System, method and article of manufacture for mobile communication utilizing an interface support framework6356905Abstract A system is disclosed that facilitates web-based information retrieval and display system. A wireless phone or similar hand-held wireless device with Internet Protocol capability is combined with other peripherals to provide a portable portal into the Internet. The wireless device prompts a user to input information of interest to the user. This information is transmitted a query to a service routine (running on a Web server). The service routine then queries the Web to find price, shipping and availability information from various Web suppliers. This information is then available for use by various applications through an interface support framework. Claims What is claimed is: Description FIELD OF THE INVENTION
A.1.1.1.1.1 Public Type tMeetingRecord
sUserID As String (user id given by Munin)
sTitleOrig As String (original non stop listed title we need to keep
around to send back to Munin)
sTitleKW As String (stoplisted title with only keywords)
sBodyKW As String (stoplisted body with only keywords)
sCompany() As String (companies identified in title or body through
pattern matching)
sTopic() As String (topics identified in title or body through
pattern matching)
sPeople() As String (people identified in title or body through
pattern matching)
sWhen() As String (time identified in title or body through pattern
matching)
sWhere() As String (location identified in title or body through
pattern matching)
sLocation As String (location as passed in by Munin)
sTime As String (time as passed in by Munin)
sParticipants() As String (all participants engaged as passed in by
Munin)
sMeetingText As String (the original meeting text w/o userid)
EndType
There are two other structures which are created to hold each individual pattern utilized in pattern matching. The record tAPatternRecord is an array containing all the components/elements of a pattern. The type tAPatternElement is an array of strings which represent an element in a pattern. Because there may be many "substitutes" for each element, we need an array of strings to keep track of what all the substitutes are. The structures of tAPatternElement and tAPatternRecord are presented below in accordance with a preferred embodiment.
Public Type tAPatternElement
elementArray() As String
End Type
Public Type tAPatternRecord
patternArray() As tAPatternElement
End Type
Common User Defined Constants Many constants are defined in each declaration section of the program which may need to be updated periodically as part of the process of maintaining the system in accordance with a preferred embodiment. The constants are accessible to allow dynamic configuration of the system to occur as updates for maintaining the code. Included in the following tables are lists of constants from each module which I thought are most likely to be modified from time to time. However, there are also other constants used in the code not included in the following list. It does not mean that these non-included constants will never be changed. It means that they will change much less frequently.
For the Main Module (BF.Main):
CONSTANT PRESET VALUE USE
MSGTOMUNIN_TYPE 6 Define the message number used
to identify messages between BF
and Munin
IP_ADDRESS_MUNIN "10.2.100.48" Define the IP address of the
machine in which Munin and BF
are running on so they can
transfer
data through UDP.
PORT_MUNIN 7777 Define the remote port in which
we are operating on.
TIMEOUT_AV 60 Define constants for setting time
out in inet controls
TIMEOUT_NP 60 Define constants for setting time
out in inet controls
CMD_SEPARATOR ".backslash." Define delimiter to tell which
part
of Munin's command represents
the beginning of our input meeting
text
OUTPARAM_SEPARATOR "::" Define delimiter for separating
out
different portions of the output.
The separator is for delimiting
the
msg type, the user id, the meeting
title and the beginning of the
actual stories retrieved.
For the Search Module (BF.Search):
CONSTANT CURRENT VALUE USE
PAST_NDAYS 5 Define number of days you want
to
look back for AltaVista
articles.
Doesn't really matter now
because
we aren't really doing a news
search in alta vista. We want
all
info.
CONNECTOR_AV_URL "+AND+" Define how to connect keywords.
We want all our keywords in the
string so for now use AND. If
you
want to do an OR or something,
just change connector.
CONNECTOR_NP_URL "+AND+" Define how to connect keywords.
We want all our keywords in the
string so for now use AND. If
you
want to do an OR or something,
just change connector.
NUM_NP_STORIES 3 Define the number of stories to
return back to Munin from
NewsPage.
NUM_AV_STORIES 3 Define the number of stories to
return back to Munin from
AltaVista.
For the Parse Module (BF.Parse):
CONSTANT CURRENT VALUE USE
PORTION_SEPARATOR "::" Define the separator between
different portions of the
meeting
text sent in by Munin. For
example
in "09::Meet with Chad::about
life::Chad.vertline.Denise::::::" "::" is the
separator between different
parts
of the meeting text.
PARTICIPANT_SEPARATOR ".vertline." Define the separator between
each
participant in the participant
list
portion of the original
meeting
text. Refer to example above.
