DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-23 of U.S. Patent No. 11,321,736.
The subject matter claimed in the instant application is fully disclosed in the referenced copending application and would be covered by any patent granted on that copending application since the referenced copending application and the instant application are claiming common subject matter, as follows:
19/086,610
11,321,736
A computer-implemented method comprising:
determining, by a processing system, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data,
extracting, by the processing system, a new entity from digital documents;
creating, by the processing system, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node.
A method comprising:
obtaining, by a processing system, a recipient profile indicating one or more attributes of an ideal recipient of a message to be sent on behalf of a user;
obtaining, by the processing system, message data indicating one or more of an objective of the message and a message template that comprises content to be included in the message;
determining, by the processing system, a recipient list based on the recipient profile and a knowledge graph that stores:
entity data relating to a plurality of entities, each entity being of a particular entity type of a plurality of different entity types and having one or more entity attributes that are stored in the knowledge graph, wherein the plurality of different entity types include business entities and individual entities, relationship data relating to a plurality of relationships, each relationship being of a particular relationship type of a plurality of relationship types, wherein at least a subset of the plurality of relationships represent relationships between two entities, event data relating to a plurality of detected events relating to entities and the plurality of relationships represented in the knowledge graph, wherein the recipient list identifies one or more individuals that are more likely to result in a successful outcome given the recipient profile and information represented in the knowledge graph; for each individual in the recipient list: generating, by the processing system, a personalized message that is personalized to each individual based on one or more of the entity data, the relationship data, and the event data; and
providing, by the processing system, the personalized message via a communication network; after providing the personalized message for each individual in the recipient list, receiving, by the processing system, one or more digital documents obtained from crawling a public source by one or more crawlers;
extracting, by the processing system, a new entity from the one or more digital documents obtained from the crawling; extracting, by the processing system, a new relationship relating to the new entity and a previous entity based on the one or more digital documents obtained from the crawling and the knowledge graph, the previous entity being represented in the knowledge graph by a previous node; and
updating, by the processing system, the knowledge graph with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the previous node.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art. 19/086,610 does not have the steps of from the patent obtaining, by a processing system, a recipient profile indicating one or more attributes of an ideal recipient of a message to be sent on behalf of a user; and obtaining, by the processing system, message data indicating one or more of an objective of the message and a message template that comprises content to be included in the message”. While the nature of steps are diffent, however do not result in a patentable distinction, in either case, a recipient profile and message data must be obtained from certain sources in order to determine the recipe list to generate personalized message. 19/086,610 does not have the limitation of entity data relating to a plurality of entities, each entity being of a particular entity type of a plurality of different entity types and having one or more entity attributes that are stored in the knowledge graph, wherein the plurality of different entity types include business entities and individual entities, relationship data relating to a plurality of relationships, each relationship being of a particular relationship type of a plurality of relationship types, wherein at least a subset of the plurality of relationships represent relationships between two entities, event data relating to a plurality of detected events relating to entities and the plurality of relationships represented in the knowledge graph, wherein the recipient list identifies one or more individuals that are more likely to result in a successful outcome given the recipient profile and information represented in the knowledge graph. While the nature of steps are diffent, however do not result in a patentable distinction, in either case, entity data and event data must be must be detected from certain sources in order to extract a new entity to create new relationship relating the new entity and generate and provide personalized message. Furthermore, it would have been obvious to the one ordinary skill in the art for the applicant to claim the broader limitation in order to extend patent protection.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 12,271,926.
The subject matter claimed in the instant application is fully disclosed in the referenced copending application and would be covered by any patent granted on that copending application since the referenced copending application and the instant application are claiming common subject matter, as follows:
19/086,610
12,271,926
A computer-implemented method comprising:
determining, by a processing system, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data,
extracting, by the processing system, a new entity from digital documents;
creating, by the processing system, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node.
