Title: System for interacting with participants at a web site through an interactive visual proxy
Abstract: A computer graphical user interface (GUI) that has two or more visual categories. Each of the visual categories is divided into visual subcategories of ordered levels of specificity. Each of the ordered levels of specificity is grouped into visual districts containing visual subcategories of the same levels of specificity.
Patent Number: 6,961,910 Issued on 11/01/2005 to Lee, et al.
| Inventors:
|
Lee; Alison (New York, NY);
Malkin; Peter (New York, NY);
Jung; Younghee (Helsinki, FI)
|
| Assignee:
|
International Business Machines Corporation (Armonk, NY)
|
| Appl. No.:
|
784913 |
| Filed:
|
February 16, 2001 |
| Current U.S. Class: |
715/853; 715/733; 715/738; 715/758; 715/835 |
| Intern'l Class: |
G06F 003/00 |
| Field of Search: |
715/5011,733,734,738,753,758,764,810,835,841,843,846,851,853,854,856
345/853,854,841,810,843,851,734
|
References Cited [Referenced By]
U.S. Patent Documents
Other References
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1998, pp. 400-407.
P. Curtis, "6. Mudding: Social Phenomena in Text-Based Virtual Realilties", pp. 121-142.
S. Harrison, et al., "Re-Place-ing Space: The Role of Place and Space in Collaborative
Systems", 1996, pp. 67-76.
W.C. Hill, et al., "Edit Wear and Read Wear", May 3-7, 1992 ACM, pp. 3-9.
M.L. Huang, et al., "WebOFDAV—navigating and visualizing the Web on-line
with animated context swapping," http://www.7.scu.edu.au, pp. 1-6.
S.E. Lamm, et al., "Real-Time Geographic Visualization of World Wide Web Traffic",
Fifth International World Wide Web Conference, May 6-10, 1996, Paris, France, pp. 1-14.
"III. The City Image and Its Elements", pp. 46-90.
Y.S. Maarek, et al., "WebCutter: A System for Dynamic and Tailorable Site Mapping,"
http://www.scope.gmd.de/info, pp. 1-13.
N. Minar, "Visualizing the Crowds at a Web Site," http://www.media.mit.edu/˜nelson/research/crowdvis/,
May 15-20, 1999 ACM, pp. 186-187.
Alan Munro, et al., "Chapter 1: Footprints in the Snow", Social Navigation of
Information Space, pp. 1-14.
E.D. Mynatt, et al., "Design for Network Communities," Papers, CHI 97, Mar. 22-27,
1997, pp. 210-217.
J.E. Pitkow, et al., "WebViz: A Tool for WWW Access Log Analysis," 7 pages.
F.B. Viegas, et al., "Chat Circles", Papers, CHI 99, May 15-20, 1999, pp. 9-16.
R. Xiong, et al., "PeopleGarden: Creating Data Portraits for Users," CHI Letters
vol. 1, 1, pp. 38-44.
|
Primary Examiner: Bautista; X. L.
Parent Case Text
CROSS REFERENCES AND PRIORITY DOCUMENTS
This Application claims priority to U.S. Provisional patent application No.
60/183,182, entitled "Design of an Interaction Environment for an Electronic Marketplace
Community" to Lee et al., filed on Feb. 17, 2000. This Application is herein incorporated
by reference in its entirety. This Application is related to U.S. patent application
Ser. No. 09/784,913, entitled SYSTEM, METHOD, AND PROGRAM PRODUCT FOR NAVIGATING
AND MAPPING CONTENT AT A WEB SITE to Lee et al., which is herein incorporated by
reference in its entirety.
Claims
1. A computer graphical user interface (GUI) displayed on a computer having one
or more central processing units, one or more memories, and one or more network
connections, the computer GUI further comprising: two or more visual categories,
each of the visual categories divided into visual subcategories of ordered levels
of specificity, each of the ordered levels of specificity grouped into visual districts
containing visual subcategories of the same levels of specificity, where the visual
districts are represented as shapes.
2. A computer GUI, as in claim 1, where the visual districts are spatially laid
out to show relationships with one or more other visual districts.
3. A computer GUI, as in claim 2, where the visual districts are represented
as concentric shapes.
4. A computer GUI, as in claim 2, where the visual districts are represented
as two dimensional shapes.
5. A computer GUI, as in claim 1, where the visual categories include any one
or more of the following: a product category, a service category, a category class,
a category list, a product class, a list of products in a class, a product specification,
a service class, a list of services, a service specification, a social topic, a
political topic, an educational topic, and a religious topic.
6. A computer GUI, as in claim 1, where the levels of specificity include any
one or more of the following: category class, category list, offering specification,
product class, list of products in a class, product specification, service class,
list of services, and a service specification.
7. A computer GUI, as in claim 1, further comprising one or more nodes located
on one or more of the visual districts.
8. A computer GUI, as in claim 7, where the nodes are differentiated by any one
or more of the following ways: a color, a size, a shape.
9. A computer GUI, as in claim 7, where a user rolls over one or more of the
nodes to display node information.
10. A computer GUI, as in claim 7, where a user selects one or more nodes to
execute a node function.
11. A computer GUI, as in claim 7, where a user expands one or more nodes to
expose additional node functions.
12. A computer GUI, as in claim 7, where the node functions include any one or
more of the following: providing node information, displaying a menu of one or
more other selectable node functions, displaying a menu of more node information,
initiating a chat session, causing a user to be associated with a node location,
providing access to sales information, providing access to a salesman, and changing
a browser page to one that has information relating to the node.