For Pattern Matching Module (BFPatternMatch): There are no constants in this module which require frequent updates. General Process Flow The best way to depict the process flow and the coordination of functions between each other is with the five flowcharts illustrated in FIGS. 2 to 6. FIG. 2 depicts the overall process flow in accordance with a preferred embodiment. Processing commences at the top of the chart at function block 200 which launches when the program starts. Once the application is started, the command line is parsed to remove the appropriate meeting text to initiate the target of the background find operation in accordance with a preferred embodiment as shown in function block 210. A global stop list is generated after the target is determined as shown in function block 220. Then, all the patterns that are utilized for matching operations are generated as illustrated in function block 230. Then, by tracing through the chart, function block 200 invokes GoBF 240 which is responsible for logical processing associated with wrapping the correct search query information for the particular target search engine. For example, function block 240 flows to function block 250 and it then calls GoPatternMatch as shown in function block 260. To see the process flow of GoPattemMatch, we swap to the diagram titled "Process Flow for BF's Pattern Matching Unit." One key thing to notice is that functions depicted at the same level of the chart are called by in sequential order from left to right (or top to bottom) by their common parent function. For example, Main 200 calls ProcessCommandLinc 210, then CreateStopListist 220, then CreatePatterns 230, then GoBackgroundFinder 240. FIGS. 3 to 6 detail the logic for the entire program, the parsing unit, the pattern matching unit and the search unit respectively. FIG. 6 details the logic determinative of data flow of key information through BackgroundFinder, and shows the functions that are responsible for creating or processing such information. Detailed Search Architecture Under the Simple Query Mode Search Alta Vista (Function Block 270 of FIG. 2) The Alta Vista search engine utilizes the identifies and returns general information about topics related to the current meeting as shown in function block 270 of FIG. 2. The system in accordance with a preferred embodiment takes all the keywords from the title portion of the original meeting text and constructs an advanced query to send to Alta Vista. The keywords are logically combined together in the query. The results are also ranked based on the same set of keywords. One of ordinary skill in the art will readily comprehend that a date restriction or publisher criteria could be facilitated on the articles we want to retrieve. A set of top ranking stories are returned to the calendaring system in accordance with a preferred embodiment. News Page (Function Block 275 of FIG. 2) The NewsPage search system is responsible for giving us the latest news topics related to a target meeting. The system takes all of the keywords from the title portion of the original meeting text and constructs a query to send to the NewsPage search engine. The keywords are logically combined together in the query. Only articles published recently are retrieved. The Newspage search system provides a date restriction criteria that is settable by a user according to the user's preference. The top ranking stories are returned to the calendaring system. FIG. 3 is a user profile data model in accordance with a preferred embodiment. Processing commences at function block 300 which is responsible for invoking the program from the main module. Then, at function block 310, a wrapper function is invoked to prepare for the keyword extraction processing in function block 320. After the keywords are extracted, then processing flows to function block 330 to determine if the delimiters are properly positioned. Then, at function block 340, the number of words in a particular string is calculated and the delimiters for the particular field are and a particular field from the meeting text is retrieved at function block 350. Then, at function block 380, the delimiters of the string are again checked to assure they are placed appropriately. Finally, at function block 360, the extraction of each word from the title and body of the message is performed a word at a time utilizing the logic in function block 362 which finds the next closest word delimiter in the input phrase, function block 364 which strips unnecessary materials from a word and function block 366 which determines if a word is on the stop list and returns an error if the word is on the stop list. Pattern Matching in Accordance with a Preferred Embodiment The limitations associated with a simple searching method include the following: 1. Because it relies on a stoplist of unwanted words in order to extract from the meeting text a set of keywords, it is limited by how comprehensive the stoplist is. Instead of trying to figure out what parts of the meeting text we should throw away, we should focus on what parts of the meeting text we want. 2. A simple search method in accordance with a preferred embodiment only uses the keywords from a meeting title to form queries to send to Alta Vista and NewsPage. This ignores an alternative source of information for the query, the body of the meeting notice. We cannot include the keywords from the meeting body to form our queries because this often results in queries which are too long and so complex that we often obtain no meaningful results. 3. There is no way for us to tell what each keyword represents. For example, we may extract "Andy" and "Grove" as two keywords. However, a simplistic search has no way knowing that "Andy Grove" is in fact a person's name. Imagine the possibilities if we could somehow intelligently guess that "Andy Grove" is a person's name. Information such as where he is employed and currently resides. 4. In summary, by relying solely on a stoplist to parse out unnecessary words, we suffer from "information overload". Pattern Matching Overcomes these Limitations in Accordance with a Preferred Embodiment Here is how the pattern matching system can address each of the corresponding issues above in accordance with a preferred embodiment. 1. By doing pattern matching, we match up only parts of the meeting text that we want and extract those parts. 2. By performing pattern matching on the meeting body and extracting only the parts from the meeting body that we want. Our meeting body will not go to complete waste then. 3. Pattern matching is based on a set of templates that we specify, allowing us to identify people names, company names and other items from a meeting text. 4. In summary, with pattern matching, we no longer suffer from information overload. Of course, the big problem is how well our pattern matching works. If we rely exclusively on artificial intelligence processing, we do not have a 100% hit rate. We are able to identify about 20% of all company names presented to us. Patterns A pattern in the context of a preferred embodiment is a template specifying the structure of a phrase we are looking for in a meeting text. The patterns supported by a preferred embodiment are selected because they are templates of phrases which have a high probability of appearing in someone's meeting text. For example, when entering a meeting in a calendar, many would write something such as "Meet with Bob Dutton from Stanford University next Tuesday." A common pattern would then be something like the word "with" followed by a person's name (in this example it is Bob Dutton) followed by the word "from" and ending with an organization's name (in this case, it is Stanford University). Pattern Matching Terminology The common terminology associated with pattern matching is provided below. Pattern: a pattern is a template specifying the structure of a phrase we want to bind the meeting text to. It contains sub units. Element: a pattern can contain many sub-units. These subunits are called elements. For example, in the pattern "with $PEOPLE$ from $COMPANY$", "with" "$PEOPLE$" "from" "$COMPANY$" are all elements. Placeholder: a placeholder is a special kind of element in which we want to bind a value to.Using the above example, "$PEOPLE$" is a placeholder. Indicator: an indicator is another kind of element which we want to find in a meeting text but no value needs to bind to it. There may be often more than one indicator we are looking for in a certain pattern. That is why an indicator is not an "atomic" type. Substitute: substitutes are a set of indicators which are all synonyms of each other. Finding any one of them in the input is good. There are five fields which are identified for each meeting:
.diamond-solid. Company ($COMPANY$)
.diamond-solid. People ($PEOPLE$)
.diamond-solid. Location ($LOCATION$)
.diamond-solid. Time ($TIME$)
.diamond-solid. Topic ($TOPIC_UPPER$) or ($TOPIC_ALL$)
In parentheses are the placeholders I used in my code as representation of the corresponding meeting fields. Each placeholder has the following meaning: $COMPANY$: binds a string of capitalized words (e.g., Meet with Joe Carter of <Andersen Consulting >) $PEOPLE$: binds series of string of two capitalized words potentially connected by "," "and" or "&" (e.g., Meet with <Joe Carter> of Andersen Consulting, Meet with <Joe Carter and Luke Hughes> of Andersen Consulting) $LOCATION$: binds a string of capitalized words (e.g., Meet Susan at <Palo Alto Square>) $TIME$: binds a string containing the format #:## (e.g., Dinner at <6:30 pm>) $TOPIC_UPPER$: binds a string of capitalized words for our topic (e.g., <Stanford Engineering Recruiting> Meeting to talk about new hires). $TOPIC_ALL$: binds a string of words without really caring if it's capitalized or not. (e.g., Meet to talk about <ubiquitous computing>) Here is a table representing all the patterns supported by BF. Each pattern belongs to a pattern group. All patterns within a pattern group share a similar format and they only differ from each other in terms of what indicators are used as substitutes. Note that the patterns which are grayed out are also commented in the code. BF has the capability to support these patterns but we decided that matching these patterns is not essential at this point.
