Note: Descriptions are shown in the official language in which they were submitted.
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METHOD FOR PROVIDING ACCESS TO
ONLINE EMPLOYMENT INFORMATION
FIELD OF INVENTION
The present invention relates to employment services and, in particular, to
online
recruiting or employment services.
BACKGROUND OF THE INVENTION
The rapid expansion of job postings on the Internet has created a large amount
of
employment related information, which spans hundreds of thousands of web
sites.
Initially, companies began posting their open job positions on their own
corporate web
sites. A job seeker could then readily access new employment opportunities by
visiting a
company's web site. As an increasing number of company web sites began to post
their
open jobs, however, the job search process grew proportionally. For example, a
job seeker
searching for a "software developer" position would have had to identify and
visit the web
site of every company that might have such open job positions. Thus, this
growth resulted
in a task that was cumbersome and time consuming for the job seeker.
In order to help address these issues, job board web sites have evolved on the
Internet. The original purpose of a job board was to provide a single web site
where
companies could visit to post their open job positions and job seekers could
visit to search
for new employment opportunities. The job board concept helped the job seekers
by
creating a central location that a job seeker could visit to search for jobs.
Unfortunately, however, the concept increased the work and cost for companies.
In
addition to maintaining job postings on their own corporate web sites,
companies were now
required to visit the job board sites to repost, update and delete their job
position
information as appropriate. The accuracy of the job board information was
affected when
companies changed their job information, filled open position, etc., but
failed to update the
corresponding job board postings. These job boards also often charged a fee to
the
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companies for this posting service. In addition, these job boards only
contained job
positions from companies that had actively posted jobs on the sites. In other
words,
companies that did not know about the job boards would have been prevented
from listing
the company's open positions and, consequently, eliminated opportunities for
the job
seekers as well as the company itself.
Most recently, the aggregation, accuracy, and freshness of job board postings
have
been addressed through various web spidering or crawling technologies. The
technology
of web site spidering or crawling consists of a process in which content from
a set of
source web sites is retrieved automatically. This content is typically
retrieved for purpose
of being indexed into a search engine web site in order to provide Internet
users a central
web site to use as a search tool. The type of content that is spidered is
generally not
filtered so the search engine web site often has indexed content from a wide
variety of
source web sites. New web sites that contain content to be spidered have to
register with
the search engine web site before their content is retrieved and indexed into
the search
engine. Once a new site is registered into the set of source web sites to
spider, the search
engine web site will periodically spider the site to search for new or updated
content to
index.
In these updated models, the job board periodically sends out spiders to the
web
sites of companies that register with the job board web site. The purpose of
these spiders is
to retrieve and input the latest job posting infonnation from the company web
sites and
thereby automatically update the job information listed on the job board. The
method,
however, creates a disadvantage for companies and job seekers because the
sites do not
post the numerous job positions from the companies that do not register with
or know of
the job board web site. As such, the Internet contains a vast amount of job
postings which
exist only on company job boards and which are not being collected and
displayed by the
job board web sites.
Another new approach to job posting aggregation is the master search engine
site.
In this approach, the master web site collects a job seeker's search criteria
and submits it to
multiple other job board web sites. The master search engine site aggregates
the individual
sites and presents the results to the job seeker in a single format. An
advantage to this
method is that the job seeker only needs to visit a single site to perform a
job search. The
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disadvantages of this approach are that, as described above, only a subset of
the job board
sites on the Internet are actually searched and individual company job
postings are
completely omitted. Furthermore, in these types of searches, the formatting of
the results
can vary thereby causing the job seeker to become confused when presented with
search
results.
An additional feature of prior art job board web sites is the electronic
notification of
new job opportunities. When a new job is posted that fits within his selected
category
infonnation, the job seeker automatically receives notification of the new job
via email. A
limitation to this system is that user may miss employment opportunities which
are filtered
outside of the selected category information.
Another drawback of the prior art systems relate to the search engines used
for
identifying a position of interest to the job seeker. The prior art systems
use a table, key
word or boolean driven search engine. The search engines use a pull-down menu,
keyword
or boolean search methodology that has a limited ability to implement
intelligent searches.
