DETAILED ACTION
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 .
Allowable Subject Matter
Claims 6-7, 13 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11595337 and claims 1-20 of U.S. Patent No. 12177178. Although the claims at issue are not identical, they are not patentably distinct from each other because conflicting claims are in a patent(s) by the same inventive entity and assignee.
Furthermore, where claims in the instant application are broader than the claims of the ‘337 and ‘178 patent(s), it would have been obvious to one of ordinary skill in the art at the time the invention was made to omit elements when the remaining elements perform as before. A person of ordinary skill could have arrived at the present claims by omitting the details of the ‘337 and ‘178 patent(s). See In re Karlson (CCPA) 136 USPQ 184, decided January 16, 1963 ("Omission of element and its function in combination is obvious expedient if remaining elements perform same function as before").
Instant application
11595337 patent
12177178 patent
Claim 1, 8, 14 (claim 1 exemplary)
A computer program product comprising a non-transitory, computer-readable medium embodying thereon a set of computer instructions, the set of computer instructions comprising instructions for:
accessing an electronic chat, the electronic chat embodying a set of electronic chat messages;
adaptively splitting the set of electronic chat messages into a set of conversations without regard to the content of any of the electronic chat messages, each conversation in the set of conversations comprising a subset of electronic chat messages from the set of electronic chat messages, wherein adaptively splitting the set of electronic chat messages into a set of conversations further comprises:
identifying a plurality of split points based on time gaps between adjacent messages from the set of electronic messages to determine the set of conversations by providing information relating to the time gaps to a machine learning model (Gaussian mixture model) trained to determine conversations based on time distributions of electronic messages; and
storing each conversation from the set of conversations as a separate document
Claim 1, 8, 14 (claim 1 exemplary)
A computer program product comprising a non-transitory,
computer-readable medium embodying thereon a set of
computer instructions, the set of computer instructions comprising
instructions for:
accessing an electronic chat, the electronic chat embodying a set of electronic chat messages;
adaptively splitting the set of electronic chat messages into a set of conversations, each conversation in the set of conversations comprising a subset of electronic chat messages from the set of electronic chat messages, wherein adaptively splitting the set of electronic chat messages into a set of conversations further comprises:
determining a set of time gaps between adjacent messages from the set of electronic chat messages;
learning, using the set of time gaps, a Gaussian mixture model representing a mixture of Gaussian distributions;
determining a highest mean value distribution from the mixture of Gaussian distributions;
identifying a plurality of split points based on the determined highest mean value distribution from the mixture of Gaussian distributions; and storing each conversation from the set of conversations as a separate document.
Claim 1, 8, 14 (claim 1 exemplary)
A computer program product comprising a non-transitory,
computer-readable medium embodying thereon a set of
computer instructions, the set of computer instructions comprising
instructions for:
accessing an electronic chat, the electronic chat embodying a set of electronic chat messages;
adaptively splitting the set of electronic chat messages into a set of conversations, each conversation in the set of conversations comprising a subset of electronic chat messages from the set of electronic chat messages, wherein adaptively splitting the set of electronic chat messages into a set of conversations further comprises:
determining a set of time gaps between adjacent messages from the set of electronic chat messages;
identifying a plurality of split points based on the determined set of time gaps to determine the set of conversations, wherein the plurality of split points are identified without regard to the content of any of the electronic chat messages in the set of electronic chat messages;
when the determined set of conversations results in a number of messages in a first conversation before an identified split point or a second conversation after the identified split point being less than a threshold value, ignoring the identified split point; and
storing each conversation from the set of conversations as a separate document.
Claim 2, 9, 15 (claim 2 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has associated metadata and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the associated metadata of the electronic chat messages from the set of electronic chat messages.
Claim 2, 9, 15 (claim 2 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has associated metadata and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the associated metadata of the electronic chat messages from the set of electronic chat messages.
Claim 2, 9, 15 (claim 2 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has associated metadata and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the associated metadata of the electronic chat messages from the set of electronic chat messages.
Claim 3, 10, 16 (claim 3 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages.
Claim 3, 10, 16 (claim 3 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages.
Claim 3, 10, 16 (claim 3 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages.
