WO2015183389A1 - Transformation and classification of time spent in collaborative activities for assessing organizational productivity and effectiveness - Google Patents
Transformation and classification of time spent in collaborative activities for assessing organizational productivity and effectiveness Download PDFInfo
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- WO2015183389A1 WO2015183389A1 PCT/US2015/022090 US2015022090W WO2015183389A1 WO 2015183389 A1 WO2015183389 A1 WO 2015183389A1 US 2015022090 W US2015022090 W US 2015022090W WO 2015183389 A1 WO2015183389 A1 WO 2015183389A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24554—Unary operations; Data partitioning operations
- G06F16/24556—Aggregation; Duplicate elimination
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/107—Computer-aided management of electronic mailing [e-mailing]
Definitions
- Managers in organizations often have limited visibility into how employees and teams spend their time and are forced to make many decisions based on anecdotes which may not represent what is actually happening. Due to a lack of transparency on how members of an organization is spending their time, it is difficult to have data driven discussions or to make decisions on where time should or shouldn't be invested. For some functions or industries, data on how the workers spend their time are difficult to gather.
- One example of such a function or industry is information workers who spend a large amount of time in meetings, collaborating with colleagues via email or performing other tasks.
- companies will sometimes conduct surveys or interview processes to manually gather data on where time is spent. However, the manual gathering of data through surveys or interviews is labor intensive, expensive, results in low quality self -reported data, disrupts the culture, and provides only a one-time snapshot that is marginally useful.
- a method for assessing collaboration time includes the extraction of collaboration data from a plurality of collaborators and storing the collaboration data as a dataset.
- the collaboration data includes data from sent mails in mailboxes and meetings in calendars of each of the plurality of collaborators.
- collaborators is defined, and a group of collaborators is defined by filtering based on the plurality of attributes. For the dataset, collaboration time is assigned for each member of the group using the collaboration data.
- sent mails in mailboxes and meetings in calendars of each of the plurality of collaborators are mined and stored as the dataset.
- one or more pre-computations may be performed.
- the pre-computations may include a meeting de-duplication process, an attendee de-duplication process, an hours adjustment process, and a non-meeting removal process.
- For each meeting in the meeting de-duplication process, it is determined whether two or more meetings include overlapping times for one or more attendees. If so, then the time durations for the two or more meetings are adjusted, such that the adjusted time durations do not exceed an actual time duration for the two or more meetings.
- the given meeting For each meeting, in the hours adjustment process, it is determined whether the given meeting comprises an all-day, all-week, or recurring meeting. If so, then a time duration of the given meeting is adjusted to reflect a work-day length for each day of the given meeting.
- a time allocation method is used to assign the collaboration data.
- a plurality of partitions is defined, where the plurality of partitions represent different types of collaborations involving the group.
- time allocation method for each given member of the group, it is determined if a given meeting includes the given member as an attendee. If so, then the partition matching the other attendees of the given meeting is determined, and a collaboration time for the given member is allocated to the matching partition.
- the partition matching recipients of the given electronic mail is determined. Also determined is a delta time between the sending of the given electronic mail and an immediately previously sent electronic mail by the given member, the lessor of the collaboration time or the delta time for the given member is then allocated to the matching partition.
- time allocation method for each given member of the group, it is determined whether a given electronic mail is received by the given member from a sender in the dataset. If so, then the partition matching the sender is determined, and a collaboration time for the given member is allocated to the matching partition. [0013] In the time allocation method, for each given member of the group, it is determined whether a given electronic mail is sent by the given member to a recipient not in the dataset. If so, then a mail is imputed to be received by the given member from the recipient not in the dataset. The partition matching the recipient not in the dataset is then determined, and a collaboration time for the given member is allocated to the matching partition.
- a time spent method is used to assign the collaboration data.
- Time groupings are defined, where the time groupings represent how a given member's spent time will be counted.
- time spent method it is determines whether a given meeting includes the given member as an attendee. If so, then a collaboration time is added to a total collaboration time associated with one or more of the time groupings based on each attendee of the given meeting.
