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 .
Response to Amendment
Claims 1, 11 and 12 are amended. Claims 8 and 19 are cancelled. Claims 1-7, 9-18 and 20-21 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/11/2026 was filed after the mailing date of first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicants’ arguments with respect to claims 1, 11 and 12 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.
Examiner’s Note:
If applicant incorporates the concept from claims 2 and 4 into the currently presented independent claims, all the claims will be allowed.
The examiner tried multiple times to contact several attorneys at the newly assigned law firm listed on the Power of Attorney filed May 29, 2026, but received no response.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known techniques to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestions, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
2020/0074984
Claims 1-3,7,9,11-14, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US 20200074984) and further in view of Rangarajan (US 20220093088) and further in view of Doronichev (US 20200183044)
Regarding claim 1, Ho teaches a method for generating a tracker model for identification of trackers in textual data (training a machine learning model/classifier (i.e., a tracker model) for identifying intents in a live dialog server for live automated responses, Fig 7, Para 0061-0067) , comprising: receiving an input query including at least an input sentence exemplifying a tracker of interest ( the user launches a search query into the system UI to retrieve utterances related to a semantic scope of intent input by the user,, Para 0061) , wherein the tracker is at least one word with a specific context (wherein the utterances are related to a semantic scope of intent input by the user … semantic scope of user utterance, Para 0061-0067, Fig 7-8) generating a base results set including a set of matching sentences matching the input sentence, wherein the sentences in the base results set are obtained from an index indexing textual data, wherein the index has values for the indexing textual data including the matching sentences in the base results set ( retrieving a set of 100 best matching utterances (i.e., a base results set including a set of sentences matching the input sentences.) wherein the utterances are retrieved from conversational logs (i.e., an index indexing textual data) stored as text (see Ho at Fig.8 where the retrieved utterances are displayed in text form then labeled, therefore the conversational logs are text data.) Figs. 7 - 8 and Para 0061-0067); deriving a first labeling set from the base results set, wherein the first labeling set includes sample sentences that are at least a portion of the matching sentences in the base results set )determining a subset of matching utterances and presenting a user with the subset of the matching utterances to be labeled by the user (i.e., a labeling set including at least a portion of samples of sentences from the base results set.), Fig. 7 and Para 0061-0067.), receiving labels on each sample sentence in the first labeling set (a user labeling the utterances from the subset of the utterances (i.e., receiving labels on each sentence)., Figs. 7 - 8 and Para 0061- 0067); and feeding the first labeling set and the respective labels to a machine learning algorithm to train the tracker model (training a machine learning model / classifier (i.e., a tracker model) using the corpus of conversational logs of utterances labeled by the user and propagated from the subset to the remaining (i.e., feeding the labeling set and the labels to a machine learning model for training.), Figs. 7 - 8 and Para [0061] - [0067])
Ho does not teach wherein the index has sentence embedding values for the indexing textual data including the matching sentences in the base results set; wherein deriving the first labeling set further comprises: clustering the matching sentences in the base results set into a plurality of clusters, based on their respective sentence embedding values in the index; determining eligible clusters from among the plurality of clusters by excluding clusters determined to be farther than a predefined threshold distance from the sentence embedding value of the input sentence; and selecting the sample sentences from the eligible clusters, wherein the selected sample sentences include a sample sentence from each eligible cluster
However, Rangarajan teaches wherein the index has sentence embedding values for the indexing textual data including the matching sentences in the base results set (a second sentence (from the corpus of sentences) may be selected, in view of the first sentence. In some embodiments, the selection of the second sentence may be based on one or more of a semantic context of the first sentence, a target semantic context, a semantic relationship between the first and second sentences, and/or a target semantic relationship. More specifically, selecting the second sentence from the corpus of sentences may be based on the first sentence vector (i.e., a sentence embedding of the first sentence). In some non-limiting embodiments, the sentence embedding of the first statement may be compared with the vector embeddings of the sentences included in the corpus of sentences. Thus, a corpus of vectors sentences (e.g., a database of reference sentence vectors and/or a plurality of other sentence vectors) may be searched to identify and/or select the