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 .
DETAILED ACTION
Claims 1-20 are presented for examination.
This is a Non-Final Action.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 1 recites “17/755,542” as parent application, which should be “17/475,542”.
Appropriate correction is required.
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 obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 2, 5, 8, 9, 10, 11, 12, 15, 18 and 20 of Patent No. US 12,406,012. Although the conflicting claims are not identical, they are not patentably distinct from each other because
Instant Application
US Patent: US 12,406,012
1, 12 and 20
1, 12 and 18
2 and 19
2
3 and 16
1
4 and 17
17
5
1 and 20
6
5
7
1
8
8
9 and 18
9
10 and 15
10
11
11
13
1
14
15
This is an obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
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.
Claims 1-20 rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 2008/0275701) in view of Peng et al. (US 2019/0325084)
1. Wu teaches, A computing system, comprising:
a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts (Fig 1, Paragraph 23 – user computer 102, Wu) comprising:
executing a search based upon the keyword (Fig 1:110, 112, 114 and 116, paragraph 24 – teaches a search engine 112 searches the text file 110 for words and/or phrases that present in a first data set 114, and when a match is found, searches a second data set 116 for documents containing the matched words or phrases, Wu); and
causing search results for the search to be presented on a display to the first user (Fig 1:118 and 102; Fig 3 – teaches conversations, related documents and related people displayed on monitor 118; Paragraph 24 – teaches the output is then sent to a user’s computer monitor 118, Wu).
Wu does not explicitly teach or disclose,
obtaining computer-readable text based upon an interaction between a first user and at least one of a plurality of computer-executable applications;
upon obtaining the computer-readable text, providing the computer-readable text and a context of the first user as input into an intent identification module, wherein the intent identification module generates an output indicative of an intent, wherein the context of the first user is determined based upon activity history of the first user in one or more of the plurality of computer-executable applications;
upon obtaining an output of the intent identification module indicative of a search intent, identifying a potential keyword based upon the computer-readable text;
computing a confidence score for the potential keyword based upon the context of the first user;
identifying the potential keyword as a keyword for search based upon the confidence score;
However Peng teaches,
obtaining computer-readable text based upon an interaction between a first user and at least one of a plurality of computer-executable applications (Fig 1:130, 132, 136, Fig 2:205, 210, 215, 220, Paragraph 39 – teaches obtaining computer readable text from user interaction with computer executable applications, including assistant, social networking messaging and browser applications, Peng);
upon obtaining the computer-readable text, providing the computer-readable text and a context of the first user as input into an intent identification module, wherein the intent identification module generates an output indicative of an intent, wherein the context of the first user is determined based upon activity history of the first user in one or more of the plurality of computer-executable applications (Paragraphs 40 and 41, Fig 2:210, 220, 225, 230; Fig 3:220, 225, 230, 224a, 224b – teaches providing the text and user context/profile information to an NLU module (220) which is the intent identification module; further NUL/intent classifier outputs an identified intent, Peng);
upon obtaining an output of the intent identification module indicative of a search intent, identifying a potential keyword based upon the computer-readable text (Fig 3:220, 230, 231, 232, 233, 234 & paragraph 40 – teaches potential keywords based on n-gram, entity, slot or term extracted from the computer-readable text further teaching identifying n-gram/entities/slots from the user text after NLU processing identifies intent, Peng);
computing a confidence score for the potential keyword based upon the context of the first user (Fig 2:220, 225, 230, Paragraphs 40 and 41 – teaches ranking n-grams (i.e. candidate keywords, with confidence scores based on aggregated user profile/context information, Peng);
identifying the potential keyword as a keyword for search based upon the confidence score (Fig 3:220, 230, Paragraph 55 – teaches using confidence ranked n-grams/entities to identify the semantic item used by the NLU/dialog system; Under BRI the selected ranked n-gram/entity corresponds to identifying the potential keyword as the keyword to be used for information retrieval/search, Peng).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to allow modify Wu’s conversation based automatic search system with Peng’s NLU/user context processing because Wu seeks to automatically retrieve information relevant to a user’s ongoing conversation, while Peng teaches using an NLU module with a user context engine and semantic information aggregator to identify user intent and rank n-grams/entities with confidence scores based on user profile /context information. The combination would have predictably improved Wu’s selection of conversation derived keywords and improved relevance of the search results presented to the user.
