U.S. patent application number 16/656255 was filed with the patent office on 2021-04-22 for sourcing object creation from natural language inputs.
The applicant listed for this patent is SAP SE. Invention is credited to Kumaraswamy Gowda, Nishant Kumar, Vidhya Neelakantan, Nithya Rajagopalan, Panish Ramakrishna, Erica Vandenhoek, Rajendra Vuppala.
Application Number | 20210117504 16/656255 |
Document ID | / |
Family ID | 1000005505219 |
Filed Date | 2021-04-22 |
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United States Patent
Application |
20210117504 |
Kind Code |
A1 |
Kumar; Nishant ; et
al. |
April 22, 2021 |
Sourcing Object Creation From Natural Language Inputs
Abstract
User-generated input is received that includes a sequence of
words associated with initiation of a computer-implemented event.
Thereafter, such input is parsed using at least one natural
language processing (NLP) model. This parsed input is then used by
a machine learning model to determine a suggested template having a
plurality of fields for initiating the event. The template can then
be presented in a graphical user interface. Related apparatus,
systems, techniques and articles are also described.
Inventors: |
Kumar; Nishant; (Singapore,
SG) ; Ramakrishna; Panish; (Bangalore, IN) ;
Gowda; Kumaraswamy; (Bangalore, IN) ; Vuppala;
Rajendra; (Dublin, CA) ; Neelakantan; Vidhya;
(Bangalore, IN) ; Vandenhoek; Erica; (Palo Alto,
CA) ; Rajagopalan; Nithya; (Bangalore, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SAP SE |
Walldorf |
|
DE |
|
|
Family ID: |
1000005505219 |
Appl. No.: |
16/656255 |
Filed: |
October 17, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
G06N 5/04 20130101; G06F 3/0481 20130101; G06F 40/205 20200101 |
International
Class: |
G06F 17/27 20060101
G06F017/27; G06F 3/0481 20060101 G06F003/0481; G06N 20/00 20060101
G06N020/00; G06N 5/04 20060101 G06N005/04 |
Claims
1. A method for implementation by one or more computing devices
comprising: receiving user-generated input comprising a sequence of
words associated with initiation of a computer-implemented event;
parsing the user-generated input using at least one pre-trained
language model comprising a neural network that classifies each
word to one of a plurality of pre-defined classes, a first of the
pre-defined classes being unknown and a second of the pre-defined
classes being event type; determining, by a machine learning model
using the parsed user-generated input other than the words
classified as being unknown, a suggested template comprising a
plurality of fields for initiating the event, the machine learning
model being different than the pre-trained language model and
trained using historical user selections for events having a same
event type as the initiated computer-implemented event as
determined by the parsing; and presenting, in a graphical user
interface, the suggested template.
2. The method of claim 1 further comprising: receiving, via the
graphical user interface, further user-generated input populating
at least a portion of the fields.
3. The method of claim 2 further comprising: initiating the event
once all of the fields have been completed.
4. The method of claim 3, wherein the event comprises a
computer-implemented procurement process.
5. The method of claim 1 further comprising: populating at least a
portion of the fields based on an output of the machine learning
model.
6. The method of claim 1, wherein the graphical user interface
forms part of a web browser.
7. The method of claim 1, wherein the graphical user interface
forms part of a chatbot interface.
8. The method of claim 1, wherein the user input is received via
e-mail.
9. The method of claim 1, wherein the determining by the machine
learning model comprises: assigning one of a plurality of classes
to each word, wherein the classes are used by the machine learning
model in determining the suggested template.
10. The method of claim 1, wherein the machine learning model is an
extreme gradient boosting model.
11. A system comprising: at least one data processor; and memory
storing instructions which, when executed by the at least one data
processor, result in operations comprising: parsing the
user-generated input using at least one pre-trained language model
comprising a neural network that classifies each word to one of a
plurality of pre-defined classes, a first of the pre-defined
classes being unknown and a second of the pre-defined classes being
event type; determining, by a machine learning model using the
parsed user-generated input other than the words classified as
being unknown, a suggested template comprising a plurality of
fields for initiating the event, the machine learning model being
different than the pre-trained language model and trained using
historical user selections for events having a same event type as
the initiated computer-implemented event as determined by the
parsing; and presenting, in a graphical user interface, the
suggested template.
