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 Final Action.
Response to Arguments
Applicant’s arguments with respect to claim(s) 09/03/2026 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.
The amendment to specification was approved and entered.
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 1-11 rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (US 2019/0325084) in view of He et al. (US 9,053,156) in view of further in view of Wu et al. (US 2008/0275701)
1. Peng teaches, A computing system, comprising:
a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising (Fig 9 – teaches the hardware, Peng):
obtaining computer-readable text based upon an interaction between a first user and at least one of a plurality of computer-executable applications (Figs 1-2, Paragraphs 28 and 39 – teaches using an audio speech recognition (ASR) module 210 to convert the user input into text, Peng);
upon obtaining the computer-readable text, processing the computer-readable text and a context of the first user using a natural language processing algorithm to generate an output indicative of an intent (Fig 3, Paragraph 40 – teaches NLU module using information from user context engine and semantic information aggregator to interpret textual input and identify a domain, an intent, and one or more slots from the user input, Peng), 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 (Fig 3, Paragraph 55 – Offline aggregator process past user activity including news feed posts/comments, interactions with news feed posts/comments, instant posts/comments, search history, etc., Peng);
upon the output being indicative of a search intent, identifying a potential keyword based upon the computer-readable text (Fig 3, Paragraphs 40, 54-55 – teaches intent classifier 224a and semantic information aggregator 230 process user input, including annotating n-grams of the user input, Peng);
computing a confidence score for the potential keyword based upon the context of the first user (Fig 2, Paragraph 40 – teaches the semantic information aggregator performs ranking the n-grams with confidence scores based on the aggregated information, Peng);
identifying the potential keyword as a keyword for search based upon the confidence score (Fig 3, Paragraph 40 & 55 – teaches providing ranked n-grams into features that can be used by the NLU module 220, Peng);
Peng does not explicitly teach,
determining whether the keyword can be mapped to a search domain in a plurality of search domains;
executing a search based upon the keyword when the keyword can be mapped to a search domain in the plurality of search domains;
refraining from executing the search when the keyword cannot be mapped to any search domain in the plurality of search domains; and
causing search results for the search to be presented on a display to the first user.
However, He teaches,
determining whether the keyword can be mapped to a search domain in a plurality of search domains (Fig 1, Fig 6:604, Col 3: lines 64-65 – teaches mapping component 106 can be configured to map query 104 to set of domain topics 108. The mapping uses the probability P(T|Q), He);
executing a search based upon the keyword when the keyword can be mapped to a search domain in the plurality of search domains (Fig 6:604-606; Col 7: lines 34-49 – teaches first mapping the query to domain topic, then, a set of results for the query can be identified based on the probability of a result given the domain topic, He);
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 modify Peng’s context aware natural language processing system to incorporate, He’s domain-topic mapping technique to determine whether an identified search keyword corresponds to a predefined search domain before executing a search. Such modification would have predictably improved search relevance by associating contextually identified keyword with appropriate search domains, thereby reducing irrelevant search operations and improving the accuracy of retrieved results.
However, Wu teaches,
refraining from executing the search when the keyword cannot be mapped to any search domain in the plurality of search domains (Fig 1; Paragraph 24 – teaches searching text for words or phrases in first data set and when a match is found searches a second data set for document 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 and 3, Paragraphs 24 and 28 – teaches the output is then sent to user’s computer monitor and Fig 3 further disclosing displaying related conversations, documents and contacts, Wu).
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 incorporate Wu’s automatic, topic-based search and result-presentation technique into the combination of Peng and He to automatically retrieve and present information relevant to ta user’s ongoing interaction based on contextually identified keywords mapped to search domains. Such modification would have predictably improved search efficiency and relevance by reducing manual search input and facilitating timely retrieval of contextually relevant information.
2. The combination of Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 Peng, He and Wu 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 an online chat, instant messaging application, Peng).