Claim 4, 11, 17 (claim 4 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp, and wherein the set of computer instructions further comprises instructions for:
determining a set of models that model the set of time gaps, wherein determining the set of models comprises:
determining a single Gaussian distribution of the set of time gaps;
selecting a best model from the set of models;
based on selecting a single Gaussian distribution as the best model, not splitting the electronic chat; and
based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model.
Claim 4, 11, 17 (claim 4 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp, and wherein the set of computer instructions comprises instructions for:
determining a set of models that model the set of time gaps, wherein determining the set of models comprises:
determining a single Gaussian distribution of the set of time gaps;
selecting a best model from the set of models;
based on selecting the single Gaussian distribution as the best model, not splitting the electronic chat; and
based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model
Claim 4, 11, 17 (claim 4 exemplary)
The computer program product of claim 1, wherein each electronic chat message embodied in the electronic chat has a timestamp, and wherein the set of computer instructions comprises instructions for:
determining a set of models that model the set of time gaps, wherein determining the set of models comprises:
determining a single Gaussian distribution of the set of time gaps;
selecting a best model from the set of models;
based on selecting a single Gaussian distribution as the best model, not splitting the electronic chat; and
based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model.
Claim 5, 12, 18 (claim 5 exemplary)
The computer program product of claim 4, wherein selecting the best model from the set of models comprises determining, for each model in the set of models, a Bayesian information criterion and selecting the best model from the set of models based on the Bayesian information criteria for the set of models.
Claim 5, 12, 18 (claim 5 exemplary)
The computer program product of claim 4, wherein selecting the best model from the set of models comprises determining, for each model in the set of models, a Bayesian information criterion and selecting the best model from the set of models based on the Bayesian information criteria for
the set of models.
Claim 5, 12, 18 (claim 5 exemplary)
The computer program product of claim 4, wherein selecting the best model from the set of models comprises determining, for each model in the set of models, a Bayesian information criterion and selecting the best model from the set of models based on the Bayesian information criteria for
the set of models.
Claim 6, 13, 19 (claim 6 exemplary)
The computer program product of claim 4, wherein the model is a Gaussian mixture model, the computer program product further comprising adaptively splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model comprising:
selecting a time gap from the set of time gaps:
determining a probability of the selected time gap for each Gaussian distribution in the mixture of Gaussian distributions to produce a set of probabilities for the selected time gap; and
based on a determination that a highest probability from the set of probabilities for the selected time gap is for the highest mean value distribution, splitting the electronic chat into new conversation at the selected time gap.
Claim 6, 13, 19 (claim 6 exemplary)
The computer program product of claim 4, wherein performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model comprises:
selecting a time gap from the set of time gaps:
determining a probability of the selected time gap for each Gaussian distribution in the mixture of Gaussian distributions to produce a set of probabilities for the selected time gap; and
based on a determination that a highest probability from the set of probabilities for the selected time gap is for the highest mean value distribution, splitting the electronic chat into new conversation at the
selected time gap.
Claim 6, 13, 19 (claim 6 exemplary)
The computer program product of claim 4, further comprising adaptively splitting of the set of electronic chat messages into the set of conversations based on a Gaussian mixture model comprising:
selecting a time gap from the set of time gaps:
determining a probability of the selected time gap for each Gaussian distribution in the mixture of Gaussian distributions to produce a set of probabilities for the selected time gap; and
based on a determination that a highest probability from the set of probabilities for the selected time gap is for the highest mean value distribution, splitting the electronic chat into new conversation at the selected time gap.
Claim 7, 20 (claim 7 exemplary)
The computer program product of claim 6, wherein the set of computer instructions comprises instructions for not splitting the electronic chat at the selected time gap based on a determination that the highest probability from the set of probabilities for the selected time gap is not for the highest mean value distribution.
Claim 7, 20 (claim 7 exemplary)
The computer program product of claim 6, wherein the set of computer instructions comprises instructions for not splitting the electronic chat at the selected time gap based on a determination that the highest probability from the set of probabilities for the selected time gap is not for the highest mean value distribution.
