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 .
Claims 2 - 21 are pending and claims 2, 9 and 16 are independent 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 2, 3, 5, 6 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, 6 of copending Application No. 17/463,361 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the previous patented claims anticipate the current claims as laid out in the chart below. Please note that the independent claims 9 and 16 of the current application are anticipated by the independent claims 10 and 19 of the copending application 17/463,361, as the independent claim 2 of the current application maps to the independent claim 1 of the copending application 17/463,361.
Instant Application
US Patent 17/463,361
Claim 2: A system comprising:
a non-transitory memory; and
Claim 1:
A system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
accessing a chat dialogue having a plurality of messages between a user and an agent of a customer relationship management (CRM) system, wherein the plurality of messages include content exchanged between the user and the agent in association with one or more past activities of the user;
accessing a chat dialogue between a user and a chat service of a customer relationship management (CRM) system for a service provider associated with the system, wherein the chat dialogue comprises a plurality of words in a plurality of word groupings;
determining content in the chat dialogue that is associated with one or more named entities and an assistance type associated with the user, wherein the one or more named entities were previously interacted with by the user, and wherein the assistance type was previously used or is currently being used by the user with the CRM system;
determining a past chat dialogue between the user and an assistance type provided to the user via a communication channel for the chat service of the CRM system, wherein the past chat dialogue indicates the assistance type previously used by the user and one or more named entities interacted with by the user, wherein the assistance type is associated with a service application provided by the service provider for use by the user, and wherein communication channel provides digital communications for the chat service of the CRM system;
identifying, using a named entity recognition (NER) operation comprising a machine learning (ML) model trained to identify named entities in chat dialogues, a first named entity associated with content from the plurality of messages, the one or more named entities, and the assistance type;
performing a named entity recognition (NER) operation on the plurality of words using a machine learning (ML) model trained based on the assistance type and the communication channel, wherein the NER operation identifies the one or more named entities in the chat dialogue based on one or more of the plurality of words;
determining an interaction and a time of the interaction between the user and the first named entity, wherein the interaction corresponds to at least one of the plurality of messages in the chat dialogue;
determining an interaction with the one or more named entities by the user and a time associated with the interaction based on the performing the NER operation and the chat dialogue; determining one or more labels for the one or more named entities, wherein the one or more labels are specific to the assistance type and the communication channel;
generating a description of the first named entity based at least on the interaction and the time of the interaction, wherein the description comprises a label of the first named entity based on the assistance type and additional data for the label and the interaction at the time;
determining additional data for the one or more named entities based the one or more labels and the interaction at the time, wherein the additional data includes one or more past interactions at one or more past times by the user with the merchant that correspond to the interaction.
generating a visual element configured to provide at least a portion of the description with a visual identifier in a user interface displaying the chat dialogue, wherein the first named entity appears in the plurality of messages; and
generating a description of the one or more named entities in a format compatible with a user interface displaying visual chat data for the chat dialogue based on the interaction and the additional data; and
outputting the visual element with one or more portions of the content associated with the first named entity in the user interface displaying the chat dialogue.
outputting the visual chat data having the one or more named entities marked with the one or more labels and selectable to view the description, wherein the visual chat data comprise one or more processes to view the additional data for the one or more named entities in the chat dialogue.
Claim 3: The system of claim 2, wherein the visual element comprises a highlight of text of the first named entity or the content associated with the first named entity in the plurality of messages corresponding to the first named entity, and wherein the highlight is a first color of at least two colors usable for displaying the first named entity and at least a second named entity.
Claim 2: The system of claim 1, wherein the visual chat data comprise one or more highlights of the one or more named entities over text in the chat dialogue corresponding to the one or more named entities.
Claim 5: The system of claim 4, wherein the operations further comprise:
receiving a selection of the text having the visual identifier applied;
obtaining the description for the first named entity; and
displaying the description in the window of the user interface while the chat dialogue is displayed.
Claim 3: The system of claim 1, wherein the operations further comprise:
receiving a selection of a first identification from the visual chat data; obtaining the additional data for a corresponding named entity of the one or more named entities associated with the first identification; and
displaying the additional data with the corresponding named entity in the chat dialogue.
Claim 6: The system of claim 2, wherein the description comprises historical information for the interaction between the user and the first named entity, and wherein the description becomes visible via the visual identifier when the visual identifier is at least one of selected in the user interface or hovered over by a selector in the user interface.
Claim 6: The system of claim 2, wherein the description comprises historical information for the interaction between the user and the first named entity, and wherein the description becomes visible via the visual identifier when the visual identifier is at least one of selected in the user interface or hovered over by a selector in the user interface.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 2, 5-6, 8-9, 11-13, 15-16, 18-19 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ghajar et al. Pub. No. US2018/0330278 A1 (Ghajar).