11. The combination of Peng, He and Wu 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).
Claims 12-19 rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (US 2019/0325084) in view of Wu et al. (US 2008/0275701) further in view of Donneau-Golencer (US 2015/0046435)
12. Peng teaches, A method executed by a processor of a computing system, the method comprising:
obtaining computer-readable text based upon an interaction between a first user and at least one of a plurality of computer-executable applications (Figs 1-2, Paragraphs 28 and 39 – teaches using an audio speech recognition (ASR) module 210 to convert the user input into text, Peng);
upon obtaining the computer-readable text, processing the computer-readable text and a context of the first user using a natural language processing algorithm to generate an output indicative of an intent (Fig 3, Paragraph 40 – teaches NLU module using information from user context engine and semantic information aggregator to interpret textual input and identify a domain, an intent, and one or more slots from the user input, Peng), 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 (Fig 3, Paragraph 55 – Offline aggregator process past user activity including news feed posts/comments, interactions with news feed posts/comments, instant posts/comments, search history, etc., Peng);
upon the output being indicative of a search intent, identifying a potential keyword based upon the computer-readable text (Fig 3, Paragraphs 40, 54-55 – teaches intent classifier 224a and semantic information aggregator 230 process user input, including annotating n-grams of the user input, Peng);
computing a confidence score for the potential keyword based upon the context of the first user (Fig 2, Paragraph 40 – teaches the semantic information aggregator performs ranking the n-grams with confidence scores based on the aggregated information, Peng);
identifying the potential keyword as a keyword for search based upon the confidence score (Fig 3, Paragraph 40 & 55 – teaches providing ranked n-grams into features that can be used by the NLU module 220, Peng).
Peng does not explicitly teach or suggest,
executing a search based upon the keyword to obtain search results;
selecting, as a subset of the search results, (i) user content of the first user that relates to a topic referenced in the computer-readable text when the context of the first user includes an identifier for the topic, or (ii) content that explains the topic to the first user when the context of the first user does not include the identifier for the topic; and
causing the subset of the search results to be presented on a display to the first user.
Wu teaches,
executing a search based upon the keyword to obtain search results (Fig 1, Paragraph 24 – teaches search engine 112 searches a second data set for documents containing the matched words or phrases, Wu); and
(i) user content of the first user that relates to a topic referenced in the computer-readable text when the context of the first user includes an identifier for the topic (Fig 2-3, Paragraphs 26-28 – teaches second data set includes the user’s email messages, contacts list, and/or text documents stored on the user’s computer. Fig 3 shows related emails and documents retrieved based on the conversation topic, Wu).
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 modify Wu’s automatic conversation-based search system to incorporate Peng’s context aware natural language processing and confidence-based keyword ranking techniques to identify user intent and select relevant search keywords based on the user’s activity derived context. Such modification would have predictably improved the accuracy of keyword identification and relevance of automatically retrieved search results.
However, Donneau-Golencer teaches,
selecting, as a subset of the search results (Fig 1, Paragraphs 57 and 61 – teaches that the query executer may simply display the query results that correspond to the selected topic, Donneau-Golencer), when the context of the first user includes an identifier for the topic (Fig 2, Paragraphs 75, 80 & 92 – teaches topic data object 216 containing Topic ID, description and weight. The topic analyzer 420 looks for words 212, topic 216 and/or sub-topic 218 in the user-specific profile 132, Donneau-Golencer), or (ii) content that explains the topic to the first user (Fig 1, Paragraph 45 – teaches Non user specific content includes educational information and public knowledge bases (such as WIKIPEDIA), Donneau-Golencer) when the context of the first user does not include the identifier for the topic (Fig 5, Paragraph 103 – teaches the query corrector determines whether a search term is in the user-specific profile. If absent the search term may be compared to one or more public sources such as publicly available knowledge based on dictionaries); and
causing the subset of the search results to be presented on a display to the first user (Fig 1; paragraph 57 – teaches the query executer may simply display the query results that correspond to the selected topic, Donneau-Golencer).
