Semi-automatic index term augmentation in document retrieval

US 20070027902A1
Filed: 04/03/2006
Published: 02/01/2007
Est. Priority Date: 03/31/1999
Status: Active Grant

First Claim

Patent Images

1. A method for assigning index terms to a document D_iin a collection of documents/ where other documents in the collection have previously had index terms assigned by another method, comprising:

(a) selecting a term I_jfrom among a set of terms from which the index terms are being assigned, which term I_jhas not yet been processed, (b) calculating a likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, which likelihood function is based upon the likelihood that a term occurring in the document D_ialso occurs in the document D_k, (c) repeating step (b) for a plurality of other documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (d) calculating a total score for the Document D_ifor the Index Term I_j, which total score is based upon the likelihood functions for the document D_iand the documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (e) repeating steps (a)-(d) for a plurality of other terms I_jfrom among the set of terms from which index terms are being assigned, and (f) choosing index terms to be assigned to Document D_i, from among the set of terms I_jfrom which index terms are being assigned, based upon the total scores calculated for the Document D_ifor the Index Terms I_j.

View all claims

4 Assignments

Timeline View

Assignment View

0 Petitions

Accused Products

Abstract

Disclosed are methods and systems for indexing or retrieving materials accessible through computer networks.

88 Citations

View as Search Results

70 Claims

1. A method for assigning index terms to a document D_iin a collection of documents/ where other documents in the collection have previously had index terms assigned by another method, comprising:
- (a) selecting a term I_jfrom among a set of terms from which the index terms are being assigned, which term I_jhas not yet been processed, (b) calculating a likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, which likelihood function is based upon the likelihood that a term occurring in the document D_ialso occurs in the document D_k, (c) repeating step (b) for a plurality of other documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (d) calculating a total score for the Document D_ifor the Index Term I_j, which total score is based upon the likelihood functions for the document D_iand the documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (e) repeating steps (a)-(d) for a plurality of other terms I_jfrom among the set of terms from which index terms are being assigned, and (f) choosing index terms to be assigned to Document D_i, from among the set of terms I_jfrom which index terms are being assigned, based upon the total scores calculated for the Document D_ifor the Index Terms I_j.
- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 11)
- - 2. The method of claim 1, wherein the likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, is the log likelihood ratio L(D_i, D_k) for the document D_iand the document D_k, $L = (D_{i}, D_{k}) = \log$
    - ( ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i , D k ) / ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i ) ) , where $\begin{matrix} \underset{m}{Π} (D_{i}, D_{k}) = 1, if Term m is assigned to Document D_{i} and to \\ Document D_{k} \\ = 0 otherwise \\ \underset{m}{Π} (D_{i}) = 1, if Term m is assigned to Document D_{i} \\ = 0 otherwise \\ M_{0} = the number of Terms which are in Document D_{i} . \end{matrix}$
  - 3. The method of claim 2, wherein the total score T (D_i, I_j) for the Document D_ifor the Index Term I_j, is $T$
    - ( D i , I j ) = ∑
      
      k = 1 K 0 ⁢
      
      W ⁡
      
      ( D k , I j ) * L ⁡
      
      ( D i , D k ) / ∑
      
      k = 1 K 0 ⁢
      
      W ⁡
      
      ( D k , I j ) , where K₀=the number of Documents in the collection assigned Index Term I_kby the other method, W(D_k, I_j)=a weight assigned to Index Term I_jfor Document D_k,
  - 4. The method of claim 3, wherein the documents are Web pages.
  - 5. The method of claim 3, wherein the documents are Web sites.
  - 6. The method of claim 3, wherein the documents in the collection which have previously had index terms assigned to them by another method have had one and only one index term assigned.
  - 7. The method of claim 6, wherein the weight W(D_k, I_j) assigned to the index term I_jpreviously assigned to a document D_kby another method equals 1.0.
  - 8. The method of claim 3, wherein a fixed number N of terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iare the highest are assigned as index terms for the Document D_i.
  - 9. The method of claim 8, wherein the fixed number N is equal to 1.
  - 11. The method of claim 7, wherein the term I_jwhose total score T (D_i, I_j) calculated for the Document D_iis the highest is assigned as an index term for the Document D_i.

