EASY ON THE EYES (EOE)

Title: Easy On the Eyes (EOE)

Date: June 14, 2006

From/To: Rahul / Saurabh

There is no STANDARD WAY of writing a text resume. After studying about 80/100 text resumes, I discovered the following:

• There are about 15 "AREAS" of a text resume (like vital organs in a human body).
• There are numerous ways of "describing" each of these 15 AREAS. A total of 182 different variations!
• All text resumes do not necessarily contain all 15 AREAS.
• This, coupled with 182 variations (of nomenclature), make millions of permutations/combinations in which resumes get written/constructed!
• These huge no. of combinations prevent "Apple-for-Apple" comparisons.

between any two given resumes. Even a computer would have difficulty making such "Objective Comparison"!

• These (millions) combinations make it easy for a jobseeker to skip unpleasant details/facts, without fear of being discovered/uncovered!
• Even when a candidate has no "bad facts" to hide, he would skip many broad "AREAS"—simply because, in that particular area, he has nothing to report!
• eg: he may not have
• Written any "publications"
• won any "awards"
• got any "memberships" etc.
• Our structured "Submit Resume" form covers only 4 AREAS, viz:
• Skills (skill box)
• Experience
• Education
• Personal Details

• If we try to modify our "Submit Resume" form to cover all 15 AREAS, in some structured way, it would capture a lot of valuable data (in specific fields), but it would also make the form very long/unwieldy—and jobseekers will run away!
• Only companies who have a terrific "bargaining power/clout" (IBM, Wipro, Infosys, Accenture etc.), can force/compel the candidates to fill up such lengthy forms. Someday, when we have such power, we will do the same.
• Till such time, we have to permit candidates to Cut & Paste whatever text resume he has on his harddisk.
• But, can we make an HR mgr's life easy, by "rearranging" that text resume into those 15 AREAS?
• 15 "VIEW-BOXES/POP-UP BOXES": Each view-box to contain/display all "sentences/paras", which...

Top Annotation:

"are preceded by any of those 182 'nomenclatures / naming variations'!"

Layout Structure (UI Mockup)

Section	Component	Description / Content
Header	Profiles	Displays "xxx" placeholders, likely for candidate names or basic identifiers.
Body (Upper)	GUI	Contains three graphical placeholders (charts or icons) arranged in a grid-like fashion.
Body (Middle)	TEXT RESUME	A separator bar indicating the transition to the text-based data extraction.
Main Interface	AREAS (Sidebar)	A navigational list with toggle arrows (\triangleright): * Objective * Summary * Skills * (Blank entry) * Personal Details
Main Interface	DISPLAY (Panel)	A large viewing pane with a scrollbar on the right. It shows text lines corresponding to the selected "Area" (currently showing lines for Objective and Summary).

---

Bottom Annotation:

"HR mgrs. would LOVE this!"

Signature/Mark: Located at the bottom right (stylized "V" or "LD" mark).

TEXT RESUMES: List of "Standard Headings"

#	Standard Heading (AREAS)	Naming Variations
1	Objective	7
2	Summary	11
3	Skills	19
4	Responsibilities	19
5	Experience	22
6	Achievements	10
7	Projects	7
8	Education	10
9	Professional Qualifications	14
10	Membership	4
11	Awards	3
12	Publications	3
13	Computer Skills	9
14	Miscellaneous	35
15	Personal Details	9
	TOTAL	182

Footer Note: Our structured "Submit Resume" form contains only these 4 AREAS (Skills, Experience, Education, Personal Details).

Standard Heading: Objective

Synonyms:

1. Objective
2. Job Objective
3. Career Objective
4. Personal objectives
5. Ambition
6. Aims and objectives
7. Job Demand

Standard Heading: Summary

Synonyms:

1. Summary
2. Summary of Experience
3. Quick Experience Summary
4. Qualification Summary
5. Work Experience Summary
6. Operational Sector
7. Career
8. Career Highlights
9. Technical Experience
10. Career Profile
11. Professional Summary

Standard Heading: Skills

Synonyms:

1. Skills
2. Key skills
3. Technical skills
4. Special skills
5. Technical & Management Skills
6. Strengths & Professional Skills
7. Technical Skills & Product Experience
8. Strengths
9. Skill set
10. Skill Assets
11. Strengths/Weaknesses
12. Professional Profile
13. Career Profile
14. Summary
15. Professional Summary
16. Summary of Qualifications
17. Technical Knowledge
18. Domain Experience
19. Summary of Skills & Experience

Standard Heading: Experience

Synonyms:

1. Experience
2. Work Experience
3. Previous Experience
4. Professional Experience
5. Relevant Project Experience
6. Summary of Experience
7. Experience Summary
8. Details of Work Experience
9. Detailed Work Experience
10. Experience Details
11. Job Experience
12. Professional Background
13. Employment History
14. Employment Summary
15. Brief Employment History
16. Career Progression
17. Earlier Position
18. Last opportunity
19. Previous Assignments
20. Organisational Experience
21. Worked with
22. Working Exposure

Standard Heading: Achievements

Synonyms:

1. Achievements
2. Major Achievements
3. Key Achievements
4. Significant Achievements
5. Contributions
6. Significant Contributions
7. Strategic Accomplishments
8. Literary and Cultural Achievements
9. Personal Achievements
10. Key Performance Areas

Standard Heading: Projects

Synonyms:

1. Projects
2. Projects Handled
3. Projects Profile
4. Projects Associated With
5. Present Projects
6. Present Assignment
7. Projects in Which Worked

Standard Heading: Personal Details

Synonyms:

1. Personal Details
2. Personal Data
3. Personal Profile
4. Personnel Profile \longrightarrow (spelling mistake)
5. Personal Touch
6. Personal Information
7. Personal
8. Bio Data
9. Curriculum Vitae

Standard Heading: Miscellaneous

Synonyms:

1. Family Data
2. Nationality
3. Marital Status
4. Passport Details
5. Passport Status
6. Other Activities

7-12. Salary Details / Current salary / Present Salary / Salary Expected / Last salary Drawn / Expected salary

7. Retirement Benefits
8. Administrative Performances

15-16. Willingness to relocate / Mobility

9. Extra Curricular Activities
10. Current Industry
11. Current Function

20-22. Languages known / Language Command / Languages

12. Experience Profile
13. Net Experience
14. Total Years of Experience
15. Experience as a Trainer

27-28. Other Interests / Main Interests

16. Present Location
17. Other Certificates
18. Additional Information
19. Hobbies
20. References
21. Clients
22. Date of Availability

