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Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 51, 53, 60-61 and 70 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 7-8, 10-11, 17-18 and 20 of U.S. Patent No. 10,133,375. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 51, 53, 60-61 and 70 of the instant application are similar in scope and content of the patented claims 1, 5, 7-8, 10-11, 17-18 and 20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 51, 53, 60-61 and 70 are to be found in patented claims 1, 5, 7-8, 10-11, 17-18 and 20 (as the application claims 51, 53, 60-61 and 70 fully encompasses patented claims 1, 5, 7-8, 10-11, 17-18 and 20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1, 5, 7-8, 10-11, 17-18 and 20 of the patent is in effect a “species” of the “generic” invention of the application claims 51, 53, 60-61 and 70. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 51, 53, 60-61 and 70 is anticipated by claims 1, 5, 7-8, 10-11, 17-18 and 20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/982,484
Patent No: 10,133,735
51. A method comprising: training a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receiving an input comprising a first sequence of words and a second sequence of words; inputting the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generating for display search results for the particular single query.
1. A method for training a processor-executed data model to accurately determine whether two phrases are conversationally connected, the method comprising: detecting a first phrase and a second phrase; translating the first phrase to a first string of word types by determining what type of word each word of the first phrase represents, and replacing each word of the first phrase with its respective type; translating the second phrase to a second string of word types by determining what type of word each word of the second phrase represents, and replacing each word of the second phrase with its respective type; generating a third string of word types by appending the second string to the end of the first string; determining a first degree to which the first string and the second string matches any singleton template of a plurality of singleton templates by comparing both the first string and the second string to the plurality of singleton templates; determining a second degree to which the third string matches any conversational template of a plurality of conversational templates; determining whether the first degree exceeds the second degree; in response to determining that the first degree exceeds the second degree: retrieving, from a knowledge graph stored in a database, a strength of association between the first string and a conversational category and a strength of association between the second string and the conversational category; decreasing the strength of association between the first string and the conversational category by a pre-defined amount, and decreasing the strength of association between the second string and the conversational category by the pre-defined amount; and in response to determining that the second degree exceeds the first degree: increasing the strength of association between the first string and the conversational category by the pre-defined amount, and increasing the strength of association between the second string and the conversational category by the pre-defined amount.
52. The method of claim 51, further comprising: receiving positive or negative feedback input for the display of the search results; and further training the model based on the receiving the feedback input.
53. The method of claim 51, further comprising: receiving a subsequent input comprising two sequences of words; and generating for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
5. The method of claim 1, wherein the second phrase is detected subsequent to a time at which the first phrase is detected, and wherein determining the first degree comprises: determining whether a word type of a first word of the second string is of a transitional type; and in response to determining that the word type of the first word of the second string is of the transitional type, reducing the first degree.
10. The method of claim 1, further comprising, in response to determining that the second degree exceeds the first degree, creating a combined phrase by combining the first phrase with the second phrase, and executing a search on the combined phrase.
54. The method of claim 51, wherein inputting the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generating for display search results for the two distinctive queries.
55. The method of claim 51 wherein generating for display the search results for the particular single query further comprises: receiving feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
56. The method of claim 51, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
57. The method of claim 51, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
58. The method of claim 57, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
59. The method of claim 57, further comprising determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
60. The method of claim 51, further comprising: determining a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further training the model based on the word having the higher relative importance.
7. The method of claim 1, wherein translating of either the first phrase or the second phrase comprises: extracting a word from either the first phrase or the second phrase; comparing the word to entries of a database that indicates word types of known words; determining whether a word type is known based on the comparing; and in response to determining that the word type is known, replacing the word with the word type indicated in an entry corresponding with the word.
8. The method of claim 7, further comprising, in response to determining that the word type is unknown: comparing the word to entries of a dictionary database to determine a grammatical category of the word; comparing the word to entries of a graph to determine a high-level category corresponding to the word; and extrapolating a word type based on the grammatical category and the high-level category.
61. A system comprising: control circuitry configured to: train a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receive an input comprising a first sequence of words and a second sequence of words; input the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generate for display search results for the particular single query.