12. A method for assigning index terms to documents in a collection of documents, comprising:
- (a) manually pre-assigning index terms to a subset of the documents in the collection, (b) selecting a document D_ifrom among the documents in the collection to which index terms have not yet been assigned, which document D_ihas not yet been processed, (c) selecting a term I_jfrom among a set of terms from which index terms are being assigned which term In has not yet been processed, (d) calculating a likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term manually, which likelihood function is based upon the likelihood that a term occurring in the document D_ialso occurs in the document D_k, (e) repeating step (d) for a plurality of other documents D_kin the collection to which the term I_jhas previously been assigned as an index term manually, (f) calculating a total score for the Document D_ifor the Index Term;
  
  I_jwhich total score is based upon the likelihood functions for the Document D_iand the Documents D_kin the collection to which the term I_jhas previously been assigned as an index term manually, (g) repeating steps (c)-(f) for a plurality of other terms I_jfrom among the set of terms from which index terms are being assigned, (h) choosing index terms to be assigned to Document D_i, from among the set of terms I_jfrom which index terms are to be assigned, based upon the total scores calculated for the Document D_ifor the index Terms I_j, and (i) repeating steps (b)-(h) for a plurality of other documents in the collection to which index terms have not yet been assigned which have not yet been processed.
- View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
- - 13. The method of claim 12, wherein the likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term manually;
    - is the log likelihood ratio L(D_i, D_k) for the document D_iand the document D_k $L (D_{i}, D_{k}) = \log (\sum_{m = 1}^{M_{0}} \underset{m}{Π} (D_{i}, D_{k}) / \sum_{m = 1}^{M_{0}} \underset{m}{Π} (D_{i})), where$ $\begin{matrix} \underset{m}{Π} (D_{i}, D_{k}) = 1, if Term m is assigned to Document D_{i} and to \\ Document D_{k} \\ = 0 otherwise \\ \underset{m}{Π} (D_{i}) = 1, if Term m is assigned to Document D_{i} \\ = 0 otherwise \\ M_{0} = the number of Terms which are in Document D_{i} . \end{matrix}$
  - 14. The method of claim 13, wherein the total score T (D_i, I_j) for the Document D_ifor the Index Term I_jis:
    - $T (D_{i}, I_{j}) = \sum_{k = 1}^{K_{0}} W (D_{k}, I_{j}) * L (D_{i}, D_{k}) / \sum_{k = 1}^{K_{0}} W (D_{k}, I_{j}),$ where K₀=the number of Documents in the collection assigned Index Term I_jmanually, W(D_k, I_j)=a weight assigned to Index Term I_jfor Document D_k.
  - 15. The method of claim 14, wherein the documents are Web pages.
  - 16. The method of claim 14, wherein the documents are Web sites.
  - 17. The method of claim 14, wherein one and only one index term is assigned to the documents in the collection to which index terms are assigned manually.
  - 18. The method of claim 17, wherein the weight W(D_k, I_j) assigned to the index term I_jpreviously assigned to a document D_kmanually equals 1.0.
  - 19. The method of claim 14, wherein a fixed number N of terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iare the highest are assigned as index terms for the Document D_i.
  - 20. The method of claim 19, wherein the fixed number N is equal to 1.
  - 21. The method of claim 14, wherein all terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iexceed a fixed cutoff score are assigned as index terms for the Document D_i.
  - 22. The method of claim 18, wherein the term I_jwhose total score T (D_i, I_j) calculated for the Document D_iis the highest is assigned as an index term for the Document D_i.

