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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 11/5/2025. Claims 1-6, 8-19, and 21-22 have been examined in this application.
Response to Amendment
Applicant’s amendments to claims 1, 8, and 18 are acknowledged. Applicant’s cancellation of claims 7 and 20 are acknowledged. Applicant’s addition of new claims 21-22 are acknowledged.
Response to Arguments
Applicant’s response to the 101 rejection is acknowledged on Remarks page 11. The Examiner still interprets the claims as amended to be directed to an abstract idea without significantly more. The rejection has been updated below to reflect Applicant’s amendments.
Applicant’s response to the 112 second/b rejection is acknowledged on remarks page 11. Based on Applicant’s amendments, the previous 112 second/b rejections have been withdrawn.
Applicant’s response to the claims rejection under prior art on Remarks pages 11-13 is acknowledged. The Examiner still interprets the prior art of record to teach Applicant’s amendments as updated in the prior art rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-6 and 21-22 recite a machine as the claims recite a system with a processor. Claims 8-17 recite a process as the claims recite a method. Claims 18-19 recite an article of manufacture as the claims recite a computer readable medium, and the claims are not being interpreted as signals per se based on Applicant’s specification at paragraph 0168 “Computer storage media does not comprise signals per se.”.
The claim(s) 1-6, 8-19, and 21-22 recite(s) collecting data, aggregating the information, and displaying results like recommendations or suggestions based on the collection and analyzing of data.
The claims recite observations, evaluations, judgements and opinions that can be performed in the human mind or with pen and paper in the idea of collecting data, aggregating the information, and displaying results like recommendations or suggestions based on the collection and analyzing of data, accordingly the claims recite a mental process.
Further the claims recite providing recommendations or suggestions based on the collection and analyzing of data where the data can be based on users or relationships between users which is managing personal behavior or relationships or interactions between people which is a certain method of organizing human activity.
Mental processes and certain methods of organizing human activity are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
It is noted that the Examiner has bolded and underlined additional elements beyond the abstract idea. Limitations neither bolded nor underlined are considered a part of the abstract idea.
1. A computerized system for facilitating computer-resource efficient communication between users, comprising:
at least one processor; and computer memory storing computer-useable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, each node corresponding to a contact of the user, a data object associated with the user, or a topic determined from the data object, each edge connecting two nodes and corresponding to an interaction between the contact and the data object or corresponding to a relationship between the data object and the topic;
accessing a subgraph of the knowledge graph, the subgraph of the knowledge graph including a set of nodes comprising at least two nodes from the plurality of nodes and a set of edges comprising at least one edge from the plurality of edges corresponding to a particular contact;
determining, from the set of nodes, a set of candidate topics including at least one candidate topic, the set of candidate topics determined based on a corresponding interaction stored in the subgraph and represented as a first edge between the particular contact and a corresponding data object and a corresponding relationship, represented as a second edge, between the corresponding data object and a corresponding particular candidate topic;
ranking the set of candidate topics for the particular contact based on frequency the set of candidate topics occurs within or is determined from a set of data objects associated with the user;
generating a number of topics associated with the particular contact based on the ranking of the set of candidate topics;
responsive to the particular contact within an application, causing presentation of a graphical user interface (GUI) that is separate from the application, where the GUI comprises the particular contact and a first topic from the number of topics associated with the particular contact.
2. The computerized system of claim 1, wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node.
3. The computerized system of claim 1, wherein determining the set of candidate topics further comprises: removing topics from the set of candidate topics corresponding to each of the plurality of data objects without having an activity associated therewith within a threshold period of time; removing topics from the set of candidate topics below a threshold quality value; removing topics from the set of candidate topics from a single source of in the plurality of data objects; and merging similar topics of the set of candidate topics.
4. The computerized system of claim 3, wherein merging similar topics of the set of candidate topics further comprises: merging triples of similar topics of the set of candidate topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the set of candidate topics contained within other topics of the set of candidate topics; and clustering topics in the set of candidate topics by their Levenshtein distance.
5. The computerized system of claim 1, wherein determining the set of candidate topics further comprises: ranking each topic from the set of nodes using a PageRank algorithm based on keywords corresponding to each topic in the plurality of data objects; and determining each candidate topic of the set of candidate topics based on each topic from the set of nodes above a threshold ranking in the PageRank algorithm.
6. The computerized system of claim 1, wherein determining the set of candidate topics further comprises: determining, based on the subgraph, that a first candidate topic of the set of candidate topics is associated with a plurality of contacts that exceeds a threshold percentage; and removing the first candidate topic from the set of candidate topics; and wherein ranking the set of candidate topics for the particular contact further comprises ranking each candidate topic in the set of candidate topics using term frequency–inverse document frequency.
8. A computer-implemented method for preserving computing and network resources for communications between users, the computer-implemented method being performed by a computerized system including a processor and the computer-implemented method comprising:
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, the plurality of nodes corresponding to a plurality of contacts of the user and a plurality of data objects of the user, the plurality of edges corresponding to interactions between each of the plurality of contacts and each of the plurality of data objects;
determining a set of contacts from the plurality of contacts of the knowledge graph for the user;
determining a plurality of topics from the plurality of data objects of the knowledge graph for the user; for a contact of the set of contacts, determining at least one candidate topic from the plurality of topics based on corresponding interactions stored in the knowledge graph between the contact and corresponding data objects of the plurality of data objects;
and responsive to detecting a meeting scheduled between the user and the contact causing display of a graphical user interface comprising the contact with the at least one candidate topic corresponding to the contact
9. The computer-implemented method of claim 8, wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node.
10. The computer-implemented method of claim 8, wherein the plurality of topics are stored as nodes in the knowledge graph and relationships between each of the plurality of topics and the plurality of data objects are stored as edges in the knowledge graph.