- time spent method it is determined whether a given electronic mail is sent by the given member. If so, then a collaboration time is added to a total collaboration time associated with one or more of the time groupings based on each recipient of the given electronic mail.
- time spent method it is determined whether a given electronic mail is received by the given member from a sender in the dataset. If so, then a collaboration time is added to a total collaboration time associated with one or more of the time groupings based on the sender.
- time spent method it is determined whether a given electronic mail is sent by the given member to a recipient not in the dataset. If so, then a mail is imputed to be received by the given member from the recipient not in the dataset, and a collaboration time is added to a total collaboration time associated with one or more of the time groupings based on the recipient not in the dataset.
- a method for assessing collaboration time further includes determining push and pull numbers.
- the push and pull numbers are initialized, where the push number represents other people's time that the given member initiates, and the pull number represents the given member's time initiated by the other people.
- determining the push and pull numbers it is determined whether a given meeting includes the given member as an attendee. If so, then it is determined whether the given member initiated the given meeting. If the given member initiated the given meeting, then a collaboration time is added to the push number for each attendee of the given meeting. If the given member did not initiate the given meeting, then a
- determining the push and pull numbers it is determined whether a given electronic mail is sent by the given member. If so, then a collaboration time is added to the push number for each recipient of the given electronic mail.
- determining the push and pull numbers it is determined whether a given electronic mail is received by the given member from a sender in the dataset. If so, then a collaboration time is added to the pull number.
- determining the push and pull numbers it is determined whether a given electronic mail is sent by the given member to a recipient not in the dataset. If so, then a collaboration time is added to the pull number for each recipient of the given electronic mail.
- FIG. 1 illustrates a system for assessing organizational productivity and effectiveness according to embodiments of the present invention.
- FIG. 2 illustrates a computer system according to embodiments of the present invention.
- FIG. 3 is a flowchart illustrating a method for assessing organizational productivity and effectiveness according to embodiments of the present invention.
- FIG. 4 is a flowchart illustrating in more detail the extraction of data from the mails and calendars of the collaborators in the organization, according to embodiments of the present invention.
- FIG. 5 is a flowchart illustrating a pre-computation method according to embodiments of the present invention.
- FIG. 6 is a flowchart illustrating the time allocation method according to embodiments of the present invention.
- FIG. 7 is a flowchart illustrating the time spent method according to embodiments of the present invention.
- FIG. 8 is a flowchart illustrating a method for determining the push and pull metrics according to embodiments of the present invention.
- the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements.
- the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.
- the present invention can take the form of a computer program product accessible from a computer usable or compute readable storage medium providing program code for use by or in connection with a computer or any instruction execution system.
- a computer usable or computer readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
- the medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium.
- Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk.
- Current examples of optical disks include compact disk - read only memory (CD-ROM), compact disk - read/write (CD-R/W) and DVD.
- a computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
- a data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus.
- the memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
- I/O devices including but not limited to keyboards, displays, point devices, etc.
- I/O controllers including but not limited to keyboards, displays, point devices, etc.
- Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks.
- Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified local function(s).
- the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
- FIG. 1 illustrates a system for assessing organizational productivity and effectiveness according to embodiments of the present invention.
- the system includes an organization 101, which includes a plurality of people (internal collaborators 102) engaged in collaborative activities with each other and/or with persons external to the organization 101 (external collaborators 103).
- the collaborators 102 may be all of the people at the organization 101 or some sub-set. Some, but not necessarily all, of the collaborative activities may be over a communications network 106, such as the Internet, a cellular network, or virtual private network (VPN).
- the collaborators 102 may collaborate via a server 104 providing certain services, such as e-mail hosting, inter- organizational data sharing, and/or various cloud services.
- the server 105 provides an assessment service to the organization 101, including allocating time spent on
- Each collaborator 102 and 103, and the servers 104 and 105, may be a computer system as illustrated in FIG. 2.
- FIG. 2 illustrates a computer system according to embodiments of the present invention.
- the computer system 200 is operationally coupled to a processor or processing units 206, a memory 201, and a bus 209 that couples various system components, including the memory 201 to the processor 206.