12. The system of claim 11, wherein the operations further
comprise: receiving, via the graphical user interface, further
user-generated input populating at least a portion of the
fields.
13. The system of claim 12, wherein the operations further
comprise: initiating the event once all of the fields have been
completed.
14. The system of claim 13, wherein the event comprises a
computer-implemented procurement process.
15. The system of claim 11, wherein the operations further
comprise: populating at least a portion of the fields based on an
output of the machine learning model.
16. The system of claim 11, wherein the graphical user interface
forms part of a web browser.
17. The system of claim 11, wherein the graphical user interface
forms part of a chatbot interface.
18. The system of claim 11, wherein the user input is received via
e-mail.
19. The system of claim 11, wherein the determining by the machine
learning model comprises: assigning one of a plurality of classes
to each word, wherein the classes are used by the machine learning
model in determining the suggested template; and wherein the
machine learning model is an extreme gradient boosting model.
20. A non-transitory computer program product storing instructions
which, when executed by at least one computing device, result in
operations comprising: parsing the user-generated input using at
least one pre-trained language model comprising a neural network
that classifies each word to one of a plurality of pre-defined
classes, a first of the pre-defined classes being unknown and a
second of the pre-defined classes being event type; determining, by
a machine learning model using the parsed user-generated input
other than the words classified as being unknown, a suggested
template comprising a plurality of fields for initiating the event,
the machine learning model being different than the pre-trained
language model and trained using historical user selections for
events having a same event type as the initiated
computer-implemented event as determined by the parsing; and
presenting, in a graphical user interface, the suggested template.
Description
TECHNICAL FIELD
[0001] The subject matter described herein relates to the auto
population of forms, such as for sourcing objects, using a
combination of natural language and machine learning
processing.
BACKGROUND
[0002] Conventional techniques for creating various computer-based
events, such as sourcing products as part of a larger supply chain
require a user to manually enter relevant information. Such a
process can be lengthy and, additionally, prone to error. For
example, to create a sourcing object event, a user inputs data into
fixed data entry fields through a user interface (UI). As part of
this process, the user needs to identify and select an appropriate
template for the event of interest. The user then needs to manually
populate various line items in the template (which can range to a
few items to several thousand). In some cases, some or all of the
fields can be populated using a previously created file (e.g., an
EXCEL format file, etc.).
SUMMARY
[0003] In a first aspect, user-generated input is received that
includes a sequence of words associated with initiation of a
computer-implemented event. Thereafter, such input is parsed using
at least one natural language processing (NLP) model. This parsed
input is then used by a machine learning model to determine a
suggested template having a plurality of fields for initiating the
event. The template can then be presented in a graphical user
interface.
[0004] Further user-generated input can be received populating at
least a portion of the fields. The event can be initiated once all
of the fields have been completed. The event can take various forms
including a computer-implemented procurement process.
[0005] In some cases, a machine learning model can be used to
populate at least a portion of the fields. Such machine learning
model can be the same or a different one that the model that
selected the suggested template.
[0006] The graphical user interface can form part of a web browser,
a mobile phone application, a dedicated client application, and/or
a chatbot interface.
[0007] The user-generated input can be received via different
modalities including directly into an input box, via a chatbot, via
voice instructions (by way of a computing device), and/or via
e-mail.
[0008] The machine learning model can assign one of a plurality of
classes to each word that are used to determine the suggested
template.
[0009] The machine learning model can take various forms including
an extreme gradient boosting model.
[0010] Non-transitory computer program products (i.e., physically
embodied computer program products) are also described that store
instructions, which when executed by one or more data processors of
one or more computing systems, cause at least one data processor to
perform operations herein. Similarly, computer systems are also
described that may include one or more data processors and memory
coupled to the one or more data processors. The memory may
temporarily or permanently store instructions that cause at least
one processor to perform one or more of the operations described
herein. In addition, methods can be implemented by one or more data
processors either within a single computing system or distributed
among two or more computing systems. Such computing systems can be
connected and can exchange data and/or commands or other
instructions or the like via one or more connections, including but
not limited to a connection over a network (e.g., the Internet, a
wireless wide area network, a local area network, a wide area
network, a wired network, or the like), via a direct connection
between one or more of the multiple computing systems, etc.
