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 .
Claim Status
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi (US 2019/0114290) in view of Agrawal (US 5,930,789).
Examiner Note: Hereafter, above references will be entered combination A.
retrieving a timestamp set of queries;
Yi [0003] In accordance with the present disclosure, one or more systems and/or methods for generating a query-goal-mission structure for a set of queries are provided. In an example, a set of queries may be evaluated to identify query information (e.g., terms within the queries, a location of the terms within the query, a time a query was submitted, etc.) for the queries within the set of queries. For example, a search log (e.g., a mobile search log) may be evaluated to identify the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal) for the query pairs based upon the query information. In an example, a feature may be utilized to determine the common goal probability for the query pair. In an example, the query information may be utilized to determine the feature. The query pairs may be grouped into goal clusters based upon the common goal probabilities for the query pairs exceeding a goal probability threshold. The goal clusters may be evaluated as goal cluster pairs utilizing a mission classifier to determine common mission probabilities for the goal cluster pairs. The goal cluster pairs may be grouped into mission clusters based upon the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold. A query-goal-mission structure may be generated for the set of queries based upon the goal clusters and the mission clusters.
determining a time-stable set of groups from the timestamp set of queries by:
classifying each pair of queries in the timestamp set of queries based on whether a predefined feature similarity condition between the pair is satisfied; and classifying a window-level grouping status of each pair based on whether a predefined window-level similarity condition between the pair is satisfied, wherein the time-stable set of groups comprises a set of query pairs in the timestamp set of queries having a window-level grouping status classification indicative of being grouped; and determining a time-stable set of group clusters by:
Yi discloses elements of the claimed invention as noted but does not disclose above limitation. However, Agrawal discloses:
Agrawal abstract A system and method for discovering similar time sequences in a database of time sequences includes a computer-implemented program which first breaks each sequence into small windows. The windows from the first sequence are compared to selected windows from the second sequence to determine which windows are similar. Pairs of similar windows are then stitched together when certain stitching constraints are met to establish pairs of similar subsequences. Likewise, pairs of similar subsequences are stitched together, and the lengths of the stitched subsequences are then compared to the overall length of the time sequences to determine whether the time sequences meet a similarity criteria.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yi to obtain above limitation based on the teachings of Agrawal for the purpose of comparing the windows from the first sequence to selected windows from the second sequence to determine which windows are similar, see abstract.
determining a timestamp-level group distance for each pair of groups in the time-stable set of groups, wherein each pair of groups comprises a first group and a second group;
determining, for each pair of groups in the time-stable set of groups, a timestamp-level query-pair distance for each query pair between queries of the first group and queries of the second
group by setting each timestamp-level query-pair distance between the first group and the second group to a distance based on the timestamp-level group distance between the first group and the
second group;
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features (e.g., log-likelihood ratio for two queries, entropy, the entropy of rewrite probabilities from queries which can be rewritten, pointwise mutual information, pq12, such as a normalized probability that the first query is rewritten as a second query aggregated over many user sessions, etc.), query term-pair global features (e.g., term-pointwise mutual information, t-pq12, etc.), and/or desktop query term-pair features (e.g., the same as the query term-pair global features but using desktop search logs).
determining, for each query pair between queries of the first group and queries of the second group of each pair of groups in the time-stable set of groups, a window-level query-pair distance
based on the timestamp-level query-pair distances; and clustering the time-stable set of groups, using the window-level query-pair distances.
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
wherein determining whether a predefined feature similarity condition is satisfied comprises calculating a Jacard similarity index using a first and second set of URLs, wherein the first and second sets of URLs correspond, respectively, to a first and second set of articles of a first and second query of the pair of queries.