PAT PAT
GRP # PATTERN EXAMPLE
1 a $PEOPLE$ of Paul Maritz of Microsoft
$COMPANY$
b $PEOPLE$ from Bill Gates, Paul Allen and
$COMPANY$ Paul Maritz from Microsoft
2 a $TOPIC_UPPER$ meeting Push Technology Meeting
b $TOPIC_UPPER$ mtg Push Technology Mtg
c $TOPIC_UPPER$ demo Push Technology demo
d $TOPIC_UPPER$ Push Technology interview
interview
e $TOPIC_UPPER$ Push Technology
presentation presentation
f $TOPIC_UPPER$ visit Push Technology visit
g $TOPIC_UPPER$ briefing Push Technology briefing
h $TOPIC_UPPER$ Push Technology
discussion discussion
i $TOPIC_UPPER$ Push Technology
workshop workshop
j $TOPIC_UPPER$ prep Push Technology prep
k $TOPIC_UPPER$ review Push Technology review
l $TOPIC_UPPER$ lunch Push Technology lunch
m $TOPIC_UPPER$ project Push Technology project
n $TOPIC_UPPER$ projects Push Technology projects
3 a $COMPANY$ corporation Intel Corporation
b $COMPANY$ corp. IBM Corp.
c $COMPANY$ systems Cisco Systems
d $COMPANY$ limited IBM limited
e $COMPANY$ ltd IBM ltd
4 a about $TOPIC_ALL$ About intelligent agents
technology
b discuss $TOPIC_ALL$ Discuss intelligent agents
technology
c show $TOPIC_ALL$ Show the client our
intelligent agents
technology
d re: $TOPIC_ALL$ re: intelligent agents
technology
e review $TOPIC_ALL$ Review intelligent agents
technology
f agenda The agenda is as follows:
--clean up
--clean up
--clean up
g agenda: $TOPIC_ALL$ Agenda:
--demo client intelligent
agents technology.
--demo ecommerce.
5 a w/$PEOPLE$ of Meet w/Joe Carter of
$COMPANY$ Andersen Consulting
b w/$PEOPLE$ from Meet w/Joe Carter from
$COMPANY$ Andersen Consulting
6 a w/$COMPANY$ per Talk w/Intel per Jason
$PEOPLE$ Foster
7 a At $TIME$ at 3:00 pm
b Around $TIME$ Around 3:00 pm
8 a At $LOCATION$ At LuLu's restaurant
b In $LOCATION$ in Santa Clara
9 a Per $PEOPLE$ per Susan Butler
10 a call w/$PEOPLE$ Conf call w/John Smith
B call with $PEOPLE$ Conf call with John Smith
11 A prep for $TOPIC_ALL$ Prep for London meeting
B preparation for Preparation for London
$TOPIC_ALL$ meeting
FIG. 4 is a detailed flowchart of pattern matching in accordance with a preferred embodiment. Processing commences at function block 400 where the main program invokes the pattern matching application and passes control to function block 410 to commence the pattern match processing. Then, at function block 420, the wrapper function loops through to process each pattern which includes determining if a part of the text string can be bound to a pattern as shown in function block 430. Then, at function block 440, various placeholders are bound to values if they exist, and in function block 441, a list of names separated by punctuation are bound, and at function block 442 a full name is processed by finding two capitalized words as a full name and grabbing the next letter after a space after a word to determine if it is capitalized. Then, at function block 443, time is parsed out of the string in an appropriate manner and the next word after a blank space in function block 444. Then, at function block 445, the continuous phrases of capitalized words such as company, topic or location are bound and in function block 446, the next word after the blank is obtained for further processing in accordance with a preferred embodiment. Following the match meeting field processing, function block 450 is utilized to locate an indicator which is the head of a pattern, the next word after the blank is obtained as shown in function block 452 and the word is checked to determine if the word is an indicator as shown in function block 454. Then, at function block 460, the string is parsed to locate an indicator which is not at the end of the pattern and the next word after unnecessary white space such as that following a line feed or a carriage return is processed as shown in function block 462 and the word is analyzed to determine if it is an indicator as shown in function block 464. Then, in function block 470, the temporary record is reset to the null set to prepare it for processing the next string and at function block 480, the meeting record is updated and at function block 482 a check is performed to determine if an entry is already made to the meeting record before parsing the meeting record again. Using the Identified Meeting Fields Now that we have identified fields within the meeting text which we consider important, there are quite a few things we can do with it. One of the most important applications of pattern matching is of course to improve the query we construct which eventually gets submitted to Alta Vista and News Page. There are also a lot of other options and enhancements which exploit the results of pattern matching that we can add to BF. These other options will be described in the next section. The goal of this section is to give the reader a good sense of how the results obtained from pattern matching can be used to help us obtain better search results. FIG. 5 is a flowchart of the detailed processing for preparing a query and obtaining information from the Internet in accordance with a preferred embodiment. Processing commences at function block 500 and immediately flows to function block 510 to process the wrapper functionality to prepare for an Internet search utilizing a web search engine. If the search is to utilize the Alta Vista search engine, then at function block 530, the system takes information from the meeting record and forms a query in function blocks 540 to 560 for submittal to the search engine. If the search is to utilize the NewsPage search engine, then at function block 520, the system takes information from the meeting record and forms a query in function blocks 521 to 528. Alta Vista Search Engine The strength of the Alta Vista search engine is that it provides enhanced flexibility. Using its advance query method, one can construct all sorts of Boolean queries and rank the search however you want. However, one of the biggest drawbacks with Alta Vista is that it