WO2020139865A1 - Systèmes et procédés pour des conversations automatisées améliorées - Google Patents

Systèmes et procédés pour des conversations automatisées améliorées Download PDF

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Publication number
WO2020139865A1
WO2020139865A1 PCT/US2019/068421 US2019068421W WO2020139865A1 WO 2020139865 A1 WO2020139865 A1 WO 2020139865A1 US 2019068421 W US2019068421 W US 2019068421W WO 2020139865 A1 WO2020139865 A1 WO 2020139865A1
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WO
WIPO (PCT)
Prior art keywords
message
meaning
conversation
intent
intents
Prior art date
Application number
PCT/US2019/068421
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English (en)
Inventor
Benjamin P. Brigham
Siddhartha Reddy Jonnalagadda
Kerri Louise Rapes
Cesar Alexis Flores Suazo
Original Assignee
Conversica, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US16/723,735 external-priority patent/US20200143115A1/en
Application filed by Conversica, Inc. filed Critical Conversica, Inc.
Publication of WO2020139865A1 publication Critical patent/WO2020139865A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/041—Abduction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Definitions

  • AI Artificial Intelligence
  • systems and methods are provided for parsing a message in a conversation series. This involves receiving a message, which is a collection of a series of exchanges including earlier exchanges and a current exchange. The different exchanges are identified in the message, and the earlier ones are removed to isolate the current exchange. The current exchange is then divided up into sentences, and the language being used is detected. Language detection may include running different language models on the message, and selecting the language for the model with the highest confidence.
  • the classification text is provided to sets of models for parallel prediction of the intent(s) of the message.
  • Each model is predicts the intent with a given confidence level.
  • Models are queried for based upon series of the conversation, the industry involved, the client the model is for, and the message campaign to select which of the machine learning models to use for intent determination. If a speech act was identified previously, the models used to analyze the statement may differ based upon which speech type exists. For example, some models may be well tuned for analyzing statements, but fail to have accurate results for questions.
  • Mapping rules and/or prediction machine learning models are used to convert the intents into meanings. These meanings may be filtered (e.g., by a threshold cutoff of some other filtering methodology) to arrive at the final meaning of the exchange. It is also possible to apply a decision engine policy for the determination of the meaning. The decision engine policy removes at least one of the plurality of machine learning models and replaces a probability of the predicted meaning for the removed models to 1.
  • the training of the AI system may be enabled by, or supplemented with, conventional CRM data.
  • CRM data may be particularly useful when used to augment traditional training sets, and input from the training desk.
  • social media exchanges may likewise be useful as a training source for the AI models. For example, a business often engages directly with customers on social media, leading to conversations back and forth that are again, specific and accurate to the business. As such this data may also be beneficial as a source of training material.
  • Feedback may be collected from the conversational exchanges, in many
  • the message receiver 530 may include a smart parser 531 which consumes the raw message and splits it into multiple portions, including differentiating between the salutation, reply, close, signature and other message components. This is performed by the search and removal of the outgoing message (in an email this is typically appended below a response message and should mirror the message sent originally to the contact), followed by pre-processing of the test to build features. Subsequently predictions are requested by models for what message segment a particular line of text belongs to, and the response is logically built.
  • a smart parser 531 which consumes the raw message and splits it into multiple portions, including differentiating between the salutation, reply, close, signature and other message components. This is performed by the search and removal of the outgoing message (in an email this is typically appended below a response message and should mirror the message sent originally to the contact), followed by pre-processing of the test to build features. Subsequently predictions are requested by models for what message segment a particular line of text belongs to, and the response is logical
  • the input for the smart parser may include raw email text (or other message information converted into a raw text format).
  • the input into the smart parser function may include:
  • the entire data processor 540 operates as a step function responsible for connecting all of the lambda functions.
  • the step function may start with the smart message parser, proceed to message normalization, then language detection, and then data processing, which includes its own stepwise sets of functions (e.g., critical intent checking, model query, parallel prediction, sorting, meaning mapping, etc.).
  • the message templates in the conversation are generated (at 820). If the series is populated (at 830), then the conversation is reviewed and submitted (at 840). Otherwise, the next message in the template is generated (at 820).
  • Figure 9 provides greater details of an example of this sub-process for generating message templates. Initially the user is queried if an existing conversation can be leveraged for templates, or whether a new template is desired (at 910).
  • the system may detect the language (at 1320) of the response using multiple language models and selecting the model with the highest confidence level.
  • the sentences are tokenized (at 1330) which divides the response into separate sentences. This is performed because generally each sentence of a conversation includes a separate/discrete idea or intention. By separating each sentence the risk of token

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Machine Translation (AREA)