Regarding Claim 2, Ghajar discloses a system comprising:
a non-transitory memory (Ghajar, para 0029, The computer-readable storage media are, for example, tangible and non-transitory); and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising (Ghajar, Para 29 and Figure 2, one or more processors and a memory, Memory 118 may further include computer-readable storage media. The computer-readable storage media are, for example, tangible and non-transitory; The one or more processors 116 can utilize memory 118 to execute the instructions stored therein; the system comprising: a non-transitory memory; and also one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations; i.e., Figure 2 clearly shows the coupling of the one or more processors 116 and the memory 118, i.e., a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations):
accessing a chat dialogue having a plurality of messages between a user and an agent of a customer relationship management (CRM) system, wherein the plurality of messages include content exchanged between the user and the agent in association with one or more past activities of the user (Ghajar, Para 59 and Figure 5, illustrates an exemplary chat engagement interface overlaying a portion of content; content 500 can include interaction point 502 and chat engagement 504, which can comprise chat engagement data 506, 508, and 510 as well as message bar 512; i.e. accessing a chat dialogue (chat engagement) between a user (consumer) and a chat service of a customer relationship management (CRM) system (the chat engagement interface); Ghajar, para 33, one or more key phrases and/or one or more entities; Ghajar, para 60 and Figure 5, the chat engagement data of Figure 5, Unit 506 as “the chat dialogue”; i.e. “Key phrases” or one or more words can be considered as a plurality of words, while key phrases and one or more words can form a plurality of messages);
determining content in the chat dialogue that is associated with one or more named entities and an assistance type associated with the user, wherein the one or more named entities were previously interacted with by the user, and wherein the assistance type was previously used or is currently being used by the user with the CRM system (Ghajar, 0006, there is a need for more effective, cost-efficient, intelligent, and/or real-time processes and techniques for training machine learning platforms/models—and a system for facilitating such processes and techniques—so that, for example, real-time two-way engagement platforms can more accurately interpret and respond to content consumer questions and requests as well as provide more engaging and relevant content (e.g., via guided engagement) that can enhance content consumer knowledge at minimal effort; Ghajar, Para 59 and Figure 5, illustrates an exemplary chat engagement interface; [i.e., “interpret and respond to content consumer questions and requests as well as … engaging and relevant content” as “determining content in the chat dialogue that is associated with one or more named entities and an assistance type” through the chat engagement interface currently being used by the user with the CRM system]):
identifying, using a named entity recognition (NER) operation comprising a machine learning (ML) model trained to identify named entities in chat dialogues, a first named entity associated with content from the plurality of messages, the one or more named entities, and the assistance type (Ghajar, para 0033 and Figure 2, content analyzer 206, content receiver 204 identify one or more key phrases and/or one or more entities; [i.e., identifying (recognizing/performing) a named entity through a named entity recognition (NER) on the plurality of words (“Key phrases” and/or one or more words); Ghajar, 0023, users 108 may distribute content with topically-relevant two-way engagement mechanisms over network 104 to various types of channels, such as a one-to-many social media channel (e.g., Facebook, Instagram, Twitter, Snapchat, YouTube, Reddit, LinkedIn, etc.),…messaging channel (e.g., Facebook Messenger, Slack, WeChat, Line, Blog Center, Email, etc.), and/or a static webpage channel]; Ghajar, 0023, … training machine learning platforms/models—and a system for facilitating such processes and techniques—so that, for example, real-time two-way engagement platforms can more accurately interpret and respond to content consumer questions and requests as well as provide more engaging and relevant content (e.g., via guided engagement); Ghajar, 0009, Processes and techniques for more effectively training machine learning models (i.e., via unsupervised, semi-supervised, and/or supervised training) with relation to a specific context (e.g., products, services, brands, etc.), and more specifically, to processes and techniques—and a system for facilitating such processes and techniques—for more effectively training machine learning models by leveraging the principles of content development and distribution in content marketing operations as well as two-way engagement with content consumers for the purpose of continuously improving the quality of two-way engagement (e.g. conversation) and increasing the engagement around specific topics are described herein. In one example process, a system can receive content. The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels));
determining an interaction and a time of the interaction between the user and the first named entity, wherein the interaction corresponds to at least one of the plurality of messages in the chat dialogue (Ghajar, 0009, The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels). The system can then receive interaction data, which represents interactions of the one or more consumers with the content and the one or more interaction points. After, the system generates metrics or analytics based on the interaction data, and trains one or more machine learning models using the metrics or analytics);
generating a description of the first named entity based at least on the interaction and the time of the interaction, wherein the description comprises a label of the first named entity based on the assistance type and additional data for the label and the interaction at the time (Ghajar, para 0052 and Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404… the system can tag content analyzed at block 404 with one or more tags. For example, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, 0035, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users; Ghajar, para 0035, Figure 2, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users 108 via SaaS interface 106. Tag module 210 can further include instructions for tagging content based on one or more key phrases, one or more entities, and/or one or more topics identified by content analyzer 206; Ghajar, para 0062, As a result of the one or more servers performing the process 400 steps explained above (i.e., analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags, and generating two-way engagement mechanisms and two-way engagement mechanism data based on interaction points) two-way engagement mechanism data can be topically related to content. For example, as shown in FIG. 5, Figure 5, chat engagement data 506, 508, and 510 are topically-related to content 500 (i.e., they are similarly related to “CROSS-CHANNEL TRAFFIC MANAGEMENT”). Responses to the one or more content consumers' responses, questions, and/or requests can similarly be topically-related to content 500);
generating a visual element configured to provide at least a portion of the description with a visual identifier in a user interface displaying the chat dialogue, wherein the first named entity appears in the plurality of messages (Ghajar, para 0061, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links). Additionally, or alternatively, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can perform one or more actions (e.g., make a reservation, place an order for a product, etc.); and
outputting the visual element with one or more portions of the content associated with the first named entity in the user interface displaying the chat dialogue (Ghajar, para 0052, Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags … [ i.e., displaying (outputting visually) chat data with the one or more named entities; outputting visual (displayed) chat (engagement) data (modified or generated via the two-way engagement mechanisms) comprises additional chat engagement data for one or more entities in the chat dialogue (chat engagement); “based on the one or more labels and the additional data” as “one or more portions of the content associated with the first named entity”]).