is not very good at handling a large query and is likely to give back irrelevant results. If we can identify the topic and the company within a meeting text, we can form a pretty short but comprehensive query which will hopefully yield better results. We also want to focus on the topics found. It may not be of much merit to the user to find out info about a company especially if the user already knows the company well and has had numerous meetings with them. It's the topics they want to research on. News Page Search Engine The strength of the News Page search engine is that it does a great job searching for the most recent news if you are able to give it a valid company name. Therefore when we submit a query to the news page web site, we send whatever company name we can identify and only if we cannot find one do we use the topics found to form a query. If neither one is found, then no search is performed. The algorithmn utilized to form the query to submit to Alta Vista is illustrated in FIG. 7. The algorithmn that we will use to form the query to submit to News Page is illustrated in FIG. 8. The following table describes in detail each function in accordance with a preferred embodiment. The order in which functions appear mimics the process flow as closely as possible. When there are situations in which a function is called several times, this function will be listed after the first function which calls it and its description is not duplicated after every subsequent function which calls it.
Procedure
Name Type Called By Description
Main Public None This is the main function
where the program first launches. It initializes BF with the
(BF.Main) Sub appropriate parameters
(e.g., Internet time-out, stoplist. . .) and calls GoBF to launch the
main part of the program.
ProcessCommandLine Private Main This function parses the
command line. It assumes that the delimiter indicating
(BF.Main) Sub the beginning of imput
from Munin is stored in the constant CMD_SEPARATOR.
CreateStopList Private Main This function sets up a
stop list for future use to parse out unwanted words from the
(BF.Main) Function meeting text. There are
commas on each side of each word to enable straight checking.
CreatePatterns Public Main This procedure is called
once when BF is first initialized to create all the potential
(BF.Pattern Sub patterns that portions of
the meeting text can bind to. A pattern can contain however
Match) many elements as needed.
There are two types of elements. The first type of elements are
indicators. These are
real words which delimit the potential of a meeting field (eg
company) to follow. Most
of these indicators are stop words as expected because stop
words are words usually
common to all meeting text so it makes sense they form
patterns. The second type
of elements are special strings which represent
placeholders. A
placeholder is always in the form of $*$ where * can be either
PEOPLE, COMPANY,
TOPIC_UPPER, TIME, LOCATION or TOPIC_ALL. A
pattern can begin with
either one of the two types of elements and can be however long,
involving however any
number/type of elements. This procedure dynamically creates a
new pattern record for
each pattern in the table and it also dynamically creates new
tAPatternElements for
each element within a pattern. In addition, there is the
concept of being able to
substitute indicators within a pattern. For example, the pattern
$PEOPLE$ of $COMPANY$ is
similar to the pattern $PEOPLE$ from $COMPANY$.
"from" is a substitute
for "of".
Our structure should be
able to express such a need for substitution.
GoBF Public Main This is a wrapper
procedure that calls both the parsing and the searching
(BF.Main) Sub subroutines of the BF. It
is also responsible for sending data back to Munin.
ParseMeeting Public GoBackGroundFinder This function takes the
initial meeting text and identifies the userID
Text Function of the record as well as
other parts of the meeting text including the title, body,
(BF.Parse) participant list,
location and time. In addition, we call a helper function
ProcessStopList to
eliminate all the unwanted words from the originalmeeting title and
meeting body so that only
keywords are left. The information parsed out is stored in the
MeetingRecord structure.
Note that this function does no error checking and for the most
time assumes that the
meeting text string is correctly formatted by Munin.
The important variable is
thisMeeting Record is the temp holder for all info regarding
current meeting. It's
eventually returned to caller.
FormatDelimitation Private ParseMeetingText, There are 4 ways in which
the delimiters can be placed. We take care of all these
(BF.Parse) DetermineNum cases by reducing them
down to Case 4 in which there are no delimiters around but
Words, only between fields in a
string(e.g., A::B::C)
GetAWordFrom
String
DetermineNumWords Public ParseMeeting This functions determines
how many words there are in a string (stInEvalString) The
(BF.Parse) Function Text, function assumes that
each word is separated by a designated separator as specified
ProcessStop in stSeparator. The
return type is an integer that indicates how many words have been
List found assuming each word
in the string is separated by stSeparator. This function is
used along with
GetAWordFromString and should be called before calling
GetAWordFrom String.
GetAWordFromString Public ParseMeeting This function extracts
the ith word of the string(stInEvalString)assuming that each
(BF.Parse) Function Text, word in the string is
separated by a designated separator contained in the variable
ProcessStop stSeparator. In most
cases, use this function with DetermineNumWords. The function
List returns the wanted word.
This function checks to make sure that iInWordNum is within
bounds so that i is not
greater than the total number of words in string or less than/equal
to zero. If it is out of
bounds, we return empty string to indicate we can't get anything.