11. A system for training a processor-executed data model to accurately determine whether two phrases are conversationally connected, the system comprising: communications circuitry; and control circuitry configured to detect a first phrase and a second phrase; translate the first phrase to a first string of word types by determining what type of word each word of the first phrase represents, and replacing each word of the first phrase with its respective type; translate the second phrase to a second string of word types by determining what type of word each word of the second phrase represents, and replacing each word of the second phrase with its respective type; generate a third string of word types by appending the second string to the end of the first string; determine a first degree to which the first string and the second string matches any singleton template of a plurality of singleton templates by comparing both the first string and the second string to the plurality of singleton templates; determine a second degree to which the third string matches any conversational template of a plurality of conversational templates; determine whether the first degree exceeds the second degree; in response to determining that the first degree exceeds the second degree: retrieve, from a knowledge graph stored in a database, a strength of association between the first string and a conversational category and a strength of association between the second string and the conversational category; decrease the strength of association between the first string and the conversational category by a pre-defined amount, and decrease the strength of association between the second string and the conversational category by the pre-defined amount; and in response to determining that the second degree exceeds the first degree: increase the strength of association between the first string and the conversational category by the pre-defined amount, and increase the strength of association between the second string and the conversational category by the pre-defined amount.
62. The system of claim 61, wherein the control circuitry is further configured to: receive positive or negative feedback input for the display of the search results; and further train the model based on the receiving the feedback input.
63. The system of claim 61, wherein the control circuitry is further configured to: receive a subsequent input comprising two sequences of words; and generate for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
15. The system of claim 11, wherein the second phrase is detected subsequent to a time at which the first phrase is detected, and wherein the control circuitry is configured, when determining the first degree, to: determine whether a word type of a first word of the second string is of a transitional type; and in response to determining that the word type of the first word of the second string is of the transitional type, reduce the first degree.
20. The system of claim 11, wherein the control circuitry is further configured to, in response to determining that the second degree exceeds the first degree, create a combined phrase by combining the first phrase with the second phrase, and to execute a search on the combined phrase.
64. The system of claim 61, wherein the control circuitry is configured to input the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generate for display search results for the two distinctive queries.
65. The system of claim 61 wherein the control circuitry is configured to generate for display the search results for the particular single query, the control circuitry is further configured to: receive feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
66. The system of claim 61, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
67. The system of claim 61, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
68. The system of claim 67, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
69. The system of claim 67, wherein the control circuitry is further configured to determine that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
70. The system of claim 61, wherein the control circuitry is further configured to: determine a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further train the model based on the word having the higher relative importance.
17. The system of claim 11, wherein the control circuitry is further configured, when translating either the first phrase or the second phrase, to: extract a word from either the first phrase or the second phrase; compare the word to entries of a database that indicates word types of known words; determine whether a word type is known based on the comparing; and in response to determining that the word type is known, replace the word with the word type indicated in an entry corresponding with the word.
18. The system of claim 17, wherein the control circuitry is configured, in response to determining that the word type is unknown, to: compare the word to entries of a dictionary database to determine a grammatical category of the word; compare the word to entries of a graph to determine a high-level category corresponding to the word; and extrapolate a word type based on the grammatical category and the high-level category.
Claims 51 and 61 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11, of U.S. Patent No. 10,747,960. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 51 and 61 of the instant application are similar in scope and content of the patented claims 1 and 11 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 51 and 61 are to be found in patented claims 1 and 11 (as the application claims 51 and 61 fully encompasses patented claims 1 and 11). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1 and 11 of the patent is in effect a “species” of the “generic” invention of the application claims 51 and 61. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 51 and 61 is anticipated by claims 1 and 11 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/982,484
Patent No: 10,747,960
51. A method comprising: training a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receiving an input comprising a first sequence of words and a second sequence of words; inputting the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generating for display search results for the particular single query.
1. A method for training a processor-executed data model to accurately determine whether two phrases are conversationally connected, the method comprising: generating a first string of word types based on a first phrase and a second string of word types based on a second phrase; generating a third string of word types by appending the second string of word types to the end of the first string of word types; determining a first degree of matching based on (1) comparing the first string of word types with a first singleton template and (2) comparing the second string of word types with a second singleton template; determine a second degree of matching based on comparing the third string of word types with a conversational category; determining whether the first degree of matching exceeds the second degree of matching; and in response to determining that the first degree of matching exceeds the second degree of matching, decreasing, in a graph of related entities stored in a database, a first strength of association between the first string of word types and the conversational category by a pre-defined amount; and in response to determining that the first degree of matching does not exceed the second degree of matching, increasing, in the graph of related entities stored in the database, the first strength of association between the first string of word types and the conversational category by the pre-defined amount.
52. The method of claim 51, further comprising: receiving positive or negative feedback input for the display of the search results; and further training the model based on the receiving the feedback input.