Regarding Claim 5, Ghajar discloses the system of claim 4, wherein the operations further comprise:
receiving a selection of the text having the visual identifier applied (Ghajar, para 0061, Figure 5, in some examples, the one or more servers can generate a response to the one or more content consumers' responses, questions, and/or requests such that the live and conversational interaction with the one or more content consumers (i.e., two-way engagement) can continue for as long as the one or more content consumers desire. For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., receiving a selection of the portion of the text from the live agent in the display (as a result of the interaction between the live agent and users/consumers));
obtaining the description for the first named entity (Ghajar, para 0033 and 0051, In some examples, entities can be subsets of key phrases, so a key phrase might consist of one or more entities and/or descriptors or could simply be an entity; i.e., determining description data (key words) for the named entity responsive to the selection); and
displaying the description in the window of the user interface while the chat dialogue is displayed (Ghajar, para 0033 and 0051, In some examples, entities can be subsets of key phrases, so a key phrase might consist of one or more entities and/or descriptors or could simply be an entity; para 0061, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., displaying the description (descriptor/key phrase) data in the display).
Regarding Claim 6, Ghajar discloses the system of claim 2, wherein the description comprises historical information for the interaction between the user and the first named entity, and wherein the description becomes visible via the visual identifier when the visual identifier is at least one of selected in the user interface or hovered over by a selector in the user interface (Ghajar, para 0033 and Figure 2, Content analyzer 206 and content receiver 204, include instructions for analyzing content received to identify one or more key phrases and/or one or more entities; i.e., (analyzing and) receiving a content selection of a first identification from the visual chat data).
Regarding Claim 8, Ghajar discloses the system of claim 2, wherein the chat dialogue comprises text between the user and at least one of a live agent or a chatbot, wherein the chat dialogue is one of an asynchronous chat or a real-time live chat, and wherein the identifying the first named entity is further based on one of past messages sent by the user or a plurality of agents in the asynchronous chat or a service request by the user to the CRM system for the real-time live chat (Ghajar, para 0061, In some examples, the one or more servers can generate a response to the one or more content consumers' responses, questions, and/or requests such that the live and conversational interaction with the one or more content consumers (i.e., two-way engagement). For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., the chat dialogue (the chat engagement) comprises text between the user and at least one of a live agent (with live and conversational interaction) or a chatbot for the chat service).
Regarding Claim 9, Ghajar discloses a method comprising:
receiving communications between a user and an agent of a service provider, wherein the communications comprise a plurality of words exchanged between the user and the agent during a provision of a service to the user by the agent (Ghajar, para 0018-0021, a system can receive content. The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then receive interaction data, which represents interactions of the one or more consumers with the content and the one or more interaction points. After, the system generates metrics and/or analytics based on the interaction data…users 108, user devices 110, content consumers 112, and content consumer devices 114. In some examples, users 108 on user devices 110 can communicate with server system 102 via SaaS interface 106 to create, modify, upload, and distribute content (e.g., blogs, webpages, messages, etc.), as well as to generate, modify, and distribute topically-relevant two-way engagement mechanisms that can be used for topically-relevant two-way engagement with content consumers. In some examples, SaaS interface 106 resides on server system 102 and can be accessed by user devices 104 via network 102. For example, user devices 110 can include any electronic device, such as a mobile phone, tablet computer, portable media player, desktop computer, laptop computer, or the like, and can communicate with server system 102 through network 104, which can include the Internet, an intranet, or any other wired or wireless public or private network. In some examples, in order to communicate with server system 102 via SaaS interface 106, users 108 must create a user account, which may comprise user information (e.g., name, profession, company, location, etc. of users 108));
identifying a named entity in the communications using a machine learning (ML) model trained for named entity recognition (NER), wherein the identifying the named entity includes determining an identifier of the named entity using the NER and the provision of the service to the user (Ghajar, para 0033 and Figure 2, content analyzer 206, content receiver 204 identify one or more key phrases and/or one or more entities; [i.e., identifying (recognizing/performing) a named entity through a named entity recognition (NER) on the plurality of words (“Key phrases” and/or one or more words); Ghajar, 0023, users 108 may distribute content with topically-relevant two-way engagement mechanisms over network 104 to various types of channels, such as a one-to-many social media channel (e.g., Facebook, Instagram, Twitter, Snapchat, YouTube, Reddit, LinkedIn, etc.),…messaging channel (e.g., Facebook Messenger, Slack, WeChat, Line, Blog Center, Email, etc.), and/or a static webpage channel]; Ghajar, 0023, … training machine learning platforms/models—and a system for facilitating such processes and techniques—so that, for example, real-time two-way engagement platforms can more accurately interpret and respond to content consumer questions and requests as well as provide more engaging and relevant content (e.g., via guided engagement); Ghajar, 0009, Processes and techniques for more effectively training machine learning models (i.e., via unsupervised, semi-supervised, and/or supervised training) with relation to a specific context (e.g., products, services, brands, etc.), and more specifically, to processes and techniques—and a system for facilitating such processes and techniques—for more effectively training machine learning models by leveraging the principles of content development and distribution in content marketing operations as well as two-way engagement with content consumers for the purpose of continuously improving the quality of two-way engagement (e.g. conversation) and increasing the engagement around specific topics are described herein. In one example process, a system can receive content. The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels));