For instance, a job seeker may be in search of a position in a specific
technical field. A
search of job postings with one or two keywords may identify many unrelated
jobs. It may
be very time consuming for the job seeker to review every identified job
posting. The
effort becomes even greater when compounded by the number of such searches to
be
completed at each of the numerous online employment sites. The job seeker may
use
additional keywords to reduce the number of unrelated job postings. However,
the
additional keywords often have the effect of reducing certain of the job
postings, which
may be of interest to the job seeker, but do not necessarily contain all of
the designated
keywords. In other words, the search strategy may have become too restrictive.
Therefore,
the job seeker ends up accessing only a small fraction of jobs currently
available on the
Internet.
Along with the evolution of job board related web sites, the prior art systems
have
provided job seekers the ability to post electronically their resumes. These
systems have
increased the amount of resumes available online. This increase has created
web sites,
which collect resumes into searchable databases. These web sites often sell
subscription
access to their databases, which employers and recruiters purchase in order to
search for
qualified candidates. However, these web sites suffer from the same
disadvantages and
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limitations as described in the job posting process: a) companies and job
seekers must visit .
the web sites to add and update information; b) searches are limited to
narrowly targeted
keywords; and c) job seeker resumes are sorted into restrictive categories.
Furtheiniore, if these companies do not post at the job board web sites,
without
adequate traffic to their corporate web site and employment pages, employers
cannot, on
their own, reach a sufficient number of qualified candidates. As a result, the
employers
must choose to either pay the third party job board web sites to post a
portion of their jobs
online, making these opportunities accessible to a larger candidate pool, or
miss many
qualified candidate. Despite this investment, however, the factors listed
above still limit
the effectiveness of the job boards and prevent many qualified candidates from
matching
with the opportunities employers have paid to list.
In summary, there are deficiencies in the current state of the art in the
Internet
based employment process. The gap between job board listings and actual online
jobs is
growing rapidly. Companies develop and add recruiting pages to their own web
sites much
faster than the rate at which the top job boards add clients. Moreover, the
gap between
unique job board listings and unique jobs available online is expanding at an
even faster
pace, as companies that use job boards often post the same opening to between
six and ten
sites. Furthermore, the current web site job boards fail to aggregate
completely all job
postings on the Internet. Even the sites that aggregate a larger amount of the
available job
listings are limited by the search engine technology currently used by those
job boards. In
addition, the current prior art systems are deficient in their information
exchange
capabilities. Job board web sites rely on companies and/or job seekers to
continually visit
the job board web sites and update the applicable information.
SUMMARY OF THE INVENTION
The object of the present invention is a method of managing employment data to
provide enhanced access via the Internet to the employment data.
A further object of the present invention is to provide a more thorough and
precise
searching of the employment data.
Still a further object of the present invention is to update automatically the
employment data collected by the present invention.
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Still yet a further object of the present invention is to format the
employment data
so as to allow for a more accurate and efficient search of the employment
data.
Still yet a further object of the present invention is to match automatically
users to
fulfill employment needs.
In general, the present invention consists of several key subsystems. These
subsystems are based on existing software technology, information spidering
and concept
based searching, which is new in its application to the Internet related
employment
industry.
The present invention builds on the technology of job spidering and
aggregation
and incorporates it into the employment field. For example, the working set of
web sites
which this system spiders includes the entire Internet directory ("Dot Corn
database").
Thus, both companies and job boards are included in the job posting
collection.
Furtheimore, the use of spidering technology is extended to resume collection
as well as
spidering of job postings. This allows the creation of a much more
comprehensive and
complete database of the available employment data.
The present invention also applies a concept based search engine to the
employment search and match problem. As noted above, prior art search engine
web sites
are commonly based on keyword search engine technology. In its simplest form,
a
keyword search takes a set of comma delimited user input words and scans its
document
set for one or more word or partial word matches. Keyword searches, however,
have been
enhanced to include word count statistics, i.e., how often a word appears in a
document
increases its relevancy, and boolean operators, i.e., a user can search for
specific terms to
return documents that must contain both words. Unfortunately, these searches
remain as
simple word pattern matching technology, and the casual Internet user does not
necessarily
possess a clear understanding of query word relevancy or boolean logic.