13. A computer GUI, as in claim 7, where the nodes have node information that
include any one or more of the following: a menu of one or more other selectable
node functions and a menu of more node information.
14. A computer GUI, as in claim 7, where one or more of the nodes is a landmark
that marks a salient location on one or more of the visual districts.
15. A computer GUI, as in claim 14, where the salient location is fixed and associated
with one of the categories.
16. A computer GUI, as in claim 14, where the salient location can change in
time and is associated with an activity.
17. A computer GUI, as in claim 16, where the activity is any one or more of
the following: a current "hot spot", "a list of most popular pages in a computer
section", a public chat, a sale, a special product offering, a special service
offering, and a sales agent availability.
18. A computer GUI, as in claim 14, where the salient location is personally
meaningful to the user.
19. A computer GUI, as in claim 14, where the salient location represents any
one or more of the following: a user's buddy, a chat buddy, a private chat, a user's
favorite spot, and a user with common interest.
20. A computer GUI, as in claim 14, where a user rolls over the salient location
to display salient location information.
21. A computer GUI, as in claim 20, where the salient location information includes
any one or more of the following: salient location identification and one or more
salient location functions.
22. A computer GUI, as in claim 14, where a user selects the salient location
to execute a salient location function.
23. A computer GUI, as in claim 20, where the salient location function includes
displaying a menu of one or more other functions.
24. A computer GUI, as in claim 7, further comprising one or more paths, each
path linking two or more nodes and representing one or more connectivity relationships
among the nodes.
25. A computer GUI, as in claim 24, where a path is associated with one of the
following: a user's path through one or more of the visual districts, a customer's
path through one or more of the visual districts, a preferred path of a group of
users through one or more of the visual districts, a preferred path of a group
of users with common interests through one or more of the visual districts, and
a preferred path of a group of users with complementary interests through one or
more of the visual districts.
26. A computer GUI, as in claim 7, further comprising one or more node sets,
each node set containing one or more nodes clustered in nearby locations in one
or more of the visual districts.
27. A computer GUI, as in claim 26, where a node set represents a relationship
among two or more nodes located in one or more of the visual districts.
28. A computer GUI, as in claim 26, where one or more of the node sets is associated
with one of the following: a density of users gathered in one or more adjacent
node locations, a set of node locations marking results of a search, a set of node
locations related by a semantic attribute, a set of area visited by a group of
users with common interests, a set of node locations, visited by a group of users
with complementary interests, and a crowd of users.
29. A computer GUI, as in claim 26, where one or more of the node sets has a
node set function.
30. A computer GUI, as in claim 29, where the node set function includes any
one or more of the following: providing information about the set, changing a user's
location to be associated with a node location in the set, and changing browser
page to one that has information relating to a node in the set.
31. A computer system for displaying a graphical user interface (GUI), the computer
system comprising:
one or more central processing units;
one or more memories coupled to the one or more central processing units; and
one or more network connections coupled to the one or more central processing
units;
wherein the computer system is adapted to display the GUI, the GUI comprising:
two or more visual categories, each of the visual categories divided into visual
subcategories of ordered levels of specificity, each of the ordered levels of specificity
grouped into visual districts containing visual subcategories of the same levels
of specificity, where the visual districts are represented as shapes.
Description
FIELD OF THE INVENTION
This invention relates to computer graphical user interfaces (GUIs). More specifically,
the invention relates to GUIs provided by an interactive Web site to an end user.
BACKGROUND OF THE INVENTION
Online spaces like the Well, Usenet newsgroups, and MUDs (multi-user dungeons)
have gained interest among researchers, educators and business people as objects
of study, design, and commercial application. The research community has been particularly
interested in the design and use of such systems to support groups of people collaborating.
Much of their focus has been placed on understanding the interdependence of social
and technical elements in a design space to successfully support online social
groups. More recently, the World Wide Web has been drawn to online social groups
as part of its evolution and growth. Much of this interest has been fueled by interests
in capitalizing on them in new business paradigms.
These developments are bringing the Web to an important and new stage in its
development. While the first two stages can be characterized as valuing the importance
of information and transactions, respectively, the third stage can be characterized
as valuing the role that people play on the Web. Yet, much of the commercial efforts
to enhance the participation and interactions of people, in particular, to create
online social groups at Web sites, have taken a tools focus (e.g., chat tools,
bulletin boards, and instant messaging) rather than the broader socio-technical
focus that has led to successes in formation of social groups for the Internet
and for some e-commerce sites (e.g., Motley Fool and eBay).
PROBLEMS WITH THE PRIOR ART
Current Internet/Web/e-commerce sites contain site maps that resemble store
directories (e.g., eBay's site map). These directories present text categories
and subcategories of products and services available at the site. Their main purpose
is informational; providing users with insights into the specific categories and
sub categories used to organize the products and services within the Web site.
While users have a pointer for where the information in this structure (i.e., categories,
sub categories, listings, and products and services) is located in the Web site,
this reference is an abstract one. There are no spatial cues in the textual store
directory representation that enable users to exploit them, like in the physical
world, to guide them in their search of related and unrelated products and services.
The addition of spatial cues is useful to users for formulating a mental map of
a space and for using this mental structure to perform their tasks (e.g., to access
a product through a store directory or by location). The prior art does not provide
a site map with one-to-one mapping between a semantic organization and a spatial organization.
While the particular content (i.e., products and services) in the textual store
directory representation is unique to a Web site, the organizational structure
of categories, sub categories, listings, and products and services are similar
across Web sites. This common organizational structure is not made explicit to
users to allow them to ground or relate their navigation of the products and services
with their navigation of the Web store site. The prior art neither makes the organizational
structure explicit in Web site maps, nor makes the common representational grammar,
e.g. a visual grammar, available so that it can be shared across Web sites.