Standard Heading: Computer Skills

Synonyms:

1. Computer Skills
2. Computer Qualifications
3. Computer Proficiency
4. Computer Familiarity
5. Computer Literacy
6. Computer Knowledge
7. Knowledge of Software
8. Professional Domain
9. Technical Skills

Standard Heading: Publications

Synonyms:

1. Publications
2. Papers Published & Presented
3. Author and Editor of

Standard Heading: Awards

Synonyms:

1. Awards
2. Awards Received
3. Honours & Awards

Standard Heading: Membership

Synonyms:

1. Membership
2. Member of Professional Bodies
3. Professional Membership
4. Academic & Professional Activities

Standard Heading: Professional Qualifications

Synonyms:

1. Professional Qualifications
2. Professional
3. Professional Certifications
4. Professional Courses attended
5. Summary of Skills & Professional
6. Certifications
7. Specialized Course
8. Training/s
9. Training Availed
10. Training / Special Achievement
11. Details of Training
12. Other Courses / memberships
13. Additional
14. Trainings Attended

Standard Heading: Education

Synonyms:

1. Education
2. Education Details
3. Educational Background
4. Educational Qlfy
5. Educational Qualifications
6. Technical Qualifications
7. Academics
8. Academic Qualifications
9. Academic Credentials
10. Qualifications

Front Office / Secretarial Staff

(List of keywords/categories)

1. ADMIN
2. ADMINISTRATIVE OFFICER
3. ADMINISTRATOR
4. AFFAIRS
5. APPOINTMENT
6. APPOINTMENTS
7. ARRANGEMENTS
8. ASSISTANCE
9. ATTENDANCE
10. BACK OFFICE
11. CAR
12. CARS
13. CO-ORDINATE
14. CO-ORDINATOR
15. CONFERENCE
16. CONTRACTS
17. CONTROLLING
18. COORDINATE
19. COORDINATOR
20. DICTATION
21. EVENTS
22. EXECUTIVE ASSISTANT
23. FACILITY
24. FEEDBACK
25. FILING
26. FOLLOW-UP
27. FOREIGN EXCHANGE
28. FORMS
29. FUNDS
30. GUEST RELATIONS
31. HOSPITALITY
32. HOTEL BOOKINGS
33. HOUSE KEEPING
34. HOUSEKEEPING
35. INDUCTION
36. INFORMATION SYSTEM
37. INFRASTRUCTURE
38. INSPECTION
39. INVESTMENT
40. ISO 9002
41. LEASING
42. LIAISONING
43. LIASIONING
44. LIASONING
45. LIBRARY
46. LICENSES
47. MINISTRY
48. MUNICIPAL
49. NEGOTIATING
50. OFFICE ADMINISTRATION
51. OFFICE ASSISTANT
52. OFFICE AUTOMATION
53. OFFICE MANAGER
54. OPERATOR
55. ORGANISING

Category: Front Office / Secretarial Staff

No.	Description
56	PETTY CASH
57	PREMISES
58	PRESENTATIONS
59	PRESS
60	PRINTING
61	PROPERTIES
62	PROPERTY
63	RECEPTIONIST
64	REGULATORY
65	RENOVATION
66	SECRETARY
67	SECRETARY TO VICE PRESIDENT
68	SECURITY
69	SETTLEMENT
70	SOURCING
71	STATUTORY
72	STENOGRAPHER
73	TRAINING PROGRAMS
74	TRANSPORT
75	TRANSPORTATION
76	TRAVEL
77	TRAVEL ARRANGEMENTS
78	WELFARE
79	WORKSHOPS

 

Category: Account / Finance / Audit / Taxation

No.	Description	No.	Description
1	ACCOUNT ASSISTANT	31	BANKERS
2	ACCOUNT EXECUTIVE	32	BILL DISCOUNTING
3	ACCOUNT MANAGEMENT	33	BILL PASSING
4	ACCOUNT MANAGER	34	BILLING
5	ACCOUNTANCY	35	BILLS
6	ACCOUNTANT	36	BILLS DISCOUNTING
7	ACCOUNTANT.	37	BOOKS OF ACCOUNT
8	ACCOUNTING STANDARDS	38	BOOKS OF ACCOUNTS
9	ACCOUNTING SYSTEM.	39	BRANCH ACCOUNTING
10	ACCOUNTS ASSISTANT	40	BUDGETARY CONTROL
11	ACCOUNTS ASSISTANT.	41	C.A
12	ACCOUNTS EXECUTIVE	42	C.A.
13	ACCOUNTS MANAGER	43	CA
14	ACCOUNTS OFFICER	44	CASH & BANK
15	ACCOUNTS OFFICER.	45	CASH BOOK
16	ACCOUNTS PAYABLE	46	CASH CREDIT
17	ACCOUNTS RECEIVABLE	47	CASH FLOW
18	ADVANCE TAX	48	CASH FLOW STATEMENT
19	ADVANCES	49	CASH FLOWS
20	ANNUAL ACCOUNTS	50	CHARTERED ACCOUNTANT
21	ANNUAL BUDGET	51	CHEQUE
22	ANNUAL RETURN	52	CHEQUES
23	ASSESSMENT	53	CHIEF ACCOUNTANT
24	ASSESSMENTS	54	CLAIMS
25	ASSET	55	CLOSING OF ACCOUNTS
26	ASSETS	56	COLLECTIONS
27	AUDITORS	57	COMPANY ACCOUNTS
28	B.COM.	58	COMPILATION
29	BALANCE SHEET	59	COMPLIANCE
30	BANK RECONCILIATION	60	COMPUTATION

 

Category: Account / Finance / Audit / Taxation

No.	Item	No.	Item
61	CONSOLIDATION	91	FINANCIAL STATEMENTS
62	CONTROLLER	92	FIXED ASSETS
63	CREDIT LIMITS	93	FIXED DEPOSITS
64	CREDIT NOTE	94	FORMS
65	CREDIT NOTES	95	FUND
66	CREDITOR	96	FUNDS
67	CREDITORS	97	GENERAL LEDGER
68	CREDITORS.	98	GRATUITY
69	DEBIT NOTE	99	HIRE PURCHASE
70	DEBIT NOTES	100	I.T
71	DEBTORS	101	INCOME TAX
72	DEBTORS & CREDITORS	102	INCOME TAX MATTERS
73	DEBTORS CONTROL	103	INCOME-TAX
74	DEPOSITS	104	INTEREST
75	DIPLOMA IN FINANCIAL	105	INVESTMENTS
76	DIPLOMA IN FINANCIAL MANAGEMENT	106	INVOICES
77	DISBURSEMENT	107	JOURNAL VOUCHERS
78	DUES	108	L/C
79	ESIC	109	LEASING
80	EXCISE	110	LEDGER
81	EXCISE DUTY	111	LEDGERS
82	EXECUTIVE ACCOUNTS	112	LETTER OF CREDIT
83	EXPENDITURE	113	LIABILITY
84	FILING	114	LOANS
85	FINAL ACCOUNTS	115	MAJOR ACCOUNTS
86	FINALISATION	116	MANAGER - ACCOUNTS
87	FINALIZATION	117	MANAGER ACCOUNTS
88	FINANCE & ACCOUNTS	118	MODVAT
89	FINANCIAL SERVICES	119	OVERHEADS
90	FINANCIAL STATEMENT