11. The computer-implemented method of claim 8, wherein determining the set of contacts from the plurality of contacts further comprises: removing duplicate contacts in the plurality of contacts; removing non-human contacts in the plurality of contacts; ranking each contact in the plurality of contacts based on importance correlated to how connected each contact is with the user in the knowledge graph; and determining each contact in the set of contacts above a threshold ranking.
12. The computer-implemented method of claim 8, wherein determining the plurality of topics from the plurality of data objects further comprises: determining, using a language model, a plurality of keywords in the plurality of data objects; and determining the plurality of topics from the plurality of keywords.
13. The computer-implemented method of claim 8, wherein determining the plurality of topics further comprises: removing topics from the plurality of topics from each data object in the plurality of data objects without activity within a threshold period of time; removing topics from the plurality of topics below a threshold quality value; removing topics from the plurality of topics from a single source of in the plurality of data objects; and merging similar topics of the plurality of topics.
14. The computer-implemented method of claim 13, wherein merging similar topics of the plurality of topics further comprises: merging triples of similar topics of the plurality of topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the plurality topics contained within other topics of the plurality of topics; and clustering topics in the plurality of topics by their Levenshtein distance.
15. The computer-implemented method of claim 8, wherein determining at least one candidate topic from the plurality of topics further comprises: determining a set of candidate topics from the plurality of topics; ranking each candidate topic in the set of candidate topics using a PageRank algorithm based on keywords corresponding to each candidate topic in the plurality of interactions with the set of contacts stored in the knowledge graph; and determining the at least one candidate topic of the set of candidate topics above a threshold ranking.
16. The computer-implemented method of claim 15, wherein determining the set of candidate topics from the plurality of topics further comprises: removing candidate topics from the set of candidate topics inferred for above a threshold percentage of the set of contacts; merging triples of similar candidate topics of the set of candidate topics when a Jaccard distance between any pair of candidate topics in the triples of similar candidate topics is below a certain threshold; removing candidate topics of the set of candidate topics contained within other candidate topics of the set of candidate topics; and clustering candidate topics in the set of candidate topics by their Levenshtein distance.
17. The computer-implemented method of claim 15, wherein determining the at least one candidate topic of the set of candidate topics above a threshold ranking further comprises: ranking each candidate topic using term frequency–inverse document frequency; and determining the at least one candidate topic above a threshold ranking.
18. One or more computer storage media having computer-executable instructions embodied thereon that, when executed by a computing system having at least one processor and at least one memory, cause the at least one processor to perform operations comprising:
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, the plurality of nodes corresponding to a plurality of contacts of the user, a plurality of data objects of the user, and a plurality of topics extracted from the plurality of data objects, the plurality of edges corresponding to interactions between each of the plurality of contacts and each of the plurality of data objects and relationships between each of the plurality of data objects and each of the plurality of topics;
determining a set of contacts from the plurality of contacts of the knowledge graph for the user; for each contact of the set of contacts: accessing a subgraph of the knowledge graph, the subgraph of the knowledge graph comprising a set of nodes from the plurality of nodes and a set of edges from the plurality of edges corresponding to each contact;
determining a set of candidate topics from the set of nodes based on corresponding interactions stored in the subgraph between each contact and corresponding data objects and corresponding relationships between the corresponding data objects and the set of candidate topics;
ranking the set of candidate topics for each contact; and generating a number of topics for each contact based on the ranking of the set of candidate topics;
and responsive detecting a communication with the particular contact of the set of contacts, causing display of a graphical user interface (GUI) comprising the particular user and a first topic, from the number of topics generated for the particular contact
19. The one or more computer storage media of claim 18: wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node; and wherein determining the set of candidate topics further comprises: removing topics from the set of candidate topics corresponding to each of the plurality of data objects without activity within a threshold period of time; removing topics from the set of candidate topics below a threshold quality value; removing topics from the set of candidate topics from a single source of in the plurality of data objects; and merging similar topics of the set of candidate topics.
21. The computerized system of claim 1, wherein the GUI comprises a plurality of topics from the number of topics, the plurality of topics comprising at least the first topic and being arranged in a list format adjacent to the particular contact on the GUI
22. The computerized system of claim 1, wherein first topic is included in a list of topics arranged adjacent to the particular contact on the GUI
As per claim 1, the claims recite limitations a human or humans could perform. Specifically a human or humans could collect data, generate graphs to show relationships or connections and the strength of connections like weights between the collected data, rank content of interest like topics based on the graph and frequency of occurrence, provide corresponding content based on the rankings, and provide the content on different displays responsive to desired action or trigger for example providing content when needed like communicating with a specific individual who may find certain information of more interest in an application.
The additional elements that these limitations that could be performed by a human or humans given the broad recitation in the claim are instead being performed by a computer (specifically “computerized” system, “computer” resource efficient, and at least one processor; and computer memory storing computer-useable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising) and that information is displayed in GUIs, merely results in apply it.
The claims amount to nothing more than an instruction to apply the abstract idea using a generic computer and therefore does not render an abstract idea eligible (see MPEP 2106.05(f) “ In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer). The claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the abstract to an abstract idea (e.g. a fundamental economic practical or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Further limitations that could be performed by a human or humans that instead recite it being performed by a computer (specifically “computerized” system, “computer” resource efficient, and at least one processor; and computer memory storing computer-useable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising) and that information is displayed in GUIs merely results in generally linking it to the field of computers.