The prior art has developed 2D and 3D graphical visualizations to map the structure
of the Web and Web sites. They are used to visualize search results, to aid user
navigation, to illustrate access patterns or to aid site management. These tools
are intended for exploration and analysis purposes by either end-users or Web administrators
in which the detailed representation of the structure is important for the task.
However, for the Web-based collaborative setting, the focus is supporting multi-user
interactions and providing sufficient information about the Web site structure
to facilitate such interactions. This domain requires a sharable representation
related to the context and purpose of the site that provide the common ground for
promoting collaborative interactions. Many collaborative systems like MUDs/MOOs,
virtual worlds, and avatar systems have exploited the role of space and spatial
organization to facilitate, structure, and guide social interaction. More importantly,
the spatial models provide a means to frame social information about people, activities,
and interaction within the semantic context of the site. These collaborative systems
have developed visual representations that mirror concepts from the physical world
(e.g., rooms). Aside from the problem that they do not scale effectively to large
numbers of people, these representations do not encode the inter-relationships
between spaces other than through labelling (e.g., living room, office, Sarah's
room). Therefore, the prior art neither provides a visual representation that includes
a one-to-one mapping between a semantic organization and a spatial organization
nor a simplified representation that goes beyond information browsing to support
social interactions.
Current Web sites are very subjective spaces. These sites rarely, if ever,
facilitate the creation of a common mental model among the site users. For example,
it is difficult for users of the prior art to feel they are in different sub locations
in a common (physical or cognitive) space. While different users can be found in
the same eStore, the prior arts neither indicate which of the different departments
of the store (e.g. sporting goods, clothes, housewares) these users are found nor
enable the users to be aware of this information. Essentially, the prior art does
not provide a shared cognitive space that has sub locations.
While a number of Web applications provide capabilities to visualize every
person that is at the Web site or a Web page, this representation does not scale
effectively to large numbers of people that can be at a Web site. These applications
are not provided by the Web site but by another business (third party) which require
users to use the business' proprietary application. As a result, these applications
do not capture all the users at a Web site, but only those users using the third
party application. Examples of third party applications include: "Gooey" and "Odigo".
Essentially, users of "Gooey" at eBay are unaware of users of "Odigo" at eBay.
Furthermore, the prior art has neither an improved visualization for
allowing users to be aware of other people's activities and social interactions
(i.e., social visualization) nor a spatial representation that encodes such information
to support social navigation. Social navigation is "navigation guided by other
people and/or the present or past actions of other people" like "navigation towards/away
from a crowd." While the textual store directory provides an appropriate metaphor
for information navigation, it is an inappropriate metaphor for supporting social
navigation. As a result, users neither develop a mental model of the Web site nor
have access to a spatial representation as a basis for shared cognition or shared
action (e.g., social navigation) among users. For example, a user cannot easily
drop in on a discussion that a crowd of people at the site are having about a product
and then make a decision whether or not to approach and join in the discussion.
Current Web sites are not selective in terms of which people, that are present
at the Web site, are shown to any given user. Furthermore, while current Web sites
enable users to become aware of information and activities of other people who
share a common interest with the user (e.g., Amazon.com—people like you have
bought this book) through interests identified by the user or by inference from
user's actions, they are unable to support the user in the serendipitous discovery
of common or complementary interests arising from social interactions with other
people. The prior art has no means of visualizing information about people, activities,
and social interactions at a Web site that permits users to determine common or
complementary interests among a group of users.
Since current Web sites lack visualizations of the social context (i.e., activities
and social interactions of other people), users have very limited awareness and
understanding of their social context. As a result, current Web sites can only
support formal, planned or superficial interactions while informal, unplanned,
meaningful, spontaneous or serendipitous interactions remain unsupported. Research
has shown that these other kinds of interactions are important parts of the repertoire
of social interactions. Also, research has shown that repeated social interactions
can lead to the formation of social groups and possibly the establishment of relationships.
While ID current Web sites provide users with the tools to interact with each other
(e.g., public and private chat, message boards, and instant messaging), they fail
to provide users with an interaction environment that links these tools with the
context for social interactions. As a result, the possibilities for social interactions
to occur are limited, the opportunities to discover other people who potentially
share common or complementary interests are never pursued, and the benefits that
can be derived from such interactions are never realized.
Current Web sites are very concerned with the problem of how to encourage
many people to visit their sites, to encourage repeat as well as often visits to
their sites, and to encourage people to remain at their Web site for a long period
of time. A critical mass of people is important for the survival and livelihood
of the Web site. Furthermore, research has shown that people are sociable beings.
They are drawn to where other people are and are interested in engaging others
in social interactions to benefit from each other's company. Hence, current Web
sites are interested in ways to attract and retain people. While the prior art
provides users with means for social interactions, it lacks improved mechanisms
for catalyzing social interactions that would lead to frequent and repeated visits
to the Web site for such interactions.
Current Web sites are focused on making information available to users to
facilitate the user's decision making with regards to transactions. This problem
of how to turn a user who is a looker into a booker (i.e., purchaser) is an important
one for business. In so far as a business can help the user to reach a decision
by bringing the relevant information to bear for the user, the greater the value
is the mechanism for promoting sales and transactions for the business. The prior
art has used social filtering technology to solve part of this problem and would
benefit from having additional data for its social filtering technology. Access
to this additional data can be gotten from encouraging additional formation of
social groups. Furthermore, the prior art can benefit from improved means of using
social interactions as a means of bringing relevant information to bear in a user's decision-making.