Claim 7, 20 (claim 7 exemplary)
The computer program product of claim 6, wherein the set of computer instructions comprises instructions for not splitting the electronic chat at the selected time gap based on a determination that the highest probability from the set of probabilities for the selected time gap is not for the highest mean value distribution.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-3, 8-10 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2016/0019659 to Doganata et al. (“Doganata”) in view of U.S. Patent Publication No. 2020/0311151 to Menon et al. (“Menon”) and further in view of U.S. Patent Publication No. 2021/0029065 A1 to Erhart et al. (“Erhart”).
As to claim 1, Doganata discloses a computer program product comprising a non-transitory, computer-readable medium embodying thereon a set of computer instructions (Doganata: fig 1-7, [0129-134]: computer readable storage medium retain and store instructions for use by an instruction execution device [0130]).
Doganata did not explicitly disclose accessing an electronic chat, the electronic chat embodying a set of electronic chat messages (emphasis added).
Specifically, Doganata discloses accessing an electronic tweet, the electronic tweet embodying a set of electronic tweet messages (Doganata: fig 2-3, [0016-17;34-45]: exemplary tweet messages 200 are lined up on the time axis in the order in which they are generated (accessing an electronic tweet) and tweets clustered that belong to the same conversation (the electronic tweet embodying a set of electronic tweet messages) [0034] … system 200/300 extracts tweet conversations … performs tweet grouping, group splitting, tweet group clustering, tweet group merging … tweet grouper 381 groups tweet messages in tweet streams into tweet groups responsive to hashtags and time intervals in which tweets sent and tweet splitter 382 splits tweet groups into subgroups responsive to secondary hashtags and time separation between tweet messages [0036]).
Nonetheless, Doganata did not explicitly disclose accessing an electronic chat, the electronic chat embodying a set of electronic chat messages (emphasis added).
Menon discloses accessing an electronic chat, the electronic chat embodying a set of electronic chat messages (emphasis added) (Menon: fig 1-5, [0004-63]: ... embodiments provide a method, apparatus, and system for searching within and across messages that are grouped under conversations (accessing an electronic chat(s) ...) ... a conversation (the electronic chat ...) includes a series of messages and/or replies that are transmitted among multiple participants and/or grouped under a common title and/or one or more labels and, for example, a conversation can include an email thread, a series of chats exchanged in a group or channel (... embodying a set of electronic chat messages), and/or a string of responses and/or comments to a post, article, and/ or other content [0011]).
Doganata and Menon are analogous art because they are from the same field of endeavor with respect to messages.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to substitute the strategies by Menon for chat messages into the product by Doganata for tweet messages. The suggestion/motivation would have been to provide method, apparatus and system for conversations that can include an email thread, a series of chats exchanged in a group or channel, and/or a string of responses and/or comments to a post, article, and/ or other content (Menon: [0011]).
Doganata and Menon further disclose adaptively splitting the set of electronic chat messages into a set of conversations without regard to the content of any of the electronic chat messages (Menon: fig 1-5, [0004-63]: ... message content is stored with a fixed position gap between consecutive messages (“fixed” position gap between messages and thus - adaptively splitting the set of electronic chat messages into a set of conversations without regard to the content of any of the electronic chat messages) in the second document and, in one embodiment, the fixed position gap is selected to be at least double the maximum message length of the messages (using message metadata “maximum message length” not message content - and thus - adaptively splitting the set of electronic chat messages into a set of conversations without regard to the content of any of the electronic chat messages) and, for example, the position gap is set to 16,000 virtual offsets between the beginnings of consecutive messages in the second document when the messages have a maximum message length of 8,000 tokens (using message metadata “maximum message length” not message content - and thus - adaptively splitting the set of electronic chat messages into a set of conversations without regard to the content of any of the electronic chat messages)... by storing and indexing conversation metadata and message metadata for each message in a conversation in separate documents, the disclosed embodiments allow conversation-specific metadata ( e.g., title, participants, labels, etc.) and message-specific metadata ( e.g., author, timestamp, etc.) to be separately searched and/or filtered [0014-15]),
each conversation in the set of conversations comprising a subset of electronic chat messages from the set of electronic chat messages (Menon: fig 1-5, [0004-63]: and see fig 2 ... storage nodes 204 store and/or index different subsets of data and/or metadata related to messages and conversations (... comprising a subset of electronic chat messages from the set of electronic chat messages) exchanged among users of a messaging platform (e.g., email client, chat service, SMS service, newsgroup, forum, commenting system, etc.) and, for example, each storage node includes a physical and/or virtual node or partition that stores messages (e.g., message 1104, message x 106) and/or conversations (e.g., conversation 1108, conversation y 110) (each conversation in the set of conversations comprising ... ) for a subset of users in the platform within an instance of data store 234 [0024]).