As per claim 2, the claims recite limitations a human or humans could perform. Specifically a human or humans could use weights to show the strength of a relationship between two objects and provide data that indicates an action a user took with relation to an item (like purchased a product).
There are no additional elements beyond those previously discussed above with respect to “computerized system” in claim 1 which result in merely applying it or generally linking it to the field of computers.
As per claim 3, the claims recite limitations a human or humans could perform. Specifically a human or humans could filter out data or merge topics or data accordingly to various set rules.
There are no additional elements beyond those previously discussed above with respect to “computerized system” in claim 1 which result in merely applying it or generally linking it to the field of computers.
As per claim 4, the claims recite limitations a human or humans could perform. Specifically a human or humans could filter out data or merge topics or data accordingly mathematical equations or theories like Levenshtein distance or Jaccard distance.
There are no additional elements beyond those previously discussed above with respect to “computerized system” in claim 1 which result in merely applying it or generally linking it to the field of computers.
Even if these Levenshtein distance or Jaccard distance would be considered additional elements of machine learning for which the Examiner does not contend as they are mere mathematical theories or equations being used to implement the abstract idea that could be implemented by a human or humans given the broad recitation in the claim and therefore part of the abstract idea. These would be additional elements are used to generally apply the abstract idea without placing any limitation on how the Levenshtein distance or Jaccard distance operates. The claim omits any details as to how the Levenshtein distance or Jaccard distance solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic Levenshtein distance or Jaccard distance merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 5, the claims recite limitations a human or humans could perform. Specifically a human or humans could rank each topic according to an algorithm based on keywords of a topic and determine each topic based on a threshold ranking according to an algorithm.
The additional element with respect to “computerized system” results in merely applying it or generally linking it to the field of computers as discussed above in claim 1.
The additional element of the algorithm being a “PageRank” algorithm does not amount to a practical application or significantly more. Specifically this additional element is used to generally apply the abstract idea without placing any limitation on how the PageRank algorithm operates. The claim omits any details as to how the PageRank algorithm solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic PageRank algorithm merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 6, the claims recite limitations a human or humans could perform. Specifically a human or humans could determine a fist topic associated with a contact exceeds a threshold percentage and remove the topic, and then rank the topics using a frequency algorithm.
The additional element with respect to “computerized system” results in merely applying it or generally linking it to the field of computers as discussed above in claim 1.
The additional element of the algorithm being a “term frequency inverse document frequency” algorithm does not amount to a practical application or significantly more. Specifically this additional element is used to generally apply the abstract idea without placing any limitation on how the “term frequency inverse document frequency” algorithm operates. The claim omits any details as to how the “term frequency inverse document frequency” algorithm solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic “term frequency inverse document frequency” algorithm merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 8, the claims recite limitations a human or humans could perform. Specifically a human or humans could collect data, generate graphs to show relationships or connections and the strength of connections like weights between the collected data, rank content of interest like topics based on the graph, provide corresponding content based on the rankings, determining a user has a meeting with another user, and provide the content in different displays responsive to desired action or trigger for example providing content when needed like communicating with a specific individual who may find certain information of more interest.
The additional elements that these limitations that could be performed by a human or humans given the broad recitation in the claim are instead being performed by a computer (“computerized system” including a “processor”) and that information is displayed in graphical user interfaces (“GUIs”) merely results in apply it.
The claims amount to nothing more than an instruction to apply the abstract idea using a generic computer and therefore does not render an abstract idea eligible (see MPEP 2106.05(f) “ In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer). The claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the abstract to an abstract idea (e.g. a fundamental economic practical or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Further limitations that could be performed by a human or humans that instead recite it being performed by a computer (“computerized system” including a “processor”) and that information is displayed in graphical user interfaces (“GUIs”) merely results in generally linking it to the field of computers.
As per claim 9, the claims recite limitations a human or humans could perform. Specifically a human or humans could use weights to show the strength of a relationship between two objects and provide data that indicates an action a user took with relation to an item (like purchased a product).
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
As per claim 10, the claims recite limitations a human or humans could perform. Specifically the claims recite how a human or human could set up a graph to see relationships between objects like nodes that store topics and edges that store relationships.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
As per claim 11, the claims recite limitations a human or humans could perform. Specifically a human could remove contacts according to constraints like duplicate and non human. Further a human could rank contacts according to information in the graph and determine information by comparing it to a threshold.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
As per claim 12, the claims recite limitations a human or humans could perform. Specifically a human could use a language model to determine a plurality of keywords from objects like documents and then determine a plurality of topics from keywords.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
As per claim 13, the claims recite limitations a human or humans could perform. Specifically a human could remove topics from interest that meet search constraints like below a threshold or a threshold amount of time or from a source. Further a human could merge topics that are similar together for easier record keeping.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
As per claim 14, the claims recite limitations a human or humans could perform. Specifically a human or humans could filter out data or merge topics or data accordingly mathematical equations or theories like Levenshtein distance or Jaccard distance.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
Even if these Levenshtein distance or Jaccard distance would be considered additional elements of machine learning for which the Examiner does not contend as they are mere mathematical theories or equations being used to implement the abstract idea that could be implemented by a human or humans given the broad recitation in the claim and therefore part of the abstract idea. These would be additional elements are used to generally apply the abstract idea without placing any limitation on how the Levenshtein distance or Jaccard distance operates. The claim omits any details as to how the Levenshtein distance or Jaccard distance solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic Levenshtein distance or Jaccard distance merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 15, the claims recite limitations a human or humans could perform. Specifically a human or humans could rank each topic according to an algorithm based on keywords of a topic and determine each topic based on a threshold ranking according to an algorithm.
