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 .
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments filed, with respect to the rejection of Claims 4, 12 and 19 under 35 USC 112(b), have been fully considered and are persuasive. Therefore, the rejection of Claims 4, 12 and 19 under 35 USC 112(b) has been withdrawn.
Applicant’s arguments filed, with respect to the amended independent claims (Claims 1, 9 and 16) and the previously cited prior art, have been fully considered and are persuasive. Therefore, the rejection of Claims 1, 5-9, 13-16 and 20 under 35 USC 102, and Claims 2-4, 10-12 and 17-19 under 35 USC 103, has been withdrawn. However, upon further consideration and search, a new ground of rejection is made in view of Pieper (US Pub. 2007/0250468 A1).
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.
Claims 1, 5-9, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra et al. (US Patent 12, 033,162 B2) in view of Pieper (US Pub. 2007/0250468 A1).
Regarding Claims 1, 9 and 16, Chopra teaches a method (see Fig.1, Fig.2 and Col.4, Line 41-43), comprising:
obtaining from a plurality of communications, using a processor, a plurality of words and phrases (see Fig.1 (102a), Fig.2 (202), Fig.5 (504) and Col.7, Line 47-52, words and phrases from user interactions with a VA agent);
applying, using the processor, a word embedding algorithm to the plurality of words and phrases (see Fig.1 (110) and Col.7, Line 37-44), wherein the word embedding algorithm maps the plurality of words and phrases as vectors in high-dimensional space with distances between the vectors based on dimensions assigned to the plurality of words and phrases (see Fig.2 (204), Fig.6 (606), Col.9, Line 62 – Col.10, Line 25 and Col.15, Line 30-39);
clustering, using the processor, the mapped plurality of words and phrases into a plurality of groups using the vector distances (see Fig.1 (112), Fig.2 (210) and Col.13, Line 4-17), wherein each group of the plurality of groups has a respective category (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the categories are based on tier level clusters are formed based on the named entity recognition or keywords);
applying a constraint to at least one group of the plurality of groups to traverse the groups based on their respective categories and to obtain therefrom at least one modified group of clustered words and phrases (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the tier level clusters or groups are formed based on the named entity recognition or keywords), wherein the constraint is at least one of a keyword, a phrase, or groupings of words in phrases which are in close proximity (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the tier level clusters or groups are formed based on the named entity recognition or keywords);
and determining, using the processor, a category for the at least one modified group (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the groups and subgroups are formed based on the named entity recognition or keywords).
Chopra fails to teach receiving a query including a constraint to add context to the plurality of groups, wherein the at least one modified group having a bespoke categorization unreliant on the categories for the plurality of groups.
Pieper, however, teaches receiving a query to collect context information to identify a new content domain to provide content expected to be in alignment with a user’s interest (see Fig.5 (504,506,508) and paragraph [0087]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to Chopra’s method the step for receiving a query including a constraint to add context to the plurality of groups, wherein the at least one modified group having a bespoke categorization unreliant on the categories for the plurality of groups. The motivation would be to receive additional context information to identify a new category or subcategory for a modified group.
Regarding Claims 5 and 13, Chopra further teaches wherein the category is utilized in performing the analysis of agent performance (see Fig.4 (404), Fig.5, Fig.13 and Col.12, Line 8-43, receiving feedbacks for training and refining the machine learning models to accurately identify the hierarchy of customer issues).
Regarding Claims 6 and 14, Chopra further teaches wherein one or more vectors is a real-valued vector encoding for each word or phrase of the plurality of communications (see Fig.8 and Col.9, Line 62 – Col.10, Line 25).
Regarding Claim 7, wherein the word embedding algorithm is a Bidirectional Encoder Representations Transformer BERT (see Fig.1 (112) and Col.13, Line 13-35).
Regarding Claims 8, 15 and 20, Chopra further teaches wherein applying the constraint enables traversing through the plurality of groups, wherein each group of the plurality of groups has a category (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the categories are based on tier level clusters are formed based on the named entity recognition or keywords).
Claims 2-4, 10-12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chopra et al. (US Patent 12, 033,162 B2) in view of Pieper (US Pub. 2007/0250468 A1), and in further view of Prabhavalkar et al. (US Patent 11,646,019 B2).
Regarding Claims 2, 10 and 17, Chopra Pieper teach the method of Claim 1 but fail to teach mapping a plurality of acoustic characteristics of the plurality of communications into the high-dimensional space as one or more vectors.
Prabhavalkar, however, teaches mapping acoustic characteristics from a voice input into a high-dimensional space as vectors to provide a transcription for the voice input and to identify specific words from the transcription (see Fig.4 (404,420), Col.4, Line 46-57 and Col.12, Line 8-21).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the method of Claim 1 the step for mapping a plurality of acoustic characteristics of the plurality of communications into the high-dimensional space as one or more vectors. The motivation would be to utilize acoustic features obtained from the user interactions with the automated VA to generate text for analysis and to identify specific words from the user interactions.
Regarding Claims 3, 11 and 18, Chopra teaches clustering, using the processor, the mapped plurality of words into a plurality of groups using the vector distances (see Fig.1 (112), Fig.2 (210) and Col.13, Line 4-17), but Chopra and Pieper fail to teach clustering the mapped plurality of acoustic characteristics.
Prabhavalkar, however, teaches mapping acoustic characteristics from a voice input into a high-dimensional space as vectors to provide a transcription for the voice input and to identify specific words from the transcription (see Fig.4 (404,420), Col.4, Line 46-57 and Col.12, Line 8-21).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the method of Claim 1 the step for clustering the mapped acoustic characteristics along with the mapped words and phrases. The motivation would be to identify and group similar words or phrases from the generated text of the user interactions with automated VA.
Regrading Claims 4, 12 and 19, Chopra further teaches applying a constraint to at least one group of the plurality of groups to obtain a modified group (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the tier level clusters or groups are formed based on the named entity recognition or keywords), wherein applying the constraint involves finding vectors using the vector distances (see Fig.13 and Col.15, Line 30-65); and determining, using the processor, a category for the modified group (see Fig.13, Col.14, Line 26-51 and Col.15, Line 30-65, the groups and subgroups are formed based on the named entity recognition or keywords).
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 VU B HANG whose telephone number is (571)272-0582.
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/VU B HANG/ Primary Examiner, Art Unit 2654
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