 

Category: Account / Finance / Audit / Taxation (Continued)

No.	Item	No.	Item
120	P & L	142	SENIOR ACCOUNTANT
121	PAYABLE	143	SENIOR ACCOUNTS OFFICER
122	PAYMENT	144	STOCK RECONCILIATION
123	PAYMENTS	145	STOCK STATEMENT
124	PAYROLL	146	SUNDRY DEBTORS
125	PERIODICAL	147	T.D.S
126	PETTY CASH	148	T.D.S.
127	PETTY CASH BOOK	149	TALLY 5
128	PF	150	TAX
129	PROFESSION TAX	151	TAX RETURN
130	PROFIT & LOSS	152	TAX RETURNS
131	PROFIT AND LOSS	153	TAXATION.
132	PROFITABILITY	154	TDS
133	PROVIDENT FUND	155	TRANSACTION
134	QUARTERLY	156	TRANSACTIONS
135	RECEIVABLE	157	TRIAL BALANCE
136	RECEIVABLES	158	VALUATION
137	RECOVERY	159	VERIFICATION
138	REMITTANCE	160	VERIFYING
139	RETURNS	161	VOUCHER
140	ROC	162	VOUCHERS
141	SALES TAX	163	WORKING CAPITAL

 

Category: After-Sales Service

1. AFTER SALES
2. AFTER SALES SERVICE
3. AFTER SALES SUPPORT
4. AMC
5. APPLIANCES
6. BREAKDOWN MAINTENANCE
7. CALL CENTRE
8. CUSTOMER CARE
9. CUSTOMER SATISFACTION
10. CUSTOMER SERVICE
11. CUSTOMER SUPPORT
12. DEALER
13. DELIVERY
14. DEMONSTRATION
15. FEEDBACK
16. FIELD CO-ORDINATION
17. FIELD SERVICE
18. HOME APPLIANCES
19. INSTALLATION
20. INSTALLATION AND COMMISSIONING
21. INSTALLATIONS
22. QUALITY CHECKING
23. SERVICE
24. SERVICE ENGINEER
25. SERVICE ENGINEERS
26. SERVICE EXECUTIVE
27. SERVICE MANAGER
28. SPARE PARTS
29. SPARE PARTS.
30. SPARES
31. SR. SERVICE ENGINEER
32. WARRANTY
33. WORKSHOP

Title: ALL FUNCTION LIST

This document contains a database table with the following columns: FUNCTIONID, FUNCTIONNM, ENTERDT, COMPANYCD, and INUSE.

FUNCTIONID	FUNCTIONNM	ENTERDT	COMPANYCD	INUSE
1	Account / Finance / Audit / Taxation	55:58.0	NULL	Y
2	Front Office / Secretarial Staff	55:58.0	NULL	Y
3	After-Sales Service	55:58.0	NULL	N
4	Analysis	55:58.0	NULL	N
5	Civil / Architecture / Interior Design	55:58.0	NULL	Y
6	Audit	55:58.0	NULL	N
7	Banking / Insurance	55:58.0	NULL	Y
8	Branch Management	55:58.0	NULL	N
9	Business Development	55:58.0	NULL	N
10	Legal / Law	55:58.0	NULL	Y
11	Advertising / Media / PR / Corp. Communication / Journalism	55:58.0	NULL	Y
12	Construction Mgt.	55:58.0	NULL	N
13	Cost Accounting	55:58.0	NULL	N
14	Credit Management	55:58.0	NULL	N
15	Call Centre / Customer Care/ITES	55:58.0	NULL	Y
16	IT - DBA / Datawarehousing	55:58.0	NULL	Y
17	Supply Chain / Logistics / Purchase	55:58.0	NULL	Y
18	Engineering	55:58.0	NULL	N
19	Import / Export / International Trading	55:58.0	NULL	Y
20	Finance	55:58.0	NULL	N
21	Gen. Management-Strategy	55:58.0	NULL	N
22	HR / Admin	55:58.0	NULL	Y
23	IT - eCommerce / Internet	55:58.0	NULL	Y
24	Maintenance-Utilities	55:58.0	NULL	N
25	Maintenance / Service Engineering / Production / Manufacturing	55:58.0	NULL	Y
26	Marketing	55:58.0	NULL	N
27	Biotech / Pharmaceutical	55:58.0	NULL	Y
28	IT - Hardware / Networking / System Admin	55:58.0	NULL	Y
30	Others	55:58.0	NULL	N
31	Product and Brand Mgt.	55:58.0	NULL	N
32	Product Development	55:58.0	NULL	N
33	Production Planning and Control	55:58.0	NULL	N
34	Site Egg. / Project Mgmt.	55:58.0	NULL	Y
35	Purchase-Vendor Devp-Inventory Control	55:58.0	NULL	N
36	IT - OA / QC / Testing	55:58.0	NULL	Y
37	R&D / Engineering Design	55:58.0	NULL	Y
38	Sales / Marketing	55:58.0	NULL	Y
39	IT - Software Development	55:58.0	NULL	Y
40	Education / Teaching / Training	55:58.0	NULL	Y
41	Technical Writing and Documentation	55:58.0	NULL	N
42	Telecom / ISP	00:00.0	NULL	Y
43	IT - ERP / CRM	NULL	NULL	Y
44	Arts / Entertainment / TV	NULL	NULL	Y
45	Automotive	NULL	NULL	Y
46	Hospitality / Tourism / Airline / Travel	NULL	NULL	Y
47	Hotels / Restaurant	NULL	NULL	Y
48	Medical / Healthcare	NULL	NULL	Y
49	Top Management	NULL	NULL	Y

 

Handwritten Notes and Workflow

Workflow Diagram:

1. 3PClient.asp \longrightarrow 3PAdvtDetail.asp?ADVTID=*
2. (Arrow pointing down to box): emailing.asp?strAdvtId=*

List of IDs (marked with asterisk):

13 / 14 / 18 / 20 / 21 / 22 / 23 / 24 / 25 / 26 / 34 / 35 / 36 / 37 / 38 / 39 / 40 / 41 / 42 / 43 / 44 / 45 / 46 / 47 / 56 / 57 / 59 / 60 / 61 / 62 / 63 / 5 / 6 / 7 / 8 / 9 /

Would you like me to cross-reference these handwritten IDs with the names in the first table to see which categories they represent?