We try to make sure this
doesn't happen by calling DetermineNumWords first.
ParseAndCleanPhrase Private ParseMeetingText This function first
grabs the word and send it to CleanWord in order strip the stuff that
(BF.Parse) Function nobody wants. There are
things in parseWord that will kill the word, so we will need a
method of looping through
the body and rejecting words without killing the whole
function i guess keep
CleanWord and check a return value ok, now I have a word so
I need to send it down
the parse chain. This chain goes ParseCleanPhrase ->
CleanWord ->
EvaluateWord. If the word gets through the entire chain without
being killed, it will be
added at the end to our keyword string. first would be the function
that checks for "/" as a
delimiter and extracts the parts of that. This I will call
"StitchFace" (Denise is
more normal and calls it GetAWordFromString) if this finds
words, then each of these
will be sent, in turn, down the chain.
If these get through the
entire chain without being added or killed then they will be
added rather than tossed.
FindMin Private ParseAndCleanPhrase This function takes in 6
input values and evaluates to see what the minimum non zero
(BF.Parse) Function value is. It first
creates an array as a holder so that can sort the five input values
in ascending order. Thus
the minimum value will be the first non zero value element
of the array. If we go
through entire array without finding a non zero value, we know
that there is an error
and we exit the function.
CleanWord Private ParseAndCleanPhrase This function tries to
clean up a word in a meeting text. It first of all determines if
(BF.Parse) Function the string is of a valid
length. It then passes it through a series of tests to see it is and
when needed, it will edit
the word and strip unnecessary characters off of it. Such tests
includes getting rid of
file extensions, non chars, numbers etc.
EvaluateWord Private ParseAndCleanPhrase This function tests to
see if this word is in the stop list so it can determine
(BF.Parse) Function whether to eliminate the
word from the original meeting text. If a word is not in the
stoplist, it should stay
around as a keyword and this function exits beautifully with
no errors. However, if
the words is a stopword, an error must be returned. We must
properly delimit the
input test string so we don't accidentally retrieve substrings.
GoPatternMatch Public GoBF This procedure is called
when our QueryMethod is set to complex query meaning
(BF.Pattern Sub we do want to do all the
pattern matching stuff. It's a simple wrapper function which
Match) initializes some arrays
and then invokes pattern matching on the title and the body.
MatchPatterns Public GoPattern Match This procedure loops
through every pattern in the pattern table and
(BF.Pattern Sub tries to identify
different fields within a meeting text specified by sInEvalString. For
Match) debugging purposes it
also tries to tabulate how many times a certain pattern was
triggered and stores it
in gTabulateMatches to see which pattern fired the most.
gTabulateMatches is
stored as a global because we want to be able to run a batch file
of 40 or 50 test strings
and still be able to know how often a pattern was triggered.
MatchAPattern Private MatchPatterns This function goes
through each element in the current pattern. It first evaluates to
(BF.Pattern Function determine whether element
is a placeholder or an indicator. If it is a placeholder, then it
Match) will try to bind the
placeholder with some value. If it is an indicator, then we try to
locate it. There is a
trick however. Depending on whether we are at current element is
the head of the pattern
or not we want to take different actions. If we are at the head,
we want to look for the
indicator or the placeholder. If we can't find it, then we
know that the current
pattern doesn't exist and we quit. However, if it is not
the head, then we
continue looking, because there may still be a head somewhere.
We retry in this case.
etingField Private MatchAPattern This function uses a big
switch statement to first determine what kind of placeholder we
(BF.Pattern Function are talking about and
depending on what type of placeholder, we have specific
Match) requirements and
different binding criteria as specified in the subsequent functions
called such as BindNames,
BindTime etc. If binding is successful we add it to our
FIG. 6 is a flowchart of the actual code utilized to prepare and submit searches to the Alta Vista and Newspage search engines in accordance with a preferred embodiment. Processing commences at function block 610 where a command line is utilized to update a calendar entry with specific calendar information. The message is next posted in accordance with function block 620 and a meeting record is created to store the current meeting information in accordance with function block 630. Then, in function block 640 the query is submitted to the Alta Vista search engine and in function block 650, the query is submitted to the Newspage search engine. When a message is returned from the search engine, it is stored in a results data structure as shown in function block 660 and the information is processed and stored in summary form in a file for use in preparation for the meeting as detailed in function block 670. FIG. 7 provides more detail on creating the query in accordance with a preferred embodiment. Processing commences at function block 710 where the meeting record is parsed to obtain potential companies, people, topics, location and a time. Then, in function block 720, at least one topic is identified and in function block 720, at least one company name is identified and finally in function block 740, a decision is made on what material to transmit to the file for ultimate consumption by the user. FIG. 8 is a variation on the query theme presented in FIG. 7. A meeting record is parsed in function block 800, a company is identified in function block 820, a topic is identified in function block 830 and finally in function block 840 the topic and or the company is utilized in formulating the query. Alternative embodiments for adding various specific features for specific user requirements are discussed below. Enhance Target Rate for Pattern Matching To increase BF's performance, more patterns/pattern groups are added to the procedure "CreatePatterns." The existing code for declaring patterns can be used as a template for future patterns. Because everything is stored as dynamic arrays, it is convenient to reuse code by cutting and pasting. The functions BindName, BindTime, BindCompanyLocTopic which are responsible for associating a value with a placeholder can be enhanced. The enhancement is realized by increasing the set of criteria for binding a certain meeting field in order to increase the number of binding values. For example, BindTime currently accepts and binds all values in the form of ##:## or #:##. To increase the times we can bind, we may want BindTime to also accept the numbers 1 to 12 followed by the more aesthetic time terminology "o'clock," Vocabulary based recognition algorithms and assigning an accuracy rate to each guess BF makes allowing only guesses which meet a certain threshold to be valid. Depending on what location the system identifies through pattern matching or alternatively depending on what location the user indicates as the meeting place, a system in accordance with a preferred embodiment suggests a plurality of fine restaurants whenever it detects the words lunch/dinner/breakfast. We can also use a site like company finder to confirm what we got is indeed a company name or if there is no company name that pattern matching can identify, we can use a company finder web site as a "dictionary" for us to determine