53. The method of claim 51, further comprising: receiving a subsequent input comprising two sequences of words; and generating for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
54. The method of claim 51, wherein inputting the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generating for display search results for the two distinctive queries.
55. The method of claim 51 wherein generating for display the search results for the particular single query further comprises: receiving feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
56. The method of claim 51, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
57. The method of claim 51, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
58. The method of claim 57, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
59. The method of claim 57, further comprising determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
60. The method of claim 51, further comprising: determining a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further training the model based on the word having the higher relative importance.
61. A system comprising: control circuitry configured to: train a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receive an input comprising a first sequence of words and a second sequence of words; input the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generate for display search results for the particular single query.
11. A system for training a processor-executed data model to accurately determine whether two phrases are conversationally connected, the system comprising: communications circuitry; and control circuitry configured to: generate a first string of word types based on a first phrase and a second string of word types based on a second phrase; generate a third string of word types by appending the second string of word types to the end of the first string of word types; determine a first degree of matching based on (1) comparing the first string of word types with a first singleton template and (2) comparing the second string of word types with a second singleton template; determine a second degree of matching based on comparing the third string of word types with a conversational category; determine whether the first degree of matching exceeds the second degree of matching; and in response to determining that the first degree of matching exceeds the second degree of matching, decrease, in a graph of related entities stored in a database, a first strength of association between the first string of word types and the conversational category by a pre-defined amount; and in response to determining that the first degree of matching does not exceed the second degree of matching, increase, in the graph of related entities stored in the database, the first strength of association between the first string of word types and the conversational category by the pre-defined amount.
62. The system of claim 61, wherein the control circuitry is further configured to: receive positive or negative feedback input for the display of the search results; and further train the model based on the receiving the feedback input.
63. The system of claim 61, wherein the control circuitry is further configured to: receive a subsequent input comprising two sequences of words; and generate for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
64. The system of claim 61, wherein the control circuitry is configured to input the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generate for display search results for the two distinctive queries.
65. The system of claim 61 wherein the control circuitry is configured to generate for display the search results for the particular single query, the control circuitry is further configured to: receive feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
66. The system of claim 61, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
67. The system of claim 61, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
68. The system of claim 67, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
69. The system of claim 67, wherein the control circuitry is further configured to determine that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
70. The system of claim 61, wherein the control circuitry is further configured to: determine a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further train the model based on the word having the higher relative importance.
Claims 51, 60-61 and 70 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 10, 11-13 and 20, of U.S. Patent No. 11,687,729. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 51, 60-61 and 70 of the instant application are similar in scope and content of the patented claims 1-3, 10, 11-13 and 20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 51, 60-61 and 70 are to be found in patented claims 1-3, 10, 11-13 and 20 (as the application claims 51, 60-61 and 70 fully encompasses patented claims 1-3, 10, 11-13 and 20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-3, 10, 11-13 and 20 of the patent is in effect a “species” of the “generic” invention of the application claims 51, 60-61 and 70. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 51, 60-61 and 70 is anticipated by claims 1-3, 10, 11-13 and 20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/982,484
Patent No: 11,687,729
51. A method comprising: training a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receiving an input comprising a first sequence of words and a second sequence of words; inputting the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generating for display search results for the particular single query.
1. A method comprising: detecting a first phrase and a second phrase; identifying a first template, of a plurality of templates associated with a first category, that is a closest match to the first phrase, wherein the identifying the first template comprises calculating a first matching score based on the first phrase and the first template; generating a third phrase by combining the first phrase and the second phrase; identifying a second template, of a plurality of templates associated with a second category, that is a closest match to the third phrase, wherein the identifying the second template comprises calculating a second matching score based on the third phrase and the second template; comparing the first matching score to the second matching score; in response to determining that the first matching score exceeds the second matching score, decreasing, in a graph of related entities stored in a database, a strength of association between the first phrase and the second category by a pre-defined amount; receiving a fourth phrase, and generating for display search results for the fourth phrase, wherein the search results are based on the decreasing of the strength of association between the first phrase and the second category by the pre-defined amount.
2. The method of claim 1, further comprising: in response to determining that the first matching score exceeds the second matching score, decreasing, in the graph of related entities stored in a database, a strength of association between the second phrase and the second category associated with the second template by the pre-defined amount.
3. The method of claim 2, further comprising: generating for display search results for the first phrase; and generating for display search results for the second phrase.
52. The method of claim 51, further comprising: receiving positive or negative feedback input for the display of the search results; and further training the model based on the receiving the feedback input.