In order to improve the user search experience, concept based search engines
were
created. The premise of a concept based search engine is that it is able to
"learn" thematic
information regarding the documents that it indexes. This
learning is typically
accomplished by applying Bayesian reasoning and neural network technology to
each
document when it is indexed. Users are often able to search the database by
using full
sentence, natural language queries instead of keyword sets and boolean logic.
As a concept
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based search engine learns its document set, it can also make distinctions and
relations.
This learned infounation allows a user to search effectively for infonnation
without
knowing exactly what is being sought or how the query should be phrased.
Another important feature of a concept based search engine is that the user
will
always be provided with some form of results. The results from such a search
engine are
typically returned in descending weight order. A result with 100% weight is
highly
relevant to the user's query, while a result with 1% weight contains little or
no relevance to
the search. This behavior is a key feature of the concept based search engine,
because it
allows a programmatic decision to be made based on the "goodness" of a
particular result.
The use of a concept based search engine in the present invention eliminates
the
need for the user to categorize a job posting or resume into a fixed category
list and to rely
on simple keyword based searches to find information, thereby providing an
accurate and
thorough search result. The present invention then automatically spiders job
and resume
related web sites for content, indexes the content into its concept based
search engines,
matches the content between jobs and resumes, and notifies companies and job
seekers of
new mutual opportunities. This process occurs continuously to maximize the
timeliness
and freshness of the information exchange.
Also, the present invention is able to accept a wide range of job posting
formats and
resume formats. The format of a job posting or resume will vary, often
significantly, from
web site to web site and job seeker to job seeker. By enhancing the process
with newly
developed software, which targets the online employment information, the
system is able
to index this diverse data into a common format. Once in a common format,
matches
within the data between job postings and resumes are efficiently performed.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a functional block diagram of the system of the present invention.
Fig. 2 shows a functional flowchart for creating and accessing a database of
employment data available on the Internet.
Fig. 3 shows a flow chart for determining if the visited web sites meet the
employment criteria.
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Fig. 4 shows a flow chart for updating automatically the employment data
stored in
the database.
Fig. 5 shows a flow chart for formatting and parsing the employment data.
Fig. 6 shows a flow chart for adjusting the revisitation period of the visited
web
sites.
Fig. 7 shows a flowchart showing the aging and deletion step.
Fig. 8 shows a flow chart for collecting subscriber search criteria and
conducting a
concept-based search using the criteria.
Fig. 9 shows a flowchart of matching the employment data and notifying the
users.
Fig. 10 provides a table depicting employment data.
DETAILED DESCRIPTION OF THE INVENTION
With reference to FIG 1, a system 10 of managing employment data is shown. The
system 10 includes a dedicated spidering server 12, a dedicated search,
retrieve and
process server 14 and a database 16. The system 10 provides users (not shown)
with the
ability to search, via the Internet 18, for employment data located at public
job boards 20,
corporate web sites 22 and other web sites 24. Users are provided access to
the system 10
via user Internet connections 26. The Internet connections 26 may be personal
computers,
for example.
The dedicated spidering server 12 is used to search the Internet for the
employment
data. FIG 10 provides a table showing an example of employment data 28 or
information
available via the Internet 18. Once the employment data is located, relevant
information is
loaded into the database 16. The dedicated search, retrieve and process server
14 provides
the user the ability to search the database 16 for employment data. Users
include
corporation representatives seeking to fill a position, agents working for the
corporations,
as well as individuals seeking an employment position. The dedicated search,
retrieve and
process server 14 also conducts automatic searches of the database for
matching
employment data (i.e., matching jobs and resumes).