Abstract

L'invention concerne des systèmes et des procédés d'analyse d'un message dans une série de conversations. Ceci consiste à recevoir un message, à isoler l'échange actuel, à le diviser en phrases et à détecter la langue utilisée. Les phrases du message sont normalisées et tout « acte de parole » est identifié. De même, toute « intention critique » est identifiée. S'il n'y a pas d'intention critique, le texte de classification est fourni à des ensembles de modèles pour une prédiction parallèle des une ou plusieurs intentions du message. Des modèles sont interrogés en fonction d'une série de la conversation, de l'industrie impliquée, du client auquel est destiné le modèle, de la campagne de messagerie et de tout acte de parole présent. Des règles de mappage et/ou des modèles d'apprentissage machine de prédiction sont utilisés pour convertir les intentions en significations, qui sont filtrées. Il est également possible d'appliquer une politique de moteur de décision pour la détermination de la signification. Ceci est suivi par une extraction d'entité et une génération de réponse par mappage des significations à des actions.
PCT/US2019/068421 2018-12-24 2019-12-23 Systèmes et procédés pour des conversations automatisées améliorées WO2020139865A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201862784696P 2018-12-24 2018-12-24
US62/784,696 2018-12-24
US16/723,735 2019-12-20
US16/723,735 US20200143115A1 (en) 2015-01-23 2019-12-20 Systems and methods for improved automated conversations

Publications (1)

Publication Number Publication Date
WO2020139865A1 true WO2020139865A1 (fr) 2020-07-02

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112559689A (zh) * 2020-12-21 2021-03-26 广州橙行智动汽车科技有限公司 一种基于车载问答的数据处理方法和装置
CN112926326A (zh) * 2021-02-20 2021-06-08 深圳追一科技有限公司 命名实体识别方法、装置、计算机设备和存储介质
CN112988993A (zh) * 2021-02-19 2021-06-18 车智互联（北京）科技有限公司 一种问答方法和计算设备
CN113239188A (zh) * 2021-04-21 2021-08-10 上海快确信息科技有限公司 一种一套金融交易对话信息分析技术方案
CN114077831A (zh) * 2020-08-21 2022-02-22 北京金山数字娱乐科技有限公司 一种问题文本分析模型的训练方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050050469A1 (en) * 2001-12-27 2005-03-03 Kiyotaka Uchimoto Text generating method and text generator
US20070192309A1 (en) * 2005-10-12 2007-08-16 Gordon Fischer Method and system for identifying sentence boundaries
US20140172417A1 (en) * 2012-12-16 2014-06-19 Cloud 9, Llc Vital text analytics system for the enhancement of requirements engineering documents and other documents
US20150340033A1 (en) * 2014-05-20 2015-11-26 Amazon Technologies, Inc. Context interpretation in natural language processing using previous dialog acts
US20180314689A1 (en) * 2015-12-22 2018-11-01 Sri International Multi-lingual virtual personal assistant

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050050469A1 (en) * 2001-12-27 2005-03-03 Kiyotaka Uchimoto Text generating method and text generator
US20070192309A1 (en) * 2005-10-12 2007-08-16 Gordon Fischer Method and system for identifying sentence boundaries
US20140172417A1 (en) * 2012-12-16 2014-06-19 Cloud 9, Llc Vital text analytics system for the enhancement of requirements engineering documents and other documents
US20150340033A1 (en) * 2014-05-20 2015-11-26 Amazon Technologies, Inc. Context interpretation in natural language processing using previous dialog acts
US20180314689A1 (en) * 2015-12-22 2018-11-01 Sri International Multi-lingual virtual personal assistant

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114077831A (zh) * 2020-08-21 2022-02-22 北京金山数字娱乐科技有限公司 一种问题文本分析模型的训练方法及装置
CN112559689A (zh) * 2020-12-21 2021-03-26 广州橙行智动汽车科技有限公司 一种基于车载问答的数据处理方法和装置
CN112988993A (zh) * 2021-02-19 2021-06-18 车智互联（北京）科技有限公司 一种问答方法和计算设备
CN112988993B (zh) * 2021-02-19 2023-10-20 车智互联（北京）科技有限公司 一种问答方法和计算设备
CN112926326A (zh) * 2021-02-20 2021-06-08 深圳追一科技有限公司 命名实体识别方法、装置、计算机设备和存储介质
CN112926326B (zh) * 2021-02-20 2024-01-19 深圳追一科技有限公司 命名实体识别方法、装置、计算机设备和存储介质
CN113239188A (zh) * 2021-04-21 2021-08-10 上海快确信息科技有限公司 一种一套金融交易对话信息分析技术方案

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