied to claim 17 above, and further in view of Zhang et al. (US 2020/0311203 A1, “Zhang ‘203”).
Zhu in view of Cavalin and Zhang differs from claim 18 in that it does not teach training the machine learning module as specifically recited.
Zhang ‘203 teaches:
word tokenizing at least some comments of the plurality of training comments (para. 0041);
processing the at least some comments to unify a format of the at least some comments (para. 0043);
removing one or more of single-letter words, two-letter words, words on a pre-defined list, punctuation, or numbers from each of the at least some comments to form sets of remaining words for each of the at least some comments (para. 0040, 0043);
lemmatizing the sets of remaining words for each of at least some comments to generate a plurality of known terms (para. 0040, 0043, 0055); and
associating the at least some comments, the plurality of known terms, and the particular polarity of sentiment with one another in a database (Zhu: Fig. 5A-5F; Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin and Zhang with the above teaching of Zhang ‘203 in order to train the machine learning model for improved sentiment analysis.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, Zhang and Zhang ‘203, as applied to claim 18 above, and further in view of Parent et al. (US 10,546,027 B1, “Parent”).
Zhu in view of Cavalin, Zhang and Zhang ‘203 teaches:
process at least some comments of the plurality of comments to generate individual terms (Zhu: para. 0045), but differs from claim 19 in that it does not teach:
compare the individual terms to the plurality of known terms that are each associated with a particular polarity of sentiment.
Parent teaches determining whether a customer review is positive, negative or neutral by comparing identified adjectives with a predefined list of keywords (col. 6, lines 39-53; col. 10, lines 35-43). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin, Zhang and Zhang ‘203 with the above teaching of Parent in order to more accurately determine a sentiment metric.
Claim(s) 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cavalin, Zhang and Zhang ‘203, as applied to claim 18 above, and further in view of He et al. (US 2018/0293499 A1, “He”).
Zhu in view of Cavalin, Zhang and Zhang ‘203 differs from claim 20 in that it does not teach: training the machine learning module further comprises:
analyzing the database to identify a set of most important terms;
outputting a vector of each of the most important terms and a weight for each of the at least some comments; and
using each vector and each weight as an input to train the machine learning module.
He teaches sentiment analysis including training a neural attention model by identifying important terms, outputting a vector and a weight, and using each vector and each weight to train the neural attention model (Abstract; para. 0014, 0020, 0024, 0054). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu in view of Cavalin, Zhang and Zhang ‘203 with the above teaching of He in order to provide a trained machine learning model for improved sentiment analysis.
As to claim 21, Zhu in view of Cavalin, Zhang, Zhang ‘203 and He teaches: each weight comprises a term frequency inverse document frequency calculation (Zhang ‘203: para. 0045).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 11,842,156 in view of Cavalin.
Claim 2 (present application)
Claim 1 (US 11,842,156)
A method of providing sentiment analysis, comprising:
A method of providing sentiment analysis, comprising:
aggregating, by a processor, a plurality of comments from one or more data sources;
aggregating, by a processor, a plurality of text-based comments from one or more data sources, wherein at least some of the plurality of text-based comments provided by the one or more data sources include a score, the score being indicative of a polarity of sentiment associated with a respective one of the plurality of text-based comments, wherein each of the plurality of text-based comments is associated with a particular product;
classifying, by the processor, the plurality of comments as being associated with a polarity of sentiment using a machine learning module that is trained to detect sentiment; and
classifying, by the processor, the plurality of text-based comments as being associated with a polarity of sentiment, wherein the at least some of the plurality of text-based comments are classified based on the score associated with each of the at least some of the plurality of text-based comments;
providing, by the processor, an output indicative of the polarity of the sentiment.
outputting, by the processor, a graphic that includes the predetermined number of most common phrases for the particular polarity of sentiment.
Claim 1 of the patent differs from claim 2 of the present application in that it does not recite: identifying, by the processor, a trend in the polarity of the sentiment over a period of time;
identifying, by the processor, at least one most common phrase associated with the polarity of the sentiment over the period of time; and
determining, by the processor, a cause of the trend in the polarity of the sentiment based on the at least one most common phrase.
Cavalin teaches using sentiment analysis to identify trends based on most commonly occurring words/phrases/topics within a time period of interest (Abstract; para. 0032-0033). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the remoted method of claim 2 with the above teaching of Cavalin in order to make use of other well known trending metrics, such as the most commonly occurring phrases taught by Cavalin.
Response to Arguments
Applicant’s arguments with respect to claim(s) 2-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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 Stella L Woo whose telephone number is (571)272-7512. The examiner can normally be reached Monday - Friday, 8 a.m. to 5 p.m.
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STELLA L. WOO
Primary Examiner
Art Unit 2693
/Stella L. Woo/Primary Examiner, Art Unit 2693