whether certain capitalized words represent a company name. We can even display stock prices and breaking news for a company that we have identified. Wireless Bargain Identification in Accordance with a Preferred Embodiment FIG. 9 is a flow diagram that depicts the hardware and logical flow of control for a device and a software system designed to allow Web-based comparison shopping in conventional, physical, non-Web retail environments. A wireless phone or similar hand-held wireless device 920 with Internet Protocol capability is combined with a miniature barcode reader 910 (installed either inside the phone or on a short cable) and used to scan the Universal Product Code (UPC) bar code on a book or other product 900. The wireless device 920 transmits the bar code via an antennae 930 to the Pocket BargainFinder Service Module (running on a Web server) 940, which converts it to (in the case of books) its International Standard Book Number or (in the case of other products) whatever identifier is appropriate. The Service Module then contacts the appropriate third-party Web site(s) to find price, shipping and availability information on the product from various Web suppliers 950. This information is formatted and displayed on the hand-held device's screen. The IP wireless phone or other hand held device 920 utilizes a wireless modem such as a Ricochet SE Wireless Modem from Metricom. Utilizing this device, a user can hang out in a coffee shop with a portable computer perched on a rickety little table, with a latte sloshing dangerously close to the keyboard, and access the Internet at speeds rivaling direct connect via a telephone line. The 8-ounce Ricochet SE Wireless Modem is about as large as a pack of cigarettes and setup is extremely simple, simply attach the modem to the back of your portable's screen with the included piece of Velcro, plug the cable into the serial port, flip up the stubby antenna, and transmit. Software setup is equally easy: a straightforward installer adds the Ricochet modem drivers and places the connection icon on your desktop. The functional aspects of the modem are identical to that of a traditional telephone modem. Of course, wireless performance isn't nearly as reliable as a traditional dial-up phone connection. We were able to get strong connections in several San Francisco locations as long as we stayed near the windows. But inside CNET's all-brick headquarters, the Ricochet couldn't connect at all. When you do get online, performance of up to 28.8 kbps is available with graceful degradation to slower speeds. But even the slower speeds didn't disappoint. Compared to the alternative--connecting via a cellular modem--the Ricochet is much faster, more reliable, and less expensive to use. Naturally, the SE Wireless is battery powered. The modem has continuous battery life of up to 12 hours. And in accordance with a preferred embodiment, we ran down our portable computer's dual cells before the Ricochet started to fade. Thus, utilizing the wireless modem, a user may utilize the web server software 940 to identify the right product 950 and then use an appropriate device's key(s) to select a supplier and place an order in accordance with a preferred embodiment. The BargainFinder Service Module then consummates the order with the appropriate third-party Web supplier 960. mySite! Personal Web Site & Intentions Value Network Prototype mySite! is a high-impact, Internet-based application in accordance with a preferred embodiment that is focused on the theme of delivering services and providing a personalized experience for each customer via a personal web site in a buyer-centric world. The services are intuitively organized around satisfying customer intentions--fundamental life needs or objectives that require extensive planning decisions, and coordination across several dimensions, such as financial planning, healthcare, personal and professional development, family life, and other concerns. Each member owns and maintains his own profile, enabling him to create and browse content in the system targeted specifically at him. From the time a demand for products or services is entered, to the completion of payment, intelligent agents are utilized to conduct research, execute transactions and provide advice. By using advanced profiling and filtering, the intelligent agents learn about the user, improving the services they deliver. Customer intentions include Managing Daily Logistics (e.g., email, calendar, contacts, to-do list, bill payment, shopping, and travel planning); and Moving to a New Community (e.g., finding a place to live, moving household possessions, getting travel and shipping insurance coverage, notifying business and personal contacts, learning about the new community). From a consumer standpoint, mySite! provides a central location where a user can access relevant products and services and accomplish daily tasks with ultimate ease and convenience. From a business standpoint, mySite! represents a value-added and innovative way to effectively attract, service, and retain customers. Intention value networks allow a user to enter through a personalized site and, and with the assistance of a learning, intelligent agent, seamlessly interact with network participants. An intention value network in accordance with a preferred embodiment provides superior value. It provides twenty four hour a day, seven days a week access to customized information, advice and products. The information is personalized so that each member views content that is highly customized to assure relevance to the required target user. Egocentric Interface An Egocentric Interface is a user interface crafted to satisfy a particular user's needs, preferences and current context. It utilizes the user's personal information that is stored in a central profile database to customize the interface. The user can set security permissions on and preferences for interface elements and content. The content integrated into the Egocentric Interface is customized with related information about the user. When displaying content, the Egocentric Interface will include the relationship between that content and the user in a way that demonstrates how the content relates to the user. For instance, when displaying information about an upcoming ski trip the user has signed up for, the interface will include information about events from the user's personal calendar and contact list, such as other people who will be in the area during the ski trip. This serves to put the new piece of information into a context familiar to the individual user. FIG. 10A describes the Intention Value Network Architecture implementation for the World Wide Web. For simplification purposes, this diagram ignores the complexity pertaining to security, scalability and privacy. The customer can access the Intention Value Network with any Internet web browser 1010, such as Netscape Navigator or Microsoft Internet Explorer, running on a personal computer connected to the Internet or a Personal Digital Assistant with wireless capability. See FIG. 17 for a more detailed description of the multiple methods for accessing an Intention value Network. The customer accesses the Intention Value Network through the unique name or IP address associated with the Integrator's Web Server 1020. The Integrator creates the Intention Value Network using a combination of resources, such as the Intention Database 1030, the Content Database 1040, the Supplier Profile Database 1050, and the Customer Profile Database 1060. The Intention Database 1030 stores all of the information about the structure of the intention and the types of products and services needed to fulfill the intention. Information in this database includes intention steps, areas