determining an interaction between the named entity and the user based on the communications a past interaction between the named entity and the user (Ghajar, 0009, The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels). The system can then receive interaction data, which represents interactions of the one or more consumers with the content and the one or more interaction points. After, the system generates metrics or analytics based on the interaction data, and trains one or more machine learning models using the metrics or analytics);
generating a description of the named entity based at least on the interaction (Ghajar, para 0052 and Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404… the system can tag content analyzed at block 404 with one or more tags. For example, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, 0035, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users; Ghajar, para 0035, Figure 2, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users 108 via SaaS interface 106. Tag module 210 can further include instructions for tagging content based on one or more key phrases, one or more entities, and/or one or more topics identified by content analyzer 206; Ghajar, para 0062, As a result of the one or more servers performing the process 400 steps explained above (i.e., analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags, and generating two-way engagement mechanisms and two-way engagement mechanism data based on interaction points) two-way engagement mechanism data can be topically related to content. For example, as shown in FIG. 5, Figure 5, chat engagement data 506, 508, and 510 are topically-related to content 500 (i.e., they are similarly related to “CROSS-CHANNEL TRAFFIC MANAGEMENT”). Responses to the one or more content consumers' responses, questions, and/or requests can similarly be topically-related to content 500); and
configuring an interface displaying the communications to display a highlight of associated with the named entityhighlight includes identifier that is selectable in the interface display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags … [ i.e., displaying (outputting visually) chat data with the one or more named entities; outputting visual (displayed) chat (engagement) data (modified or generated via the two-way engagement mechanisms) comprises additional chat engagement data for one or more entities in the chat dialogue (chat engagement)).
Regarding Claim 11, Ghajar discloses the method of claim 9, wherein the highlight is associated with one or more processes to cause the description of the named entity to be displayed in a window of the interface with the communications (Ghajar, para 0052, Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags).
Regarding Claim 12, Ghajar discloses the method of claim 11, further comprising:
receiving a selection of the highlight (Ghajar, para 0061, Figure 5, in some examples, the one or more servers can generate a response to the one or more content consumers' responses, questions, and/or requests such that the live and conversational interaction with the one or more content consumers (i.e., two-way engagement) can continue for as long as the one or more content consumers desire. For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., receiving a selection of the portion of the text from the live agent in the display (as a result of the interaction between the live agent and users/consumers));
obtaining the description for the named entity (Ghajar, para 0033 and 0051, In some examples, entities can be subsets of key phrases, so a key phrase might consist of one or more entities and/or descriptors or could simply be an entity; i.e., determining description data (key words) for the named entity responsive to the selection); and
further configuring the interface while the communications are displayed to display the description (Ghajar, para 0033 and 0051, In some examples, entities can be subsets of key phrases, so a key phrase might consist of one or more entities and/or descriptors or could simply be an entity; para 0061, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., displaying the description (descriptor/key phrase) data in the display).
Regarding Claim 13, Ghajar discloses the method of claim 9, wherein the description comprises historical information for the named entity, and wherein the description becomes visible via the highlight when the highlight is selected in the interface or hovered over by a selector in the interface (Ghajar, para 0033 and Figure 2, Content analyzer 206 and content receiver 204, include instructions for analyzing content received to identify one or more key phrases and/or one or more entities; i.e., (analyzing and) receiving a content selection of a first identification from the visual chat data).
Regarding Claim 15, Ghajar discloses the method of claim 9, wherein the identifying the named entity is further based on past messages sent by the user and/or a plurality of agents (Ghajar, para 0047-0051, learning module 222 may include instructions for instructing content analyzer 206 to modify its content analysis (e.g., modifying the identification of key phrases and/or entities by content analyzer 206). Additionally, or alternatively, learning module 222 may include instructions for instructing interaction point module 208 to modify one or more of the interaction points it has previously inserted in content. Additionally, or alternatively, learning module 222 may include instructions for instructing two-way engagement engine 212 to modify one or more two-way engagement mechanisms it has previously inserted in content and/or the two-way engagement mechanism data it generates… identify one or more key phrases and/or one or more entities; [i.e., “previously …content” as “past content/entity”]).