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features (e.g., log-likelihood ratio for two queries, entropy, the entropy of rewrite probabilities from queries which can be rewritten, pointwise mutual information, pq12, such as a normalized probability that the first query is rewritten as a second query aggregated over many user sessions, etc.), query term-pair global features (e.g., term-pointwise mutual information, t-pq12, etc.), and/or desktop query term-pair features (e.g., the same as the query term-pair global features but using desktop search logs).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Hegde (US 2007/0100958).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein determining a window-level grouping status comprises performing a voting routine that combines a current timestamp-level grouping status of the pair with the pair's timestamp-level grouping status at one or more prior time slots. However, Hegde discloses:
Hegde [0044] A bypassed domain name server is accessed based on an expiration of the associated timestamp at step 640. In one embodiment, expiration of the associated timestamp is determined by comparing the current time with the timestamp to determine if the timestamp is prior or earlier in time than the timestamp. Current time is determined using any appropriate method, such as a gettimeofday( ) query. In one embodiment, the timestamp is replaced with a zero value based on a determination that the timestamp has expired.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Hegde for the purpose of accessing a domain name server from the domain name server table based on the timestamp, see abstract.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view Van Der Zaag (US 2024/0105333).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein determining the timestamp-level group distance comprises determining, for each article of the first group, distance to each article of the second group, wherein distance is determined based on cosine similarity of article content and entity embeddings. However, Van Der Zaag discloses:
Van Der Zaag [0099] Various methods may be used to identify groups of trends with group-specific temporal behaviour patterns. For example, a clustering analysis may be performed to identify different groups of trends. Clustering analysis, or clustering, is an unsupervised learning technique, which aims at grouping a set of trends into clusters, i.e., groups, so that trends in the same clusters should be similar as possible, whereas trends in one cluster should be as dissimilar as possible from trends in other clusters. Clustering analysis aims to group a collection of patterns into clusters based on similarity. A typical clustering technique uses a similarity function for comparing various data items. In particular, clustering is done based on a similarity measure to group similar data objects together. This similarly measure may be based on distance functions such as Euclidean distance, Manhattan distance, Minkowski distance, Cosine similarity, etc. to group trends in clusters. The clusters may be formed in such a way that any two trends within a cluster have a minimum distance value and any two trends across different clusters have a maximum distance value. In other words, the similarity measure between the trends in the same trend group lies within a pre-determined range.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Van Der Zaag for the purpose providing a method to identify groups of trends in each of genetic changes with similar genetic changes over time and to analyse these groups to determine the patients response to therapy, see abstract.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A
Combination A discloses setting a final group distance for a pair of groups based on a minimum window-level query-pair distance associated with the pair.
Agrawal col 4 lines 25-37, Per the present invention, the system includes a hierarchical database which is accessible by the computer for electronically storing the windows as corresponding points in .omega.-dimensional space. Also, the system includes means for identifying one or more second windows as being similar to one or more corresponding first windows when the second window lies within a predetermined distance of the first window in the .omega.-dimensional space. Thereby, a pair of similar windows is established. And, the system includes means for stitching pairs of first and second windows together when the windows satisfy one or more predetermined stitching criteria to identify similarities in the first and second time sequences.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Thiel (US 2025/0156447).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein the clustering is performed using DBSCAN. However, Thiel discloses:
Thiel [0045] In comparison to computing an exact clustering by DBSCAN from scratch the index query according to an aspect of the invention is more efficient, in particular in terms of the number and complexity of the range queries required to compute the neighborhoods for any threshold distance less or equal to the predefined threshold distance. The DBSCAN algorithm requires for each object a range query over the entire dataset. In contrast, index querying requires range queries only for the candidate records over the collection of core records (or vice versa).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Thiel for the purpose of computing an index for a density-based clustering of a collection of records, see [0001].
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Lea (US 2025/0088293).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the predefined feature similarity condition is satisfied if the calculated Jacard similarity index is 0.2 or more. However, Lea discloses:
Lea [0082] In the cell environment, one device, Device D.sub.a, is able to see three cells, c1, c2, c3, and another device, Device D.sub.b, is able to see three cells, c2, c3, c4. Thus, Device D.sub.a and Device D.sub.b are able to be described using the above Jacard Similarity Index, which ranges from 0 to 1, where the closer the Jacard Similarity Index is to 1, the more similar the two sets of data are (or the closer Device D.sub.a and Device D.sub.b are to each other), and where the closer the Jacard Similarity Index is to 0, the less similar the two sets of data are (or the farther apart Device D.sub.a and Device D.sub.b are to each other).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Lea for the purpose of determining the closer the Jacard Similarity Index is to 1, such that the more similar the two sets of data are to each other.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Van Der Zaag in view of Buterbaugh (US 2024/0310514).