- the bus 209 represents one or more of any of several types of bus structure, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.
- the memory 201 may include computer readable media in the form of volatile memory, such as random access memory (RAM) 202 or cache memory 203, or non- volatile storage media 204.
- RAM random access memory
- the memory 201 may include at least one program product having a set of at least one program code module 205 that are configured to carry out the functions of embodiment of the present invention when executed by the processor 206.
- the computer system 200 may also communicate with one or more external devices 211, such as a display 210, via I/O interfaces 207.
- the computer system 200 may communicate with one or more networks via network adapter 208.
- FIG. 3 is a flowchart illustrating a method for assessing organizational
- the server 105 implements the method with the cooperation of the computer systems 200 at the collaborators 102 and/or the server 104.
- Mails sent and meetings attended by the collaborators 102 within an organization are used as representative of the collaborative activities.
- the method extracts the data from the sent mails and calendars of the collaborators 102 in the organization 101 and stores the data as a dataset (301).
- the server 105 may access the mailboxes and calendars of the collaborators 102 from their computer systems or the server 104.
- Attributes are defined for each of the collaborators 102 (302).
- Example attributes may include, but are not limited to: role; level; location; region; department; function; and domain.
- a "group" of the collaborators 102 is defined (303), the members of which whose time is being allocated. Then, for the dataset, the server 105 assigns the collaboration time for each member of the group (304). The results may then be presented for assessment purposes (306).
- FIG. 4 is a flowchart illustrating in more detail the extraction of data from the mails and calendars of the collaborators in the organization, according to embodiments of the present invention.
- the server 105 mines the sent mails in the mailboxes and the meetings on the calendars of a plurality of collaborators (401). In this embodiment, only the headers of each sent mail are extracted for privacy and data security purposes.
- Example header data that are extracted includes, but are not limited to: identity of the sender; identity of each recipient; subject line; and date and time sent.
- the data that is extracted includes, but is not limited to: identity of the collaborator on whose calendar is meeting is found; identity of each attendee; duration of the meeting; identity of the meeting organizer; and location of the meeting.
- Text files are then produced by the server 105 for the mined mails and meetings (402). In this embodiment, one folder is created for each mailbox mined. Each mail and meeting is disassembled into components for easier storage in the text files. The text files are then compressed and encrypted (403) and stored as the dataset in a database (404).
- FIG. 5 is a flowchart illustrating a pre-computation method according to embodiments of the present invention. The computations illustrated here may instead be performed during the time allocation process, described further below.
- the text files of the dataset are decompressed and decrypted.
- the mails are then each re-assembled and pseudonymized (501) and stored back into the database (502).
- each email address is associated with a unique identifier for privacy and data security purposes.
- the mapping between the email addresses and the identifiers are stored in a separate mapping file, which is not loaded for the time allocation process.
- the pseudonymizing of the mail is optional and may be omitted when non-anonymous reports are desired.
- the pre-computations may include a meeting de-duplication process (503) and an attendee de- duplication process (504).
- the meeting de-duplication process identifies overlapping meetings, i.e., where an attendee is scheduled to attend two or more meetings with overlapping times.
- the meeting time for each meeting is adjusted such that the adjusted time durations (i.e., the meeting time to be assigned) does not exceed the actual time duration for the meetings.
- the total meeting time to be allocated here is 1 hour.
- 0.5 hours of the meeting time would be allocated to the meeting with B, and 0.5 hours would be allocated to the meeting with C.
- B would be allocated 1 hour, and C would be allocated 0.5 hours.
- the attendee de-duplication process identifies multiple responses to a meeting invitation from the same attendee, which may cause this attendee to be listed twice for the same meeting. In this embodiment, the later response by the attendee is favored, and the earlier response is removed.
- the pre-computations for meetings further include an hours adjustment process (505) and a process to remove non-meeting appointments (506).
- hours adjustment process hours for certain meetings are adjusted to more accurately reflect the
- collaboration time For example, all-day, all- week, or recurring meetings are adjusted to be no more than a work day length per day, such as 8 hours of collaboration time a day. This is to avoid a 24 hour time allocation.