23. A method for assigning categories of items to supercategories, comprising:
- (a) assigning a subset of the categories in the collection to supercategories manually, (b) selecting a category C_ifrom among the categories in the collection not yet assigned to supercategories which has not yet been processed, (c) calculating a likelihood function L_ikfor the category C_iand a category C_kin the collection which has previously been assigned to a supercategory S_jmanually, which likelihood function is based upon the likelihood that a term occurring in the category C_ialso occurs in the category C_k, (d) repeating step (c) for a plurality of other categories C_kin the collection which have previously been assigned to a supercategory S_jmanually, (e) assigning the category C_ito a supercategory S_jbased on the likelihood functions L_ikthat a term occurring in the category C_ialso occurs in the category C_kwhich is assigned to supercategory S_j, and (f) repeating steps (b)-(e) for a plurality of other categories in the collection which have not yet been assigned to supercategories and which have not yet been processed.
- View Dependent Claims (24, 25, 26)
- - 24. The method of claim 23, wherein the categories correspond to varieties of products or services.
  - 25. The method of claim 24, wherein the items in the categories correspond to merchants, stores or other sources of the category'"'"'s product or service, and the merchants, stores or other sources may be associated with more than one category of products or services.
  - 26. The method of claim 24, wherein the supercategories correspond to groups of categories as to which it is desired to present the same banner advertisement to a user accessing the categories.

27. A method for assigning categories of items to supercategories, comprising:
- (a) assigning a subset of the categories in the collection to supercategories manually, (b) selecting a category C_ifrom among the categories in the collection not yet assigned to supercategories which has not yet been processed, (c) selecting a supercategory S_jfrom among the set of supercategories, (d) calculating a likelihood function for the category C_iand a category C_kin the collection which has previously been assigned to the supercategory S_jmanually, which likelihood function is based upon the likelihood that a term occurring in the category C_ialso occurs in the category C_k, (e) repeating step (d) for a plurality of other categories C_kin the collection which have previously been assigned to the supercategory S_jmanually, (f) calculating a total score for the category C_ifor the supercategory S_j, which total score is based upon the likelihood functions for the category C_iand the categories C_kin the collection which have previously been assigned to the supercategory S_jmanually, (g) repeating steps (c)-(f) for a plurality of other supercategories S_j, (h) assigning category C_ito the supercategory for which the total score calculated for the category C_iis the highest, and (i) repeating steps (b)-(h) for a plurality of other categories in the collection which have not yet been assigned to supercategories and which have not yet been processed
- View Dependent Claims (10, 28, 29, 30, 31, 32, 33, 34, 35)
- - 10. The method of claim 33, wherein all terms I_jwhose total scores T ( D_i, I_j) calculated for the Document D_iexceed a fixed cutoff score are assigned as index terms for the Document D_i.
  - 28. The method of claim 27, wherein the likelihood function for the category C_iand a category C_kin the collection which has previously been assigned to the supercategory S_jmanually, is the log likelihood ratio L(C_i, C_k) for the category C_iand the category C_k, $L$
    - ( C i , C k ) = log ⁡
      
      ( ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( C i , C k ) / ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( C i ) ) , ⁢
      
      where $\begin{matrix} \underset{m}{Π} (C_{i}, C_{k}) = 1, if item m is assigned to category C_{i} \\ and to category C_{k} \\ = 0 otherwise \\ \underset{m}{Π} (C_{i}) = 1, if item m is assigned to category C_{i} \\ = 0 otherwise \\ M_{0} = the number of items which are assigned \\ to category C_{i}, \end{matrix}$
  - 29. The method of claim 28, wherein the total score T (C_i, S_j) for the category C_ifor the supercategory S_jis $T$
    - ( C i , S j ) ⁢
      
      ∑
      
      k = 1 K 0 ⁢
      
      L ⁡
      
      ( C i , C k ) / K 0 , where K₀=the number of categories in the collection assigned to supercategory S_jmanually.
  - 30. The method of claim 29, wherein the categories correspond to varieties of products or services.
  - 31. The method of claim 30, wherein the items in the categories correspond to merchants stores or other sources of the category'"'"'s product or service, and the merchants, stores or other sources may be associated with more than one category of products or services.
  - 32. The method of claim 31, wherein the supercategories correspond to groups of categories as to which it is desired to present the same banner advertisement to a user accessing the categories.
  - 33. The method of claim 32, wherein there are 20,000 categories of products or services.
  - 34. The method of claim 33, wherein there are 2,000 categories of products or services assigned to supercategories manually.
  - 35. The method of claim 34, wherein there are 50 supercategories.