53. The method of claim 51, further comprising: receiving a subsequent input comprising two sequences of words; and generating for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
54. The method of claim 51, wherein inputting the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generating for display search results for the two distinctive queries.
55. The method of claim 51 wherein generating for display the search results for the particular single query further comprises: receiving feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
56. The method of claim 51, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
57. The method of claim 51, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
58. The method of claim 57, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
59. The method of claim 57, further comprising determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
60. The method of claim 51, further comprising: determining a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further training the model based on the word having the higher relative importance.
10. The method of claim 9, further comprising, in response to determining that the word type is unknown: comparing the word to entries of a dictionary database to determine a grammatical category of the word; determining a high-level category corresponding to the word; and extrapolating a word type based on the grammatical category and the high-level category.
61. A system comprising: control circuitry configured to: train a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receive an input comprising a first sequence of words and a second sequence of words; input the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generate for display search results for the particular single query.
11. A system comprising: input/output (I/O) circuitry configured to: detect a first phrase and a second phrase; control circuitry configured to: identify a first template, of a plurality of templates associated with a first category, that is a closest match to the first phrase, wherein the identifying the first template comprises calculating a first matching score based on the first phrase and the first template; generate a third phrase by combining the first phrase and the second phrase; identify a second template, of a plurality of templates associated with a second category, that is a closest match to the third phrase, wherein the identifying the second template comprises calculating a second matching score based on the third phrase and the second template; calculate a second matching score based on the third phrase and the second template; in response to determining that the first matching score exceeds the second matching score, decrease, in a graph of related entities stored in a database, a strength of association between the first phrase and the second category by a pre-defined amount; receiving a fourth phrase, and generating for display search results for the fourth phrase, wherein the search results are based on the decreasing of the strength of association between the first phrase and the second category by the pre-defined amount.
12. The system of claim 11, wherein the control circuitry is further configured to: in response to determining that the first matching score exceeds the second matching score, decrease, in the graph of related entities stored in a database, a strength of association between the second phrase and the second category associated with the second template by the pre-defined amount.
13. The system of claim 12, wherein the control circuitry is further configured to: generate for display search results for the first phrase; and generate for display search results for the second phrase.
62. The system of claim 61, wherein the control circuitry is further configured to: receive positive or negative feedback input for the display of the search results; and further train the model based on the receiving the feedback input.
63. The system of claim 61, wherein the control circuitry is further configured to: receive a subsequent input comprising two sequences of words; and generate for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
64. The system of claim 61, wherein the control circuitry is configured to input the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generate for display search results for the two distinctive queries.
65. The system of claim 61 wherein the control circuitry is configured to generate for display the search results for the particular single query, the control circuitry is further configured to: receive feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
66. The system of claim 61, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
67. The system of claim 61, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
68. The system of claim 67, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
69. The system of claim 67, wherein the control circuitry is further configured to determine that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
70. The system of claim 61, wherein the control circuitry is further configured to: determine a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further train the model based on the word having the higher relative importance.
20. The system of claim 19, wherein the control circuitry is further configured to, in response to determining that the word type is unknown: compare the word to entries of a dictionary database to determine a grammatical category of the word; determine a high-level category corresponding to the word; and extrapolate a word type based on the grammatical category and the high-level category.
Claims 51, 54, 60-61 and 70 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8-11, and 18-20, of U.S. Patent No. 12,204,868. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 51, 54, 60-61 and 70 of the instant application are similar in scope and content of the patented claims 1-4, 8-11, and 18-20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 51, 54, 60-61 and 70 are to be found in patented claims 1-4, 8-11, and 18-20 (as the application claims 51, 54, 60-61 and 70 fully encompasses patented claims 1-4, 8-11, and 18-20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-4, 8-11, and 18-20 of the patent is in effect a “species” of the “generic” invention of the application claims 51, 54, 60-61 and 70. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 51, 54, 60-61 and 70 is anticipated by claims 1-4, 8-11, and 18-20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/982,484
Patent No: 12,204,868
51. A method comprising: training a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receiving an input comprising a first sequence of words and a second sequence of words; inputting the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generating for display search results for the particular single query.
1. A method comprising: detecting a first phrase and a second phrase; identifying a first template, of a plurality of templates associated with a first category, that is a closest match to the first phrase, wherein the identifying the first template comprises calculating a first matching score based on the first phrase and the first template; generating a third phrase by combining the first phrase and the second phrase; identifying a second template, of a plurality of templates associated with a second category, that is a closest match to the third phrase, wherein the identifying the second template comprises calculating a second matching score based on the third phrase and the second template; determining whether the first matching score exceeds the second matching score; and in response to determining that the first matching score does not exceed the second matching score, generating for display search results for the third phrase.