It will become clear from FIG 2 that the database 16 of FIG 1 represents
multiple
databases having individual functions. FIG. 2 discloses a process or
functional block
diagram of the present invention. In particular, FIG 2 discloses a process
which
dynamically retrieves and indexes large amounts of web employment data and
processes
this information in an efficient and timely manner. The Dot Corn database 30
contains a
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listing of all the active domain names on the Internet 18. The prequalify
dictionary 32
consists of a concept based search engine that has been loaded with template
documents to
identify web pages that contain job posting or resume information. The site
prequalification step 34 receives input from the Dot Corn database 30 and the
prequalify
dictionary 32. The site prequalification step 34 filters web sites that
contain job postings or
resumes. The output of step 34 includes URL records, which are stored in the
active
spider's database 36. Step 34 is shown in greater detail in FIG. 3. Begin site
prequalification at step 3.1 Step 3.2 of FIG 3 begins with reading the
prequalify dictionary
32. Step 3.3 reads the next record from the Dot Corn data base 30. Determine
if end of
records at step 3.4. If end, then done at step 3.14. Step 3.5 consists of
determining
whether the record is scheduled for a check. At step 3.6, each record is
checked against the
Internet domain named service (DNS) to verify whether an active web site
exists for the
domain name. Determine if DNS valid at step 3.7. In the event it is determine
that an
active web site does not exist, then step 3.13 consists of scheduling the web
site or record
for a future check. In the event the web site is active, step 3.8 consists of
fetching the
content of the web site. Step 3.10 consists of checking the site content
against the
prequalify dictionary 32. The prequalify dictionary 32 contains a concept base
search
engine which has been configured with template sample documents of job
postings and
resumes. Each page of site content that is retrieved at step 3.8 is presented
as a query input
to the prequalify dictionary concept based search engine at step 3.10.
Determine if check
successful at step 3.11 and if so, add site to active spider database at step
3.12. The search
engine returns a rated percent result, which indicates how relevant a
particular site page is
with respect to job postings or resumes. If a web site is determined to
contain documents
of sufficient relevancy, the site is stored in the active spider's database
36, enabling the site
to be regularly spidered for its content. The retrieve content is stored in
the spidered
content database 38. If a web site does not exist or has no relevant content,
it is scheduled
at step 3.13 for a future check, at which time the site prequalification step
34 will revisit
the site to repeat the foregoing process.
The site prequalification step 34 contains several key operating parameters,
including the maximum number of pages to retrieve from a single web site, the
amount of
time to spend spidering a single web site and a threshold relevancy wait that
is used to
indicate whether the site contains job postings or resumes of related content.
Critical to
this step is the configuration of the prequalify dictionary 32, as its
document set is the
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mechanism that controls which web sites are accepted as valid and which are
rejected. The
architecture of a site group prequalification step 34 is readily scalable, as
in practice
several services can be operating in parallel on the Dot Corn data base 30 to
perform the
web site validation process. By scaling services in this manner, the
information scan rate
of the millions of records of the Dot Corn database 30 is easily controlled.
The periodic spidering step 40 of FIG 2 is responsible for running each of the
spiders in the active spider's database 36 on a regular, scheduled basis. FIG
4 discloses the
periodic spidering step 40 in greater detail. Begin periodic spidering at step
4.1. Step 4.2
consists of reading the next record from the active spider's database 36.
Determine if end
of records at step 4.3 and if so, done at step 4.11. Step 4.4 determines
whether the web
site corresponding to the record is scheduled to be spidered. In the event the
web site is
scheduled to be spidered, step 4.5 fetches the site content. Step 4.7
determines whether the
newly fetched content has changed from the corresponding content previously
stored in
the spidered content database 38 (FIG. 2) to determine whether the web site
has changed.
If a change has occurred, the new content is stored in the spider content
database 38 for
further processing. If content changed, then update spidered content database
at step 4.8.
If it is determined at step 4.6 that the spider fails when accessing a
particular web
site, step 4.9 consists of identifying the site as "failed" and removing the
sit& from the
active spider's database 36. Step 4.10 updates the Dot Corn database 30 to
schedule the site
to be requalified at a later time.
Step 40 is designed to run continuously to ensure that when the content of
each
source site changes, it is quickly updated in the spider content database 38.
Thus, the
timeliness and freshness of the information is preserved. Step 40 is readily
scalable, as in
practice several services can be operated and parallel to perform this
spidering process. As
additional spiders are created, additional service can be added to handle the
new load.
The content processing step 42 of FIG. 2 consists of further processing the
content,
which is temporarily stored in the spider content database 38. The processing
dictionary 44
consists of a concept based search engine, which is similar to the prequalify
dictionary 32.
The search engine has been loaded with additional template documents that
enable
spidered content to be parsed and scrubbed prior to being loaded into the
searchable
content database 46. The content processing step 42 is shown in greater detail
in FIG 5.