36. A device for assigning index terms to a document D_iin a collection of documents, where other documents in the collection have previously had index terms assigned by another method, comprising:
- (a) means for selecting a term I_jfrom among a set of terms from which the index terms are being assigned, which has not yet been processed, (b) means for calculating a likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, which likelihood function is based upon the likelihood that a term occurring in the document D_ialso occurs in the document D_k(c) means for repeating step (b) for a plurality of other documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (d) means for calculating a total score for the Document D_ifor the Index Term I_j, which total score is based upon the likelihood functions for the document D_iand the documents D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, (e) means for repeating steps (a)-(d) for a plurality of other terms I_jfrom among the set of terms from which index terms are being assigned, and (f) means for choosing index terms to be assigned to Document D_i, from among the set of terms from which index terms are being assigned, based upon the total scores calculated for the Document D_ifor the Index Terms I_j.
- View Dependent Claims (37, 38, 39, 40, 41, 42, 43, 44, 45, 46)
- - 37. The device of claim 36, wherein the likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term by another method, is the log likelihood ratio L(D_i, D_k) for the document D_iand the document D_k, $L$
    - ( D i , D k ) ⁢
      
      log ⁡
      
      ( ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i , D k ) / ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i ) ) , where $\begin{matrix} \underset{m}{Π} (D_{i}, D_{k}) = 1, if Term m is assigned to Document D_{i} \\ and to Document D_{k} \\ = 0 otherwise \end{matrix}$ $\begin{matrix} \underset{m}{Π} (D_{i}) = 1, if Term m is assigned to Document D_{i} \\ = 0 otherwise \\ M_{0} = the number of Terms which are in Document D_{i} . \end{matrix}$
  - 38. The device of claim 37, wherein the total score T (D_i, I_j) for the Document D_ifor the Index Term I_j, is $T$
    - ( D i , I j ) = ∏
      
      k = 1 K 0 ⁢
      
      ⁢
      
      W ⁡
      
      ( D k , I j ) * L ⁡
      
      ( D i , D k ) / ∏
      
      k = 1 K 0 ⁢
      
      ⁢
      
      W ⁡
      
      ( D k , I j ) , where K₀=the number of Documents in the collection assigned Index Term I_jby the other method, W(D_k, I_j)=a weight assigned to Index Term I_jfor Document D_k,
  - 39. The device of claim 38, wherein the documents are Web pages.
  - 40. The device of claim 38, wherein the documents are Web sites.
  - 41. The device of claim 38, wherein the documents in the collection which have previously had index terms assigned to them by another method;
    - have had one and only one index term assigned.
  - 42. The device of claim 41, wherein the weight W(D_k, I_j) assigned to the index term I_jpreviously assigned to a document D_kby another method equals 1.0.
  - 43. The device of claim 38, wherein a fixed number N of terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iare the highest are assigned as index terms for the Document D_i.
  - 44. The device of claim 43, wherein the fixed number N is equal to 1.
  - 45. The device of claim 38, wherein all terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iexceed a fixed cutoff score are assigned as index terms for the Document D_i.
  - 46. The device of claim 42, wherein the term I_jwhose total score T (D_i, I_j) calculated for the Document D_iis the highest is assigned as an index term for the Document D_i.

47. A device for assigning index terms to documents in a collection of documents, comprising:
- (a) means for manually pre-assigning index terms to a subset of the documents in the collection, (b) means for selecting a document D_ifrom among the documents in the collection to which index terms have not yet been assigned, which document D_ihas not yet been processed, (c) means for selecting a term I_jfrom among a set of terms from which index terms are being assigned, which term I_jhas not yet been processed, (d) means for calculating a likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term manually, which likelihood function is based upon the likelihood that a term occurring in the document D_ialso occurs in the document D_k, (e) means for repeating step (d) for a plurality of other documents D_kin the collection to which the term I_jhas previously been assigned as an index term manually, (f) means for calculating a total score for the Document D_ifor the Index Term I_jwhich total score is based upon the likelihood functions for the Document D_iand the Documents D_kin the collection to which the term I_jhas previously been assigned as an index term manually, (g) means for repeating steps (c)-(f) for a plurality of other terms I_jfrom among the set of terms from which index terms are being assigned, (h) means for choosing index terms to be assigned to Document D_i, from among the set of terms from which index terms are to be assigned, based upon the total scores calculated for the Document D_ifor the Index Terms I_j, and (i) means for repeating steps (b)-(h) for a plurality of other documents in the collection to which index terms have not yet been assigned which have not yet been processed.
- View Dependent Claims (48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
- - 48. The device of claim 47, wherein the likelihood function for the document D_iand a document D_kin the collection to which the term I_jhas previously been assigned as an index term manually, is the log likelihood ratio L(D_i, D_k) for the document D_iand the document D_k $L$
    - ( D i , D k ) = log ⁡
      
      ( ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i , D k ) / ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( D i ) ) , where $\begin{matrix} \underset{m}{Π} (D_{i}, D_{k}) = 1, if Term m is assigned to Document D_{i} \\ and to Document D_{k} \\ = 0, otherwise \end{matrix}$ $\begin{matrix} \underset{m}{Π} (D_{i}) = 1, if Term m is assigned to Document D_{i} \\ = 0 otherwise \\ M_{0} = the number of Terms which are in Document D_{i} . \end{matrix}$
  - 49. The device of claim 48, wherein the total score T (D_i, I_j) for the Document D_ifor the Index Term I_jis:
    - $T (D_{i}, I_{j}) = \sum_{k = 1}^{K_{0}} W (D_{k}, I_{j}) * L (D_{i}, D_{k}) / \sum_{k = 1}^{K_{0}} W (D_{k}, I_{j}),$ where K₀=the number of Documents in the collection assigned Index Term I_jmanually, W(D_k, I_j)=a weight assigned to Index Term I_jfor Document D_k.
  - 50. The device of claim 49, wherein the documents are Web pages.
  - 51. The device of claim 49, wherein the documents are Web sites.
  - 52. The device of claim 49, wherein one and only one index term is assigned to the documents in the collection to which index terms are assigned manually.
  - 53. The device of claim 52, wherein the weight W (D_k, I_j) assigned to the index term I_jpreviously assigned to a document D_kmanually equals 1.0.
  - 54. The device of claim 49, wherein a fixed number N of terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iare the highest are assigned as index terms for the Document D_i.
  - 55. The device of claim 54, wherein the fixed number N is equal to 1.
  - 56. The device of claim 49, wherein all terms I_jwhose total scores T (D_i, I_j) calculated for the Document D_iexceed a fixed cutoff score are assigned as index terms for the Document D_i.
  - 57. The device of claim 53, wherein the term I_jwhose total score T (D_i, I_j) calculated for the Document D_iis the highest is assigned as an index term for the Document D_i.

58. A device for assigning categories of items to supercategories, comprising:
- (a) means for assigning a subset of the categories in the collection to supercategories manually, (b) means for selecting a category C_ifrom among the categories in the collection not yet assigned to supercategories which has not yet been processed, (c) means for calculating a likelihood function L_ikfor the category C_iand a category C_kin the collection which has previously been assigned to a supercategory S_jmanually, which likelihood function is based upon the likelihood that a term occurring in the category C_ialso occurs in the category C_k, (d) means for repeating step (c) for a plurality of other categories C_kin the collection which have previously been assigned to a supercategory S_jmanually, (e) means for assigning the category C_ito a supercategory S_jbased on the likelihood functions L_ikthat a term occurring in the category C_ialso occurs in the category C_kwhich is assigned to supercategory S_j, and (f) means for repeating steps (b)-(e) for a plurality of other categories in the collection which have not yet been assigned to supercategories and which have not yet been processed.
- View Dependent Claims (59, 60, 61)
- - 59. The device of claim 58, wherein the categories correspond to varieties of products or services.
  - 60. The device of claim 59, wherein the items in the categories correspond to merchants, stores or other sources of the category'"'"'s product or service, and the merchants, stores or other sources may be associated with more than one category of products or services.
  - 61. The device of claim 59, wherein the supercategories correspond to groups of categories as to which it is desired to present the same banner advertisement to a user accessing the categories.