The additional element with respect to “computer” results in merely applying it or generally linking it to the field of computers as discussed above in claim 8.
The additional element of the algorithm being a “PageRank” algorithm does not amount to a practical application or significantly more. Specifically this additional element is used to generally apply the abstract idea without placing any limitation on how the PageRank algorithm operates. The claim omits any details as to how the PageRank algorithm solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic PageRank algorithm merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 16, the claims recite limitations a human or humans could perform. Specifically a human or humans could filter out data or merge topics or data accordingly mathematical equations or theories like Levenshtein distance or Jaccard distance.
There are no additional elements beyond those previously discussed above with respect to “computer” in claim 8 which result in merely applying it or generally linking it to the field of computers.
Even if these Levenshtein distance or Jaccard distance would be considered additional elements of machine learning for which the Examiner does not contend as they are mere mathematical theories or equations being used to implement the abstract idea that could be implemented by a human or humans given the broad recitation in the claim and therefore part of the abstract idea. These would be additional elements are used to generally apply the abstract idea without placing any limitation on how the Levenshtein distance or Jaccard distance. The claim omits any details as to how the Levenshtein distance or Jaccard distance solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic Levenshtein distance or Jaccard distance merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 17 the claims recite limitations a human or humans could perform. Specifically a human or humans could determine a first topic exceeds a threshold percentage, and then rank the topics using a frequency algorithm.
The additional element with respect to “computer” results in merely applying it or generally linking it to the field of computers as discussed above in claim 8.
The additional element of the algorithm being a “term frequency inverse document frequency” algorithm does not amount to a practical application or significantly more. Specifically this additional element is used to generally apply the abstract idea without placing any limitation on how the “term frequency inverse document frequency” algorithm operates. The claim omits any details as to how the “term frequency inverse document frequency” algorithm solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a generic “term frequency inverse document frequency” algorithm merely as a tool to make the recited mathematical calculation rather than purporting to improve the technology or computer. Therefore these limitations are no more than mere instructions to apply the judicial exception on a computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the environment of computers (additionally see USPTO Example 48).
As per claim 18, the claims recite limitations a human or humans could perform. Specifically a human or humans could collect data, generate graphs to show relationships or connections and the strength of connections like weights between the collected data, rank content of interest like topics based on the graph, provide corresponding content based on the rankings, and provide the content responsive to desired action or trigger in a different display for example providing content when needed like communicating with a specific individual who may find certain information of more interest in an application. The additional elements that these limitations that could be performed by a human or humans given the broad recitation in the claim are instead being performed by a computer (specifically “one or more computer storage media having computer-executable instructions embodied thereon that, when executed by a computing system having at least one processor and at least one memory, cause that at least one processor to perform operations comprising:”) and that the displays are “graphical user interfaces (GUIs)” merely results in apply it.
The claims amount to nothing more than an instruction to apply the abstract idea using a generic computer and therefore does not render an abstract idea eligible (see MPEP 2106.05(f) “ In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer). The claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the abstract to an abstract idea (e.g. a fundamental economic practical or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Further limitations that could be performed by a human or humans that instead recite it being performed by a computer (specifically “one or more computer storage media having computer-executable instructions embodied thereon that, when executed by a computing system having at least one processor and at least one memory, cause that at least one processor to perform operations comprising:”) and that the displays are “graphical user interfaces (GUIs)” merely results in generally linking it to the field of computers.
As per claim 19, the claims recite limitations a human or humans could perform. Specifically a human or humans could generate a graph to show the relationship between collected objects. A human could use weights to show the strength of a relationship between two objects and provide data that indicates an action a user took with relation to an item (like purchased a product). Further a human or humans could filter out data or merge topics or data accordingly to various set rules.
There are no additional elements beyond those previously discussed above with respect to “one or more computer storage media” in claim 18 which result in merely applying it or generally linking it to the field of computers.
As per claim 21, the claims recite mental process and or human activities steps. Specifically the claims recite displaying data from different topics and displaying information in a list format next to a contact. Therefore this is part of the abstract idea. The additional element that the display is a “GUI” and it is being performed by a “computerized” system merely results in apply it or generally linking it to the field of computers, as discussed in claim 1 above.
As per claim 22, the claims recite mental process and or human activities steps. Specifically the claims recite displaying a first topic in a list of topics arranged adjacent to the a particular content in a display. Therefore this is part of the abstract idea. The additional element that the display is a “GUI” and it is being performed by a “computerized” system merely results in apply it or generally linking it to the field of computers, as discussed in claim 1 above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite: (1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above with respect to the practical application step.
Claim Objections
Claim 18 is objected to because of the following informalities: There appears to be a typo in the claim amendments. For the purposes of this examination the Examiner will interpret the claim as follows: responsive to detecting a communication with the particular contact rather than the currently recited responsive detecting a communication with the particular contact. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 8-13, 18-19, and 21-22 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222).
As per claim 1,Stickler et al. teaches A computerized system for facilitating computer-resource efficient communication between users, comprising: (see paragraph 0011, Examiner’s note: implemented as a computing system).
at least one processor; and computer memory storing computer-useable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: (see paragraphs 0011, 0092-0093, and claim 20, Examiner’s note: software running on a computer to perform operations).