Doganata did not explicitly disclose wherein adaptively splitting the set of electronic chat messages into a set of conversations further comprises: identifying a plurality of split points based on time gaps between adjacent messages from the set of electronic messages to determine the set of conversations by providing information relating to the time gaps to a machine learning model trained to determine conversations based on time distributions of electronic messages.
Erhart discloses wherein adaptively splitting the set of electronic chat messages into a set of conversations further comprises (Erhart: fig 1-24, [0004-89; 90-132]: system is capable of determining the resumption of a prior conversation versus as new conversation on a different topic, determining likelihood of disengagement, measuring engagement, predicting likely re-engagement after disengagement and estimating times of predicted re-engagement and tracking periods of engagement to facilitate best times for offers to resume conversation [0013] fig 3A-F … customer 116 C is interacting with contact center over chat channel … corresponds to any type of asynchronous communication channel [0111]):
identifying a plurality of split points based on time gaps between adjacent messages from the set of electronic messages to determine the set of conversations (Erhart: fig 1-24, [0090-132]: fig 3A-F … because communication channel is asynchronous and persists over an extended period of time, it is not appropriate to assume that newly receive message is continuation of the topic(s) being discussed in first portion of conversation 308a (split point(s) based on time gaps) … analyzes content of newly received message and determine topic with which newly received message is associated (identifying a plurality of split points based on time gaps to determine the set of conversations) [0113] … conversation velocity corresponds to measure of conversation speed as a number of messages exchanged over a period of time (identifying a plurality of split points based on time gaps to determine the set of conversations) [0041] … velocity of exchanges or message turns and rate of conversation (split point(s) based on time gaps) can show … whether conversation is slowing down and no resolution to issue occurs in specific period of time (identifying a plurality of split points based on time gaps to determine the set of conversations) [0012])
by providing information relating to the time gaps to a machine learning model trained to determine conversations based on time distributions of electronic messages (Erhart: fig 1-24, [0004-89; 90-132]: ... methods described or claimed herein can be performed with traditional executable instruction sets that are finite and operate on a fixed set of inputs to provide one or more defined outputs and alternatively or additionally, methods described or claimed herein can be performed using AI, machine learning, neural networks, or the like (see with [0113; 41;12] above - by providing information relating to the time gaps to a machine learning model trained to determine conversations based on time distributions of electronic messages) [0057]).
Doganata , Menon and Erhart are analogous art because they are from the same field of endeavor with respect to messages.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Erhart into the product by Doganata and Menon. The suggestion/motivation would have been to enable automated system to analyze a new customer input after a period of disengagement (time gap) to see if the newly received message is continuing the existing topic or starting a new topic (Erhart: [0013]).
Doganata , Menon and Erhart further disclose storing each conversation from the set of conversations as a separate document (Menon: fig 1-5, [0004-63]: fig 1 ... data-processing system 102 organizes and/or stores messages generated by the users by grouping messages that share certain attributes under conversations (e.g., conversation 1108, conversation y 110) (storing each conversation from the set of conversations as a separate document) [0018] ... data-processing system 102 uses a document structure 116 to store data related to messages and conversations under which the messages are grouped and document structure 116 includes conversation metadata 118, message metadata 120, and message content 122 (storing each conversation from the set of conversations as a separate document) [0019];
Doganata: fig 2-6, [0016-17;34-128]: … system continuously collects tweets associated with each tweet conversation and dynamically generates features from existing tweet sets for each tweet conversation and these feature vectors (storing each conversation from the set of conversations as a separate document) may change in time since tweets keep streaming around the same hashtag [0056] … the feature vector (storing each conversation from the set of conversations as a separate document) of a tweet conversation H(a) is defined as F(A) where f(A(j)) is the value of the jth feature [0072]; Conley: fig 3-5, [0012-28; 41-53]: fig 3 … inputs 300 are messages, such as text messages, posts, chats, tweets and the like [0041]).