[0011] The subject matter described herein provides many technical
advantages. For example, by using natural language processing
techniques as described herein, user can create events (e.g.,
sourcing events, etc.) using any communication medium including,
but not limited to, voice, e-mail, and chatbot interfaces. Further,
the techniques described herein are also advantageous in that the
underlying algorithms are language agnostics provided that they are
trained with appropriate samples.
[0012] The details of one or more variations of the subject matter
described herein are set forth in the accompanying drawings and the
description below. Other features and advantages of the subject
matter described herein will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0013] FIG. 1 is a process diagram illustrating creation of event
parameters from user-generated input;
[0014] FIG. 2 is an example user-generated input which is parsed
into different classes;
[0015] FIG. 3 is a view of a graphical user interface illustrating
a suggested template;
[0016] FIG. 4 is a process flow diagram illustrating creation of a
sourcing object using a natural language user input; and
[0017] FIG. 5 is a diagram illustrating a computing device for
implementing aspects of the current subject matter.
DETAILED DESCRIPTION
[0018] The current subject matter is directed to the use of a
combination of natural language processing and machine learning to
parse a user input, select an appropriate UI-based template (e.g.,
a web-based form to create a new sourcing object event as part of a
supply chain procurement process, etc.), and at least partially
populate such template with values.
[0019] While the current subject matter is directed to the creation
of a sourcing object (sometimes referred to herein as a sourcing
event involved with a computer-implemented procurement process), it
will be appreciated that the current subject matter can be applied
to the intelligent selection of a user interface template and,
additionally in some cases, to the population of some or all of the
fields of such template. Once populated, the interface can provide
for the user to modify or otherwise alter such populated fields as
may be needed/desired.
[0020] A user can provide user input comprising a natural language
string (i.e., free running text, etc.) via various modalities
including e-mail, chatbot, and/or a document (e.g., a spreadsheet
document, etc.). Once provided, the user input can be parsed using
natural language processing (NLP) as follows. First, various
sourcing object headers can be identified including one or more of:
title of an event (if the user input is e-mail based, the subject
line can be the event title, etc.), type of event (RFP, RFI,
auction, etc.), event start and end dates, geographic region and
corresponding currency, and an associated commodity (i.e., good,
object, etc.). Next, using a machine learning model trained using
historical data with known outcomes, a template can be selected and
recommended to the user (e.g., by presenting such template in a
graphical user interface). The historical data can vary depending
on user preferences or otherwise. For example, the historical data
can be based on commodity, region, user, process owner, item to be
procured, last or previously used templates, etc.
[0021] The template, when presented to the user in the graphical
user interface, can, in some variations, include a plurality of
fields, some of which are prepopulated with data (based on the user
input).
[0022] The machine learning model can take a variety of forms
including, without limitation, an extreme gradient boosting
algorithm, a neural network model (including deep learning), a
logistic regression model, a support vector machine, a random
forest, a nearest neighbor model, a Bayesian model, a genetic
algorithm, and/or the like.
[0023] With reference to diagram 100 of FIG. 1, a text corpus can
be generated 110 in connection with the NLP using text from various
sources. For example, online resources (especially those having
same or similar text across multiple languages such as WIKIPEDIA)
can be accessed. Other textual documents such as product
documentation and the like can be used for creating vector
representations of the corresponding text.
[0024] Next, various information within the vectorized text is
identified and tagged to facilitate training. A pre-trained
language model 105 (i.e., a model trained using a large corpus of
text such as from WIKIPEDIA, blogs, books, and the like that
understands semantics and compositional structure of the language
of interest, etc.) can be applied to transfer learning 115 in a
domain of interest (e.g., procurement) to extract objects of
interest (e.g., a sourcing object, etc.) from the vectorized text.