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, each node corresponding to a contact of the user, a data object associated with the user, or a topic determined from the data object, each edge connecting two nodes and corresponding to an interaction between the contact and the data object or corresponding to a relationship between the data object and the topic; (see paragraphs 0006 and 0031-0034, Examiner’s note: teaches a graph with nodes and edges).
accessing a subgraph of the knowledge graph, the subgraph of the knowledge graph including a set of nodes comprising at least two nodes from the plurality of nodes and a set of edges comprising at least one edge from the plurality of edges corresponding to a particular contact; determining, from the set of nodes, a set of candidate topics including at least one candidate topic, the set of candidate topics determined based on a corresponding interaction stored in the subgraph and represented as a first edge between the particular contact and a corresponding data object and a corresponding relationship, represented as a second edge, between the corresponding data object and a corresponding particular candidate topic; ranking the set of candidate topics for the particular contact based on a frequency the set of candidate topics occurs within or is determined from a set of data objects associated with the user; generating a number of topics associated with the particular contact based on the ranking of the set of candidate topics; and responsive to navigating to the particular contact within an application, causing presentation of a graphical user interface (GUI) that is separate from the application, where the GUI comprises the particular contact and a first topic from the number of topics associated with the particular contact. (see paragraphs 0048-0055 and Figures 2A-2C, Examiner’s note: here paragraphs 0048-0055 and corresponding Figures 2A-2D, show how the system could perform specific searches with respect to specific users or groups of users (for example the user selecting information and Liz in the above example). Highest ranking edges are returned. This is interpreted as a subgraph as the system is looking at the user selecting information and Liz information and interactions in the above example rather than all the users of the system).
As per claim 2, Stickler teaches
wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node. (see paragraphs 0006 and 0031-0035, Examiner’s note: teaches a graph with nodes and edges (See paragraphs 0006 and 0031-0034). Further teaches applying weights to edges based on actions (see paragraph 0035)).
As per claim 3, Stickler teaches
wherein determining the set of candidate topics further comprises: removing topics from the set of candidate topics corresponding to each of the plurality of data objects without having an activity associated therewith within a threshold period of time; removing topics from the set of candidate topics below a threshold quality value; removing topics from the set of candidate topics from a single source of in the plurality of data objects; (see paragraphs 0035, 0065, and 0072, Examiner’s note: teaches using weighting different interactions related to time, source, and value differently, and using weights to match content).
and merging similar topics of the set of candidate topics (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
As per claim 8, Stickler teaches A computer-implemented method for preserving computing and network resources for communications between users, the computer-implemented method being performed by a computerized system including a processor and the computer-implemented method comprising, comprising: (see paragraphs 0011, 0099, and claim 12, Examiner’s note: computer process (see paragraph 0011). Further teaches this Stickler is implemented with a processor (see claim 12 and paragraph 0099)).
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, the plurality of nodes corresponding to a plurality of contacts of the user and a plurality of data objects of the user, the plurality of edges corresponding to interactions between each of the plurality of contacts and each of the plurality of data objects; (see paragraphs 0006,0031-0034, Examiner’s note: teaches a graph with nodes and edges).
determining a set of contacts from the plurality of contacts of the knowledge graph for the user; determining a plurality of topics from the plurality of data objects of the knowledge graph for the user; for a contact of the set of contacts, determining at least one candidate topic from the plurality of topics based on corresponding interactions stored in the knowledge graph between the contact and corresponding data objects of the plurality of data objects; (see paragraphs 0048-0055 and Figures 2A-2C, Examiner’s note: here paragraphs 0048-0055 and corresponding Figures 2A-2D, show how the system could perform specific searches with respect to specific users or groups of users (for example the user selecting information and Liz in the above example). Highest ranking edges are returned. the system is looking at the user selecting information and Liz information in the above example rather than all the users of the system).
Responsive to detecting a meeting scheduled between the user and the contact causing display of a graphical user interface (GUI) comprising the contact with the at least one candidate topic corresponding to the contact (see paragraphs 0030, 0035, 0047, and Figure 2A, Examiner’s note: teaches determining connections based on meetings (see paragraphs 0030 and 0035) and then teaches providing information in a graphical user interface based on connections, here the example is Liz (see paragraph 0047 and Figure 2A)).
As per claim 9, Stickler teaches
wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node. (see paragraphs 0006 and 0031-0035, Examiner’s note: teaches a graph with nodes and edges (See paragraphs 0006 and 0031-0034). Further teaches applying weights to edges based on actions (see paragraph 0035)).
As per claim 10, Stickler teaches
wherein the plurality of topics are stored as nodes in the knowledge graph and relationships between each of the plurality of topics and the plurality of data objects are stored as edges in the knowledge graph. (see paragraphs 0006 and 0031-0035, Examiner’s note: teaches a graph with nodes and edges (See paragraphs 0006 and 0031-0034). Further teaches applying weights to edges based on actions (see paragraph 0035)).
As per claim 11, Stickler teaches
wherein determining the set of contacts from the plurality of contacts further comprises: removing duplicate contacts in the plurality of contacts; (see paragraph 0038, Examiner’s note: teaches performing a process instead of duplicating files).
removing non-human contacts in the plurality of contacts; ranking each contact in the plurality of contacts based on importance correlated to how connected each contact is with the user in the knowledge graph; and determining each contact in the set of contacts above a threshold ranking. (see paragraphs 0035, 00052, 067, 0072, Examiner’s note: teaches using weights to determine relevance and providing information according to a threshold. Here information is removed from being returned if the weighting is low for example on comments, follows, emails, like, shares).