OBJECTS OF THE INVENTION
An object of this invention is an improved system and method for social visualization
on a Web site that does not rely on third party applications.
An object of this invention is to provide a visual map of a Web site that relates
the semantic organization of the product classes and degree of specificity about
products at the Web site with the location of the product classes and products
in the spatial representation.
An object of this invention is to provide a visual map of a Web site showing
the
product classes and degree of specificity about products at the Web site along
with tools for navigating over the visual map, i.e. a common cognitive model.
An object of this invention is an improved system and method for creating a visual
map of a Web site that provides accurate information about the people, activities
and social interactions occurring at the Web site.
SUMMARY OF THE INVENTION
The present invention is a computer graphical user interface (GUI) displayed
on a computer having one or more central processing units, one or more memories,
and one or more network connections. The GUI has two or more visual categories.
Each of the visual categories is divided into visual subcategories of ordered levels
of specificity. Each of the ordered levels of specificity is grouped into visual
districts containing visual subcategories of the same levels of specificity.
BRIEF DESCRIPTION OF THE FIGURES
The foregoing and other objects, aspects, and advantages will be better understood
from the following non limiting detailed description of preferred embodiments of
the invention with reference to the drawings that include the following:
FIG. 1 is a block diagram of one preferred system architecture.
FIG. 2 is a block diagram of a mapping data structure.
FIG. 3, comprising FIGS. 3A, 3B, 3C, and 3D, is a diagram
of various graphic user interface (GUI) elements. FIG. 3D shows mapping of internal
eStore catalog (represented as a tree) to external representation.
FIG. 4 is a block diagram of a server architecture.
FIG. 5 is a flow chart of a server process for updating changes from one or
more clients and sending changes to one or more clients.
FIG. 6 is a flow chart of an eStore mapper process.
FIG. 7 is a flow chart of a eStore server log mapper process.
FIG. 8 is flow chart of a client activity logger process.
FIG. 9 is a flow chart of an eStore map an data sender process.
FIG. 10 is a flow chart of an eStore location mapper process.
FIG. 11 is a flow chart of a closeness retriever process.
FIG. 12 is a flow chart of a closeness updater.
FIG. 13 is a client architecture.
FIG. 14 a flow chart of a client process for updating changes from one or more
ePlace servers and/or internal queries.
FIG. 15 is a flow chart of eStore map and data requester process.
FIG. 16 is a flow chart of a site map drawer process.
FIG. 17 is a flow chart of a site map update drawer.
FIG. 18 is a flow chart of a site map interaction handler.
DETAILED DESCRIPTION OF THE INVENTION
The present invention is a system, method and program product that enables participant
at a Web site to interact with other participants using an interactive visual proxy.
The present invention consists of a computer graphical user interface (GUI) displayed
on a computer. This GUI overlays information about people, activities, and social
interactions at a Web site onto the same visual representation that exposes the
semantic structure of the contents of the Web site. By grounding the social information
within the same representation used to express the purpose and function of the
Web site, the GUI provides a context for interpreting a person's location and activities.
The GUI also incorporates mechanisms that enable participants to engage in social
interactions with other participants. This GUI represents the semantic structure
as two or more visual categories. Each of the visual categories is divided into
visual subcategories of ordered levels of specificity. Each of the ordered levels
of specificity is grouped into visual districts containing visual subcategories
of the same levels of specificity. One result of this invention is a GUI that provides
the user with a "visual closeness" between two or more items in different categories.
In a preferred embodiment, the GUI is represented as a 2D, spatially organized,
interactive site-map. The present invention has applications for the Web as well
as the Internet and other online interaction systems.
FIGS. 1,
3, and
13-
18 described below refer to the present
GUI invention. Other Figures and descriptions refer to the over system, invention
uses, and business methods that are more particularly described and claimed in
U.S. patent application numbers 09/784,913, entitled SYSTEM, METHOD, AND PROGRAM
PRODUCT FOR NAVIGATING AND MAPPING CONTENT AT A WEB SITE to Lee et al., which is
herein incorporated by reference in its entirety.
FIG. 1 depicts an overall logical network topology within which the preferred
embodiment of the current invention can be implemented. This topology includes
a network (
100), through which clients (
1300) and servers (
400
and
200) communicate. This network includes, but is not limited to the Internet
(both wired and wireless). The servers include an ePlace Server (
400) (described
in detail with reference to FIG.
4), and an eStore Server (
200) which
provides the data about a given store or company. These two servers (
200,
400) can be any network (
100) accessible data source and computing
node, including, but not limited to an HTTP (web) server (such as that sold by
IBM under the trademark WebSphere), or SQL database (such as that sold by IBM under
the trademark DB2). They are able to retrieve and transmit data, to process received
data, and to provide transactions. They run on machines such as the RS/6000, S390,
or PowerPC sold by IBM. A given client (
1300) can be any computing node
able to transmit data over the network (
100), to process received data,
and then to provide a graphic rendering of the processed data. Such devices include
personal computers, such as IBM's ThinkPad; a personal data assistant (PDA), such
as IBM's WorkPad, or a network-communication-enabled cellular phone, such as the
Nokia 9000. Although only three clients (
1300) are explicitly shown in FIG.
1, the present invention is applicable to any number of clients.
FIG. 2 depicts an example of the Mapping Data Structure (
2000) used by
the preferred implementation of the current invention to hold, access, and modify
its data. Those with regular skill in the art will appreciate that this data structure
can be implemented many different ways, including, but not limited to a set of
relational tables held and accessed through a relational database like DB2 sold
by IBM, or as a set of objects accessed via a program written in an object-oriented
language like Java. As shown, there are 10 different types of structures, each
specifying a particular type relation.