 

scan0028.jpg: Salary Graph Analysis

This page contains two hand-drawn bell curves (normal distribution graphs) relating to professional salary distributions and Six Sigma principles.

Top Graph: Salary Graph

• Header: "Salary Graph [Covers SIX SIGMA (= 99% of professionals)]"
• X-Axis Markers: Annotated with "3", "6", and "8".
• Annotations: * A horizontal arrow spanning the bulk of the curve is labeled 6\sigma.
• Shaded "tail" areas indicate outliers at the low and high ends of the distribution.

Bottom Graph: Percentile Distribution

• Y-Axis: labeled "\% of Co." (Percentage of Company/Professionals).
• X-Axis: labeled "Percentile".
• Key Markers: Vertical lines at the 40th and 70th percentiles.
• Annotation: The area between specific markers is labeled "99% of co profess," with a span indicated as 6\sigma.

KarmaScope Matrix

This page outlines a structured evaluation table titled "KarmaScope." It is designed as a 2 \times 3 grid for assessing different levels of professional functions.

Function Category	Primary Function [✓]	Secondary Function [□]	Tertiary Function [□]
Areas of Strength	List items 1, 2, 3... up to 25. Includes scroll bar indicators (\triangle/\nabla).	List items. Includes scroll bar indicators (\triangle/\nabla).	List items. Includes scroll bar indicators (\triangle/\nabla).
Areas of Improvement?	List items 1, 2, 3... up to 25. Includes scroll bar indicators (\triangle/\nabla).	List items. Includes scroll bar indicators (\triangle/\nabla).	List items. Includes scroll bar indicators (\triangle/\nabla).

 

Web Interface Wireframe

This page contains UI/UX sketches for a website's "Home Page" and a payment information section.

Top Section: Home Page UI

• Title: "from Home Page"
• Sub-header: "What do you Pay For."
• Wireframe Box: A rectangular box representing a UI element.
• Contains two radio buttons/bullet points on the left.
• A large "X" on the right (likely a placeholder for an image or close button).
• Callout: An arrow points from the box to the text "Tariff Table."

Bottom Section: Payment Data

• Title: "from what do you Pay"
• Data Table (Numeric Values):

| 145 | 107 |

| 107 | 142 |

| 144 | 198 |

New Database for Karmascope

Requirement:

• The Jobseeker decides what are his/her strong/weak points.
• Then he/she clicks on save and the specific data (strong/weak points) get saved onto a New database.
• When HR Manager decides to view KARMASCOPE — he/she will be able to see the saved data by the Jobseeker.
• So the Jobseeker decides his/her presentable data.

Database:

• New dataTable — GRKARMASCOPE
• New stored procedures — SP_INSERTKARMASCOPE, KARMASCOPE
• ??? SP_UPDATEKARMASCOPE

Table: GRKARMASCOPE

• PKID (Auto Incr)
• PEN
• FUNCTION ID
• KEYWORDNM
• STRENGTH (Y/N)

Stored Procedure: SP_INSERTKARMASCOPE

• Parameters: @int PEN, @int FUNCTIONID, @str KEYWORDNM, @str STRENGTH
• Logic: Insert into GRKARMASCOPE values (1, 2, 3, 4)

Stored Procedure:  KARMASCOPE

• Parameters: @int Penno, @F1, @F2, @F3
• Logic:
1. select * from kscc where Pen=@int Pen and functionID=F1 & strength='Y'
2. select ... strength='N'

In this ( Ksc) since we write 6 select statements — 6 tables are passed thru dataset (Table 0 ... Table 5).

DECISION:— (SK/RD)

• 3 SP — insert / get / delete
• NO PKID

GR KSC

• Pen
• FId
• KNm
• Strength

getting it to frontend

Successful 10/5/2006 12:45

Karma Scope

• Table?
• Columns?
• Relns?

Function keyword defined

• Function Id | KeywordNM | Keyword weight

GR Person Function

• PKid | Pen | PtId | FunctionId | FunctionRank | Entrdt | Updtd | Fn expressn

GR Function List

• FunctionId | FunctionName | Entrdt | CompanyCd | Inuse

Stored Procedure List:

• Function population by Pen
• Function list
• person function
• person key wordby Pen
• person Function Profile Mdfes by Pen
• Person keyword

Procedure

• Create AUC (uc Jobseeker Karma scope)
• Design acc to requirements —
• Place 2 frames
• 6 list boxes
• Call sp's to get the fn names / keywords for a specific Pen.
• get all keywords related to that fn name.
• check if present & place Top 25 in list boxes.

Compose a Job Description — III

Date: 15/04/06

From: Rajeev | cc: Rahul /Saurabh / Pranav / Vikram

This has ref. to our discussion today. As discussed,

• Every time a HR mgr. selects a sentence from the DISPLAY BOX and clicks TRANSFER, we must store that sentence in a folder with: Folder Name = Position/Vacancy Name

So, there will eventually be as many folders as there are UNIQUE positions. In any given (one) folder, dozens of HR mgrs may "click & deposit" thousands of "sentences", many of which, selected/clicked/transferred again & again & again (the POPULAR sentences).

The POPULARITY of a sentence will be determined by its "frequency of selection". Now, every time, our s/w will display the "most popular" sentence at the top in the DISPLAY BOX (in descending order of usage). Next to each sentence, we may even display the NUMBER (of usage) in a BOX! eg:

• Candidate should be well-versed in JAVA [ 563 ]
• Candidate should have exposure to .Net [ 496 ]

Job Posting

Date: 14-04-06

From: Rajeev | cc: Rahul \rightarrow Saurabh \rightarrow Pranav \rightarrow Vikram

Earlier we thought of the "Archival" method where a complete/old job-advt (maybe of a competitor or your own) will be edited & reposted/resubmitted.

But we have a problem there in the form of Monster/Naukri logos, which constitute an integral part of the advts. It was not possible to remove these logos thru editing. So, this idea became a "non-starter". Back to square one!

But there is a way to make a HR manager's life simple.

