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
1. This in response to amendment filed 01/15/2026. No claims have been added. No claims have been canceled. Claims 1, 14 and 20 have been amended. Claims 1-20 are still pending in this application.
Claim Rejections - 35 USC § 103
2. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCourt (US PAT # 12/230,253 B2) in view of Babu Balasubramani et al. (US PAT # 11,252,052 B1) and further in view of Consul et al. (US PAT # 8,620,869 B2).
Regarding claims 1 and 14, McCourt teaches method and system of managing interaction transcripts based on rules, the method comprising:
identifying one or more decision parameters in an interaction transcript (reads on the model uses call transcripts words by each party and topic/word probabilities as features (i.e., parameters), see col. 5, lines 1-38, see also step 202 call transcripts received, wherein feature model extracts topics/word distributions from the transcript, see col. 8, lines 11-29); and
determining at least one decision category for a rule from the one or more identified decision parameters (reads on classification model produces label given input features (parameters), see col. 7, lines 25-33. Also, Fig. 3 and corresponding text).
McCourt does not specifically teach “calculating probabilities for the at least one determined decision category for the rule using the one or more decision parameters”.
However, Babu teaches assigning scores to predictions (see Fig. 6, col. 16, line 63 through col. 19, line 10) and determining confidence score ranges (high, moderate, low) (see col. 27, line through col. 28 line 17). Note that examiner interpreting the confidence scores represent likelihood values associated with outcomes.
Thus, it would have been obvious for one of an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the confidence score determination of Babu into the system of McCourt in order to associate likelihood values with the classification outcomes generated from interaction transcripts. Note that combining the two will associate likelihood values with the determined decision categories, thereby improving decision reliability and enabling more informed downstream processing based on those categories.
McCourt and Babu features have been addressed in the above rejection. Note that the combination of McCourt and Babu does not specifically teach “applying the rule by selecting one or more action categories for the interaction transcript based on the calculated probabilities for the at least one decision category”.
Yet, Consul teaches that data items may be associated with retention policy tags that automatically trigger one or more action categories (e.g., retain, copy or delete) and that such tags include expiration periods for each action (see col. 2, lines 44-56).
Thus, it would have been obvious for one of an ordinary skill in the art before the effective filing date of the claimed invention to apply the automated policy actions, as taught by Consul to the interaction transcript classification of McCourt in view of Babu, so that transcripts are managed based on their predicted categories and probabilities.
In McCourt, the claimed “a computing device” as recited in independent claim 14 reads on computing device 120 (as shown in Fig. 1), “a memory” as recited in independent claim 14 reads on memory 506 (as shown in Fig.5) and “a processor” as recited in independent claim 14 reads on reads on processor 504 (as shown in Fig. 5).
Regarding claims 2 and 15, the combination of McCourt, Babu and Consul teaches method according to claim 1, wherein when the selection of the one or more action categories is based on a single-level selection, the rule is based on the decision category with the highest probability (McCourt teaches identifying decision parameters in an interaction script, and determine one decision category from those parameters and calculating probabilities for the categories (see col.1, line 63 through col. 2, line 3 and col. 4, lines 54-67 and Fig. 4A). McCourt classifier outputs a probability distribution for multiple categories. Thus, it would have been obvious to a one of an ordinary skill in the art to select the single category with the highest probability as the operative decision. This being the well-known maximum likelihood principle used in classification models to simplify rule application)).
Regarding claims 3 and 16, the combination of McCourt, Babu and Consul teaches wherein when the selection of the one or more action categories is based on a multi-level selection, the rule is based on the at least one decision category whose calculated probabilities lie above a pre-set threshold value (McCourt teaches computing probabilities for multiple decision categories (see col. 7, lines 25-33. Also, Fig. 3 and corresponding text). Consul teaches using rules that apply to multiple messages tagged for retention or deletion when criteria exceed a threshold (see col. 5, lines 4-17). Thus, it would have been obvious to extend McCourt probabilistic classification by selecting all categories whose probabilities exceed thresholds, similar to Consul rule trigger threshold, in order to handle multiple outcomes per transcript).
Regarding claims 4 and 17, the combination of McCourt, Babu and Consul teaches wherein the identified one or more decision parameters are normalized (McCourt teaches extracting linguistic and acoustic features from transcripts, see col.5, lines 1-38, see also step 202 call transcripts received, wherein feature model extracts topics/word distributions from the transcript at col. 8, lines 11-29. Consul describes applying standardized or normalized policy parameters to ensure consistent evaluation across data items, see col. 3, lines 1-18. Thus, it would have been obvious to normalize McCourt decision parameters, to align with the uniform rule evaluation process of Consul).