The Category Data Structure (
2100) describes the internal organization
of the eStore's (
200) data content. As shown, each instance of this structure
describes a data point's location in the internal structure. It includes the following
identifiers (i.e., named components): its internal identifier (
2105) (e.g.,
"2345" a unique category identifier), its internal name (
2110) (e.g., "superball"),
its level of specificity (
2115) (e.g., 3), its section (
2120) (e.g.,
"22" a unique section identifier), its node (
2125) (e.g., "123abc" a unique
data node identifier), its parent (
2130) (e.g., the identifier of the given
category's parent), its child (
2135) (e.g., the identifier of its child),
and next (
2140) (e.g., the identifier of the given category's next sibling).
The Node Data Structure (
2200) describes the data structure for the node
component (
3210,
3230) of the external visual representations (
3100,
3200,
3300, described in detail with reference to FIGS. 3A,
3B,
and
3C respectively). As shown, each instance of this data structure describes
the location of an eStore's data point in the visual representation. It includes
the following identifiers: its node id (
2205) (e.g., "123abc" a unique data
node identifier), its category identifier (
2210) (e.g., "2345" a unique
category identifier), its section (
2215) (e.g., "22" a unique section identifier),
its x and y coordinate (
2220,
2225) within the section (e.g., "10",
"20") and its external name (
2230) (e.g., "superball toys").
The Section Data Structure (
2300) describes the data structure for the
section component (
3170) of the external visual representation (
3100).
Sections are embedded in a district (
3180). As shown, each instance of this
data structure describes the spatial layout and location of a section. It includes
the following identifiers: its section (
2305) (e.g., "22" a unique section
identifier), the principal category data point's identifier (
2310) for the
section (e.g., "2345" a unique category identifier), its level of specificity (
2315)
(e.g., 3), its origin expressed in terms of a pair of x and y coordinates (
2320,
2325) within a district (e.g., "125", "120"), its spatial extent expressed
in terms of outer radius (
2330) (e.g., "10"), inner radius (
2335)
(e.g., "5"), its beginning angle (
2340) (e.g., "110" degrees), and its ending
angle (
2345) (e.g., "120" degrees), and the number of nodes (
2350)
located within it.
The District Data Structure (
2400) describes the data structure for the
district component (
3180) of the external visual representation (
3100).
As shown, each instance of this data structure describes a section contained in
the district. It includes the following identifiers: the level of specificity (
2405)
(e.g., 3), main category for the section (
2410) (e.g., "2345" a unique category
identifier) and the section (
2415) (e.g., "22" a unique section identifier).
The Section Closeness Data Structure (
2500) describes the closeness between
two sections. As shown, each instance of this data structure includes the following
identifiers: the first and second section identifiers (
2505,
2510)
(e.g., "22" and "35") and the closeness value (
2515) (e.g., "33").
The Landmark Data Structure (
2600) describes the data structure for the
landmark component (
3105,
3110,
3120,
3130,
3150)
of the external visual representation (
3100). As shown, each instance of
this data structure includes the following identifiers: the node (
2605)
(e.g., "458ytr" a unique node identifier), its name (
2610) (e.g., "Mary")
and its type (
2615) (e.g., "customer service agents").
The Path Data Structure (
2700) describes the data structure for the path
component (
3220) of the external visual representation (
3200). As
shown, each instance of this data structure describes a set of connected nodes.
It includes the following identifiers: the path (
2705) (e.g., "7341" a unique
path identifier), a node in the path (
2710) (e.g., "743opi" a unique node
identifier), path's type (
2715) (e.g., "popular route"), a user ID (
2720)
(e.g., "first-time visitors"), and the next node in the path (
2725) (e.g.,
the identifier of the given path's next sibling).
The Node Set Data Structure (
2800) describes the data structure for the
node-set component (
3310) of the external visual representation (
3300).
As shown, instances of this data structure describe a set of related nodes. They
include the following identifiers: the node set (
2805) (e.g., "5431" a unique
node-set identifier), a node in the node set (
2810) (e.g., "982owe" a unique
node identifier), node-set's type (
2815) (e.g., "bought similar items"),
and a user ID (
2820) (e.g., "science fiction").
The Crowd Data Structure (
2850) describes the data structure for the crowd
component (
3190) of the external visual representation (
3100). As
shown, each instance of this data structure describes the crowd size at a given
node. It includes the following identifiers: the node (
2855) (e.g., "743opi"
a unique node identifier), the time (
2860) ("12:45"), and the density (
2865)
of the crowd (e.g., "
876 customers per node").
The Activity Data Structure (
2900) describes the data structure for selected
activities occurring at the ePlace Server (
400) and the eStore Server (
200).
These are also known as transient landmarks (
3120,
3130,
3150)
and are further differentiated as either as a business landmark (
3150) or
a landmark of personal interest to the user (
3120,
3130). As shown,
each instance of this data structure describes an activity occurring at a given
node. It includes the following identifiers: the activity (
2905) (e.g.,
"a187" a unique activity identifier), the activity type (
2910) (e.g., "public
chat"), the time (
2915) ("13:15"), a user ID (
2920) (e.g., "John"),
and the node (
2925) (e.g., "874iyt" a unique node identifier).
FIGS. 6 thru
11 describe how these data structures are instantiated.
Beyond these descriptions, there are two comments to be made about how the Path
Data Structure can be additionally populated and how the Node Set Data Structure
is populated. In addition to the way the Path Data Structure (
2700) is populated
(described in detail with reference to FIG.