Combination A in view of Van Der Zaag discloses elements of the claimed invention as noted but does not disclose setting the timestamp-level group distance to equal the average of the top-3- minimum distances. However, Buterbaugh discloses:
Buterbaugh [0019] In one aspect, a method of tracking a vehicle may include initializing a plurality of traffic control devices, wherein the traffic control devices are configured to determining a distance to the vehicle and record a timestamp at which a minimum distance is recorded, sending a start signal to the plurality of traffic control device, receiving a minimum distance and a timestamp from at least two of the plurality of traffic control devices, generating a map of the plurality of traffic control devices, and generating, using, the minimum distances and the timestamps, a representation of a line of the vehicle.
Interpreted per specification [0076.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A in view of Van Der Zaag to obtain above limitation based on the teachings of Buterbaugh for the purpose of providing sensors for a traffic control device, and more particularly to a traffic control device including a distance detector, see [0001).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Van Der Zaag in view of Bellinin (US 12,530,399).
Combination A in view of Van Der Zaag discloses elements of the claimed invention as noted but does not disclose wherein at least a portion of the content and entity embeddings are retrieved from a caching component. However, Bellini discloses:
Bellini col 9 lines 40-48 The first section 602a can generate an output based on the input that indicates a content entity embedding vector 604 (e.g., an embedding vector of relevance scores for a specific content entity). This content entity embedding vector 604 can be cached (e.g., by the computer system 104 of FIG. 1, the device 102, or any other device) for low latency retrieval until a request associated with the content entity is received.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A in view of Van Der Zaag to obtain above limitation based on the teachings of Bellini for the purpose of providing artificial intelligence (AI) model to support tasks for different program codes.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Aggarwal (US 7,793,297).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein clustering the time-stable set of groups generates a time-stable set of group clusters, and further comprising: serving the time-stable set of group clusters to a website for display in a ranked list of trending topics. However, Aggarwal discloses:
Aggarwal col 1 lines 50-60, For example, a news organization may have a website that normally uses 100 servers, with 5 to 15 additional servers provisioned for the website application cluster during peak times. If a highly significant news event occurs, the 100 additional servers that the news organization's human resource department normally uses may need to be temporarily provisioned to the website application cluster in order prevent the application from becoming overloaded beyond its capacity to respond to client requests.
Examiner Note: for display in a ranked list of trending topics is drawn to intended use and is not given patentable weight.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Aggarwal for the purpose of providing method, apparatus, and computer program product in a data processing system for intelligent resource provisioning based on on-demand weight calculation, see abstract.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi (US 2019/0114290) in view of Agrawal (US 5,930,789).
Examiner Note: Hereafter, above references will be entered combination A.
retrieving a timestamp set of queries;
Yi [0003] In accordance with the present disclosure, one or more systems and/or methods for generating a query-goal-mission structure for a set of queries are provided. In an example, a set of queries may be evaluated to identify query information (e.g., terms within the queries, a location of the terms within the query, a time a query was submitted, etc.) for the queries within the set of queries. For example, a search log (e.g., a mobile search log) may be evaluated to identify the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal) for the query pairs based upon the query information. In an example, a feature may be utilized to determine the common goal probability for the query pair. In an example, the query information may be utilized to determine the feature. The query pairs may be grouped into goal clusters based upon the common goal probabilities for the query pairs exceeding a goal probability threshold. The goal clusters may be evaluated as goal cluster pairs utilizing a mission classifier to determine common mission probabilities for the goal cluster pairs. The goal cluster pairs may be grouped into mission clusters based upon the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold. A query-goal-mission structure may be generated for the set of queries based upon the goal clusters and the mission clusters.