Regarding claims 5 and 18, the combination of McCourt, Babu and Consul teaches encoding the identified one or more decision parameters into a numerical format (McCourt teaches extracting transcript features for ML processing (see col. 9, lines 24-23). Consul teaches encoding policy metadata and conditions in machine readable numerical identifier (see col. 3, line 60 through line 8). Thus, it would have been obvious to encode McCourt identified parameters into numerical format to enable efficient digital rule processing consistent with Consul encoded rule approach).
Regarding claims 6 and 19, the combination of McCourt, Babu and Consul teaches wherein the one or more action categories are selected from one or more of: a retention decision, a deletion decision, a copy decision or a combination thereof (see col. 2, lines 44-56).
Regarding claim 7, the combination of McCourt, Babu and Consul teaches wherein when the selected action category is a retention action, calculating a retention period and storing the interaction transcript for the retention period (Consul teaches a retention policy that when a retention action is applied, calculate retention period and stores data for that duration, see col. 2, lines 44-56) .Note that it would have been obvious to incorporate timed retention mechanism as taught by Consul into the teaching of action logic as taught by McCourt, so that selected retention action causes the transcript to be stored for a computed period.
Regarding claim 8, the combination of McCourt, Babu and Consul teaches wherein when the selected action category is a deletion action, calculating a deletion period and storing the interaction transcript until expiry of the deletion period (McCourt teaches applying rules selecting an action category, see col. 7, lines 25-33. Also, Fig. 3 and corresponding text. Now, Consul teaches a deletion policy specifies an expiration or deletion period after which the data is automatically removed, see col.2 and lines 44-56. Thus, it would have been obvious to extend McCourt framework such that when a deletion action is selected, the transcript is held until expiry of the deletion period, as taught by Consul.
Regarding claim 9, the combination of McCourt, Babu and Consul teaches wherein when the selected action category is a copy action, calculating a copy period and copying the interaction transcript after expiry of the copy period (McCourt teaches applying an action to a transcript, see col. 7, lines 25-33. Also, Fig. 3 and corresponding text. Consul teaches that items subject to a copy or archive policy may be duplicated after a retention interval, see col.6, lines 39-48. Thus, it would have been obvious to configure McCourt system so that when a copy action is selected, the transcript is copied after a defined copy period as taught by Consul).
Regarding claim 10, the combination of McCourt, Babu and Consul teaches wherein the one or more decision parameters are converted into binary vectors (note that McCourt teaches identifying linguistic decision parameters but does not define a data structure for them, see col. 5, lines 1-38. Consul teaches encoding rule parameters and policy flags in binary indicators, see table in Col. 7 (bit representing if the (flag) called Retention Flags. Thus, it would have been obvious to represent McCourt parameters as binary vectors-each feature encoded as 1 or 0 to facilitate rule-based computation similar to Consul’s bit-flag policy encoding).
Regarding claim 11, the combination of McCourt, Babu and Consul teaches wherein applying the rule comprises automatically generating rule recommendations using machine learning for managing the interaction transcript (McCourt teaches applying ML-based rules to interaction transcripts, see col. 1, line 40 through col. 2, line 3. Consul teaches automatic policy recommendation and updates based on historical data usage patterns, see col. 9, lines 40-49. Thus, it would have been obvious to generate rule recommendation using ML within McCourt framework to automatically manage transcripts according to learned patterns of data retention and action application, consistent with Consul adaptive policy learning).
Regarding claim 12, the combination of McCourt, Babu and Consul teaches wherein the generated rule recommendations for managing the interaction transcript are automatically applied to the interaction transcript (reads on automatic routing/action based on classification, see McCourt, see col. 1, lines 40-50).
Regarding claim 13, the combination of McCourt, Babu and Consul teaches wherein the selected one or more action categories within the applied rule are periodically updated using machine learning (reads on retraining the classification model using feedback data to improve accuracy, see McCourt, col. 4, lines 28-42).
Independent claim 20 is rejected for the same reasons addressed in independent claims 1 and 14.
Response to Arguments
3. Applicant’s arguments have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Conclusion
4. 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 extension fee 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 date of this final action.
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rasha S. AL-Aubaidi whose telephone number is (571) 272-7481. The examiner can normally be reached on Monday-Friday from 8:30 am to 5:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ahmad Matar, can be reached on (571) 272-7488.
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/RASHA S AL AUBAIDI/ Primary Examiner, Art Unit 2693
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