Most other fields in "Post a Job" form are either simple drop-lists or are STATIC information (eg: Job Advertiser's Contact Details) — which can be auto-filled from data stored during "Registration".

We decided that even "Keywords" box will get automatically filled-up, as soon as HR mgr. selects a FUNCTION (from Function droplist).

We will display (in this box), the same 20/30 keywords, which we are using in GumMine, to draw the Function Profile Graph. Of course, HR mgr can add/delete/edit.

And of course, we will store in a separate database, all such "new/added" keywords — against each FUNCTION, and call this database, CONSENSUS KEYWORDS for [function ABC].

Over a long period, we will compute their "frequency distribution" and then add those which are on the top of the heap (most frequent), to our list for computing Function Profile Graphs.

This will enable us to capture the "knowledge" of thousands of HR mgrs, automatically and make our PROFILES more & more relevant/accurate.

So, the only tedious (and mentally very demanding) work left in filling up "Post a Job" form is "Job Description" details.

And, if you have to type/write job-description for SAME position again & again, it is very stressful. There is a danger of missing out on some important skill/knowledge/expertise.

On top of it, most HR mgrs are not "aware" of what each job demands — and they are very poor writers. User departments (where that candidate is likely to work), do not provide sufficient "inputs" to HR mgrs.

So, I feel, HR mgrs would welcome any help in the form of writing good/accurate/meaningful job descriptions.

I have described such a tool in enclosed pages. I feel all mgrs will use this tool ONLINE while interviewing candidates! A bye-product.

Note in box: This (14th April) was my last working day with L&T in 1990. [Signed 14-04-06]

Title: indiarecruiter.net – Compose a Job Description

This page serves as the landing/interface for creating reusable "Master Job Descriptions."

1. Introduction for HR Managers

The text addresses HR Managers, asking if they are tired of retyping job descriptions. It proposes a solution:

• Create a database of MASTER Job Descriptions.
• When posting a job later, select from your master list to auto-fill the description box.
• The tool can be used to create an organizational Manual of Job Descriptions.

2. The Interface (Interface Layout)

Feature	Details
I want to create a MASTER Job Description for	A "Position" text input field with a "Submit" button.
DISPLAY BOX (Left)	Shows job descriptions used by other companies for similar positions. Includes a "TRANSFER" button to move text to the right.
COMPOSE BOX (Right)	A workspace to edit the description. Functions include: Highlight & Delete, Type new words, and Rearrange sequence.
Action Buttons	Delete, Save Master, Download, E-mail.

 

Title: Post a Job

This page shows the wireframe for the actual job posting form where the "Master" templates are utilized.

Job Related Info Section

• Master Job Descriptions List: A sidebar on the left showing previously created masters.
• Job Title / Position / Vacancy: Main header field.
• Job Description Box: A large text area that auto-populates when a Master is selected from the list (can still be edited).
• Logistics Fields: * Posting City (Dropdown)
• Minimum Experience (Years)
• Approx. Gross Annual Salary (Numeric only)

Additional Sections

• Man-Specifications: A dedicated area for specific candidate requirements.
• Job Advertiser's Contact Details: A section that auto-fills but remains editable.

 

Title: Memo: "Compose a Job Description" – II

From: Rajeev | Date: 15/04/06 | To: Rahul, Saurabh, Pranav, Vikram

This memo explains the strategic value of the "Compose Job Description" feature.

Feature Use-Case Diagram

• Benefit 1: To create a MANUAL OF JOB DESCRIPTIONS for your company/org.
• Benefit 2: To COMPOSE a "Job Description" for any given vacancy/position (Job-advt).
• Benefit 3 (The Interview Aid): To use it ONLINE during an interview. Because it contains keywords regarding skills, knowledge, and expertise, it helps the interviewer generate ideas for questions to ask the candidate.

But I suspect that when we "extract" job-description paras from several job-advts for SAME vacancy/position-name (eg. 13 advts for position = Web Designer)

and then, when we "parse" these paras, to convert into a list of bulleted "sentences", as

• Sentence #1
• Sentence #2
• Sentence #3

and then, add-up all the parsed sentences from all job description paras of all job-advts (for same Position Name/Title), — totalling 243 sentences.

Then, following can be expected:

• Many sentences may be perfect duplications or partial duplications.
• Many sentences cannot be called "Job-Descriptions" at all! They may refer to:
• Advertiser Company
• City of Posting
• Working Hrs
• email IDs / Phone no.
• Address
• junk etc. etc.

Obviously, if we permit such "garbage" go unchecked / unfiltered / un-edited, then it would make a very poor impression on the HR mgrs who like the idea and want to use this tool.

If the very first impression is bad, they are not going to comeback. Worse, they may spread "bad words" about this feature! That would spoil our reputation!

So, we must remove such garbage from all the parsed/accumulated sentences, BEFORE loading these into the database. Something like what I am doing for the last 15 days in VERIFIER tool (I have only reached upto alphabet D!). This is a painfully slow & agonizing / tiring process!

So, whereas we do need a GARBAGE REMOVAL TOOL on which, one/two persons may work to look-up each & every parsed sentence and then remove/delete the "Garbage Sentences", such a tool has to be a "self-learning" tool.

It must "learn" from the human expert working on it. It must "observe" what human-expert is doing.

That is, the tool must "store" into its (separate?) database, every sentence that the human expert is deleting (i.e. treating as garbage).

The tool may have to "store" into its memory, say 10,000, so-called "garbage" sentences.

Let us say, altogether, these 10,000 sentences contain 100,000 words. The s/w will calculate the "frequency of usage" of these 100,000 words and arrange these words, in the descending order of usage. So, you may get, no more than 2000 unique words.

Of these, perhaps, the top 200 would make-up for 90% of all occurrences! (A/B/C analysis).

So, now we have a list of 200 culprit words!

CONCLUSION?

if any of these (200) words, is appearing in any sentence

then that sentence must be a "garbage" sentence!

This is how BAYESIAN SPAM FILTER learns — and continuously improves, as it goes on "rejecting" more & more sentences, which contain any of the "garbage word".

And as each "newly discovered" garbage-sentence is added up (to the 10,000 with which we — i.e. human expert — started), — and further broken-up into words, and further "calculate" fresh "frequency of occurrence", you have got a SELF-LEARNING SOFTWARE!

No rocket-science here. Simple common-sense based "predictions" by observing "trends".

Now that the SELF-LEARNING S/W has learned (—and keeps learning), it would, on its own, eliminate/remove from lakhs & lakhs of Parsed sentences, all those sentences, which it concludes are "Garbage sentences" based on what is "observes" (i.e. checks out for the garbage words).