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 modify the combined system of Wu and Peng to incorporate Donneau-Golencer’s user-specific topic identification and content-selection techniques to select search results based on associations between identified topics and the user’s activity-derived context. Such modification would have predictably improved search relevance by prioritizing user-specific content associated with recognized topics and facilitating access to publicly available information for topics not represented in the user’s profile.
13. The combination of Peng, Wu and Donneau-Golencer 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 Peng, Wu and Donneau-Golencer 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 rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (US 2019/0325084) in view of Wu et al. (US 2008/0275701) further in view of Lawrence (US 8,041,713)
20. Peng teaches, A non-transitory computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising:
obtaining computer-readable text based upon an interaction between a first user and at least one of a plurality of computer-executable applications (Figs 1-2, Paragraphs 28 and 39 – teaches using an audio speech recognition (ASR) module 210 to convert the user input into text, Peng);
upon obtaining the computer-readable text, processing the computer-readable text and a context of the first user using a natural language processing algorithm to generate an output indicative of an intent (Fig 3, Paragraph 40 – teaches NLU module using information from user context engine and semantic information aggregator to interpret textual input and identify a domain, an intent, and one or more slots from the user input, Peng), 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 (Fig 3, Paragraph 55 – Offline aggregator process past user activity including news feed posts/comments, interactions with news feed posts/comments, instant posts/comments, search history, etc., Peng);
upon the output being indicative of a search intent, identifying a potential keyword based upon the computer-readable text (Fig 3, Paragraphs 40, 54-55 – teaches intent classifier 224a and semantic information aggregator 230 process user input, including annotating n-grams of the user input, Peng);
computing a confidence score for the potential keyword based upon the context of the first user (Fig 2, Paragraph 40 – teaches the semantic information aggregator performs ranking the n-grams with confidence scores based on the aggregated information, Peng);
identifying the potential keyword as a keyword for search based upon the confidence score (Fig 3, Paragraph 40 & 55 – teaches providing ranked n-grams into features that can be used by the NLU module 220, Peng).
Peng does not explicitly teach or suggest,
maintaining a list of keywords used to obtain search results previously presented to the first user;
executing a search based upon the keyword when the keyword is not included in the list;
refraining from executing the search when the keyword is included in the list; and
causing search results for the search to be presented on a display to the first user.
Lawrence teaches,
maintaining a list of keywords used to obtain search results previously presented to the first user (Col 19:lines 63-27- Col 20: lines 1-20; Fig 9:908 – teaches store keyword(s) associated with interest signal, further teaching storing the query associated with retrieved result and the keywords associated with the user’s interaction with displayed content, Lawrence);
executing a search based upon the keyword when the keyword is not included in the list (Col 18: lines 47-50 – teaches when a second implicit query is generated, the system may compare the two queries to determine whether or not to execute the second query, Lawrence);
refraining from executing the search when the keyword is included in the list (Col 18:lines 47-50 – teaches comparing successive implicit queries to determine whether or not to execute the second query, Lawrence).
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 modify Peng’s context aware natural language processing system to incorporate Lawrence’s techniques for maintaining previously used search keywords and comparing newly generated search queries with prior queries to determine whether subsequent searches should be executed. Such modification would have predictably improved search efficiency by reducing redundant search operations and avoiding repetitive presentation of substantially similar search results.
Wu teaches,
causing search results for the search to be presented on a display to the first user (Fig 1 and 3; Paragraph 24 and 28 – teaches that the search engine outputs the results of the search to monitor. Fig 3 illustrates related conversations, documents and people, Wu).
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 modify the combined system of Peng and Lawrence to incorporate Wu’s automatic conversation-based search and result-presentation technique to retrieve and present information relevant to a user’s ongoing interaction based on contextually identified keywords, while avoiding redundant searches using previously executed query information. Such modification would have predictably improved search efficiency and relevance by reducing manual search input, minimizing repetitive operations, and providing timely access to contextually relevant information.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Itay et al. (US 2014/0280046) – teaching filtering keywords based on search domains.
THIS ACTION IS MADE FINAL. 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 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