A computer-implemented method comprising:
dynamically updating a knowledge data structure to reflect a new entity identified by a crawler of a crawling system extracting information from an external data source over a communication network by:
identifying, by a machine learning system utilizing a classification model, the new entity from the information extracted by the crawler; and
dynamically updating the knowledge structure for the new entity by creating a new node and a new edge between the new node and an existing node within the knowledge data structure, wherein the new node represents the new entity and the new edge represents a new relationship identified by the machine learning system between the new entity and an existing entity represented by the existing node, wherein the knowledge structure is populated with:
entity data representing business entities and individual entities stored as objects within a customer relationship database (CRM) system and tracked using the knowledge structure; relationship data of relationships between the business entities and individual entities; and event data relating to events associated with the business entities and individual entities, wherein the event data is associated by the knowledge structure with the new node and the new edge connecting the new node to the existing node based upon the event data specifying an event that occurred between the new entity represented by the new node and the existing entity represented by the existing node; determining, by a processing system using attributes of an ideal recipient identified based upon an objective of a message and historical data related to outcomes associated with previously sent messages generated to achieve the objective of the message, a recipient list based on a recipient profile and the knowledge data structure that stores entity data relating to entities and relationships between the entities;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationships, wherein the processing system automatically generates the personalized message by:
automatically inferring a message template from historical data of users; constructing personalized content based upon an objective of communicating with the individual; and populating the message template with the personalized content to automatically generate the personalized message, wherein the message template is populated with directed content generated using the event data associated by the knowledge structure with the new node and the new edge.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art. 19/086,610 recites “recipient list based upon a recipient profile and knowledge data” vs “using attributed of an ideal receipt identified based upon an objective of message and historical data” . While the wording are different however, do not result in a patentable distinction in either case because both invention automates the process of selecting targe recipient using stored data and predefine criteria. 19/086,610 recites “extracting a new entity form digital document” vs “identifying the new entity from the information extracted by the crawler”. While the nature of the steps are different, however do not result in a patentable distinction, in either case, entity must be identified and received from a certain source to be processed. Furthermore, it would have been obvious to the one ordinary skill in the art for the applicant to claim the broader limitation in order to extend patent protection.
Furthermore, there is no apparent reason why applicant would be prevented from presenting claims corresponding to those of the instant application in the other copending application. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
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.
Step 1: The claims 1-16 and 20 are a method, claims 17-19 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: independent claims (1, 17 and 20) recite determining, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships; generating and providing, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data, extracting, a new entity from digital documents; creating, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and updating, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node. Theses limitation as drafted is a process, under its broadest reasonable interpretation, covers the generation personalized message content and sending to the chosen recipients or their organization based on extracted names, jobs, event etc., which fail into methods of organizing human behavior or manipulating data for the recitation of generic computer components. That is, other than reciting “by a processing system” nothing in the claimed element precluded the step from practically being performed by human. Simply put, these limitation merely describe generating personalized message content and sending to the chosen recipients or their organization based on extracted names, jobs, event etc., which is clearly a business arrangement in its purest form. Claims 2-16, and 18-19 , merely provide additional abstract concepts and narrow the abstract idea of claims 1, 17 and 20. Further claims 1-20 are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select a template, receiving input, and creating a notification can be performed by a human using "pencil and paper," it may be rejected as a mental process.
Step 2A-Prong 2: The claim recites one additional element: that a processing system or is used to perform determining, generating, extracting and updating step. The processing system in these seps is recited at a high level of generality, i.e., as a generic processing system performing a generic computer function of processing data (generating and sending personalized message). This generic processing system limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) s 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US Pub., No., 2017/0255862 A1) in view of Akella et al (US Patent No., 7, 539, 697 B1)
With respect to claim 1, Li teaches a computer-implemented method (paragraph [0008], discloses methods, systems, and programming for user profiling for content recommendation over the internet) comprising:
determining, by a processing system, a recipient list based on a recipient profile (paragraph [0009], discloses generating user profiles based on the set of hierarchal cluster so that the user profile is to be used to personalized content recommendation, paragraph [0014], discloses user interest are organized in a hierarchical structure to form the set of user profile and each of the set of user profiled defines an aspect of user interest and paragraph [0015], discloses generating user profile with semantic knowledge comprise a user activity analyzer configured to obtain first information assocted with a user ) and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships (paragraph [0010], discloses identifying at lease on related entity from the second information, that related to the one or more entities.., paragraphs [0011]-[0012], discloses estimating user interest with respect to the set of augmented entity and incorporating the user interest with respect to the set of augmented entities into the profile and paragraph [0056], discloses clustering unit w. r. t. entity name 706 may use the .., network structure having one known entity and top five inferred entities);