Maybe, there is no need to develop such a FILTERING TOOL!

From internet, just download (for free), one (or more) of the three "BAYESIAN SPAM FILTERS" (names given by Reena to Rahul).

• spambully.com
• death2spam.net
• spambayes.sourceforge.net
• http://all-free-info.com/anti-spam-softwares/
• www.comodoantispam.com

Maybe all 3, to tryout our experiment on all 3, to see which one is better).

NOW each of these spam filters will treat each "Job Description" paragraph as an "E-mail Document" (which it is) and dump the "SPAM JOB DESCRIPTIONS" into a separate folder! And it will keep learning.

All that we need to do, is to supply the spam-filter with a list of "Garbage Words" to begin with. Nothing to do thereafter. It will learn on its own!

Only Problem with this approach is:—

• We do not want the filter to reject an entire job-description para (of say 10 sentences)

but,

• We want it to reject only 2 garbage sentences — out of the total 10 sentences.

I suppose this problem can be solved as follows:

• To a spam filter, do not submit an "entire" job-description para as INPUT DOCUMENT.

Instead

• Submit each "sentence" as an Input document (to be accepted or rejected).

Perhaps, this (Bayesian Spam Filter) approach will also make human-expert's job easy.

Now, having been presented with 10,000 parsed sentences (in a tool),

• all he needs to do is to look at each sentence,
• spot, what he considers, a "garbage" word in that sentence (— one or more words, that make the sentence, a "garbage sentence").
• highlight those words & SAVE

Hopefully, by the time he has gone thru 10,000 sentences, he would have spotted & highlighted ALL possible "garbage words".

Now your SPAM FILTER is all set/ready to process all email documents presented to it (Viz: One million sentences) and sort into BAD vs. GOOD!

[Signature]

Rajeev cc: Rahul -> Saurabh -> Pranav -> Vikram 15/04/06

Compose a Job Description — III

This has ref. to our discussion today. As discussed, — everytime a HR mgr. selects a sentence from the DISPLAY BOX and clicks TRANSFER we must store that sentence in a folder with Folder Name = Position/Vacancy Name

So, there will, eventually be, as many folders as there are UNIQUE positions. In any given (one) folder, dozens of HR mgrs may "click & deposit" thousands of "sentences", many of which, selected/clicked/transferred again & again & again (the POPULAR sentences).

The POPULARITY of a sentence, will be determined by its "frequency of selection". Now, everytime, our s/w will display the "most popular" sentence at the top in the DISPLAY BOX (in descending order of usage). Next to each sentence, we may even display the NUMBER (of usage) in a BOX! eg:

• Candidate should be well-versed in JAVA [568]
• Candidate should have exposure to .Net [496]

[Signature]

Rahul cc: Saurabh -> Pranav -> Vikram -> Rajeev 01/05/06

KarmaScope

This has ref. to my telecon yesterday with Rahul / Saurabh, re: "How to make Knowledge Profile compelling?"

Whereas DNA spiral type animation may look real "intriguing", it has following limitations:

1. It will take a longtime to develop (— whereas we are running against time)
2. It may pose display-problem, when a jobseeker wants to either download or email his ImageBuilder — which, I expect, will be very common. Even, there could be problems with Resume Blast / Resume Courier.

So, what we need (in the ImageBuilder) is a STATIC frame, which we can develop quickly and which does not pose problems described above.

I enclose my proposal KarmaScope — cast in the fashion of a horoscope.

(You may find a better illustration in Google "horoscope" (Images) — 226,000 results).

Maybe one at www.liveindia.com could be useful. (—the colourful Kundli with its mystical symbols).

To begin with space/area inside the diamond will be blank, but the 4 corners will get filled with the keywords extracted by GuruMine + keywords added by jobseeker in the "keyword box" in his "submit resume" form.

Each Keyword will be a hyperlink — clicking on any, will:

• insert that keyword in the Google sets
• click "submit"
• return resulting WORDS and display the same <u>inside</u> the diamond.

<u>Question</u> All the words found in...

Google Sets are themselves "hyperlinks".

When our s/w fetches these words and display inside our diamond, will they continue to remain hyperlinks?

If they do, then a HR mgr or even the concerned jobseeker, may be tempted to click on some and immediately discover that, all our razzle-dazzle magic is due to Google!

So, it is very important to remove these hyperlinks while displaying these words inside the diamond.

But each & every keyword OUTSIDE the diamond, will be a hyperlink.

All this clicking / fetching / displaying will be possible, only when the concerned jobseeker or the HR manager, is

• Online
• On our website (or any partner website)

This feature won't work offline.

And for each & every click on any keyword, we will charge HR mgr, ONE CREDIT POINT (currently = Re. 1/=).

By keeping the tariff so low, I want to encourage HR mgrs to keep clicking! Many HR mgrs like to impress the candidate (and even more, to impress their other interview-panel colleagues!) with their display of subject-knowledge by asking all sorts of questions—relevant or irrelevant!—even when they don't know the right answer themselves!

KarmaScope will satisfy this "Self-Ego Boosting" need of HR managers!

And then there are thousands of HR managers, who sit-in on interviews, without any real "subject-knowledge". They are merely "generalists" without domain knowledge.

They can judge a candidate's:

• Personality
• Expression
• Attitude etc. etc.

but they cannot ask him any technical questions related to a candidate's expertise.

Sometimes interviewers are "owners" or managers from other disciplines. They will find this feature very useful. They are in no position to hire "subject-experts".

Now, for the first time, ImageBuilder, thru its KarmaScope feature, give them a cheap—but—very powerful tool. They would simply "love" it!

I have repeatedly said,

"We need to activate 40,000 HR mgrs/owners/placement agencies, to bring pressure on jobseekers to change over to ImageBuilder. If they get "SOLD" on the ImageBuilder, then, in turn, they will start refusing to entertain plain text resumes. They will do the "selling" for us."

KarmaScope is such an Interview Tool that no jobsite offers—our USP.

clicking on a keyword & fetching Google-set words, would, I suppose, work like "Harvester". Vikram said this was possible.

You may even get away by modifying the source-code of HARVESTER!

But, if this is going to take some (long) time, I suggest we Introduce/implement KarmaScope in 2 stages, viz:

STAGE 1

Simply display keywords—outside the diamond—without hyperlinks. This should be done, along with the launch.

STAGE 2

Develop "WORDFETCHER" software and activate hyperlinks.

From Rajeev's ImageBuilder, I tested all 57 keywords in GoogleSets and got reasonably relevant results (WORDS) in all but 8 cases.