As per claim 12, Stickler teaches
wherein determining the plurality of topics from the plurality of data objects further comprises: determining, using a language model, a plurality of keywords in the plurality of data objects; and determining the plurality of topics from the plurality of keywords (see paragraph 0059 and claims 11 and 20, Examiner’s note: teaches using natural language processing to provide information related to trains with input of “modes of transportation).
As per claim 13, Stickler teaches
wherein determining the plurality of topics further comprises: removing topics from the plurality of topics from each data object in the plurality of data objects without activity within a threshold period of time; removing topics from the plurality of topics below a threshold quality value; removing topics from the plurality of topics from a single source of in the plurality of data objects; (see paragraphs 0035, 0065, and 0072, Examiner’s note: teaches using weighting different interactions related to time, source, and value differently, and using weights to match content).
and merging similar topics of the plurality of topics. (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
As per claim 18, Stickler teaches One or more computer storage media having computer-executable instructions embodied thereon that, when executed by a computing system having at least one processor and at least one memory, cause the at least one processor to perform operations comprising: (see paragraphs 0011, 0092-0093, and claim 20, Examiner’s note: software running on a computer to perform operations).
accessing a knowledge graph for a user, the knowledge graph comprising a plurality of nodes and a plurality of edges, the plurality of nodes corresponding to a plurality of contacts of the user, a plurality of data objects of the user, and a plurality of topics extracted from the plurality of data objects, the plurality of edges corresponding to interactions between each of the plurality of contacts and each of the plurality of data objects and relationships between each of the plurality of data objects and each of the plurality of topics; (see paragraphs 0006, 0031-0034, Examiner’s note: teaches a graph with nodes and edges).
determining a set of contacts from the plurality of contacts of the knowledge graph for the user; for each contact of the set of contacts: accessing a subgraph of the knowledge graph, the subgraph of the knowledge graph comprising a set of nodes from the plurality of nodes and a set of edges from the plurality of edges corresponding to each contact; determining a set of candidate topics from the set of nodes based on corresponding interactions stored in the subgraph between each contact and corresponding data objects and corresponding relationships between the corresponding data objects and the set of candidate topics; ranking the set of candidate topics for each contact; and generating a number of topics for each contact based on the ranking of the set of candidate topics; and responsive detecting a communication with the particular content of the set of contacts, causing display of a graphical user interface (GUI) comprising the particular user and a first topic, from the number of topics, generated for the particular contact (see paragraphs 0048-0055 and Figures 2A-2C, Examiner’s note: here paragraphs 0048-0055 and corresponding Figures 2A-2D, show how the system could perform specific searches with respect to specific users or groups of users (for example the user selecting information and Liz in the above example). Highest ranking edges are returned. This is interpreted as a subgraph as the system is looking at the user selecting information and Liz information and corresponding information in the above example rather than all the users of the system).
As per claim 19, Stickler teaches
wherein the knowledge graph further comprises edge weights corresponding to a strength of each interaction and relationship, each of the nodes ranked by importance, and each feature stored with respect to each node corresponding to each of the plurality of data objects, each feature indicating an action of the user with respect to the node; (see paragraphs 0006 and 0031-0035, Examiner’s note: teaches a graph with nodes and edges (See paragraphs 0006 and 0031-0034). Further teaches applying weights to edges based on actions (see paragraph 0035)).
and wherein determining the set of candidate topics further comprises: removing topics from the set of candidate topics corresponding to each of the plurality of data objects without activity within a threshold period of time; removing topics from the set of candidate topics below a threshold quality value; removing topics from the set of candidate topics from a single source of in the plurality of data objects; (see paragraphs 0035, 0065, and 0072, Examiner’s note: teaches using weighting different interactions related to time, source, and value differently, and using weights to match content).
and merging similar topics of the set of candidate topics. (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
As per claim 21, Stickler teaches
Wherein the GUI comprises a plurality of topics from the number of topics, the plurality of topics comprising at least the first topic and being arranged in a list form adjacent to the particular contact on the GUI (see paragraphs 0048-0052 and Figures 2A-2C, Examiner’s note: Figures 2A-2C show results being shown in a list related to a particular contact like Liz in the displayed example. Paragraphs 0048-0052 teach that a user may pivot between the boards based on selection and the boards can be landing pages based on selection of a user interface).
As per claim 22, Stickler teaches
Wherein the first topic is included in a list of topics arranged adjacent to the particular contact on the GUI (see paragraphs 0048-0052 and Figures 2A-2C, Examiner’s note: Figures 2A-2C show results being shown in a list related to a particular contact like Liz in the displayed example. Paragraphs 0048-0052 teach that a user may pivot between the boards based on selection and the boards can be landing pages based on selection of a user interface).
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) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222) further in view of Neela et al. (United States Patent Application Publication Number: US 2017/0206272) further in view of Lightner et al. (United States Patent Application Publication Number: US 2015/0154265).
As per claim 4, Stickler teaches
wherein merging similar topics of the set of candidate topics further comprises: (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
Stickler does not expressly teach merging triples of similar topics of the set of candidate topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the set of candidate topics contained within other topics of the set of candidate topics; and clustering topics in the set of candidate topics by their Levenshtein distance.
However, Neela which is in the art of classifying text values according to clusters (see abstract) teaches merging triples of similar topics of the set of candidate topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the set of candidate topics contained within other topics of the set of candidate topics; (see Figure 9 and paragraphs 0016, 0038, and 0019, Examiner’s using a Jaccard distance to cluster and remove content based on distance).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler with the aforementioned teachings from Neela with the motivation of providing a way to classify different topics together according to a known classification method (see Neela Figure 9 and paragraphs 0016, 0038, and 0019), when merging or clustering similar topics together is known (see Stickler paragraph 0059).