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data (paragraph [0044], discloses provide personalized content recommendation .., paragraph [0050], discloses generate a user profile 126 to be used to personalized content recommendation, and paragraph [0051] discloses content pool 150 may be a general content pool to serve all users or have personalized content pools with respect to different users.., content pool 150 may be constructed as a hierarchal structures with a top layer ),
extracting, by the processing system, a new entity from digital documents (Fig. 9, 902, 904, discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], paragraph [0015], discloses an entity extractor configured to identify one or more entities from the first information, paragraph [0044], discloses utilizing the related entities extracted from a knowledge based paragraph [0046], discloses entity extractor, paragraph [0051], discloses determines whether is a newly detected interest from user…, when there is a newly detected interest from the user, entity extractor may be triggered to obtain information from newly updated content pool and identifies newly discover entity related to the newly deterred user enters);
Li teaches the above elements including: creating, by the processing system a new relationship relating to new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node(Fig. 7, discloses entity clustering model .., Fig. 9, 902, 904, 906 discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], and clustering the known entities and the inferred entities based on entity name using selected clustering model [creating a new relationship relating to new entity], paragraph [0016], discloses the set of hierarchal cluster defines a hierarchical relationship among at least part of the set of augmented entities, paragraph [0047], discloses entity managing module 104 first links the extracted known entities to corresponding entities in knowledge graph[creating a new relationship relating to the new entity] .., knowledge archive 120 is based on an extract match or a similarly match, for example the linking of known entity “Steve Jobs” to “Tim Cook” .., based on similarity match …, paragraph [0049], discloses number of clusters to be generated paragraph [0053], discloses an entity learing unit and an entity enriching unit and paragraph [0057] disclose a mesh network structure having known entities Ek and Em and inferring entity En. Ek and Em are assigned with weight 0.2 and 0.1 respectively based on relational inference. En is newly inferred entity from the knowledge archive [ a new relationship relating to new] and paragraph [0068], discloses plurality of content source may supply new entities ); and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship(paragraph [0063], discloses newly identified named entity is utilized to incrementally update the multiple hierarchical entity cluster based profile of each user , paragraph [0066], discloses consequently, local user profile 1502is updated to new version 1506 that includes related entities from pre-constructed user profile 1504 and pre-constructed user profile 1504 is updated new version 1508 that includes related entities from local profile 1502) and information includes cluster nodes information, for example, the name of the entity donated by the cluster node and the initial weight of the entity .., cluster node donated as a linkage with between two cluster noted.. (Fig. 10 and paragraph [0060]). Li failed to explicitly teach two nodes is created a new relationship related to the new entity and wherein the new edge connects the new node to the existing node.
However, Akella teaches creating, by the processing system, a new relationship relating to the new entity and wherein the new edge connects the new node to the existing node (Col. 2, lines 6-20, discloses data received for entity is used to create a new node , a strength of relationship value is calculated for each relationship between the new entity and entity represented an existing node and assign to an edge is created to represent each relationship and cause new nod to be updated ..) and updating a new relationship with new node (Fig. 5A, 505, discloses update relationship graph). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for cluster nodes within each clusters, the graphic structure of each cluster of Li by adding edge with calculated weight of Akella in order factored into the path calculation (see Akella , Col. 3, lines 65-66).
With respect to claim 2, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the recipient list identifies individuals that are more likely to result in a successful outcome given the recipient profile and information represented in the knowledge data structure (Fig. 14, 1404, discloses generate local user profiles based on the user activity and information provided by a knowledge archive, Fig. 15 discloses local user profile .., user profile updating module and paragraph [0009], discloses generating user profiles with semantic knowledge …, generating a set of user profiles based on the set of hierarchical clusters …).
With respect to claim 3, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the knowledge data structure stores a plurality of nodes(paragraph [0049], discloses clustering models 122 may define a number of clusters to be generated, a number of cluster nodes within each cluster, the graphical structure of each cluster and paragraph [0065], discloses the hierarchical clusters or a knowledge graph) and a plurality of edges that connect respective nodes from the plurality of nodes, wherein each node represents a respective entity of a respective entity type and each edge corresponds to a respective relationship of a respective relationship type(Fig. 10 and paragraph [0060], discloses cluster node information for example , the name of the entity cluster nodes donated as a linkage weight between two cluster nodes ..).
With respect to claim 4, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the knowledge data structure stores event data relating to a plurality of detected events relating to the entities and the relationships represented in the knowledge data structure(paragraph [0049], discloses clustering models 122 may define a number of clusters to be generated, a number of cluster nodes within each cluster, the graphical structure of each cluster and paragraph [0065], discloses the hierarchical clusters or a knowledge graph and Fig. 10 and paragraph [0060], discloses cluster node information for example , the name of the entity cluster nodes donated as a linkage weight between two cluster nodes) ; and
the method further comprising:
extracting, by the processing system, a new event corresponding to the new entity, wherein extracting the new relationship is further based on the new event (paragraph [0051], disclose entity extractor 102 may be triggered to obtain information from updated content pool 150 and identifies newly discover entities related to the newly detected user interest).