Please give me a timeframe.

[Signature]

01/05/06

(Rajeev's Keywords)

This page contains a list of 57 technical keywords used for testing.

Status	Keyword	Status	Keyword
✓	Billing	✓	XML
✓	Coding	✓	Visual Basic
✓	Creating	✓	.Net Architecture
✓	Data Warehousing	✓	ASP
	Tracking	✓	ASP.Net
✓	Automated Backup	✓	C++
✓	Quotation	?	Component
✓	Modules	✓	Core
X	Package	✓	CRM
✓	Payment	?	Data Entry
✓	Payroll	✓	Database
✓	Programmer	✓	Delivery
X	QS	✓	Deployment
	~~Quotation~~	✓	Evaluate
✓	Recruitment	✓	Feedback
✓	Reports	?	Follow-up
✓	Requisition	✓	Forms
✓	Service	✓	Fox-Pro
✓	Shell	✓	FTP
X	Software Package	✓	IIS
✓	Specifications	✓	Information System
✓	SQL Server	✓	Integration
✓	Diploma in Computer	?	Interact
✓	Supply Chain	✓	ISO 14000
✓	Triggers	✓	ISO 9000
✓	VB	✓	QS 9000
✓	VB.Net	✓	SMTP
		✓	TQM
		?	Transactions
		✓	Travel

 

(My KarmaScope)

This image represents a central dashboard or interface design titled "My KarmaScope."

Main Header

Any Questions? ASK (Attributes – Skills – Knowledge) — Click any Keyword

Layout Description

The page is divided by a large central diamond shape labeled with "Knowledge Horizon" on all four slanted edges.

• The 4 Corners:
• Text Note: "In these 4 corners, we will display keywords extracted by Gurumine (+ entered by Jobseeker) — each with a hyperlink to Google Sets."
• The corners contain horizontal placeholders for various keywords.
• The Central Diamond:
• Text Note: "Inside this diamond, we will display words picked up from Google Sets when any Keyword (outside diamond) is clicked."
• Contains several horizontal bars representing dynamically generated word results.

(Tenure Profile & Analysis)

This page focuses on the data backend or specific metrics for a "Senior" level employee profile.

Table 1: Tenure Profile

Level	Range	Fats (or Ints)	%
Senior	35	[Blank]	[Blank]
Result	[Blank]	[Blank]	[Blank]

 

Analysis Calculations

There is a mathematical breakdown shown on the right side:

• Ratio: 29 / 1000
• Total Arrow: Points to the number 1000.

Data Mapping (Arrows)

The sketch uses arrows to map specific data points to categories:

• Equal: Linked to a specific row/data point.
• Behind: Linked to the value 35.
• Ahead: Linked to the value 50.
• Total No: Linked to the bottom of the column grid.

(Tenure Table Template)

This is a simplified template for displaying the tenure data.

Header: TENURE

Range	Total	Tenure Perc (%)
[Empty Grid]	[Empty Grid]	[Empty Grid]

 

Header: Rahul \ Saurabh \ Pranav (3/2)

Examine the words used to describe this technology:

(Includes a news clipping titled "Internet abridged to fit in laptop" about Webroo/Google/Yahoo dated 11-04-06)

• Snapshot
• Subset
• Vast storehouse
• Optimized
• Content Density
• Captured & Compressed info
• Sample
• millions of answers
• Google / Yahoo

All of the above, apply to a specific "Search Query" (as in Google/Yahoo) and the "Search Results."

Our function-profile graphs too, are such a "snapshot" / a photograph:

• Function: Sales
• Total Population: 18293
• Sub-population: 265

(Graph follows showing a bell curve with a highlighted sub-population between the 70th and 90th percentile).

Header: 2/2

Data/info about 18293 executives is "squeezed / condensed" into a small graph! Hence, graph has a very high CONTENT DENSITY.

And someday, one of our RESUME SEARCH methods, will involve:

• Displaying the graph, based on a HR mgr's "search parameters."
• Enabling a HR mgr to place his cursor on 70% percentile—then dragging to 90% percentile (thereby highlighting graph area in between) & clicking.
• This will result in a SHORT DISPLAY TABULATION, containing one-line summary of only those executives, whose percentile score lies between 70% & 90%—in descending order too! Even before clicking, the "highlighting" action has told him that he can expect to see results for 265 executives, meeting his criteria. Any area shaded on graph will tell him (in advance), the no. of executives covered in that range!

Signature: [Initialed] 12/04/06.

Header: Rahul | ADMIN TOOL | 12/04/06

Title: Jobseeker’s Dashboard

In this dashboard, one of the links should be Function Profile History.

After log-in, clicking on this link will reveal a tabulated history as follows:

Select [ Year | v ]

Your Functional Profile as on last day of Month (For Primary Profile Only)

Month	Raw Score	Population of Executives belonging to same FUNCTION	Percentile Score
Jan
Feb

 

These values, as of last date of each month to be stored & displayed. Of course, Raw Score No. will change only if-and-when, that jobseeker edits his resume. But percentile-score will keep changing all the time as more & more jobseekers (belonging to same PRIMARY FUNCTION) submit their resumes. Someday later, we will enable the jobseeker to see a graph as follows:

(Graph showing Percentile Score (0-100) on the Y-axis and Months (Jan, Feb, March, April) on the X-axis for the year 2006).

Header: Rahul \ Saurabh \ Pranav (1/6)

Title: RELEVANT SEARCH | 12/04/06

See this news item:

(Includes a news clipping "Google gets advanced search code" regarding an algorithm by Ori Alon called Orion, dated 11-04-06)

What can we learn from this?

eg: Jobsearch is also searching for information—info specific to job-advts.

So this concept could be applied to "Job-search" as well—and to "Resume search" also.

Q: "the text will only appear..... if ....."

• (What) "texts" are supposed to appear, when conducting a Job Search?
• Obviously, the "Job Advts" texts.
• (Which) "texts" should appear?
• Again, obviously, if those "texts" (i.e. Job-Advts), contain "Keywords" relevant to the Search Query.

Header: 2/6

Hence, in jobsearch box, we have already provided a box for candidate to type-in "Keywords".

This Jobsearch U/I looks like:

(Drawing of a UI box)

• Job Search
• Keywords: [______________________]
• Tip: Ideal job-advt. should contain these keywords
• Min Exp. asked for: [___] yrs.
• Industry: [_________ v]
• Function: [_________ v]
• Desi. Level: [_________ v]
• Job Location: [_________ v]
• [SUBMIT]

We expect that the jobseeker will imagine / visualize in his mind, an ideal job-advt (from his viewpoint) and closing his eyes, mentally SCAN that advt and highlight those Keywords (again in his imagination). He will memorize these keywords. Then he will open his eyes and feverishly start typing those keywords before his memory evaporates!