Stickler in view of Neela does not expressly teach and clustering topics in the set of candidate topics by their Levenshtein distance.
However, Lightner et al. which is in the art of search suggestions (see abstract) teaches and clustering topics in the set of candidate topics by their Levenshtein distance (see paragraph 0037, Examiner’s note: providing information based on levenshtein distance related to the entity Michael).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Neela with the aforementioned teachings from Lightner et al. with the motivation of providing a known way to provide information related to a user search to a user (see Lighter et al. paragraph 0037), when merging or clustering similar topics together is known (see Stickler paragraph 0059).
As per claim 14, Stickler teaches
wherein merging similar topics of the plurality of topics further comprises: (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
Stickler does not expressly teach merging triples of similar topics of the plurality of topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the plurality topics contained within other topics of the plurality of topics; and clustering topics in the plurality of topics by their Levenshtein distance.
However, Neela which is in the art of classifying text values according to clusters (see abstract) teaches merging triples of similar topics of the plurality of topics when a Jaccard distance between any pair of topics in the triples of similar topics is below a certain threshold; removing topics of the plurality topics contained within other topics of the plurality of topics (see Figure 9 and paragraphs 0016, 0038, and 0019, Examiner’s using a Jaccard distance to cluster and remove content based on distance).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler with the aforementioned teachings from Neela with the motivation of providing a way to classify different topics together according to a known classification method (see Neela Figure 9 and paragraphs 0016, 0038, and 0019), when merging or clustering similar topics together is known (see Stickler paragraph 0059).
Stickler in view of Neela does not expressly teach and clustering topics in the plurality of topics by their Levenshtein distance.
However, Lightner et al. which is in the art of search suggestions (see abstract) teaches and clustering topics in the plurality of topics by their Levenshtein distance (see paragraph 0037, Examiner’s note: providing information based on levenshtein distance related to the entity Michael).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Neela with the aforementioned teachings from Lightner et al. with the motivation of providing a known way to provide information related to a user search to a user (see Lighter et al. paragraph 0037), when merging or clustering similar topics together is known (see Stickler paragraph 0059).
Claim(s) 5 and 15 is rejected under 35 U.S.C. 103 as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222) further in view of Singh et al. (United States Patent Application Publication Number: US 2007/0203720).
As per claim 5, Stickler et al. does not expressly teach wherein determining the set of candidate topics further comprises: ranking each topic from the set of nodes using a PageRank algorithm based on keywords corresponding to each topic in the plurality of data objects; and determining each candidate topic of the set of candidate topics based on each topic from the set of nodes above a threshold ranking in the PageRank algorithm.
However, Singh et al. which is in the art of providing information (see abstract) teaches wherein determining the set of candidate topics further comprises: ranking each topic from the set of nodes using a PageRank algorithm based on keywords corresponding to each topic in the plurality of data objects; and determining each candidate topic of the set of candidate topics based on each topic from the set of nodes above a threshold ranking in the PageRank algorithm. (see paragraph 0054, Examiner’s note: teaches using a PageRank algorithm to provide relevant content where this is when the content is above a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler with the aforementioned teachings from Singh et al. with the motivation of using a known algorithm to provide relevant information to the user (See Singh et al. paragraph 0054), when providing relevant information according to a highest ranking edge is known (see Stickler paragraph 0052 and Figures 2B-2C).
As per claim 15, Stickler et al. does not expressly teach wherein determining at least one candidate topic from the plurality of topics further comprises: determining a set of candidate topics from the plurality of topics; ranking each candidate topic in the set of candidate topics using a PageRank algorithm based on keywords corresponding to each candidate topic in the plurality of interactions with the set of contacts stored in the knowledge graph; and determining the at least one candidate topic of the set of candidate topics above a threshold ranking.
However, Singh et al. which is in the art of providing information (see abstract) teaches wherein determining at least one candidate topic from the plurality of topics further comprises: determining a set of candidate topics from the plurality of topics; ranking each candidate topic in the set of candidate topics using a PageRank algorithm based on keywords corresponding to each candidate topic in the plurality of interactions with the set of contacts stored in the knowledge graph; and determining the at least one candidate topic of the set of candidate topics above a threshold ranking. (see paragraph 0054, Examiner’s note: teaches using a PageRank algorithm to provide relevant content where this is when the content is above a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler with the aforementioned teachings from Singh et al. with the motivation of using a known algorithm to provide relevant information to the user (See Singh et al. paragraph 0054), when providing relavant information according to a highest ranking edge is known (see Stickler paragraph 0052 and Figures 2B-2C).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222) further in view of Chen et al. (United States Patent Application Publication Number: US 2014/0279773).
As per claim 6, Stickler teaches
wherein determining the set of candidate topics further comprises: determining, based on the subgraph, that a first candidate topic of the set of candidate topics is associated with a plurality of contacts(see paragraphs 0006 and 0031-0035, Examiner’s note: teaches a graph with nodes and edges (See paragraphs 0006 and 0031-0034). Further teaches applying weights to edges based on actions (see paragraph 0035)).
Stickler does not expressly teach determining contacts that exceeds a threshold percentage; and removing the first candidate topic from the set of candidate topics; and wherein ranking the set of candidate topics for the particular contact further comprises ranking each candidate topic in the set of candidate topics using term frequency–inverse document frequency.