Same motivation applies as mentioned above to make the proposed modification.
As to claim 2, Doganata , Menon and Erhart disclose wherein each electronic chat message embodied in the electronic chat has associated metadata and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the associated metadata of the electronic chat messages from the set of electronic chat messages (Doganata: fig 2-3, [0016-17;34-45]: system 200/300 extracts tweet conversations … performs tweet grouping, group splitting, tweet group clustering, tweet group merging … tweet grouper 381 groups tweet messages in tweet streams into tweet groups responsive to hashtags and time intervals (associated metadata) in which tweets sent and tweet splitter 382 splits tweet groups into subgroups (adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering) responsive to secondary hashtags and time separation between tweet messages (based on the associated metadata of the electronic chat messages from the set of electronic chat messages) [0036]; Menon: fig 1-5, [0004-63]: ... embodiments provide a method, apparatus, and system for searching within and across messages that are grouped under conversations (accessing an electronic chat(s) ...) ... a conversation (the electronic chat ...) includes a series of messages and/or replies that are transmitted among multiple participants and/or grouped under a common title and/or one or more labels and, for example, a conversation can include an email thread, a series of chats exchanged in a group or channel (... embodying a set of electronic chat messages), and/or a string of responses and/or comments to a post, article, and/ or other content [0011]).).
For motivation, see rejection of claim 1.
As to claim 3, Doganata , Menon and Erhart disclose wherein each electronic chat message embodied in the electronic chat has a timestamp (Doganata: fig 2-3, [0016-45]: … tweet groups may be refined, for example, based on, but not limited to time stamps , list of account holders, and/or frequency and occurrence of keywords … [0033])
and wherein adaptively splitting the set of electronic chat messages into the set of conversations comprises clustering the set of electronic chat messages into clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages (Doganata: fig 2-3, [0016-45]: … stream of tweet messages first grouped based on hashtags and time interval (clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages) in which they were sent and the groups that are separated from each other from each other in time by more than a certain amount are considered different conversations … each group is further split into subgroups responsive to hashtags and time intervals … time separation between tweet messages (clusters based on the timestamps of the electronic chat messages from the set of electronic chat messages) … splitting groups of tweets to identify more refined conversations [0033; 36; 40] (adaptively splitting the set of electronic chat messages into the set of conversations) [0033; 36; 38-44]).
For motivation, see rejection of claim 1.
As to claims 8-10, see similar rejection to claims 1-3, respectively, where the method is taught by the product.
As to claims 14-16, see similar rejection to claims 1-3, respectively, where the system is taught by the product.
Claims 4-5, 11-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2016/0019659 to Doganata et al. (“Doganata”) in view of U.S. Patent Publication No. 2020/0311151 to Menon et al. (“Menon”), U.S. Patent Publication No. 2021/0029065 A1 to Erhart et al. (“Erhart”) and further in view of U.S. Patent Publication No. 2019/0251166 to Penrose et al. (“Penrose”).
As to claim 4, Doganata , Menon and Erhart disclose wherein each electronic chat message embodied in the electronic chat has a timestamp (Doganata: fig 2-3, [0016-45]: … tweet groups may be refined, for example, based on, but not limited to time stamps , list of account holders, and/or frequency and occurrence of keywords … [0033]), and wherein the set of computer instructions further comprises instructions for:
determining a set of models that model the set of time gaps (Doganata: fig 2-5, [0016-128]: conversations include, but not limited to, …splitting a group into sub-groups if they are separated in time more than N minutes (determining a set of models that model the set of time gaps) [0038-44] fig 5 … system 500 includes tweet conversation extractor 380 (fig 3), input files database 515, feature extractor 520, a prediction model 530 [0058] … the prediction model 530 can be used to provide solution to equation (9) and deliver optimum feature weight vector W={w(0)0 w(1) … w(m)} [0059] … training the feature weights … in order to obtain the coefficients of the feature weights w(p), that minimize prediction error, training data is used … features selected for tweet conversations … if a feature does not have significance, we drop that feature from the model [0122-125]).