7), it can be populated based
on aggregation and analyses of the personal history path (e.g., common paths taken
by particular user groups such as newcomers) or through user queries (e.g., paths
taken by users who are regulars and are interested in "science fiction"). The Node
Set Data Structure (
2800) is created either through aggregation and analyses
of the Web pages viewed by users (e.g., Web pages viewed by all members of a particular
user groups such as newcomers) or through user queries (e.g., Web pages viewed
by all regulars who are interested in "science fiction" or reputable sellers of
all portables with 96M of memory). Those with regular skill in the art will appreciate
that database queries and statistical analyses can be used to provide the aggregation
and analyses functions used to populate some of the instances of the Path Data
Structure and the Node Set Data Structure.
FIG. 3 contains three variants of the external visual representation (
3100,
3200,
3300) highlighting various visual components used by the preferred
implementation of the current invention to visualize the eStore's data content,
the activities occurring at the ePlace Server (
400) and the eStore Server
(
200), and the people present at the eStore. Those with regular skill in
the art will appreciate that the representations and components can be visualized
using one of many different geometric shapes and iconic representations, including,
but not limited to a 2-dimensional shape, such as a semi-circle drawn using a graphics
package like Java 2D, or by a program written in an object-oriented language like
Java. Visual representations
3100,
3200,
3300 contain visual
examples of landmarks and activities (
3105,
3110,
3120,
3130,
3150), a category (
3140), a level of specificity (
3160), a
section (
3170), a district (
3180), a crowd (
3190), nodes (
3210,
3230), a path (
3220), a node set (
3310) and layer tab (
3320).
Although only a few examples of the components are explicitly shown in FIG. 3,
the present visual representation can display any number of these components.
FIG. 3 also contains an example illustration (
3400) of how the internal
catalog structure (
3410) (represented as a tree) is mapped to the external
visual site-map representation. Each major branch of the tree is a main category
(
3420) and elements of the tree are mapped to particular levels of specificity
(
3430). Each node of the tree is a category and maps to a node in the external
representation (
3440) and a user's traversal from node to node is indicated
by path (
3450). The site-map maps the hierarchical structure of a Web site
onto a semi-circle, with each major category mapped to a pie segment. The sub-rings
represent levels of specificity and pie segments represent principal categories
in a tree hierarchy (
3400). This tree hierarchy is the internal representation
of the eStore's data content. All Web pages in the leaf nodes of the hierarchy
are mapped to the inner-most ring representing the third level of specificity.
The parent of the leaf nodes are mapped into the middle ring; the second level
of specificity. All non-leaf nodes that are not parents of a leaf node are mapped
into the outer-most ring; the first level of specificity. Those with regular skill
in the art will appreciate that an alternative mapping of the level of specificity
to a ring is possible; one that maps leaf nodes onto the outer-most ring and the
non-parents of leaf nodes onto the inner-most ring. This example shows one embodiment
which uses three levels of specificity and pie segments as visual categories but
the present invention is not restricted in the numbers of level of specificity
or shapes for visual categories.
FIG. 4 shows a block diagram of the major components of the ePlace Server (
400),
that aggregates, calculates and accesses dynamically updated data provided by the
current invention.
The ePlace Server (
400) preferably includes a Processor (
410),
memory (
405) such as RAM, and a storage device (
415) such as a disk
or CD-ROM. According to an embodiment of the present invention, the ePlace Server
Program (
500) (described in detail with reference to FIG. 5) is used to
operate the server and is preferably embodied as computer executable code that
is loaded remotely over the network (
100), or locally from storage (
415)
into memory (
405) for execution by Processor (
410). The memory (
405)
preferably includes the ePlace Server Program (
500), the eStore Mapper (
600),
the eStore Server Log Mapper (
700) (described in detail with reference to
FIG.
7), the Client Activity Logger (
800) (described in detail with
reference to FIG.
8), the eStore Map and Data Sender (
900) (described
in detail with reference to FIG.
9), the eStore Location Mapper (
1000)
(described in detail with reference to FIG.
10), the Closeness Retriever
(
1100) (described in detail with reference to FIG.
11), the Closeness
Updater (
1200), the Miscellaneous Handler (
420, not described since
it is beyond the scope of the current invention), and the Mapping Data Structures
(
2000) (described in detail with reference to FIG.
2).
FIG. 5 shows a block diagram of the program logic and flow of the ePlace Server
(
400) invoking each of the program elements resident in memory (
405).
The ePlace Sever (
400) waits for input (
510) from other ePlace Server
processes, the eStore Server (
200) or Clients (
1300), and then processes
each such request with the appropriate program element. These program elements
include: the eStore Mapper (
600), the eStore Server Log Mapper (
700)
(described in detail with reference to FIG.
7), the Client Activity Logger
(
800) (described in detail with reference to FIG.
8), the eStore
Map and Data Sender (
900) (described in detail with reference to FIG.
9),
the eStore Location Mapper (
1000) (described in detail with reference to
FIG.
10), the Closeness Retriever (
1100) (described in detail with
reference to FIG.
11), the Closeness Updater (
1200), the Miscellaneous
Handler (
420, not described since it is beyond the scope of the current
invention), and the Mapping Data Structures (
2000) (described in detail
with reference to FIG.
2).
FIG. 6 illustrates how a request to build the eStore Map (
520) is handled
by the eStore Mapper (
600). This request creates the instances of the data
records for the Category Data Structure (
2100), Node Data Structure (
2200),
Section Data Structure (
2300), District Data Structure (
2400), Section
Closeness Data Structure (
2500) and the Landmark Data Structure (
2600).