The content processing step 42 is responsible for processing each retrieved
document into
a format that is suitable for indexing into the searchable content database
36. The
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processing dictionary 44 contains a concept based search engine, which has
been
configured with documents that contain specific job titles, job descriptions
and resume
descriptions. The dictionary 44 is used to measure the relevance of each
spidered content
document to determine whether it should be classified as a job-posting, resume
or
irrelevant, at which time the content is discarded. Another task of step 42 is
the parsing
and analysis of web pages, which contain multiple sets of information. For
example, a
single web page, which contains 15 different job postings, is broken down into
15 separate
documents utilizing available advanced document parsing technology. Each
document
would contain its own title and specific job location information. The
improved content
results in a search experience that is clear and concise to the user.
Begin content processing at step 5.1. Step 5.2 consists of reading the
processing
dictionary 44. Step 5.3 consists of reading the next record from the spidered
content data
base 38. Determine if end of records at step 5.4 and if so, done at step 5.14.
Step 5.5 strips
the document of its hypertext markup language (HTML) commands. The stripped
document is evaluated by step 5.6 for its length requirements, and is scanned
at step 5.7
and 5.8 to identify the location information (city, state, and zip code), and
the e-mail
address information.
The document is then presented as query input through the processing
dictionary
44. The concept based search engine is used to further identify the document
as a job
posting or resume as well as determine its title information and amount of
different
information which the document may contain (see step 5.9). Documents that do
not meet
minimum relevancy requirements as a job posting or resume are discarded (step
5.10 and
5.12). Scan for document body content at step 5.11. Documents that pass the
noted criteria
are indexed into the searchable content database 46 as a job posting or resume
(step 5.13).
After a document passes through this process, its record in the searchable
content
database 46 represents a uniform entry, which is consistent with the other
records. The
content processing step 42 is designed to run continuously as new information
is placed
into the spidered content database 38. Thus, the timeliness and freshness of
the
information is preserved. Step 42 is readily scalable, as in practice several
servers can be
operating in parallel to perform the content processing. As the input
spidering process
information flow increases, additional servers can be added to handle the new
content
processing load.
The spider adaptation step 48 of FIG 2 is responsible for dynamically
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operating parameters of each spider. The adaptation step 48 is shown in
greater detail in
FIG. 6. Begin spider adaptation at step 6.1. Step 6.2 consists of reading the
next site of
which the content was previously processed and stored in the searchable
content database
46. Determine whether end of sites at step 6.3 and if so, done at step 6.9 In
the event it is
determined at step 6.4 that the particular spider failed or retrieved
irrelevant content (not
job posting or resume related content), then step 6.10 sets the spider status
as "failed" in
the active spider data base 36, and at step 6.11, the Dot Corn data base 30 is
updated to
requalify the failed site at a later time.
Step 6.5 compares the content retrieved at step 6.2 with the content
previously
stored in the searchable content database 46. Step 6.6 determines whether the
changed
limit has been exceeded. Based on the amount of changes that have occurred,
the spider
schedule will be adjusted accordingly. In the event the change limit has been
exceeded,
then step 6.12 will set the spider to run again the following day. In the
event the change
limit has not exceeded, then step 6.7 and 6.8 will increase the spider
frequency for that
particular site by an additional day if the delay is presently less than 30
days. The spider
adaptation step 48 is designed to run continuously as a feedback loop between
the content
processing step 42 and the periodic spidering step 40. Step 48 is readily
scalable, as in
practice several servers can be operating in parallel to perform this step 48.
As the input
spidering process information flow increases, additional service can be added
to handle the
new load.
The aging and deletion step 50 is responsible for expiring old information in
the
searchable content database 46. The aging and deletion step 50 is shown in
greater detail
in FIG 7. Begin aging and deletion at step 7.1. Step 7.2 reads the next record
from the
searchable content data base 46. Determine if end of records at step 7.3 and
if so, done at
step 7.6. Step 7.4 determines whether the document date has expired. In the
event the
document date has expired, step 7.5 deletes the document from the searchable
content
database 46. Step 50 ensures that old web sites that have been removed from
the Internet
are identified, and their content document sets are purged from the overall
system. The
aging and deletion step 50 is designed to run continuously, and it is readily
scalable, as in
practice several servers can be operating in parallel to perform this aging
and deletion step.