2. The combination of Wu and Peng teach, The computing system of claim 1, the acts further comprising:
indicative of the spoken words; and performing automatic speech recognition (ASR) on the audio data, wherein the computer-readable text is obtained based upon results of the ASR (Paragraph 24 teaches “the user’s speech is provided to an automatic speech recognition (ASR) module 108 which produces a text 110 containing a transcript of at least the side of the telephone conversation input via telephone 100”, Wu).
3. The combination of Wu and Peng teach, The computing system of claim 1, wherein the interaction between the first user and the at least one of a plurality of computer-executable applications involves a second user interacting with the at least one of the plurality of computer executable applications, and wherein the computer-readable text is indicative of a conversation between at least the first user and the second user (Paragraphs 29 – teaches an ongoing conversation searches and providing the searches on a computer (i.e. GUI) and Paragraph 48 – teaches ASR for real time streams, Wu).
4. The combination of Wu and Peng teach, The computing system of claim 3, the acts further comprising:
causing the search results for the search to be presented to the second user concurrently with the conversation (Paragraphs 29 – teaches an ongoing conversation searches and providing the searches on a computer (i.e. GUI) and Paragraph 48 – teaches ASR for real time streams, Wu).
5. The combination of Wu and Peng teach, The computing system of claim 3, wherein the one or more of the plurality of computer-executable applications from which the context of the first user is determined is different from the at least one of the plurality of computer-executable applications involving an interaction between the first user and the second user ((Paragraph 24 teaches “the user’s speech is provided to an automatic speech recognition (ASR) module 108 which produces a text file 110 containing a transcript of at least the side of the telephone conversation input via telephone 100”; Paragraph 23 teaches that a the telephone can be a “software-based telephone running on the user’s computer 102”, i.e., a “real-time meeting application”, Paragraph 53 – teaches utilizing google Desktop API for indexing and searching documents, thus teaching different applications involving to gather context and to hold a conversation between first and second user, Wu).
6. The combination of Wu and Peng teach, The computing system of claim 1, wherein identifying the potential keyword in the computer-readable text comprises assigning part of speech tags to each word in the computer-readable text, wherein the potential keyword is identified based upon the part of speech tags (Paragraph 49 – teaches tagged clips based on recognized words and phrases, Wu).
7. The combination of Wu and Peng teach, The computing system of claim 1, wherein the context of the first user is determined based upon activity history of the first user comprising the interaction between the first user and the at least one of a plurality of computer-executable applications (Fig 1:130, 132, 134, 136, fig 2:220, 225, 230, Paragraphs 27 and 40 – teaches that the user’s context is based on activity from interactions with multiple computer-executable applications/services, including assistant, messaging, social networking/news feed, search and browser/application interactions, Peng).
8. The combination of Wu and Peng teach, The computing system of claim 1, wherein the potential keyword comprises a first potential keyword and a second potential keyword, wherein a first confidence score is computed for the first potential keyword and a second confidence score is computed for the second potential keyword, wherein the first potential keyword is identified as the keyword for search based upon the first confidence score being greater than the second confidence score (Fig 3:230, 231, 232, 233, 234, Abstract Paragraphs 40 & 55 – teaches annotating n-grams of user input and ranking the n-grams with confidence scores based on aggregated user profile/context information. Because the n-grams are ranked according to their confidence scores, a first n-gram having a greater confidence score than a second n-gram would be ranked higher and selected as the relevant candidate term/keyword for further NLU processing and retrieval, thereby teaching, or at least rendering obvious identifying a first potential keyword as the keyword for search based upon the first confidence score being greater than the second confidence score, Peng).