This process begins by creating the instances of Landmark Data Records (
2600)
for the Permanent Business Landmarks (
620) based on the design specification
for the particular eStore. This design specification is created by the eStore designers
who determine the main categories, the layout and size of the GUI elements for
categories, districts, sections, and permanent business landmarks (e.g., FIG.
3A).
Given the main categories (
615) from the design, each category is handled
in turn (
620) to create an instance of Category Data Record (
625),
to create instances of the Section Closeness Data Records (
630) and District
Data Records (
635); one for each Level of Specificity. As well, a hierarchical
tree (
640) is built from the eStore Catalog rooted at the main category
with every tree node being found in only one main category tree. The eStore Catalog
structure is encoded in a database, such as that sold by IBM under the trademark
of Websphere Commerce Suite. Those with regular skill in the art will appreciate
that such a spanning tree rooted at the main category may be constructed out of
a network structure by pruning cycles and duplicates. [See
Graph Theory with
Applications, Chapter 2 entitled "Trees", by Bondy and Murty 1976]. For each
tree node in the main category tree (
645), an instance of the Category Data
Record (
625) is created or updated with repect to its parent, child, and
next values (
650). Then, its level of specificity is determined through
the following process. If there are no child for the tree node (
655), then
its level of specificity is set to 3 (
660). Otherwise, if the child of the
child for the tree node (
665), then its level of specificity is set to 2
(
670). Otherwise, the tree node's level of specificity is set to 1. An instance
of the Node Data Record for the tree node is created using the Section identifier
corresponding to the main category and the tree node's level of specificity (
680).
The (x, y) coordinate for the node in the Section is obtained either directly from
the design or computed by some heuristic (e.g., hashing function based on attribute
of the tree node's category information). When all the tree nodes of the main category
tree is processed (
645), the next main category is handled (
620).
When all the main categories are processed, then for each section, a Section Closeness
Data Record is created (
685) based on a measure of closeness between each
section and every other section. Those with regular skill in the art will appreciate
that a variety of metrics may be used to relate the closeness of items in one section
with items in another section. In particular, measures based on physical closeness
of section items as found in a physical store, semantic closeness of section items
based on descriptions of section items, temporal closeness of section items based
on patterns of access between items in two sections, and behavioral closeness of
two items in different sections for one or more individuals based on purchase behaviors
(e.g., people like you who bought videos bought books). Another example is to use
the semantic distance (measuring the similarity of the descriptions of the catalog
items) between the two clusters each containing the catalog items found in the
two sections. A third example is to use the probability that people viewing a catalog
item in the first section will then view a catalog item in the second section.
This probability can be based on the history of groups of people's movement from
one section to another section.
FIG. 7 illustrates how a request to process the eStore Server Log into constituent
elements (
530) is handled by the eStore Server Log Mapper (
700).
This process creates and/or updates instances of the Crowd Data Structure (
2850),
and possibly a new instance of the Path Data Structure (
2700). Each access
to an eStore Web page is logged by the eStore Server (such as is the case with
IBM's WebSphere HTTP server) and the entries are retrieved by the ePlace Server
(
710). Each entry in the log is processed in turn (
715). For every
record that represents a valid page request (
720), its instance in the Category
Data Structure is retrieved based on the page information obtained from the log
record (
730). The Node identifier is extracted from the Category Data Record
instance (
740). The instance for the Crowd Data Structure (
2850)
with the particular Node identifier is updated or created (
750) by incrementing
or initializing the density field. If a user identifier is included in the log
information, then the process creates (
760) an instance of the Path Data
Structure (
2700) with the given user identifier and page information and
links (
770) this new instance to the end of the Path chain for the user.
FIG. 8 illustrates how the Client Activity Logger (
800) processes the
client activity log requests (
540). This process either deletes or creates
an instance of the Activity Data Structure (
2900), and possibly creates
or deletes an instance of the Landmark Data Structure (
2700). If the request
is to terminate an activity (e.g., chat) (
810), the relevant instance of
the Activity Data Structure (
2900) is deleted (
815). If this activity
is the last instance (
820), then the instance of the Landmark Data Structure
(
2700) for this activity at this node is deleted (
825). The process
then terminates (
895). Otherwise if the original request is not to terminate
the activity, an instance of the Activity Data Structure (
2900) is created
through a process that retrieves from the client, the Location for the Node Field
(
830), the User ID for the User ID Field (
840), the Activity Type
for the Type Field (
850), the Time for the Time Field (
860). If this
activity request is to join (
870), then the Activity ID is added to the
Activity Field (
875). Otherwise, a new unique Activity ID is generated and
added to the Activity Field (
880) and a new instance of the Landmark Data
Structure (
2600) for the particular activity at the particular Node is created
(
885). The resulting instance of the Activity Data Structure is added to
the database (
890) and the process then terminates (
895).
FIG. 9 illustrates how the eStore Map and Data Sender (
900) processes
the eStore map requests (
550). This process responds to a client requesting
information that will enable the client to create or update the eStore Map. If
the request is not an update request (
910), the process must provide additional
information. This includes sending the background image of the business (
920),
the data for drawing the sections (
925), the data about the static business
landmarks (
930), the data about the static landmarks for people, activities
and events of interest to the user (
935), the static path data of interest
to the user (
940), and the static node set data of interest to the user
(
945). All requests handled by this process would send along the crowd data
(
950), the data for the transient business landmarks (
955), the data
for the transient landmarks for people, activities, and events of interest to the
user (
960), the transient path data of interest to the user (
965),
and the transient node set data of interest to the user (
970).