With respect to claim 5, Li in view of Akella teaches elements of claim 4, furthermore, Li teaches the method wherein the new event is extracted using an event classification model that is trained to identify events indicated in documents (Fig. 114, discloses training Module , Fig. 12. 1210 discloses train the weighted entities clusters using a training model, and paragraph [0051], discloses when there is a newly detected interest from the user, entity extractor 102 may be triggered to obtain identifies newly discovered entities related to the newly detected user interest) .
With respect to claim 6, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the knowledge data structure is a knowledge graph(paragraph [0043], discloses knowledge graph, and paragraph [0049] discloses entity cluster module 106 may select one or more clustering models 122 to generate the set of hierarchical clusters) .
With respect to claim 7, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the new entity is extracted using an entity classification model that is trained to identify entities indicated in the digital documents(Fig. 114, discloses training Module , Fig. 12. 1210 discloses train the weighted entities clusters using a training model, and paragraph [0051], discloses when there is a newly detected interest from the user, entity extractor 102 may be triggered to obtain identifies newly discovered entities related to the newly detected user interest) .
With respect to claim 8, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the recipient profile indicates attributes of an ideal recipient of the message to be sent on behalf of a user (paragraph [0006], discloses product recommendation based on receipt .., you tube’s recommended videos-based viewing history .., such as user demographic profile [attribute]).
With respect to claim 9, Li in view of Akella teaches elements of claim 8, furthermore, Li teaches the method wherein the determining the recipient list comprises filtering entities from the plurality of entities represented in the knowledge data structure based on the attributes(paragraph[0060], discloses ranking unit 1006 rank the rescored entities …,a user profiles is then generated including one or more profile pages corresponding to the test o hierarchal clusters).
With respect to claim 10, Li in view of Akella teaches elements of claim 8, furthermore, Li teaches the method wherein the determining the recipient list comprises, for each individual of a subset of the individuals represented in the knowledge data structure, determining a lead score of each individual based on the attributes of the recipient profile using a machine-learned scoring model(paragraph [0060], discloses a user profile is then generated .., each of the known entities and paragraph [0061], discloses user interest estimating.., generated based on cluster .., “Stave Jobs” is increase from 0.2 to 0.3 in accordance with the assignment of user interest .., and paragraph [0062], discloses entities with each cluster are ranked at 1208.., user profile including a plurlity of weights entity cluster in hierarchical structure are generated at 1212).
With respect to claim 11, Li in view of Akella teaches elements of claim 10, furthermore, Li teaches the method wherein the lead score of each individual is further based on an event related to an organization of each individual(paragraph [0062], discloses entities with each cluster are ranked at 1208 , the weighted entities-based cluster are further trained using a training model at .., a plurality of weighted entity cluster in hierarchical structure are generated at 1212).
With respect to claim 12, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method wherein the generating the personalized message for the individual comprises: generating directed content based on retrieved entity data retrieved from the knowledge data structure, wherein the directed content comprises a phrase with information corresponding to the retrieved entity data, wherein the personalized message is generated based upon the directed content and a message template(paragraph [0047], discloses entity managing module 104 first links the extracted known entities to corresponding entities in knowledge graph[creating a new relationship relating to the new entity] .., knowledge archive 120 is based on an extract match or a similarly match, for example the linking of known entity “Steve Jobs” to “Tim Cook” .., based on similarity match …, paragraph [0049], discloses number of clusters to be generated, paragraph [0057] disclose a mesh network structure having known entities Ek and Em and inferring entity En. Ek and Em are assigned with weight 0.2 and 0.1 respectively based on relational inference. En is newly inferred entity from the knowledge archive [ a new relationship relating to new] and paragraph [0068], discloses a training machine, a user profile generated engine , a content recommending engine ).
With respect to claim 13, Li in view of Akella teaches elements of claim 12, furthermore, Li teaches the method wherein the directed content is generated based on a machine-learned generative model that is trained to generate text given entity data of an entity and a particular objective of the personalized message, and wherein message data indicates the particular objective of the personalized message(paragraph [0008], discloses profiming for content recommendation over the internet, paragraph [0009], discloses generating a set of user profiles based on the set of hierarchical clusters so that the user profile is to be used to personalized content recommendation, paragraph [0050], discloses user profile generating module is configured to receive the set of hierarchical clusters indicting a set of user interest aspect and generate a set of user profile .., and paragraph [0068], discloses a training machine, a user profile generated engine , a content recommending engine).