Regarding Claim 16, Ghajar discloses a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations (Ghajar, para 0029, Figure 2, Memory 118 may further include computer-readable storage media. The computer-readable storage media are, for example, tangible and non-transitory. For example, memory 118 can include high-speed random access memory and can also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. In some examples, the computer-readable storage media of memory 118 store instructions for performing the methods and processes described herein; i.e., A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations) comprising:
receiving a chat dialogue between a user and an agent of a service provider, wherein the chat dialogue comprises a plurality of words exchanged between the user and the agent (Ghajar, para 0018-0021, a system can receive content. The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then receive interaction data, which represents interactions of the one or more consumers with the content and the one or more interaction points. After, the system generates metrics and/or analytics based on the interaction data…users 108, user devices 110, content consumers 112, and content consumer devices 114. In some examples, users 108 on user devices 110 can communicate with server system 102 via SaaS interface 106 to create, modify, upload, and distribute content (e.g., blogs, webpages, messages, etc.), as well as to generate, modify, and distribute topically-relevant two-way engagement mechanisms that can be used for topically-relevant two-way engagement with content consumers. In some examples, SaaS interface 106 resides on server system 102 and can be accessed by user devices 104 via network 102. For example, user devices 110 can include any electronic device, such as a mobile phone, tablet computer, portable media player, desktop computer, laptop computer, or the like, and can communicate with server system 102 through network 104, which can include the Internet, an intranet, or any other wired or wireless public or private network. In some examples, in order to communicate with server system 102 via SaaS interface 106, users 108 must create a user account, which may comprise user information (e.g., name, profession, company, location, etc. of users 108));
determining an assistance type and a plurality of named entities associated with the chat dialogue, wherein the assistance type was previously provided to the user or is being provided to the user via the agent, and wherein the plurality of named entities were previously interacted with by the user (Ghajar, 0006, there is a need for more effective, cost-efficient, intelligent, and/or real-time processes and techniques for training machine learning platforms/models—and a system for facilitating such processes and techniques—so that, for example, real-time two-way engagement platforms can more accurately interpret and respond to content consumer questions and requests as well as provide more engaging and relevant content (e.g., via guided engagement) that can enhance content consumer knowledge at minimal effort; Ghajar, Para 59 and Figure 5, illustrates an exemplary chat engagement interface; [i.e., “interpret and respond to content consumer questions and requests as well as … engaging and relevant content” as “determining an assistance type” through the chat engagement interface /the communication channel for the chat service of the CRM platform/]);
identifying a named entity in the chat dialogue using a machine learning (ML) model trained for named entity recognition (NER), wherein the identifying the named entity includes determining a label of the named entity based at least one the assistance type and a communication channel used by the user to interact with the agent (Ghajar, para 0033 and Figure 2, content analyzer 206, content receiver 204 identify one or more key phrases and/or one or more entities; [i.e., identifying (recognizing/performing) a named entity through a named entity recognition (NER) on the plurality of words (“Key phrases” and/or one or more words); Ghajar, para 0023, users 108 may distribute content with topically-relevant two-way engagement mechanisms over network 104 to various types of channels, such as a one-to-many social media channel (e.g., Facebook, Instagram, Twitter, Snapchat, YouTube, Reddit, LinkedIn, etc.),…messaging channel (e.g., Facebook Messenger, Slack, WeChat, Line, Blog Center, Email, etc.), and/or a static webpage channel]; Ghajar, 0023, … training machine learning platforms/models—and a system for facilitating such processes and techniques—so that, for example, real-time two-way engagement platforms can more accurately interpret and respond to content consumer questions and requests as well as provide more engaging and relevant content (e.g., via guided engagement); Ghajar, 0009, Processes and techniques for more effectively training machine learning models (i.e., via unsupervised, semi-supervised, and/or supervised training) with relation to a specific context (e.g., products, services, brands, etc.), and more specifically, to processes and techniques—and a system for facilitating such processes and techniques—for more effectively training machine learning models by leveraging the principles of content development and distribution in content marketing operations as well as two-way engagement with content consumers for the purpose of continuously improving the quality of two-way engagement (e.g. conversation) and increasing the engagement around specific topics are described herein. In one example process, a system can receive content. The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels));
determining an interaction between the named entity and the user based at least on the chat dialogue (Ghajar, 0009, The system can analyze the content to identify one or more key phrases and one or more entities, wherein the one or more key phrases and the one or more entities correspond to one or more topics. The system can then generate one or more interaction points based on the one or more key phrases, one or more entities, and one or more topics. After generating the one or more interaction points, the system can insert the one or more interaction points into the content and distribute the content with the one or more inserted interaction points to consumers (e.g., via one or more channels). The system can then receive interaction data, which represents interactions of the one or more consumers with the content and the one or more interaction points. After, the system generates metrics or analytics based on the interaction data, and trains one or more machine learning models using the metrics or analytics);