second sentence. In some embodiments, a mapping (e.g., a lookup table) between the sentences of the sentence corpus and the sentence vectors of the sentence vector corpus may be stored and consulted when translating between sentences and their corresponding sentence embeddings., Para 0283) ; wherein deriving the first labeling set further comprises: clustering the matching sentences in the base results set into a plurality of clusters (To select the second sentence, a first sentence cluster of the set of sentence clusters may be selected. Selecting the first sentence cluster may be based on the first sentence vector and the centroid for each sentence cluster in the set of sentence clusters. The second sentence vector may correspond to (or at least identify) the first sentence cluster and/or a first centroid of the first sentence cluster., Para 0331) , based on their respective sentence embedding values in the index( in the corpus, Para 0282, 0292) ; determining eligible clusters from among the plurality of clusters by determining a predefined threshold distance from the sentence embedding value of the input sentence ( The second sentence vector may correspond to (or at least identify) the first sentence cluster and/or a first centroid of the first sentence cluster. Selecting the second sentence vector may further be based on the spatial relationship between the first and second sentence vectors (which indicates the semantic relationship between the first and second semantic contexts via the spatial relationship-to-semantic relationship mapping) and a target semantic relationship. The spatial relationship may be based on the distance metric., Para 0259, 0313, 0331, Claim 8) ; and selecting the sample sentences from the eligible clusters, wherein the selected sample sentences include a sample sentence from each eligible cluster ( selecting a second sentence, where second sentence can be plurality of other sentences, Para 0282; also from Para 0331, Fig 11)
It would have been obvious to POSITA having the teachings of Ho to further include the concept of Rangarajan before effective filing date since clustering is a well-known technique for selecting sentences which are closer to the input sentence and by applying this technique, the system is able to find an answer based on semantic similarity of one or more sentences and train the model to improve performance of the model ( Para 0286-0288, Rangarajan)
Ho modified by Rangarajan does not explicitly teach selecting eligible cluster by excluding clusters determined to be farther than a predefined threshold distance
However, Doronichev selecting eligible clusters by excluding clusters determined to be farther than a predefined threshold distance (the threshold identifies a maximum distance between clusters for filtering the clusters (212) of the cluster model (210), Para 0034, 0064)
It would have been obvious to POSITA having the teachings of Ho and Rangarajan to further include the concept of Doronichev before effective filing date to find the optimal match for the data
Regarding claim 2, Ho as above in claim 1, teaches , wherein when the tracker model is not ready further comprising: iteratively generating a second labeling set from the base results set; (Ho teaches presenting a user with alternate queries that result in additional to-be-labeled utterances from the conversational logs (i.e., the user is presented with additional sentences for labeling.) Figs. 7 - 8 and Para 0061-0067. The additional queries presented to the user which yield additional results similar to the query would logically be labeled as the interface shown in Fig. 8 of Ho suggests the ability to create a new labeling set by using the "new intent" button or altering the text field and interacting with the "Search" button an additional time.)
receiving labels on each sentence in the second labeling set; (presenting a user with additional sentences for labeling after processing additional queries. Para 0061 - 0067 and Figs. 7 - 8. As such, labeling the utterances by the user amounts to the system receiving labels for each sentence (utterance).)
and feeding the second labeling set and the respective labels to the machine learning algorithm to further train the tracker model. (using the user-defined labels (i.e., the labeling sets) to train a machine learning model / classifier on the labeled data. Ho at Figs. 7 - 8, and Para 0061-0067.)
Regarding claim 3, Rangarajan as above in claim 1, teaches indexing textual data stored in a corpus to generate the index (corpus, Para 0259, 0282, 0313, 0331, Claim 8)
Regarding claim 7, Ho as above in claim 1, teach wherein generating the base results set further comprises: computing the sentence embedding value of the input sentence; determining, based on their respective sentence embedding values in the index, all the matching sentences in the index that are close to the sentence embedding value of the input sentence; and including all the determined matching sentences in the base results set ( second sentences including all the other sentences, Para 0282)
Regarding claim 9, Ho teaches further comprising: generating the second labeling set from the base results set and labels generated based on the first labeling set. (propagating labels from a subset of utterances (i.e., a first labeling set) throughout a corpus of utterances to yield a labeled corpus (i.e., a second labeling set generated based on the base results set and labels of the first labeling set.) Para [0061] - [0067].)