52. The method of claim 51, further comprising: receiving positive or negative feedback input for the display of the search results; and further training the model based on the receiving the feedback input.
53. The method of claim 51, further comprising: receiving a subsequent input comprising two sequences of words; and generating for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
54. The method of claim 51, wherein inputting the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generating for display search results for the two distinctive queries.
2. The method of claim 1, further comprising: in response to determining that the first matching score exceeds the second matching score, decreasing, in a graph of related entities stored in a database, a strength of association between the first phrase and the second category by a pre-defined amount.
3. The method of claim 1, further comprising: in response to determining that the first matching score exceeds the second matching score, decreasing, in a graph of related entities stored in a database, a strength of association between the second phrase and the second category a pre-defined amount.
4. The method of claim 3, further comprising: generating for display search results for the first phrase; and generating for display search results for the second phrase.
55. The method of claim 51 wherein generating for display the search results for the particular single query further comprises: receiving feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
56. The method of claim 51, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
57. The method of claim 51, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
58. The method of claim 57, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
59. The method of claim 57, further comprising determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
60. The method of claim 51, further comprising: determining a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further training the model based on the word having the higher relative importance.
8. The method of claim 1, wherein: determining the first template that is the closest match to the first phrase comprises: generating a first string of word types based on the first phrase; generating a second string of word types based on the second phrase; determining the second template that is the closest match to the third phrase comprises: generating a third string of word types by appending the second string of word types to an end of the first string of word types; performing the calculating of the first matching score comprises comparing the first string of word types to the first template and comparing the second string of word types to a template associated with the first category; and performing the calculating of the second matching score comprises comparing the third string of word types to the second template.
9. The method of claim 8, wherein generating the first string of word types based on the first phrase and the second string of word types based on the second phrase comprises: extracting a word from either the first phrase or the second phrase; comparing the word to database entries that indicate word types of known words; determining whether a word type is known based on the comparing; and in response to determining that the word type is known, replacing the word with the word type indicated in a database entry, of the database entries, corresponding to the word.
10. The method of claim 9, further comprising, in response to determining that the word type is unknown: comparing the word to entries of a dictionary database to determine a grammatical category of the word; determining a high-level category corresponding to the word; and extrapolating a word type based on the grammatical category and the high-level category.
61. A system comprising: control circuitry configured to: train a model that receives as an input two sequences of words and outputs a probability that the two sequences of words form a single query; receive an input comprising a first sequence of words and a second sequence of words; input the first sequence of words and the second sequence of words into the trained model, wherein the model is configured to: generate a context dependent intermediary representation of each word of the first sequence of words based on at least one respective adjacent word in the first sequence of words; generate a context dependent intermediary representation of each word of the second sequence of words based on at least one respective adjacent word in the second sequence of words; and output a particular probability indicative of whether the first sequence of words and the second sequence of words form a particular single query based on the generated intermediary representations; and based at least in part on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query, generate for display search results for the particular single query.
11. A system comprising: input/output circuitry configured to: receive a first phrase and a second phrase; control circuitry configured to: identify a first template, of a plurality of templates associated with a first category, that is a closest match to the first phrase, wherein the identifying the first template comprises calculating a first matching score based on the first phrase and the first template; generate a third phrase by combining the first phrase and the second phrase; identify a second template, of a plurality of templates associated with a second category, that is a closest match to the third phrase, wherein the identifying the second template comprises calculating a second matching score based on the third phrase and the second template; and determine whether the first matching score exceeds the second matching score; and wherein the input/output circuitry is configured to: in response to determining that the first matching score does not exceed the second matching score, generate for display search results for the third phrase.
62. The system of claim 61, wherein the control circuitry is further configured to: receive positive or negative feedback input for the display of the search results; and further train the model based on the receiving the feedback input.
63. The system of claim 61, wherein the control circuitry is further configured to: receive a subsequent input comprising two sequences of words; and generate for display subsequent search results for the subsequent input, wherein the subsequent search results are based on the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query.
64. The system of claim 61, wherein the control circuitry is configured to input the first sequence of words and the second sequence of words into the trained model, the model is further configured to: output a particular probability indicative of whether the first sequence of words and the second sequence of words form two distinctive queries based on the generated intermediary representations; and based at least in part on determining the particular probability output by the model that the first sequence of words and the second sequence of words form the two distinctive queries, generate for display search results for the two distinctive queries.