All these, so that our s/w can "match" these keywords in the texts of job-advts.

Header: 3/6

This is too much to expect from a jobseeker! We must make his life SIMPLE.

And, surprisingly, it is very easy for us (—but almost impossible to replicate / duplicate / copy by Monster/Naukri etc.! another USP for us).

All we need to do is:

#1 Rearrange "Job-Search" U/I as follows:

(Drawing of a revised UI box)

• Jobsearch
• Please show me job-advts which match following:
• Function: [_________ v]
• Industry: [_________ v]
• Desi. Level: [_________ v]
• Job Location: [_________ v]
• Min Exp. asked for: [___] yrs.
• Keywords: [ (Large Text Box) ]
• Note: Delete from this box, those keywords which are not "relevant" from your viewpoint. Feel free to add any, which you consider "relevant" but which we have missed out.
• [SUBMIT]

Rahul | 11-04-06

Capturing Jobseekers’ Knowledge (-to make GuruMine a self-learning software)

We discussed this today.

In IndiaRecruiter, a jobseeker has to identify 3 industries & 3 functions, where he claims to have strong background. But, Which of these 3 (industries & functions) are,

• most “relevant”? (where he feels superbly confident to succeed)
• quite “relevant”? (where he still feels quite comfortable)
• somewhat “relevant”? (where he can “get into the groove” with some brushing-up).

Our existing “Submit Resume” form does not bring-out these subtle differences/nuances between those 3 industries / 3 functions.

But, we have a STRONG need to capture these.

To do this, let us modify “Submit Resume” form as follows:

[UI Mockup Table]

• What is your background in terms of: Industry | Functions | Keywords
• (Three dropdown/input boxes are shown below these headers)

It is quite unlikely that you feel equally comfortable with your choices of Industries & Functions. Which do you consider:

• Most relevant (Where you feel superbly confident to succeed) ---- Rank [ 1 ]
• Quite relevant (Where you still feel quite comfortable) ---- Rank [ 2 ]
• Somewhat relevant (Where you can “get back into the groove” with some brushing-up) ---- Rank [ 3 ]

In the box below, please “Rank” your choices (to help us recommend to you the ideal jobs).

My Ranking is as follows (Table showing Industry and Function selections with corresponding Rank boxes 1, 2, and 3)

Cut & Paste your text resume in the box below [_________________________________________]

Once we capture the ranking, we will store these in our database against the name of the concerned candidate.

That will enable us to create “sub-populations” of candidates

• Industry-wise
• Function-wise

Next Step For all candidates belonging to industry ABC, add-up all the keywords contained in their “Knowledge Profile” Boxes.

Then calculate

• “Frequency of Occurrence” of each of those keywords (probability of occurrence).

Since each candidate has identified himself as belonging to:

• Industry = ABC
• Function = XYZ

and, he has himself used/selected certain “Keywords” in his resume (in Knowledge Profile box), we can safely “assume” that these keywords belong to those Industry/Functions!

So, now, instead of one or two “Experts” deciding “Which keywords signify/denote which Industry? / Function?”, we get thousands of real experts (i.e., the candidates themselves) to certify this relationship (between “keywords” on one hand, and “Ind/Func” on other).

This is exactly the future path of YAHOO’s search engine, viz: evolve a “Social Consensus” thru a large no. of USERS voting/ranking/rating on items’ importance/relevance to the “Search Query”. (Like AUDIENCE POLL in KBC!)

More & more search-engines are adopting this technique to arrange/display search—

results, in the descending order of the “Rank/Score” awarded by previous visitors.

This method (of creating smaller sub-populations) will also dramatically reduce server's / software's burden of computing “Frequency of Usage”. This is because total candidate population (of say, a million resume), will now (possibly) get broken up into 30,000 resume sub-populations (30 of them)!

Within each sub-population's “keywords”, quite likely, the top 50 (arranged in descending order of frequency-of-usage), will add-upto 90% of the sum-total of probability (i.e. add upto 0.9 probability). Subsequently, for plotting percentile-graphs, we need to use only these top 50 (or 60 or 40) keywords for matching/finding from next arriving resume, to give Raw Score.

Fresh computing of “Frequency of Usage” taking ALL keywords in any given “sub-population” (of Industry or Function), may be done once-a-week (over the weekend?).

[Signature] | 11/04/06

(Alternative Sequence)

#2 Auto fill-up “Keyword” box

The moment a jobseeker selects any (one) “Function” from the “function” drop list, our S/W will pick up (some 20/30) keywords which our FUNCTION PROFILE GRAPH uses (to draw the graph).

These will be the TOP 20/30 keywords—in terms of their frequency of occurrence, those having highest weightage—in the descending order of weightage.

Besides truly “amazing” the jobseeker with this magical appearance of keywords, now, we have made his life SIMPLE! NO excruciating “mental exercise” for him to “conjure-up” a set of Keywords. He is already presented with a SET of keywords, which, we know, are truly relevant! Given a set, it is easy for him to “edit”. Now, it is also easy for him to see “what words are missing?—conspicuous by their absence?”.

And, whatever new/fresh words that he adds to the SET, are very valuable to us. We will store these in a separate database, called,

“Jobseeker Suggested Keywords”. We will store these against each “function”.

Then, we can think of modifying our job-search U/I as follows:

[Job Search UI Mockup]

• Function: [________]
• Ind: [________]
• Desig level: [________]
• Job loc: [________]
• Min Exp: [________]
• Keywords (Suggested by us): [ Box ]
• Keywords (Suggested by previous users): [ Box ]
• Delete from this box: [ -------- ]
• [ Submit ]

Also, whatever “new/additional” keywords that jobseekers suggest/add in the box, we will keep adding-up their “frequency with which being suggested”.

Then modify our keyword profile as follows: OLD (Starting database)

• (Table showing ranks 1-20. Rank 19: Mktg (0.03), Rank 20: Sales (0.02))

NEW keyword | Weight

• (Arrow pointing from Sales weight 0.02 to a new entry: After Sales Service (0.025))

The moment the weightage of any NEW keyword exceeds the weightage of the bottom-most OLD keyword, then that new keyword will push-out/replace the old one.

So, now our s/w has become self-learning and based on “Social Consensus” / Audience Poll! We can do same with “Resume Search” U/I also.

[Signature]