62. A device for assigning categories of items to supercategories, comprising:
- (a) means for assigning a subset of the categories in the collection to supercategories manually, (b) means for selecting a category C_ifrom among the categories in the collection not yet assigned to supercategories which has not yet been processed, (c) means for selecting a supercategory S_jfrom among the set of supercategories, (d) means for calculating a likelihood function for the category C_iand a category C_kin the collection which has previously been assigned to the supercategory S_jmanually, which likelihood function is based upon the likelihood that a term occurring in the category C_ialso occurs in the category C_k(e) means for repeating step (d) for a plurality of other categories C_kin the collection which have previously been assigned to the supercategory S_jmanually, (f) means for calculating a total score for the category C_ifor the supercategory S_j, which total score is based upon the likelihood functions for the category C_iand the categories C_kin the collection which have previously been assigned to the supercategory S_jmanually, (g) means for repeating steps (c)-(f) for a plurality of other supercategories S_j, (h) means for assigning category C_ito the supercategory for which the total score calculated for the category C_iis the highest, and (i) means for repeating steps (b)-(h) for a plurality of other categories in the collection which have not yet been assigned to supercategories and which have not yet been processed.
- View Dependent Claims (63, 64, 65, 66, 67, 68, 69, 70)
- - 63. The device of claim 62, wherein the likelihood function for the category C_iand a category C_kin the collection which has previously been assigned to the supercategory S_jmanually, is the log likelihood ratio L(C_i, C_k) for the category C_iand the category C_k, $L$
    - ( C i , C k ) = log ⁡
      
      ( ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( C i , C k ) / ∑
      
      m = 1 M 0 ⁢
      
      Π
      
      m ⁡
      
      ( C i ) ) , where $\begin{matrix} \prod_{m} (C_{i}, C_{k}) = 1, if item m is assigned to category C_{i} \\ and to category C_{k} \\ = 0 otherwise \end{matrix}$ $\begin{matrix} \prod_{m} (C_{i}) = 1, if item m is assigned to category C_{i} \\ = 0 otherwise \end{matrix}$ $M_{0} = the number of items which are assigned to category C_{i},$
  - 64. The device of claim 63, wherein the total score T (C_i, S_j) for the category C_ifor the supercategory S_jis $T$
    - ( C i , S j ) = ∑
      
      k = 1 K 0 ⁢
      
      L ⁡
      
      ( C i , C k ) / K 0 , where K₀=the number of categories in the collection assigned to supercategory S_jmanually.
  - 65. The device of claim 64, wherein the categories correspond to varieties of products or services.
  - 66. The device of claim 65, wherein the items in the categories correspond to merchants, stores or other sources of the category'"'"'s product or service, and the merchants, stores or other sources may be associated with more than one category of products or services.
  - 67. The device of claim 66, wherein the supercategories correspond to groups of categories as to which it is desired to present the same banner advertisement to a user accessing the categories.
  - 68. The device of claim 67, wherein there are 20,000 categories of products or services.
  - 69. The device of claim 68, wherein there are 2,000 categories of products or services assigned to supercategories manually.
  - 70. The device of claim 69, wherein there are 50 supercategories.

Specification

Resources

Litigation Campaign Assessment

Current Assignee
Apple Inc.
Original Assignee
Verizon Laboratories Incorporated (Verizon Communications Inc.)
Inventors
Ponte, Jay

Granted Patent

US 8,572,069 B2
Time in Patent Office

Days
Field of Search
US Class Current

1/1
CPC Class Codes

G06F 16/24578   using ranking

G06F 16/313   Selection or weighting of t...

G06F 16/951   Indexing; Web crawling tech...

Semi-automatic index term augmentation in document retrieval

First Claim

4 Assignments

0 Petitions

Accused Products

Abstract

88 Citations

70 Claims

Specification

Solutions

Use Cases

Quick Links

Semi-automatic index term augmentation in document retrieval

First Claim

4 Assignments

Subscription Required

Subscription Required

0 Petitions

Subscription Required

Accused Products

Subscription Required

Abstract

88 Citations

70 Claims

Specification

Subscription Required

Solutions

Use Cases

Quick Links