Regarding claim 11, arguments analogous to claim 1, are applicable.
Regarding claim 12, arguments analogous to claim 1, are applicable.
Regarding claim 13, arguments analogous to claim 2 are applicable.
Regarding claim 14, arguments analogous to claim 3, are applicable.
Regarding claim 18, arguments analogous to claim 7, are applicable.
Regarding claim 20, arguments analogous to claim 9, are applicable.
Claims 4-6 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US 20200074984) and further in view of Rangarajan (US 20220093088) and further in view of Doronichev (US 20200183044) and further in view of Mahmoud (US 20220156298)
Regarding claim 4, Ho as above in claim 3, teaches wherein indexing the textual data further comprises: splitting each record in the corpus into a plurality of sentences (Fig. 11); computing a vector representation to each of the plurality of sentences, wherein the vector representation includes a sentence embedding value; (embedding values, Para 0282-0283);
Ho modified by Rangarajan and Doronichev does not teach associating metadata fields with the vector representation, and saving a sentence with its respective vector representation and metadata fields as a vector included in as entry in the index
However Mahmoud teach associating metadata fields with the vector representation, and saving a sentence with its respective vector representation and metadata fields as a vector included in as entry in the index ( the knowledge-based storage 212 stores an index of each subdocument and its embedding representation (a fixed-length vector semantic representation that makes it easier to perform further processing) where the embeddings, their respective text and metadata and other data can be indexed using an indexing service (e.g., Elasticsearch), Hierarchical Navigable Small World graphs, any system that supports Approximate Nearest Neighbor (ANN) search, or exhaustive (brute-force) search, etc., Para 0050, 0047; orphaned queries associated with the user has a metadata associated, Para 0094)
It would have been obvious to POSITA having the teachings of Ho modified by Rangarajan and Doronichev to further include the concept of Mahmoud before effective filing date to improve search system by having more relined searched (Para 0049-0050, Mahmoud)
Regarding claim 5, Ho as above in claim 4, teach, wherein records in the corpus include at least transcripts of calls and email messages related to sales in an organization (corpus, and related to sales is an intended purpose, Para 0061-0067)
Regarding claim 6, Ho modified by Mahmoud as above in claim 5, teach wherein the metadata fields are retrieved from a customer relationship management (CRM) system of the organization (customer knowledge base system, Para 0024)
Regarding claim 15, arguments analogous to claim 4, are applicable.
Regarding claim 16, arguments analogous to claim 5, are applicable.
Regarding claim 17, arguments analogous to claim 6, are applicable.
Claims 10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US 20200074984) and further in view of Rangarajan (US 20220093088) and further in view of Doronichev (US 20200183044) and further in view of Rogynskyy (US 20200372075)
Regarding claim 10, Ho modified by Rangarajan and Doronichev does not teach receiving a transcript of a new sales call; and identifying, using the tracker model, a tracker in the transcript of a new sales call.
Rogynskyy, however, teaches receiving a transcript of a new sales call; and identifying, using the tracker model, a tracker in the transcript of a new sales call. (sales representatives of an organization making calls and taking part in meetings. Tracking elements of sales made by sales representatives, Para 0053. Using machine learning to identify trends and behaviors not tracked by the managers. (i.e., identifying using a tracker model, a tracker in the transcript). Receiving information from various systems that may include telephone transcripts. (Para [0332]). Ingesting new electronic activities (e.g., new sales calls, new email chain, new message, etc.). Para [0075]. Rogynskyy teaches that the system is used to tag electronic activities relating to sales, recruiting, or other business-related activities, Para 0143. Further, electronic activities are calls, meetings, and others relating to deals or opportunities sales representatives are working on, Para 0053)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to combine the teachings of Ho modified by Rangarajan and Doronichev with the teachings of Rogynskyy to receive a transcript of a new sales call and identify, using the tracker model, a tracker in the transcript of a new sales call. Doing so would save users time and effort by reducing the amount of time required to generate reports and gather information automatically (Para 0053, Rogynskyy)
Regarding claim 21, arguments analogous to claim 10, are applicable.
Conclusion
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 Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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