generating a description of the named entity based at least on the label and the interaction (Ghajar, para 0052 and Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404… the system can tag content analyzed at block 404 with one or more tags. For example, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, 0035, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users; Ghajar, para 0035, Figure 2, Tag module 210 can include instructions for tagging content received by content receiver 204 with one or more tags and/or for facilitating the tagging of content received by content receiver 204 with one or more tags by users 108 via SaaS interface 106. Tag module 210 can further include instructions for tagging content based on one or more key phrases, one or more entities, and/or one or more topics identified by content analyzer 206; Ghajar, para 0062, As a result of the one or more servers performing the process 400 steps explained above (i.e., analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags, and generating two-way engagement mechanisms and two-way engagement mechanism data based on interaction points) two-way engagement mechanism data can be topically related to content. For example, as shown in FIG. 5, Figure 5, chat engagement data 506, 508, and 510 are topically-related to content 500 (i.e., they are similarly related to “CROSS-CHANNEL TRAFFIC MANAGEMENT”). Responses to the one or more content consumers' responses, questions, and/or requests can similarly be topically-related to content 500);
displaying, in a chat interface displaying the chat dialogue to one of the user or the agent, the label that highlights one or more of the plurality of words corresponding to the named entity in the chat dialogue, wherein the label is selected in the chat interface to cause a display of the description in the chat interface (Ghajar, para 0052, Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, para 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags … [ i.e., displaying (outputting visually) chat data with the one or more named entities; outputting visual (displayed) chat (engagement) data (modified or generated via the two-way engagement mechanisms) comprises additional chat engagement data for one or more entities in the chat dialogue (chat engagement); “based on the one or more labels and the additional data” as “one or more portions of the content associated with the first named entity”]); and
displaying, based on a selection of the label, the description with the chat dialogue in the chat interface (Ghajar, para 0033 and 0051, In some examples, entities can be subsets of key phrases, so a key phrase might consist of one or more entities and/or descriptors or could simply be an entity; para 0061, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); [i.e., displaying the description (descriptor/key phrase) data in the display]; Ghajar, para 0061, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links). Additionally, or alternatively, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can perform one or more actions (e.g., make a reservation, place an order for a product, etc.)).
Regarding Claim 18, Ghajar discloses the non-transitory machine-readable medium of claim 16, wherein the highlight is associated with one or more processes to cause the description of the named entity to be displayed in a window of the interface with the communications (Ghajar, para 0052, Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags).
Regarding Claim 19, Ghajar discloses the non-transitory machine-readable medium of wherein the description comprises historical information for the named entity, and wherein the description becomes visible via the label when the label is selected in the chat interface or hovered over by a selector in the chat interface (Ghajar, para 0033 and Figure 2, Content analyzer 206 and content receiver 204, include instructions for analyzing content received to identify one or more key phrases and/or one or more entities; i.e., (analyzing and) receiving a content selection of a first identification from the visual chat data).
Regarding Claim 21, Ghajar discloses the non-transitory machine-readable medium of wherein the identifying the named entity is further based on past messages sent by the user and/or a plurality of agents (Ghajar, para 0047-0051, learning module 222 may include instructions for instructing content analyzer 206 to modify its content analysis (e.g., modifying the identification of key phrases and/or entities by content analyzer 206). Additionally, or alternatively, learning module 222 may include instructions for instructing interaction point module 208 to modify one or more of the interaction points it has previously inserted in content. Additionally, or alternatively, learning module 222 may include instructions for instructing two-way engagement engine 212 to modify one or more two-way engagement mechanisms it has previously inserted in content and/or the two-way engagement mechanism data it generates… identify one or more key phrases and/or one or more entities; [i.e., “previously …content” as “past content/entity”]).
Claims 3, 7, 10, 14, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ghajar in view of Bar-on et al. Pub. No. US2021/0286633 A1 (Bar-on).
Regarding Claim 3, Ghajar discloses the system of claim 2.
Ghajar does not specifically teach wherein the visual element comprises a highlight of text of the first named entity or the content associated with the first named entity in the plurality of messages corresponding to the first named entity, and wherein the highlight is a first color of at least two colors usable for displaying the first named entity and at least a second named entity.
However, Bar-on, in the same field of endeavor, teaches wherein the visual element comprises a highlight of text of the first named entity or the content associated with the first named entity in the plurality of messages corresponding to the first named entity, and wherein the highlight is a first color of at least two colors usable for displaying the first named entity and at least a second named entity (Bar-on, para 0138, Though the information types recognized by the data detection operation are visually depicted with square brackets, italics, and bolding in Figures 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Bar-on with the method of Ghajar so as to highlights one or more named entities over text in the chat dialogue named entities with different format (square brackets, italics, bolding, and underlining) and display in different colors to make them more visible and recognizable.
Regarding Claim 7, Ghajar discloses the system of claim 2, wherein the identifying the first named entity using the NER (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities; [i.e., prior to identifying (analyzing and determining) that the chat text further comprises the plurality of second named entities (key phrases and/or entities identified by the content (the chat engagement data) analyzer) operation comprises:
identifying words or groups of words that correspond to one of a plurality of named entity groupings (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities).
Ghajar does not specifically disclose formatting the plurality of messages for the chat dialogue for use by a natural language processor (NLP), and processing at least one of semantic-based rules or syntax-based rules for words in the plurality of formatted messages using the NLP.