of interest, layout templates and personalization templates. The Content Database 1040 stores all of the information related to the intention, such as advice, referral information, personalized content, satisfaction ratings, product ratings and progress reports. The Supplier Profile Database 1050 contains information about the product and service providers integrated into the intention. The information contained in this database provides a link between the intention framework and the suppliers. It includes product lists, features and descriptions, and addresses of the suppliers' product web sites. The Customer Profile Database 1060 contains personal information about the customers, such as name, address, social security number and credit card information, personal preferences, behavioral information, history, and web site layout preferences. The Supplier's Web Server 1070 provides access to all of the supplier's databases necessary to provide information and transactional support to the customer. The Product Information Database 1080 stores all product-related information, such as features, availability and pricing. The Product Order Database 1090 stores all customer orders. The interface to this database may be through an Enterprise Resource Planning application offered by SAP, Baan, Oracle or others, or it may be accessible directly through the Supplier's Web Server or application server. The Customer Information Database 1091 stores all of the customer information that the supplier needs to complete a transaction or maintain customer records. FIG. 10B is a flowchart providing the logic utilized to create a web page within the Egocentric Interface. The environment assumes a web server and a web browser connected through a TCP/IP network, such as over the public Internet or a private Intranet. Possible web servers could include Microsoft Internet Information Server, Netscape Enterprise Server or Apache. Possible web browsers include Microsoft Internet Explorer or Netscape Navigator. The client (i.e. web browser) makes a request 1001 to the server (i.e. web server) for a particular web page. This is usually accomplished by a user clicking on a button or a link within a web page. The web server gets the layout and content preferences 1002 for that particular user, with the request to the database keyed off of a unique user id stored in the client (i.e. web browser) and the User profile database 1003. The web server then retrieves the content 1004 for the page that has been requested from the content database 1005. The relevant user-centric content, such as calendar, email, contact list, and task list items are then retrieved 1006. (See FIG. 11 for a more detailed description of this process.) The query to the database utilizes the user content preferences stored as part of the user profile in the User profile database 1003 to filter the content that is returned. The content that is returned is then formatted into a web page 1007 according to the layout preferences defined in the user profile. The web page is then returned to the client and displayed to the user 1008. FIG. 11 describes the process of retrieving user-centric content to add to a web page. This process describes 1006 in FIG. 10B in a more detailed fashion. It assumes that the server already has obtained the user profile and the existing content that is going to be integrated into this page. The server parses 1110 the filtered content, looking for instances of events, contact names and email addresses. If any of these are found, they are tagged and stored in a temporary holding space. Then, the server tries to find any user-centric content 1120 stored in various databases. This involves matching the tagged items in the temporary storage space with calendar items 1130 in the Calendar Database 1140; email items 1115 in the Email Database 1114; contact items 1117 in the Contact Database 1168; task list items 1119 in the Task List Database 1118; and news items 1121 in the News Database 1120. After retrieving any relevant user-centric content, it is compiled together and returned 1122. User Persona The system allows the user to create a number of different person as that aggregate profile information into sets that are useful in different contexts. A user may create one persona when making purchases for his home. This persona may contain his home address and may indicate that this user is looking to find a good bargain when shopping. The same user may create a second persona that can be used when he is in a work context. This persona may store the user's work address and may indicate that the user prefers certain vendors or works for a certain company that has a discount program in place. When shopping for work-related items, the user may use this persona. A persona may also contain rules and restrictions. For instance, the work persona may restrict the user to making airline reservations with only one travel agent and utilizing booking rules set up by his employer. FIG. 12 describes the relationship between a user, his multiple person as and his multiple profiles. At the User Level is the User Profile 1200. This profile describes the user and his account information. There is one unique record in the database for each user who has an account. Attached to each user are multiple Personas 1220, 1230 & 1240. These Personas are used to group multiple profiles into useful contexts. For instance, consider a user who lives in San Francisco and works in Palo Alto, but has a mountain cabin in Lake Tahoe. He has three different contexts in which he might be accessing his site. One context is work-related. The other two are home-life related, but in different locations. The user can create a Persona for Work 1220, a Persona for Home 1230, and a Persona for his cabin home 1240. Each Persona references a different General Profile 1250, 1260 and 1270 which contains the address for that location. Hence, there are three General Profiles. Each Persona also references one of two Travel Profiles. The user maintains a Work Travel Profile 1280 that contains all of the business rules related to booking tickets and making reservations. This Profile may specify, for instance, that this person only travels in Business or First Class and his preferred airline is United Airlines. The Work Persona references this Work Travel Profile. The user may also maintain a Home Travel Profile 1290 that specifies that he prefers to travel in coach and wants to find non-refundable fairs, since they are generally cheaper. Both the Persona for Home and the Persona for the cabin home point to the Home Travel Profile. FIG. 13 describes the data model that supports the Persona concept. The user table 1310 contains a record for each user who has an account in the system. This table contains a username and a password 1320 as well as a unique identifier. Each user can have multiple Personas 1330, which act as containers for more specialized structures called Profiles 1340. Profiles contain the detailed personal information in Profile Field 1350 records. Attached to each Profile are sets of Profile Restriction 1360 records. These each contain a Name 1370 and a Rule 1380, which define the restriction. The Rule is in the form of a pattern like (if x then y), which allows the Rule to be restricted to certain uses. An example Profile Restriction would be the rule that dictates that the user cannot book a flight on a certain airline contained in the list. This Profile Restriction could be contained in the "Travel" Profile of the "Work" Persona set up by the user's employer, for instance. Each Profile Field also contains a set of Permissions 1390 that are contained in that record. These permissions dictate who has what access rights to that particular