The result of the foregoing provides a searchable content database 46 of job
positions and resumes, which may be "manually" searched by users as well as
searched via
an automatic process.
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The "manual" search is initiated at the user search step 52 and continues with
the
concept phase step 54, the keyword phase step 56 and concludes with the search
results
58. FIG 8 discloses additional details as to the user search. Begin user
search at step 8.1.
Step 8.2 consists of reading the user search input. Step 8.3 determines
whether the title,
description or key words have been entered. If yes, determine if location
entered at step
8.4. If location not entered, clear search start index at step 8.5. However,
the user may
further include information such as the city, state, range of location and
number of results
returned, etc. The concept phase step 54 occurs at step 8.6 whereupon concept
searching is
conducted upon the searchable content database 46 using the user input.
Determine if
results at step 8.7. The results are processed at step 8.8 whereupon
traditional text
processes and techniques are used on the result to produce a filtered result
set. Step 8.9
determines whether the quantity of the results meets the users specified
quantity in order
to determine whether the search may be concluded. If yes, then done at step
8.10. If the
location is entered at step 8.4, then validate city and state location,
determine if location
valid, determine if location range entered, and if yes, build concept location
search spec.
The user search step provides a front-end, manual interface for job seekers
and
employers or recruiters to search for employment data, i.e., job postings or
resumes,
respectively. The job seeker's search is provided as a free service, whereas
the resume
search is sold as a subscription service.
The user search is designed to run on user demand, and is readily scalable, as
in
practice several servers can be operating in parallel to service multiple user
search
requests. As the number of new users searching the system increases,
additional servers
can be added to handle the new load.
The automatic match step 60 is responsible for identifying matches between the
employer's (job postings) and job seekers (resumes). As matches are
identified, both the
employer and job seeker are notified via e-mail. FIG 9 discloses the automatic
match step
60 in greater detail.
Begin automatic match at step 9.1. Step 9.2 consists of reading the next new
job
posting from the searchable content database 46. Determine if end of records
at step 9.3.
Step 9.4 consists of using the contents of the new job posting as query input
to perform a
concept based search on the resumes in the searchable content data base 46.
Determine if
results found at step 9.5. The results of this search consist of a set of
resumes that meet a
relevant percent rate with respect to the job posting content. The candidates
of these
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resumes are identified as "good matches" for a particular job posting. At
steps 9.6 and 9.7,
the employer corresponding to the new job posting and the candidates
corresponding to
the identified resumes, are contacted via e-mail.
Step 9.8 consists of reading the next new resume from the searchable content
data
base 46. Determine if end of records at step 9.1., and if so, done at step
9.14. At step 9.10,
the contents of the new resume are used as query input to perform a concept
based search
on the job postings in the searchable content database 46. Determine if
results found at
step 9.11. The results of this search consist of a set of job postings that
meet a relevant
percent rate with respect to the resume content. The jobs are identified as
"good matches"
for the particular candidate. Steps 9.12 and 9.13 consist of contacting the
employers
corresponding to the job posting results, and the candidate corresponding to
the new
resume.
When a candidate receives an e-mail message containing the job description(s),
the
candidate is able to access the job posting details, company information, etc.
free of
charge. Once the candidate reviews this information, the candidate may choose
to apply to
a job, also free of charge. When an employer or recruiter receives the e-mail
message
identifying an eligible candidate(s) and the qualification summaries, the
employer or
recruiter may elect to purchase a web site subscription, which allows access
to each
candidate's resume and contact information. Furthermore, when an employer or
recruiter
subscribes to the web site and accesses various candidate information, the
employer or
recruiter may also elect to engage recruiting services to assist in pursuing
the
candidate.
The automatic match step 60 is designed to run continuously as new job
postings
and resumes are added to the searchable content database 46. The match step 60
is
scalable, as in practice several servers can be operated in parallel to
perform this matching
and e-mail notification process. As the input information flow to the
searchable content
database 46 increases, additional servers can be added to handle the new load.
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