- meetings on the calendar which are likely non-meetings are removed from the dataset. For example, meetings tagged as vacations, doctor's appointments, pick up kids, etc. are removed as they are not actual meetings. Meetings with zero attendees may also be removed as non-meeting appointments.
- a machine-learning model may be used to predict whether a meeting on a calendar is "real" or a non-meeting. The cleaned meetings are stored back into the database (507).
- FIG. 6 is a flowchart illustrating the time allocation method according to embodiments of the present invention.
- a group of collaborators and a plurality of partitions are defined (600).
- a group is defined by filtering the attributes associated with a plurality of collaborators 102. It is for this group whose time is being allocated.
- a single person "group" is possible.
- a group may be defined as collaborators in a specific region or a particular business unit within the organization 101. Partitions representing different types of collaborations involving the group are also defined.
- partitions may be by "internal/external", where “internal” represents collaborators 102 within the organization 101 and "external” represents collaborators 103 external to the organization 101.
- partitions may be by function or by domain.
- example partitions may be:
- example partitions may be:
- example partitions may be:
- A's time will be allocated based on the identities of the recipients.
- a recipient includes those identified in the cc and bcc fields.
- h is the collaboration time for A.
- the value of h may vary depending on the collaboration activity type, and may be tunable.
- h time duration of the meeting to be allocated, possibly adjusted as described above with reference to FIG. 5.
- the server 105 determines the partition that matches the meeting attendees (other than A) (603). If any the adjustments to h described above with reference to FIG. 5 were not performed as part of the pre-computation process, then these adjustment can be performed during the time allocation process prior to the matching to a partition. Then, h is allocated to the matching partition (604).
- the server 105 determines the partition that matches the recipient(s) of the mail (606). The server 105 also determines the time between this mail and any mail sent by A immediately prior (At) (607). The server 105 then allocates the lesser of h and At to the matching partition (608). In this way, double-counting time for multiple emails can be avoided. For example, assume that 5 minutes is the amount of time to be allocated for each sent mail. Assume also that A sends a first mail at 8:00 am and a second mail at 8:02 am. For the first mail, 5 minutes of A's time is allocated, while the second mail is allocated 2 minutes. This ensures that the time allocated for mails sent within a certain time period does not exceed the length of the time period. Further, in this embodiment, no time is allocated for any mail sent by A where the only recipient is A.
- A's collaboration time as a mail recipient would be captured when this sender's sent mails are processed.
- the server 105 determines the partition matching the sender (610), and h is then allocated to the matching partition (611).
- collaboration time for A as a mail recipient cannot be captured directly since this sender's mailbox is not included in the dataset.
- A is assumed to receive mail from recipients not in the dataset in response to a mail sent to the recipient by A. In other words, A is imputed to receive mail from this recipient as a sender (613).
- the server 105 determines the partition that matches the (imputed) sender (614) and allocates h to the matching partition (615).
- Steps 601 - 615 are repeated for each member of the group.
- the Group includes Alice and Bob. Charlie, Dave, and Eddie are internal collaborators who are not from the Group. Fred, Gene, Heidi and Iris are external collaborators. Assume that the following partitions are defined by interactions between groups of people:
- Eddie has no mailbox and is thus a recipient not in the dataset (612). Alice is imputed to receive a mail from Eddie (613), and 2.5 minutes are allocated to the "Other internal" partition (614-615);
- 2.5 minutes are allocated to the Within-group partition for Bob as the recipient in the dataset (610-611).
- 2.5 minutes are allocated to OPS in the Other Internal partition, imputed for Eddie as a recipient not in the dataset (613-615).
- 2.5*4 minutes are allocated to the External Only partition, imputed for Fred, Gene, Heidi, and Iris as recipients not in the dataset (613-615).
- 2.5 minutes are allocated to the Internal partition for Bob as the recipient in the dataset (606-608).
- 2.5 minutes are allocated to the Internal partition for an imputed mail from Eddie to Alice (613-615).
- 2.5 minutes are allocated to Domain 1 in the
- time allocation method is described using sent mails and meetings in the above as representative of collaboration activities, other data may also be used, either alone or in combination, without departing from the spirit and scope of the present invention.