determining a time-stable set of groups from the timestamp set of queries by:
classifying each pair of queries in the timestamp set of queries based on whether a predefined feature similarity condition between the pair is satisfied; and classifying a window-level grouping status of each pair based on whether a predefined window-level similarity condition between the pair is satisfied, wherein the time-stable set of groups comprises a set of query pairs in the timestamp set of queries having a window-level grouping status classification indicative of being grouped; and determining a time-stable set of group clusters by:
Yi discloses elements of the claimed invention as noted but does not disclose above limitation. However, Agrawal discloses:
Agrawal abstract A system and method for discovering similar time sequences in a database of time sequences includes a computer-implemented program which first breaks each sequence into small windows. The windows from the first sequence are compared to selected windows from the second sequence to determine which windows are similar. Pairs of similar windows are then stitched together when certain stitching constraints are met to establish pairs of similar subsequences. Likewise, pairs of similar subsequences are stitched together, and the lengths of the stitched subsequences are then compared to the overall length of the time sequences to determine whether the time sequences meet a similarity criteria.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yi to obtain above limitation based on the teachings of Agrawal for the purpose of comparing the windows from the first sequence to selected windows from the second sequence to determine which windows are similar, see abstract.
determining a timestamp-level group distance for each pair of groups in the time-stable set of groups, wherein each pair of groups comprises a first group and a second group;
determining, for each pair of groups in the time-stable set of groups, a timestamp-level query-pair distance for each query pair between queries of the first group and queries of the second
group by setting each timestamp-level query-pair distance between the first group and the second group to a distance based on the timestamp-level group distance between the first group and the
second group;
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features (e.g., log-likelihood ratio for two queries, entropy, the entropy of rewrite probabilities from queries which can be rewritten, pointwise mutual information, pq12, such as a normalized probability that the first query is rewritten as a second query aggregated over many user sessions, etc.), query term-pair global features (e.g., term-pointwise mutual information, t-pq12, etc.), and/or desktop query term-pair features (e.g., the same as the query term-pair global features but using desktop search logs).
determining, for each query pair between queries of the first group and queries of the second group of each pair of groups in the time-stable set of groups, a window-level query-pair distance
based on the timestamp-level query-pair distances; and clustering the time-stable set of groups, using the window-level query-pair distances.
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
wherein determining whether a predefined feature similarity condition is satisfied comprises calculating a Jacard similarity index using a first and second set of URLs, wherein the first and second sets of URLs correspond, respectively, to a first and second set of articles of a first and second query of the pair of queries.
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features (e.g., log-likelihood ratio for two queries, entropy, the entropy of rewrite probabilities from queries which can be rewritten, pointwise mutual information, pq12, such as a normalized probability that the first query is rewritten as a second query aggregated over many user sessions, etc.), query term-pair global features (e.g., term-pointwise mutual information, t-pq12, etc.), and/or desktop query term-pair features (e.g., the same as the query term-pair global features but using desktop search logs).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Hegde (US 2007/0100958).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein determining a window-level grouping status comprises performing a voting routine that combines a current timestamp-level grouping status of the pair with the pair's timestamp-level grouping status at one or more prior time slots. However, Hegde discloses:
Hegde [0044] A bypassed domain name server is accessed based on an expiration of the associated timestamp at step 640. In one embodiment, expiration of the associated timestamp is determined by comparing the current time with the timestamp to determine if the timestamp is prior or earlier in time than the timestamp. Current time is determined using any appropriate method, such as a gettimeofday( ) query. In one embodiment, the timestamp is replaced with a zero value based on a determination that the timestamp has expired.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Hegde for the purpose of accessing a domain name server from the domain name server table based on the timestamp, see abstract.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view Van Der Zaag (US 2024/0105333).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein determining the timestamp-level group distance comprises determining, for each article of the first group, distance to each article of the second group, wherein distance is determined based on cosine similarity of article content and entity embeddings. However, Van Der Zaag discloses:
Van Der Zaag [0099] Various methods may be used to identify groups of trends with group-specific temporal behaviour patterns. For example, a clustering analysis may be performed to identify different groups of trends. Clustering analysis, or clustering, is an unsupervised learning technique, which aims at grouping a set of trends into clusters, i.e., groups, so that trends in the same clusters should be similar as possible, whereas trends in one cluster should be as dissimilar as possible from trends in other clusters. Clustering analysis aims to group a collection of patterns into clusters based on similarity. A typical clustering technique uses a similarity function for comparing various data items. In particular, clustering is done based on a similarity measure to group similar data objects together. This similarly measure may be based on distance functions such as Euclidean distance, Manhattan distance, Minkowski distance, Cosine similarity, etc. to group trends in clusters. The clusters may be formed in such a way that any two trends within a cluster have a minimum distance value and any two trends across different clusters have a maximum distance value. In other words, the similarity measure between the trends in the same trend group lies within a pre-determined range.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Van Der Zaag for the purpose providing a method to identify groups of trends in each of genetic changes with similar genetic changes over time and to analyse these groups to determine the patients response to therapy, see abstract.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A
Combination A discloses setting a final group distance for a pair of groups based on a minimum window-level query-pair distance associated with the pair.