Transfer learning 115, in this context, refers to the ability to
train a model on one dataset and then adapt that model to perform
different NLP functions on a different dataset. In this case, the
pre-trained language model 105 was trained on Wikipedia and books
and then applied to handle procurement problems. A sequence model
140 can be used to tag each word in the text to one of a plurality
of pre-defined classes. An example class set can include, for
example, {Unknown, Event Type, Start and Closing Date, Currency,
etc}. The sequence model 140 is configured to understand sequences
of words to perform task like translation, pronoun understanding
etc. As an example, a user enters the following free text: "create
an auction which will run from 18.sup.th to 20.sup.th August". To
understand 20.sup.th August is the closing date of event, the
sequence model 140 needs to understand token arrangement in the
sentence.
[0025] During a training phase 145, free text is collected from
multiple sources from which an event is to be created. Free text
120 is collected from multiple sources from which an event is to
created. This collected text is then tokenized and such token are
manually annotated 130 to be labeled as relating to a particular
class to result in an enriched language model 125. All input words
can then be converted to word embeddings 135 (i.e., vectors, etc.)
using the pre-trained language model 105. ElmoEmbeddings is one
example of such a model. Machine learning algorithms typically work
using numbers and the current techniques provides several ways to
convert words to numbers. One class of representation is called
word embeddings. In this approach, word is represented by set of
100 numbers called vectors. The vectors itself are the output of
machine learning algorithm. These vectors are semantically aware
i.e vectors of two close word like King and monarch will be very
similar. With general word embeddings, words like "Bank" will have
same vector even when used as "Bank of America" versus "River
bank". Elmo embeddings addresses such situations by generating
different vectors of same word when used in different context.
[0026] A sequence model architecture 140 can then be used to map
the input words to one of the classes. An example sequence model
architecture can utilize one or more neural networks. For example,
the architecture can include a BiLSTM layer and softmax at the end.
Once the training 145 is completed, weights assigned to each
feature are stored so that they can be later used in real-time when
processing a user input.
[0027] During a real time tagging phase, learned weights can be
loaded into the memory with a neural network architecture during
service startup phase. The service startup phase can also be
referred to as an inference phase during which the trained model is
loaded and inference is done real time when the user makes the
requests. The inference phase starts when the model is loaded into
memory once when server comes online.
[0028] When the system receives free text (i.e., user input) from
email, chatbot, etc, such text is converted to vectors (i.e.,
vectorized) using the pre-trained language model 105. The
pre-trained language model can be a neural network which is trained
using a corpus of text such as using articles on WIKIPEDIA, a
newspaper, etc. A feed forward phase (i.e., an online inference
phase) can get activated which classifies each input words (i.e.,
text string) to one of the predefined classes (e.g., event type,
closing date, unknown, etc.).
[0029] For example if input text is "Create an RFP which runs from
10/4 till 18/4", each of the words will go through the pre-trained
langue model 105 (i.e., a neural network model, etc.) and the
output can be as follows:
[0030] Create=>UNKNOWN
[0031] An=>UNKNOWN
[0032] RFP=>EVENT_TYPE
[0033] Which=>UNKNOWN
[0034] Runs=>UNKNOWN
[0035] From=>UNKNOWN
[0036] 10/4=>UNKNOWN
[0037] Till=>UNKNOWN
[0038] 18/4=>CLOSING DATE
[0039] After ignoring all unknown words, event parameter extracted
from the text is {"EVENT_TYPE": "RFP", "CLOSING DATE":"18/4"}
[0040] As noted in the above example, all words classified as
`Unknown` are ignored and the classified words are considered as
event parameters 150. These event parameters 150 are then used by
one or more machine learning models (e.g., extreme gradient
boosting, etc.) which are trained using historical data to select
an optimal template (either as a suggestion requiring the user to
accept or simply providing such template in an interface). Stated
differently, after the extraction of the event parameters (150), a
next task is identify which template to use. This identification is
based on another machine learning model trained using historical
user selections when, for example, when event type is RFP. This
template selection machine learning model can take various forms
including an extreme gradient boosting algorithm. Other machine
learning models including classification algorithms such as
logistic regression, Random Forests and the like can be
utilized.