However, Bar-on, in the same field of endeavor, discloses:
formatting the plurality of messages for the chat dialogue for use by a natural language processor (NLP) (Bar-on, para 0138-0139, Though the information types recognized by the data detection operation are visually depicted with square brackets, italics, and bolding in FIGS. 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text. FIG. 7B depicts the example graphical user interface of FIG. 7A with an image 716 within a second input area 706b. In this example, the image 716 may be detected by the information aggregation system 700 and may be determined to be a graphic (e.g., by natural language processor 208);
processing at least one of semantic-based rules or syntax-based rules for words in the plurality of formatted messages using the NLP (Bar-on, para 0078-0079, The natural language processor 208 may also analyze other kinds of content other than text. For example, the natural language processor 208 may analyze content such as videos, audio, images, links, and photos. For example, if a user inputs an image, the natural language processor 208 may be able to determine the presence of the image and may be able to determine a file type of the image (e.g., Portable Network Graphics (PNG) or JPEG) or a subject type (e.g., a picture of a tree or animal). In some embodiments, a data detection operation may be utilized in order to analyze the content input into an input area (e.g., to detect the presence of content pertaining to a respective categorization block). The data detection operation may leverage any suitable method to determine a category and/or information type for content input into an input area including, but not limited to: determining file extensions; leveraging regular expressions for known syntax types).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Ghajar with the method of Bar-on so as to highlight the first named entity in the chat text, and receiving a selection of the highlighted first named entity with different format (square brackets, italics, bolding, and/or underlining) and display in different colors to make the selected entity more visible and recognizable (Bar-on, para 0001).
Regarding Claim 10, Ghajar discloses the method of claim 9.
Ghajar does not specifically teach wherein the highlight is of a name of the named entity in the communications, and wherein the highlight is a first color of at least two colors usable for displaying the named entity or another named entity.
However, Bar-on, in the same field of endeavor, teaches wherein the highlight is of a name of the named entity in the communications, and wherein the highlight is a first color of at least two colors usable for displaying the named entity or another named entity (Bar-on, para 0138, the data detection operation are visually depicted with square brackets, italics, and bolding in Figures 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text);
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Ghajar with the method of Bar-on so as to highlight the first named entity in the chat text, and receiving a selection of the highlighted first named entity with different format (square brackets, italics, bolding, and/or underlining) and display in different colors to make the selected entity more visible and recognizable (Bar-on, para 0001).
Regarding Claim 14, Ghajar discloses the method of claim 9, wherein, prior to the identifying the named entity (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities; [i.e., prior to identifying (analyzing and determining) that the chat text further comprises the plurality of second named entities (key phrases and/or entities identified by the content (the chat engagement data) analyzer), the method further comprises:
identifying words or groups of words that correspond to one of a plurality of named entity groupings (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities).
Ghajar does not specifically disclose formatting the communications for a natural language processor (NLP), and processing at least one of semantic-based rules or syntax-based rules in the formatted communications using the NLP.
However, Bar-on, in the same field of endeavor, discloses:
formatting the communications for a natural language processor (NLP) (Bar-on, para 0138-0139, Though the information types recognized by the data detection operation are visually depicted with square brackets, italics, and bolding in FIGS. 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text. FIG. 7B depicts the example graphical user interface of FIG. 7A with an image 716 within a second input area 706b. In this example, the image 716 may be detected by the information aggregation system 700 and may be determined to be a graphic (e.g., by natural language processor 208);
processing at least one of semantic-based rules or syntax-based rules in the formatted communications using the NLP (Bar-on, para 0078-0079, The natural language processor 208 may also analyze other kinds of content other than text. For example, the natural language processor 208 may analyze content such as videos, audio, images, links, and photos. For example, if a user inputs an image, the natural language processor 208 may be able to determine the presence of the image and may be able to determine a file type of the image (e.g., Portable Network Graphics (PNG) or JPEG) or a subject type (e.g., a picture of a tree or animal). In some embodiments, a data detection operation may be utilized in order to analyze the content input into an input area (e.g., to detect the presence of content pertaining to a respective categorization block). The data detection operation may leverage any suitable method to determine a category and/or information type for content input into an input area including, but not limited to: determining file extensions; leveraging regular expressions for known syntax types).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Ghajar with the method of Bar-on so as to highlight the first named entity in the chat text, and receiving a selection of the highlighted first named entity with different format (square brackets, italics, bolding, and/or underlining) and display in different colors to make the selected entity more visible and recognizable (Bar-on, para 0001).
Regarding Claim 17, Ghajar discloses the non-transitory machine-readable medium of the non-transitory machine-readable medium (Ghajar, para 0029, Memory 118 may further include computer-readable storage media. The computer-readable storage media are, for example, tangible and non-transitory).
Ghajar does not specifically teach wherein the label highlights one of a name or an identifier of the named entity, and wherein the highlight is a first color of at least two colors usable for displaying the named entity or another named entity.