However, Chen et al which is in the art of determining concepts of resources (see paragraphs 0001-0002) teaches determining contacts that exceeds a threshold percentage; and removing the first candidate topic from the set of candidate topics; and wherein ranking the set of candidate topics for the particular contact further comprises ranking each candidate topic in the set of candidate topics using term frequency–inverse document frequency (see paragraphs 0006, 0037-0038, Examiner’s note: teaches using frequency inverse document frequency to filter out data, and then that filtered data is scored to provide similar or contextually relevant information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler with the aforementioned teachings from Chen et al. with the motivation of providing a way to filter out information that is popular but not relevant to the content (See Chen paragraphs 0006, 0037-0038), when providing relevant information to the user id known (see Stickler paragraphs 0052-0054 and 0059).
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222) further in view of Singh et al. (United States Patent Application Publication Number: US 2007/0203720) further in view of Neela et al. (United States Patent Application Publication Number: US 2017/0206272) further in view of Lightner et al. (United States Patent Application Publication Number: US 2015/0154265) further in view of Chen et al. (United States Patent Application Publication Number: US 2014/0279773).
As per claim 16, Stickler teaches
wherein determining the set of candidate topics from the plurality of topics further comprises: (see paragraph 0059, Examiner’s note: here teaches merging similar topics by providing a document related to a train when a user searches modes of transportation).
Stickler in view of Singh et al. does not expressly teach (1) merging triples of similar candidate topics of the set of candidate topics when a Jaccard distance between any pair of candidate topics in the triples of similar candidate topics is below a certain threshold; removing candidate topics of the set of candidate topics contained within other candidate topics of the set of candidate topics, (2) removing candidate topics from the set of candidate topics inferred for above a threshold percentage of the set of contacts; (3) and clustering candidate topics in the set of candidate topics by their Levenshtein distance.
However, Neela which is in the art of classifying text values according to clusters (see abstract) teaches (1) merging triples of similar candidate topics of the set of candidate topics when a Jaccard distance between any pair of candidate topics in the triples of similar candidate topics is below a certain threshold; removing candidate topics of the set of candidate topics contained within other candidate topics of the set of candidate topics (see Figure 9 and paragraphs 0016, 0038, and 0019, Examiner’s using a Jaccard distance to cluster and remove content based on distance).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Singh et al. with the aforementioned teachings from Neela with the motivation of providing a way to classify different topics together according to a known classification method (see Figure 9 and paragraphs 0016, 0038, and 0019), when merging or clustering similar topics together is known (see Stickler paragraph 0059).
Stickler in view of Singh et al. in view of Neela does not expressly teach (2) removing candidate topics from the set of candidate topics inferred for above a threshold percentage of the set of contacts; (3) and clustering candidate topics in the set of candidate topics by their Levenshtein distance.
However, Lightner et al. which is in the art of search suggestions (see abstract) teaches (3) and clustering candidate topics in the set of candidate topics by their Levenshtein distance (see paragraph 0037, Examiner’s note: providing information based on levenshtein distance related to the entity Michael).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Singh et al. in view of Neela with the aforementioned teachings from Lightner et al. with the motivation of providing a known way to provide relevant information related to a user search to a user (see Lighter et al. paragraph 0037), when merging or clustering similar topics together to provide relevant content related to a search is known (see Stickler paragraph 0059).
Stickler in view of Singh et al. in view of Neela in view of Lightner et al. does not expressly teach (2) removing candidate topics from the set of candidate topics inferred for above a threshold percentage of the set of contacts.
However, Chen et al which is in the art of determining concepts of resources (see paragraphs 0001-0002) teaches determining contacts that exceeds a threshold percentage; (2) removing candidate topics from the set of candidate topics inferred for above a threshold percentage of the set of contacts. (see paragraphs 0006, 0037-0038, Examiner’s note: teaches using frequency inverse document frequency to filter out data, and then that filtered data is scored to provide similar or contextually relevant information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Singh et al. in view of Neela in view of Lightner et al. with the aforementioned teachings from Chen et al. with the motivation of providing a way to filter out information that is popular but not relevant to the content (See Chen paragraphs 0006, 0037-0038), when providing relevant information to the user is known (see Stickler paragraphs 0052-0054 and 0059).
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Stickler et al. (United States Patent Application Publication Number: US 2015/0248222) further in view of Singh et al. (United States Patent Application Publication Number: US 2007/0203720) further in view of Chen et al. (United States Patent Application Publication Number: US 2014/0279773).
As per claim 17, Stickler et al. in view of Singh et al. does not expressly teach wherein determining the at least one candidate topic of the set of candidate topics above a threshold ranking further comprises: ranking each candidate topic using term frequency–inverse document frequency; and determining the at least one candidate topic above a threshold ranking.
However, Chen et al which is in the art of determining concepts of resources (see paragraphs 0001-0002) teaches determining contacts wherein determining the at least one candidate topic of the set of candidate topics above a threshold ranking further comprises: ranking each candidate topic using term frequency–inverse document frequency; and determining the at least one candidate topic above a threshold ranking (see paragraphs 0006, 0037-0038, Examiner’s note: teaches using frequency inverse document frequency to filter out data, and then that filtered data is scored to provide similar or contextually relevant information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Stickler in view of Singh et al. with the aforementioned teachings from Chen et al. with the motivation of providing a way to filter out information that is popular but not relevant to the content (See Chen paragraphs 0006, 0037-0038), when providing relevant information to the user (see Stickler paragraphs 0052-0054 and 0059) is known.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
York et al. (United States Patent Application Publication Number: US 2013/0275429) teaches providing contextual recommendations based on social graphs (see abstract)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626