- telephone logs, IM, and any other collaboration means that has a digital footprint may be considered.
- end-user feedback may also be incorporate, for example, to assist in identifying calendar entries as non-meeting.
- meeting and/or mail time allocated may be further adjusted in other ways, for example, for mails sent by an attendee during a meeting,
- FIG. 7 is a flowchart illustrating the time spent method according to embodiments of the present invention.
- time groupings are defined (700).
- the time groupings represent how A's spent time will be counted.
- the groups may be defined by person, function, or domain.
- h A's time
- T total time spent by A (701).
- h would be counted as follows:
- h For each mail sent by A (704), h is added to the T's associated with one or more groups based on each recipient (705). For the following example groups, h would be counted as follows:
- h For each mail received by A from a sender in the dataset (706), h is added to the T associated with one or more groups based on the sender (707). For the following example groups, h would be counted as follows:
- a mail is imputed to be received by A from the recipient as sender (709), and h is added to the T's associated with one or more groups based on the recipient (710).
- groups h would be counted as follows:
- T is determined as follows (703):
- A's spent time grouped by person 1 hour of time added to the T associated with each of the other attendees;
- A's spent time grouped by function 1 hour is added to each of the T's associated with Sales, HR, and OPS;
- A's spent time grouped by region 1 hour added to each of the T's associated with West, East, and Central;
- T the mail sent by Alice (704), T is determined as follows:
- A's spent time grouped by person 5 minutes is added to the T associated with each of the recipients (705); 2.5 minutes are added to each of the T's associated with Eddie, Fred, Gene, Heidi, and Iris for imputed mail received by Alice (709- 710);
- FIG. 8 is a flowchart illustrating a method for determining the push and pull metrics according to embodiments of the present invention.
- h time to be counted (801).
- h is added to A' s PullT.
- h is added to the PushT for each recipient (who is not A) (807).
- h is added to A' s PullT (809).
- A's push/pull with a specific group can be determined by considering the attendees/recipients/senders who are in this group.
Abstract
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EP15799460.9A EP3149668A4 (en) | 2014-05-30 | 2015-03-23 | Transformation and classification of time spent in collaborative activities for assessing organizational productivity and effectiveness |
CN201580028985.1A CN106471529B (en) | 2014-05-30 | 2015-03-23 | Transformation and classification of time spent in collaborative activities for assessment of tissue productivity and efficacy |
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US14/292,753 US9652500B2 (en) | 2014-05-30 | 2014-05-30 | Transformation and classification of time spent in collaborative activities for assessing organizational productivity and effectiveness |
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2015
- 2015-03-23 CN CN201580028985.1A patent/CN106471529B/en active Active
- 2015-03-23 EP EP15799460.9A patent/EP3149668A4/en not_active Ceased
- 2015-03-23 WO PCT/US2015/022090 patent/WO2015183389A1/en active Application Filing
Patent Citations (4)
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US8271631B1 (en) * | 2001-12-21 | 2012-09-18 | Microsoft Corporation | Methods, tools, and interfaces for the dynamic assignment of people to groups to enable enhanced communication and collaboration |
US20130006690A1 (en) * | 2002-09-17 | 2013-01-03 | International Business Machines Corporation | Keeping Working Hours and Calendar Entries Up-to-Date |
US20050164651A1 (en) * | 2004-01-28 | 2005-07-28 | Microsoft Corporation | Offline global address list |
US20130117058A1 (en) * | 2010-01-27 | 2013-05-09 | Kenneth S. Norton | Systems and Methods for Scheduling Events |
Non-Patent Citations (1)
Title |
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See also references of EP3149668A4 * |
Also Published As
Publication number | Publication date |
---|---|
CN106471529B (en) | 2020-03-03 |
US20160371334A1 (en) | 2016-12-22 |
EP3149668A1 (en) | 2017-04-05 |
EP3149668A4 (en) | 2017-10-25 |
CN106471529A (en) | 2017-03-01 |
US9652500B2 (en) | 2017-05-16 |
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