[0041] For the sourcing scenario, the historical data used to train
the sequence model 140 can include for example, Template used
(target column), Category/Commodity, Region, Department, Date on
which the template was used, Currency, Baseline spend, and the
like. The sequence model 140 can be retrained over pre-defined
intervals (e.g., every week, every X events, etc.) to include newly
initiated events (i.e., the sequence model 140 can be adaptive in
nature based on continued use).
[0042] FIG. 2 is a sample user-generated input 210 in which certain
words are parsed into different classes which form event parameters
150 to be subsequently used by the sequence model 140. For example,
RFI 220 is classified as an event type, 30.sup.th October 230 is
classified as an end date and Indian Rupees 240 being classified as
currency.
[0043] FIG. 3 is a view 300 of a graphical user interface in which
a particular template is being suggested in response to
user-generated input (whether entered into a prompt on a web
browser, received via a chatbot, received via phone, and/or by
e-mail, etc.). In this particular example, the suggested template
is a request for information (RFI) template.
[0044] The current subject matter can be implemented with an
architecture such as that described in co-pending application
entitled: "Scalable Architecture for Machine Learning Model Reuse"
filed concurrently herewith and assigned U.S. patent application
Ser. No. 16/655,886, the contents of which are hereby incorporated
by reference.
[0045] FIG. 4 is a process flow diagram 400 in which, at 410,
user-generated input is received that includes a sequence of words
associated with initiation of a computer-implemented event.
Thereafter, at 420, the user-generated input is parsed using at
least one natural language processing (NLP) model. This parsed
user-generated input is then used, at 430, by a machine learning
model to determine a suggested template comprising a plurality of
fields for initiating the event. The machine learning model can
take various forms including, for example, an extreme gradient
boosting algorithm, a neural network model (including deep
learning), a logistic regression model, a support vector machine, a
random forest, a nearest neighbor model, a Bayesian model, a
genetic algorithm, and/or the like. Attributes extracted from the
parsed user-generated input can be used to specify an event type
which, in turn, can be used by the model (along with other
extracted attributes from the parsed user-generated input) to
identified the suggested template. The suggested template is then
presented, at 440, in a graphical user interface.
[0046] FIG. 5 is a diagram 500 illustrating a sample computing
device architecture for implementing various aspects described
herein. A bus 504 can serve as the information highway
interconnecting the other illustrated components of the hardware. A
processing system 508 labeled CPU (central processing unit) (e.g.,
one or more computer processors/data processors at a given computer
or at multiple computers), can perform calculations and logic
operations required to execute a program. A non-transitory
processor-readable storage medium, such as read only memory (ROM)
512 and random access memory (RAM) 516, can be in communication
with the processing system 508 and can include one or more
programming instructions for the operations specified here.
Optionally, program instructions can be stored on a non-transitory
computer-readable storage medium such as a magnetic disk, optical
disk, recordable memory device, flash memory, or other physical
storage medium.
[0047] In one example, a disk controller 548 can interface with one
or more optional disk drives to the system bus 504. These disk
drives can be external or internal floppy disk drives such as 560,
external or internal CD-ROM, CD-R, CD-RW or DVD, or solid state
drives such as 552, or external or internal hard drives 556. As
indicated previously, these various disk drives 552, 556, 560 and
disk controllers are optional devices. The system bus 504 can also
include at least one communication port 520 to allow for
communication with external devices either physically connected to
the computing system or available externally through a wired or
wireless network. In some cases, the at least one communication
port 520 includes or otherwise comprises a network interface.
[0048] To provide for interaction with a user, the subject matter
described herein can be implemented on a computing device having a
display device 540 (e.g., a CRT (cathode ray tube) or LCD (liquid
crystal display) monitor) for displaying information obtained from
the bus 504 via a display interface 514 to the user and an input
device 532 such as keyboard and/or a pointing device (e.g., a mouse
or a trackball) and/or a touchscreen by which the user can provide
input to the computer. Other kinds of input devices 532 can be used
to provide for interaction with a user as well; for example,
feedback provided to the user can be any form of sensory feedback
(e.g., visual feedback, auditory feedback by way of a microphone
536, or tactile feedback); and input from the user can be received
in any form, including acoustic, speech, or tactile input. The
input device 532 and the microphone 536 can be coupled to and
convey information via the bus 504 by way of an input device
interface 528. Other computing devices, such as dedicated servers,
can omit one or more of the display 540 and display interface 514,
the input device 532, the microphone 536, and input device
interface 528.