Profile Field's information. Intention-Centric Interface Satisfying Customer Intentions, such as Planning for Retirement or Relocating requires a specialized interface. Customer Intentions require extensive planning and coordination across many areas, ranging from financial security, housing and transportation to healthcare, personal and professional development, and entertainment, among others. Satisfying Intentions requires a network of complementary businesses, working across industries, to help meet consumers' needs. An Intention-Centric Interface is a user interface designed to help the user manage personal Intentions. At any given point, the interface content is customized to show only content that relates to that particular Intention. The Intention-Centric Interface allows the user to manage the process of satisfying that particular Intention. This involves a series of discrete steps and a set of content areas the user can access. At any point, the user can also switch the interface to manage a different Intention, and this act will change the content of the interface to include only that content which is relevant to the satisfaction of the newly selected Intention. FIG. 14 provides a detailed description of the data model needed to support an Intention-Centric Interface. Each User Persona 1410 (see FIG. 13 for a more detailed description of the Persona data model.) has any number of active User Intentions 1420. Each active User Intention is given a Nickname 1430, which is the display name the user sees on the screen. Each active User Intention also contains a number of Data Fields 1440, which contain any user data collected throughout the interaction with the user. For instance, if the user had filled out a form on the screen and one of the fields was Social Security Number, the corresponding Data Field would contain Name="SSN" 1450, Value="999-99-9999" 1460. Each User Intention also keeps track of Intention Step 1470 completion status. The Completion 1480 field indicates whether the user has completed the step. Every User Intention is a user-specific version of a Generic Intention 1490, which is the default model for that Intention for all users. The Generic Intention is customized through Custom Rules 1411 and 1412 that are attached to the sub-steps in the Intention. These Custom Rules are patterns describing how the system will customize the Intention for each individual user using the individual user's profile information. Statistical Agent An agent keeps track of key statistics for each user. These statistics are used in a manner similar to the Tamagochi virtual reality pet toy to encourage certain behaviors from the user. The statistics that are recorded are frequency of login, frequency of rating of content such as news articles, and activity of agents, measured by the number of tasks which it performs in a certain period. This information is used by the system to emotionally appeal to the user to encourage certain behaviors. FIG. 15 describes the process for generating the page that displays the agent's current statistics. When the user requests the agent statistics page 1510 with the client browser, the server retrieves the users' statistics 1520 from the users' profile database 1530. The server then performs the mathematical calculations necessary to create a normalized set of statistics 1540. The server then retrieves the formulas 1550 from the content database 1560 that will be used to calculate the user-centric statistics. Graphs are then generated 1570 using the generic formulas and that user's statistics. These graphs are inserted into a template to create the statistics page 1580. This page is then returned to the user 1590. Personalized Product Report Service The system provide Consumer Report-like service that is customized for each user based on a user profile. The system records and provides ratings from users about product quality and desirability on a number of dimensions. The difference between this system and traditional product quality measurement services is that the ratings that come back to the users are personalized. This service works by finding the people who have the closest match to the user's profile and have previously rated the product being asked for. Using this algorithm will help to ensure that the product reports sent back to the user only contain statistics from people who are similar to that user. FIG. 16 describes the algorithm for determining the personalized product ratings for a user. When the user requests a product report 1610 for product X, the algorithm retrieves the profiles 1620 from the profile database 1630 (which includes product ratings) of those users who have previously rated that product. Then the system retrieves the default thresholds 1640 for the profile matching algorithm from the content database 1650. It then maps all of the short list of users along several dimensions specified in the profile matching algorithm 1660. The top n (specified previously as a threshold variable) nearest neighbors are then determined and a test is performed to decide if they are within distance y (also specified previously as a threshold variable) of the user's profile in the set 1670 using the results from the profile matching algorithm. If they are not within the threshold, then the threshold variables are relaxed 1680, and the test is run again. This processing is repeated until the test returns true. The product ratings from the smaller set of n nearest neighbors are then used to determine a number of product statistics 1690 along several dimensions. Those statistics are inserted into a product report template 1695 and returned to the user 1697 as a product report. Personal Profile and Services Ubiquity This system provides one central storage place for a person's profile. This storage place is a server available through the public Internet, accessible by any device that is connected to the Internet and has appropriate access. Because of the ubiquitous accesibility of the profile, numerous access devices can be used to customize services for the user based on his profile. For example, a merchant's web site can use th is profile to provide personalized content to the user. A Personal Digital Assistant (PDA) with Internet access can synchronize the person's calendar, email, contact list, task list and notes on the PDA with the version stored in the Internet site. This enables the person to only have to maintain one version of this data in order to have it available whenever it is needed and in whatever formats it is needed. FIG. 17 presents the detailed logic associated with the many different methods for accessing this centrally stored profile. The profile database 1710 is the central storage place for the users' profile information. The profile gateway server 1720 receives all requests for profile information, whether from the user himself or merchants trying to provide a service to the user. The profile gateway server is responsible for ensuring that information is only given out when the profile owner specifically grants permission. Any device that can access the public Internet 1730 over TCP/IP (a standard network communications protocol) is able to request information from the profile database via intelligent HTTP requests. Consumers will be able to gain access to services from devices such as their televisions 1740, mobile phones, Smart Cards, gas meters, water meters, kitchen appliances, security systems, desktop computers, laptops, pocket organizers, PDAs, and their vehicles, among others. Likewise, merchants 1750 will be able to access those profiles (given permission from the consumer who owns each profile), and will be able to offer customized, personalized services to consumers because of th | ||||||