FIG. 10 illustrates how the eStore Location Mapper (
1000) processes the
eStore location map requests (
560). This process responds to a client requesting
the Node information for an associated Web page. The process maps (
1010)
the Web page to its Category Data Structure instance. The Node Identifier from
this instance is used to retrieve (
1020) the corresponding instance of the
Node Data Structure. Both pieces of information for this Node record instance are
forwarded (
1030) to the client.
FIG. 11 illustrates how the Closeness Retriever (
1100) processes the
closeness requests (
570). Given two Web page locations represented as Ni
and Nj, the process retrieves the instance of the Node Data Structure for Ni (
1110)
and Nj (
1120). From the corresponding Node instance, the process retrieves
the Section identifier for Ni (
1130) and Nj (
1140); known as Si and
Sj respectively. Using the retrieved Section identifiers, the process retrieves
(
1150) the instance of the Section Closeness Structure containing Si and
Sj. The process (
1160) returns the Section Closeness record. Closeness between
pie segments or between two node locations can be exploited to convey information
about the relationship between the items. Specifically, related categories (i.e.,
pie segments) are placed in close proximity to each other and related Web pages
are clustered in a district. Proximity is important for enabling site participants
to make use of semantic similarity to establish common ground with other participants.
Also, the spatial representation of the semantic structure of Web pages contributes
to rooting in a semantic context a person's location and her activities. The relationships
that are expressed in the category structure enable the visitor to draw certain
inferences about relationships amongst the users of those categories. Thus it makes
available a semantic component which has been shown to be important for helping
people to establish common ground. The site-map is intended to be the basis of
a sharable representation, in time; much like city maps are.
FIG. 12 is a flow chart of a Closeness Updater (
1200) process that responds
to periodic requests (
580) to update the closeness value for two sections,
represented as Si and Sj. It retrieves (
1220) the instance of the relevant
Section Closeness Data Structure (
2500) instances represented by Si and
Sj. Its closeness value is updated (
1230). In the example of semantic distance
as a measure of closeness, the periodic updates occur when new catalog items are
added/removed from a section. A re-computation of the closeness measure between
this section and every other section in the eStore will result in updates to the
relevant instances where the closeness values for (Si, Sj) are changed. In the
example based on probability of moving from one section to another, the periodic
updates may be recomputed hourly or daily, for example, based on the browsing behavior
of people over the hour or day.
FIG. 13 shows a block diagram of the major components of the Clients (
1300)
that transmit user data and requests to the ePlace Server (
400) and eStore
Server (
200), to process received data, and to provide a graphic rendering
of the processed data provided by the current invention. The ePlace Client (
1300)
preferably includes a Processor (
1310) and memory (
1305) such as
RAM. According to an embodiment of the present invention, the Client Program (
1400)
(described in detail with reference to FIG. 14) is used to operate the client and
is preferably embodied as computer executable code that is loaded remotely over
the network (
100) into memory (
1305) for execution by Processor (
1310).
The memory (
1305) preferably includes the Client Program (
1400) (described
in detail with reference to FIG.
14), the eStore Map and Data Requester
(
1500) (described in detail with reference to FIG.
15), the Site
Map Drawer (
1600) (described in detail with reference to FIG.
16),
the Site Map Update Drawer (
1700) (described in detail with reference to
FIG.
17), the Site Map Interaction Handler (
1800) (described in detail
with reference to FIG.
18), and the Mapping Data Structures (
2000)
(described in detail with reference to FIG.
2).
FIG. 14 provides the client program logic and flow view of the Client (
1300)
invoking each of the program elements resident in memory (
1305). The Client
program starts up (
1405) by requesting the eStore Map and Data (
1500),
invoking the Site Map Drawer (
1600) to draw the eStore Map (FIG. 3A) and
then sitting in a loop waiting for input (
1410) from its own client processes
or ePlace Server (
200). An input request to terminate (
1420) will
end the client program (
1425). A request to process a user action (
1430)
will invoke the Site Map Interaction Handler (
1800). A request to Update
the Site Map (
1435) will invoke the eStore Map and Data Requester for Update
(
1500) to obtain updates to its local information about the eStore map followed
by an invocation of the Site Map Update Drawer (
1700) to update the visual
representation of the map. Otherwise, a miscellaneous handler is invoked (
1440).
On return from processing each of these input requests, control returns to the
handler awaiting for further input (
1420).
FIG. 15 illustrates the program flow and logic of the handler for the eStore
Map and Data Requester (
1500). If the request is not an update request (
1510),
the process must perform additional operations. They include requesting a complete
set of data for the eStore map (
1520) from the ePlace Server (
900)
and processing the response to the request by receiving the Background image of
Business (
1525), data for Sections (
1530), static business landmarks
data (
1535), static landmarks data for people, activities, and events of
interest to the user (
1540), the static path information of interest to
the user (
1545), static node set information of interest to the user (
1550),
the crowd data (
1565), transient business landmarks data (
1570),
transient landmarks data for people, activities, and events of interest to the
user (
1575), transient path information of interest to the user (
1580),
and transient node set information of interest to the user (
1585). If the
request is an update request, the process simply Requests Update of eStore Map
and Data (
1560) from the eStore Server (
900). This results in the
receipt of the crowd data (
1565), transient business landmarks data (
1570),
transient landmarks data for people, activities, and events of interest to the
user (
1575), transient path information of interest to the user (
1580),
and transient node set information of interest to the user (
1585). The handler
(
1595) returns the information obtained from each branch of the handler.
The site map displays people's presence, activities, and interactions within
the same representation used to locate the Web pages. The presence and location
of all the visitors to the Web site is rendered using crowd landmarks. Specifically,
the number of individuals