65. The system of claim 61 wherein the control circuitry is configured to generate for display the search results for the particular single query, the control circuitry is further configured to: receive feedback indicating that the first sequence of words and the second sequence of words form two distinctive queries rather than the particular single query; and based at least in part on receiving the feedback, generating for display search results for the two distinctive queries.
66. The system of claim 61, wherein the particular probability indicative of whether the first sequence of words and the second sequence of words form the particular single query is increased based at least in part on detecting a transitional word in the second sequence of words.
67. The system of claim 61, wherein generating for display search results for the particular single query is based at least in part on determining that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query exceeds a threshold.
68. The system of claim 67, wherein the threshold is a dynamic threshold based on receiving positive or negative feedback input for the display of the search results.
69. The system of claim 67, wherein the control circuitry is further configured to determine that the particular probability output by the model that the first sequence of words and the second sequence of words form the particular single query is below the threshold; and based at least in part on the determination that the particular probability is below the threshold, generating for display search results for a first query corresponding to the first sequence of words and search results for a second query corresponding to the second sequence of words.
70. The system of claim 61, wherein the control circuitry is further configured to: determine a word in the first sequence of words or the second sequence of words, based on the context dependent intermediary representation of each word in the first sequence of words and the second sequence of words, to have a higher relative importance; and based on determining a word in the first sequence of words or the second sequence of words to have the higher relative importance, further train the model based on the word having the higher relative importance.
18. The system of claim 11, wherein the control circuitry is further configured to: in determining the first template that is the closest match to the first phrase: generate a first string of word types based on the first phrase; generate a second string of word types based on the second phrase; in determining the second template that is the closest match to the third phrase: generate a third string of word types by appending the second string of word types to an end of the first string of word types; perform the calculating of the first matching score by comparing the first string of word types to the first template and comparing the second string of word types to a template of the first category; and perform the calculating of the second matching score by comparing the third string of word types to the second template.
19. The system of claim 18, wherein the control circuitry is further configured to, in generating the first string of word types based on the first phrase and the second string of word types based on the second phrase: extract a word from either the first phrase or the second phrase; compare the word to database entries that indicate word types of known words; determine whether a word type is known based on the comparing; and in response to determining that the word type is known, replace the word with the word type indicated in a database entry, of the database entries, corresponding to the word.
20. The system of claim 19, wherein the control circuitry is further configured to, in response to determining that the word type is unknown: compare the word to entries of a dictionary database to determine a grammatical category of the word; determine a high-level category corresponding to the word; and extrapolate a word type based on the grammatical category and the high-level category.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Ushioda (US 2008/0097742 A1) teaches a unified-evaluated-value computing unit quantitatively combines a translation probability between words computed by a corpus-based statistical-value computing unit, dictionary information computed by a dictionary-information referring unit, and syntactic structure information computed by a syntactic-structure-value computing unit, to compute a unified evaluated-value as an index for phrase alignment.
Van Den Berg et al., (US 2009/0063550 A1) teach computer-readable media and a computer system for implementing a natural language search using fact-based structures and for generating such fact-based structures are provided. A fact-based structure is generated using a semantic structure, which represents information, such as text, from a document, such as a web page. Typically, a natural language parser is used to create a semantic structure of the information, and the parser identifies terms, as well as the relationship between the terms. A fact-based structure of a semantic structure allows for a linear structure of these terms and their relationships to be created, while also maintaining identifiers of the terms to convey the dependency of one fact-based structure on another fact-based structure. Additionally, synonyms and hypernyms are identified while generating the fact-based structure to improve the accuracy of the overall search.
Salvetti et al., (US 2009/0070322 A1) teach computer-readable media and computer systems for conducting semantic processes to facilitate navigation of search results that include sets of tuples representing facts associated with content of documents in response to queries for information. Content of documents is accessed and semantic structures are derived by distilling linguistic representations from the content. Groups of two or more related words, called tuples, are extracted from the documents or the semantic structures. Tuples can be stored at a tuple index. Representations of the relational tuples are displayed in addition to documents retrieved in response to a query.
Abir (US 2011/0202334 A1) teach a method and apparatus for automating the acquisition, reconstruction, and generation of knowledgebases of associated ideas and using such knowledgebases in many application including machine translation of human languages, search and retrieval of unstructured text, or other data, based on concept search, voice recognition, data compression, and artificial intelligence systems.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
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