[0049] One or more aspects or features of the subject matter
described herein can be realized in digital electronic circuitry,
integrated circuitry, specially designed application specific
integrated circuits (ASICs), field programmable gate arrays (FPGAs)
computer hardware, firmware, software, and/or combinations thereof.
These various aspects or features can include implementation in one
or more computer programs that are executable and/or interpretable
on a programmable system including at least one programmable
processor, which can be special or general purpose, coupled to
receive data and instructions from, and to transmit data and
instructions to, a storage system, at least one input device, and
at least one output device. The programmable system or computing
system may include clients and servers. A client and server are
generally remote from each other and typically interact through a
communication network. The relationship of client and server arises
by virtue of computer programs running on the respective computers
and having a client-server relationship to each other.
[0050] These computer programs, which can also be referred to as
programs, software, software applications, applications,
components, or code, include machine instructions for a
programmable processor, and can be implemented in a high-level
procedural language, an object-oriented programming language, a
functional programming language, a logical programming language,
and/or in assembly/machine language. As used herein, the term
"machine-readable medium" refers to any computer program product,
apparatus and/or device, such as for example magnetic discs,
optical disks, memory, and Programmable Logic Devices (PLDs), used
to provide machine instructions and/or data to a programmable
processor, including a machine-readable medium that receives
machine instructions as a machine-readable signal. The term
"machine-readable signal" refers to any signal used to provide
machine instructions and/or data to a programmable processor. The
machine-readable medium can store such machine instructions
non-transitorily, such as for example as would a non-transient
solid-state memory or a magnetic hard drive or any equivalent
storage medium. The machine-readable medium can alternatively or
additionally store such machine instructions in a transient manner,
such as for example as would a processor cache or other random
access memory associated with one or more physical processor
cores.
[0051] To provide for interaction with a user, the subject matter
described herein may be implemented on a computer having a display
device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal
display) monitor) for displaying information to the user and a
keyboard and a pointing device (e.g., a mouse or a trackball)
and/or a touch screen by which the user may provide input to the
computer. Other kinds of devices may be used to provide for
interaction with a user as well; for example, feedback provided to
the user may be any form of sensory feedback (e.g., visual
feedback, auditory feedback, or tactile feedback); and input from
the user may be received in any form, including acoustic, speech,
or tactile input.
[0052] In the descriptions above and in the claims, phrases such as
"at least one of" or "one or more of" may occur followed by a
conjunctive list of elements or features. The term "and/or" may
also occur in a list of two or more elements or features. Unless
otherwise implicitly or explicitly contradicted by the context in
which it is used, such a phrase is intended to mean any of the
listed elements or features individually or any of the recited
elements or features in combination with any of the other recited
elements or features. For example, the phrases "at least one of A
and B;" "one or more of A and B;" and "A and/or B" are each
intended to mean "A alone, B alone, or A and B together." A similar
interpretation is also intended for lists including three or more
items. For example, the phrases "at least one of A, B, and C;" "one
or more of A, B, and C;" and "A, B, and/or C" are each intended to
mean "A alone, B alone, C alone, A and B together, A and C
together, B and C together, or A and B and C together." In
addition, use of the term "based on," above and in the claims is
intended to mean, "based at least in part on," such that an
unrecited feature or element is also permissible.
[0053] The subject matter described herein can be embodied in
systems, apparatus, methods, and/or articles depending on the
desired configuration. The implementations set forth in the
foregoing description do not represent all implementations
consistent with the subject matter described herein. Instead, they
are merely some examples consistent with aspects related to the
described subject matter. Although a few variations have been
described in detail above, other modifications or additions are
possible. In particular, further features and/or variations can be
provided in addition to those set forth herein. For example, the
implementations described above can be directed to various
combinations and subcombinations of the disclosed features and/or
combinations and subcombinations of several further features
disclosed above. In addition, the logic flows depicted in the
accompanying figures and/or described herein do not necessarily
require the particular order shown, or sequential order, to achieve
desirable results. Other implementations may be within the scope of
the following claims.
* * * * *