Agrawal col 4 lines 25-37, Per the present invention, the system includes a hierarchical database which is accessible by the computer for electronically storing the windows as corresponding points in .omega.-dimensional space. Also, the system includes means for identifying one or more second windows as being similar to one or more corresponding first windows when the second window lies within a predetermined distance of the first window in the .omega.-dimensional space. Thereby, a pair of similar windows is established. And, the system includes means for stitching pairs of first and second windows together when the windows satisfy one or more predetermined stitching criteria to identify similarities in the first and second time sequences.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Thiel (US 2025/0156447).
Combination A discloses elements of the claimed invention as noted but does not disclose
wherein the clustering is performed using DBSCAN. However, Thiel discloses:
Thiel [0045] In comparison to computing an exact clustering by DBSCAN from scratch the index query according to an aspect of the invention is more efficient, in particular in terms of the number and complexity of the range queries required to compute the neighborhoods for any threshold distance less or equal to the predefined threshold distance. The DBSCAN algorithm requires for each object a range query over the entire dataset. In contrast, index querying requires range queries only for the candidate records over the collection of core records (or vice versa).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Thiel for the purpose of computing an index for a density-based clustering of a collection of records, see [0001].
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Lea (US 2025/0088293).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the predefined feature similarity condition is satisfied if the calculated Jacard similarity index is 0.2 or more. However, Lea discloses:
Lea [0082] In the cell environment, one device, Device D.sub.a, is able to see three cells, c1, c2, c3, and another device, Device D.sub.b, is able to see three cells, c2, c3, c4. Thus, Device D.sub.a and Device D.sub.b are able to be described using the above Jacard Similarity Index, which ranges from 0 to 1, where the closer the Jacard Similarity Index is to 1, the more similar the two sets of data are (or the closer Device D.sub.a and Device D.sub.b are to each other), and where the closer the Jacard Similarity Index is to 0, the less similar the two sets of data are (or the farther apart Device D.sub.a and Device D.sub.b are to each other).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Lea for the purpose of determining the closer the Jacard Similarity Index is to 1, such that the more similar the two sets of data are to each other.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Van Der Zaag in view of Buterbaugh (US 2024/0310514).
Combination A in view of Van Der Zaag discloses elements of the claimed invention as noted but does not disclose setting the timestamp-level group distance to equal the average of the top-3- minimum distances. However, Buterbaugh discloses:
Buterbaugh [0019] In one aspect, a method of tracking a vehicle may include initializing a plurality of traffic control devices, wherein the traffic control devices are configured to determining a distance to the vehicle and record a timestamp at which a minimum distance is recorded, sending a start signal to the plurality of traffic control device, receiving a minimum distance and a timestamp from at least two of the plurality of traffic control devices, generating a map of the plurality of traffic control devices, and generating, using, the minimum distances and the timestamps, a representation of a line of the vehicle.
Interpreted per specification [0076.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A in view of Van Der Zaag to obtain above limitation based on the teachings of Buterbaugh for the purpose of providing sensors for a traffic control device, and more particularly to a traffic control device including a distance detector, see [0001).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Van Der Zaag in view of Bellinin (US 12,530,399).