Doganata did not explicitly disclose wherein determining the set of models comprises: determining a single Gaussian distribution of the set of time gaps; selecting a best model from the set of models; based on selecting a single Gaussian distribution as the best model, not splitting the electronic chat; and based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model.
Penrose discloses wherein determining the set of models comprises: determining a single Gaussian distribution of the set of time gaps (Penrose: fig 4-10, [0020-26; 67-79]: fig 8 diagram 800 depicts an output from topic modelling of a) an entire conversation (a single Gaussian distribution of the set of time gaps) [0075] … );
selecting a best model from the set of models (Penrose: fig 4-10, [0067-79]: … the burst segments and reflection segments e.g. collections of the group of burst segments and group of reflection segments may then be used to determine optimal (best) topic model sizes [0025]);
based on selecting a single Gaussian distribution as the best model, not splitting the electronic chat (Penrose: fig 4-10, [0067-79]: fig 8 diagram 800 depicts an output from topic modelling of a) an entire conversation (based on selecting the single Gaussian distribution as the best model, not splitting the electronic chat) and b) conversation modelled using bursts and reflections [0075]); and
based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model (Penrose: fig 4-10, [0067-79]: fig 8 diagram 800 depicts an output from topic modelling of a) an entire conversation and b) conversation modelled using bursts and reflections [0075] … analyzed real-time chat/messages may be grouped in to two groups (burst and reflections) as inter-arrival times (time gaps) of instant messages posts are determined and/or recorded, messages may be grouped or segmented by, or according to, short and long inter-arrival times (see with [0075] - based on selecting the Gaussian mixture model as the best model, performing the adaptive splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model) [0022-23] …).
Doganata , Menon, Erhart and Penrose are analogous art because they are from the same field of endeavor with respect to real-time conversation data.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Penrose into the product by Doganata , Menon and Erhart. The suggestion/motivation would have been to provide for enhanced topic modelling operations for text segmentation (Penrose: [0076]).
As to claim 5, Doganata , Menon, Erhart and Penrose disclose wherein selecting the best model from the set of models comprises determining, for each model in the set of models, a Bayesian information criterion and selecting the best model from the set of models based on the Bayesian information criteria for the set of models (Penrose: fig 4-10, [0067-79]: fig 8 … for example, the resized topic models from operation of fig 6 may be used by topic modelling operations i.e. Biterm, LDA and the like (determining, for each model in the set of models, a Bayesian information criterion) to yield a higher precision (selecting the best model) of summary whilst respecting context [0075] … at its core, LDA (Latent Dirichlet allocation) is a three-level hierarchical Bayesian model [0021]).
For motivation, see rejection of claim 4.
As to claims 11-12, see similar rejection to claims 4-5, respectively, where the method is taught by the product.
As to claims 17-18, see similar rejection to claims 4-5, respectively, where the system is taught by the product.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
A) US 20210335367 – Graff
A text mining engine running on an artificial platform is trained to perform conversation role identification, semantic analysis, summarization, language detection, etc. The text mining engine analyzes words in a transcript that represent unique characteristics of a conversation and, based on the unique characteristics and utilizing classification predictive modeling, determines a conversation role for each participant of the conversation and metadata describing the conversation such as tonality of words spoken by a participant in a particular conversation role. Outputs from the text mining engine are indexed and useful for various purposes. For instance, because the system can identify which speaker in a customer service call is likely an agent and which speaker is likely a customer, words spoken by the agent can be analyzed for compliance reasons, training agents, providing quality assurance for improving customer service, providing feedback to improve the performance of the text mining engine, etc
B) US 20210006515 – Downs
One embodiment comprises a non-transitory computer readable medium comprising computer-executable instructions executable to access a conversation-enabled document and expose the conversation-enabled document on a first conversation channel as a conversation into the conversation-enabled document. The conversation-enabled document can comprise a conversation component for controlling a conversation interface into the conversation-enabled document, the conversation component specifying conversation steps, routing between conversation steps and a document variable to accept a conversation participant response. The computer-executable instructions can be executable to set a document variable value in the conversation-enabled document based on the participant response received via the conversation interface; and render the conversation-enabled document to a second channel using the document variable and the page template.
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/JUNE SISON/Primary Examiner, Art Unit 2455