9. The combination of Wu and Peng teach, The computing system of claim 1, wherein causing the search results for the search to be presented to the first user comprises:
identifying a storage location of a document based upon the search results (Fig 1: 112, 116, 118, Paragraph 24 – teaches search engine 112, searches text file 110 for words/phrases and when a match is found, searches second data set 116 for documents containing the matched words/phrases, and the output is then sent to a user’s computer monitor 118, Wu);
generating a link to the document based upon the storage location (Paragraph 38 – teaches finds the mentioned document on Tom’s PC and display a link to the document on Tom’s phone, Wu); and
causing display of the link on the display, wherein the document is presented on the display upon the link being selected (Fig 6 – teaches the system in which the document related information is provided to Tom’s mobile telephone; Paragraph 38 – teaches displays a link to the document on Tom’s phone, wherein Tom clicks a ‘send’ button on his phone and Bob clicks a ‘confirm’ button on his phone, Wu).
10. The combination of Wu and Peng teach, The computing system of claim 1, wherein the plurality of computer-executable applications include one or more of:
an email application ; a real-time messaging application; a web browser; or a file navigation application (Col 12: lines 35-54 – teaches a voice driven display unit which incorporates web browsing and file navigation; Fig 3 – teaches email messages, Wu; Fog 2: 205 – teaches a online chat, instant messaging application, Peng).
11. The combination of Wu and Peng teach, The computing system of claim 1, the acts further comprising:
Prior to obtaining the computer-readable text, generating the index based upon activity history of a plurality of users in the plurality of computer-executable application wherein the plurality of users include at least the first user and a second user (Paragraphs 3, 25 and 52 – teaches indexing the contents of a user’s computer, such as indexing user’s emails, (activity history), Wu).
Claim 12 is similar to claim 1 hence rejected similarly.
13. The combination of Wu and Peng teach, The method of claim 12, further comprising:
mapping the potential keyword to a search domain in a plurality of search domains, wherein upon mapping the potential keyword to the search domain, the confidence score for the potential keyword is computed based upon the search domain, the context of the first user, and prior search queries of the first user (Fig 3, 224a, 224b, 223, 225a, 225b, 230, 220, 235, 330; Paragraphs 4, 40 - teaches an NLU pipeline in which a semantic information aggregator provides ontology data associated with a plurality of predefined domains, intents and slots to an NLU module, and the NLU module performs domain classification/selection to identify a domain from the user input. Peng further teaches annotating n-grams of the user input and ranking the n-grams with confidence scores based on aggregated information including user profile/context information where the user profile includes interests/preferences aggregated through search logs and search history; therefore, teaching or rendering obvious mapping a potential keyword/n-gram to a search domain and computing a confidence score for the potential keyword based upon the search domain, the first user’s context and prior search queries/search history of the first user, Peng).
14. The combination of Wu and Peng teach, The method of claim 12, wherein the search domain comprises a plurality of relate terms, wherein mapping the potential keyword to the search domain comprises matching the potential keyword to one or more related terms in the plurality of related terms (Fig 3, 224a, 224b, 223, 225a, 225b, 230, 220, 235, 330; Paragraphs 40 & 54 – teaches that the semantic information aggregator provides ontology data associated with a plurality of predefined domains, intents and slots to the NLU module, and that dictionaries are generated from annotated entities. Peng further teaches that the slot tagger formulates each word of the user request into a vector and calculates probabilities of each word being associated with different predefined slots by comparing the word vector to vectors representing the predefined slots. Therefore teaching or at least render obvious that a search domain comprises a plurality of related terms and that mapping a potential keyword to the search domain comprises matching the potential keyword to one or more related terms in that plurality of related terms, Peng).
Claim 15 is similar to claim 10 hence rejected similarly.
Claim 16 is similar to claim 3 hence rejected similarly.
Claim 17 is similar to claim 4 hence rejected similarly.
Claim 18 is similar to claim 9 hence rejected similarly.
Claim 19 is similar to claim 2 hence rejected similarly.
Claim 20 is similar to claim 1 hence rejected similarly.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMRESH SINGH whose telephone number is (571)270-3560. The examiner can normally be reached Monday-Friday 8am-5pm.
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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