Combination A in view of Van Der Zaag discloses elements of the claimed invention as noted but does not disclose wherein at least a portion of the content and entity embeddings are retrieved from a caching component. However, Bellini discloses:
Bellini col 9 lines 40-48 The first section 602a can generate an output based on the input that indicates a content entity embedding vector 604 (e.g., an embedding vector of relevance scores for a specific content entity). This content entity embedding vector 604 can be cached (e.g., by the computer system 104 of FIG. 1, the device 102, or any other device) for low latency retrieval until a request associated with the content entity is received.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A in view of Van Der Zaag to obtain above limitation based on the teachings of Bellini for the purpose of providing artificial intelligence (AI) model to support tasks for different program codes.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi (US 2019/0114290) in view of Agrawal (US 5,930,789).
Examiner Note: Hereafter, above references will be entered combination A.
a processor; and memory comprising instructions that when executed by the
processor perform operations comprising:
Yi [0032]
retrieving a timestamp set of queries;
Yi [0003] In accordance with the present disclosure, one or more systems and/or methods for generating a query-goal-mission structure for a set of queries are provided. In an example, a set of queries may be evaluated to identify query information (e.g., terms within the queries, a location of the terms within the query, a time a query was submitted, etc.) for the queries within the set of queries. For example, a search log (e.g., a mobile search log) may be evaluated to identify the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal) for the query pairs based upon the query information. In an example, a feature may be utilized to determine the common goal probability for the query pair. In an example, the query information may be utilized to determine the feature. The query pairs may be grouped into goal clusters based upon the common goal probabilities for the query pairs exceeding a goal probability threshold. The goal clusters may be evaluated as goal cluster pairs utilizing a mission classifier to determine common mission probabilities for the goal cluster pairs. The goal cluster pairs may be grouped into mission clusters based upon the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold. A query-goal-mission structure may be generated for the set of queries based upon the goal clusters and the mission clusters.
determining a time-stable set of groups from the timestamp set of queries by:
classifying each pair of queries in the timestamp set of queries based on whether a predefined feature similarity condition between the pair is satisfied; and classifying a window-level grouping status of each pair based on whether a predefined window-level similarity condition between the pair is satisfied, wherein the time-stable set of groups comprises a set of query pairs in the timestamp set of queries having a window-level grouping status classification indicative of being grouped; and determining a time-stable set of group clusters by:
Yi discloses elements of the claimed invention as noted but does not disclose above limitation. However, Agrawal discloses:
Agrawal abstract A system and method for discovering similar time sequences in a database of time sequences includes a computer-implemented program which first breaks each sequence into small windows. The windows from the first sequence are compared to selected windows from the second sequence to determine which windows are similar. Pairs of similar windows are then stitched together when certain stitching constraints are met to establish pairs of similar subsequences. Likewise, pairs of similar subsequences are stitched together, and the lengths of the stitched subsequences are then compared to the overall length of the time sequences to determine whether the time sequences meet a similarity criteria.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yi to obtain above limitation based on the teachings of Agrawal for the purpose of comparing the windows from the first sequence to selected windows from the second sequence to determine which windows are similar, see abstract.
determining a timestamp-level group distance for each pair of groups in the time-stable set of groups, wherein each pair of groups comprises a first group and a second group;
determining, for each pair of groups in the time-stable set of groups, a timestamp-level query-pair distance for each query pair between queries of the first group and queries of the second
group by setting each timestamp-level query-pair distance between the first group and the second group to a distance based on the timestamp-level group distance between the first group and the
second group;
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features (e.g., log-likelihood ratio for two queries, entropy, the entropy of rewrite probabilities from queries which can be rewritten, pointwise mutual information, pq12, such as a normalized probability that the first query is rewritten as a second query aggregated over many user sessions, etc.), query term-pair global features (e.g., term-pointwise mutual information, t-pq12, etc.), and/or desktop query term-pair features (e.g., the same as the query term-pair global features but using desktop search logs).
determining, for each query pair between queries of the first group and queries of the second group of each pair of groups in the time-stable set of groups, a window-level query-pair distance
based on the timestamp-level query-pair distances; and clustering the time-stable set of groups, using the window-level query-pair distances.
Yi [0048] In an example, the features may comprise query-pair local features (e.g., Jaccard similarity of two query terms, normalized Levenshtein edit distance, a time interval, position difference, conxsim, etc.), query-pair global features
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161
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