However, Bar-on, in the same field of endeavor, teaches wherein the label highlights one of a name or an identifier of the named entity, and wherein the highlight is a first color of at least two colors usable for displaying the named entity or another named entity (Bar-on, para 0138, the data detection operation are visually depicted with square brackets, italics, and bolding in Figures 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text);
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Ghajar with the method of Bar-on so as to highlight the first named entity in the chat text, and receiving a selection of the highlighted first named entity with different format (square brackets, italics, bolding, and/or underlining) and display in different colors to make the selected entity more visible and recognizable (Bar-on, para 0001).
Regarding Claim 20, Ghajar discloses the non-transitory machine-readable medium of wherein, prior to the identifying the named entity (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities; [i.e., prior to identifying (analyzing and determining) that the chat text further comprises the plurality of second named entities (key phrases and/or entities identified by the content (the chat engagement data) analyzer), the operations further comprise:
identifying words or groups of words that correspond to one of a plurality of named entity groupings (Ghajar, para 0033, Figure 2, Content analyzer 206 can include instructions for analyzing content received by content receiver 204 to identify one or more key phrases and/or one or more entities).
Ghajar does not specifically disclose formatting the chat dialogue for a natural language processor (NLP), and processing at least one of semantic-based rules or syntax-based rules in the formatted chat dialogue using the NLP.
However, Bar-on, in the same field of endeavor, discloses:
formatting the chat dialogue for a natural language processor (NLP) (Bar-on, para 0138-0139, Though the information types recognized by the data detection operation are visually depicted with square brackets, italics, and bolding in FIGS. 7A-7C, this is only one possible implementation. In other embodiments, the information types may be visually displayed in a different color from non-recognized text, may be underlined, and/or may not be visually distinguished from non-recognized text. FIG. 7B depicts the example graphical user interface of FIG. 7A with an image 716 within a second input area 706b. In this example, the image 716 may be detected by the information aggregation system 700 and may be determined to be a graphic (e.g., by natural language processor 208);
processing at least one of semantic-based rules or syntax-based rules in the formatted chat dialogue using the NLP (Bar-on, para 0078-0079, The natural language processor 208 may also analyze other kinds of content other than text. For example, the natural language processor 208 may analyze content such as videos, audio, images, links, and photos. For example, if a user inputs an image, the natural language processor 208 may be able to determine the presence of the image and may be able to determine a file type of the image (e.g., Portable Network Graphics (PNG) or JPEG) or a subject type (e.g., a picture of a tree or animal). In some embodiments, a data detection operation may be utilized in order to analyze the content input into an input area (e.g., to detect the presence of content pertaining to a respective categorization block). The data detection operation may leverage any suitable method to determine a category and/or information type for content input into an input area including, but not limited to: determining file extensions; leveraging regular expressions for known syntax types).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Ghajar with the method of Bar-on so as to highlight the first named entity in the chat text, and receiving a selection of the highlighted first named entity with different format (square brackets, italics, bolding, and/or underlining) and display in different colors to make the selected entity more visible and recognizable (Bar-on, para 0001).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ghajar in view of Pande et al. Pub. No. US 20200184539 A1 (Pande).
Regarding Claim 4, Ghajar discloses the system of claim 2, wherein the visual identifier comprises a visual change of text for one of the content or the first named entity in the plurality of messages that signifies that the text corresponds to the first named entity and the text is selectable to view the description of the first named entity (Ghajar, para 0061 and Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links)); para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); i.e., displaying the description of the first named entity (additional chat engagement data for named entity, e.g., text) in response to the selection (the consumer requested content/data/entity)), and wherein the visual element is associated with one or more processes to cause the description of the first named entity to be displayed in a window of the user interface with the chat dialogue (Ghajar, para 0052, Figure 4, the one or more servers can tag content with one or more tags based on one or more key phrases, one or more entities, and/or one or more topics identified at block 404; Ghajar, para 0061, Figure 5, For example, in response to the one or more content consumer responses, questions, and/or requests, the one or more servers can generate and display (in chat engagement 504) additional chat engagement data (e.g., text and/or links); Ghajar, 0062, analyzing content, identifying key phrases and/or entities, generating interaction points based on one or more key phrases, entities, topics and/or tags).
Ghajar does not specifically teach wherein the one or more past activities are associated with least one of a transaction, shopping data for a shopping experience of the user, or a digital shopping cart (Pande, para 0021-0022, When the user is done shopping, the user is able to complete the transaction on the mobile device without having to go through a traditional in-store checkout process... In general, the digital cart management system enables guests to enhance their in-store shopping and checkout experience. Aspects of the in-store shopping experience include assisting guests with locating items in the store through the retail location's application (using voice-based or text input commands), identifying items placed in the guest's physical shopping cart (using RFID tags, bar code scans, text input, or voice input), providing item recommendations (based on items in the guest's shopping cart, shopping list, and/or previous purchases); [“previous purchases” as “past activities… associated with … transaction”]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Pande with the method of Ghajar which has the benefit that when a user is done shopping, the user is able to complete the transaction on the mobile device without having to go through a traditional in-store checkout process (Pande, Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUGETA T. DUGDA whose telephone number is (703)756-1106. The examiner can normally be reached Mon - Fri, 4:30am - 7:00pm.
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/MULUGETA TUJI DUGDA/Examiner, Art Unit 2653
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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