With respect to claim 14, Li in view of Akella teaches elements of claim 1 furthermore, Li teaches the method wherein the knowledge data structure stores event data relating to a plurality of detected events relating to the entities and the relationships represented in the knowledge data structure, wherein the personalized message is generated using directed content and a message template, and wherein the directed content is derived from the event data(, paragraph [0009], discloses generating a set of user profiles based on the set of hierarchical clusters so that the user profile is to be used to personalized content recommendation and paragraph [0051], discloses determines whether is a newly detected interest from user…, when there is a newly detected interest from the user, entity extractor may be triggered to obtain information from newly updated content pool and identifies newly discover entity related to the newly deterred user enters).
With respect to claim 15, Li in view of Akella teaches elements of claim 14, furthermore, Li teaches the method wherein the directed content is generated based on a machine-learned generative model that is trained to generate text given a particular objective of the personalized message and the event data relating to a known event that occurred with respect to an entity and the particular objective of the personalized message, and wherein message data indicates the particular objective of the personalized message (paragraph [0008], discloses profiming for content recommendation over the internet, paragraph [0009], discloses generating a set of user profiles based on the set of hierarchical clusters so that the user profile is to be used to personalized content recommendation, paragraph [0050], discloses user profile generating module is configured to receive the set of hierarchical clusters indicting a set of user interest aspect and generate a set of user profile .., and paragraph [0068], discloses a training machine, a user profile generated engine , a content recommending engine).
With respect to claim 16, Li in view of Akella teaches elements of claim 1, furthermore, Li teaches the method , wherein the processing system uses natural language processing to extract the new entity from the one or more digital documents obtained from a crawler that crawls a data source(paragraph [0051], discloses determines whether there is a newly detected interest from the user .., when there is a newly detected interest from the user, content crawler 138 fetches new content from content source .., when there is a newly detected interest from the user , entity extractor 102 may be triggered to obtain information from newly updated content pool and identifies newly discovered entities related to the newly detected user interest) .
With respect to claims 17, Li teaches a system comprising:
memory comprising instructions (paragraph [0071], discloses program storage and data storage .., memory); and
a processor configured to execute the instruction to perform operation (paragraph [0071], discloses one or more processors, for executing program instruction..) comparing:
determining, by a processing system, a recipient list based on a recipient profile (paragraph [0009], discloses generating user profiles based on the set of hierarchal cluster so that the user profile is to be used to personalized content recommendation, paragraph [0014], discloses user interest are organized in a hierarchical structure to form the set of user profile and each of the set of user profiled defines an aspect of user interest and paragraph [0015], discloses generating user profile with semantic knowledge comprise a user activity analyzer configured to obtain first information assocted with a user ) and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships (paragraph [0010], discloses identifying at lease on related entity from the second information, that related to the one or more entities.., paragraphs [0011]-[0012], discloses estimating user interest with respect to the set of augmented entity and incorporating the user interest with respect to the set of augmented entities into the profile and paragraph [0056], discloses clustering unit w. r. t. entity name 706 may use the .., network structure having one known entity and top five inferred entities);
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data (paragraph [0044], discloses provide personalized content recommendation .., paragraph [0050], discloses generate a user profile 126 to be used to personalized content recommendation, and paragraph [0051] discloses content pool 150 may be a general content pool to serve all users or have personalized content pools with respect to different users.., content pool 150 may be constructed as a hierarchal structures with a top layer ),
extracting, by the processing system, a new entity from digital documents (Fig. 9, 902, 904, discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], paragraph [0015], discloses an entity extractor configured to identify one or more entities from the first information, paragraph [0044], discloses utilizing the related entities extracted from a knowledge based paragraph [0046], discloses entity extractor, paragraph [0051], discloses determines whether is a newly detected interest from user…, when there is a newly detected interest from the user, entity extractor may be triggered to obtain information from newly updated content pool and identifies newly discover entity related to the newly deterred user enters);
Li teaches the above elements including: creating, by the processing system a new relationship relating to new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node(Fig. 7, discloses entity clustering model .., Fig. 9, 902, 904, 906 discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], and clustering the known entities and the inferred entities based on entity name using selected clustering model [creating a new relationship relating to new entity], paragraph [0016], discloses the set of hierarchal cluster defines a hierarchical relationship among at least part of the set of augmented entities, paragraph [0047], discloses entity managing module 104 first links the extracted known entities to corresponding entities in knowledge graph[creating a new relationship relating to the new entity] .., knowledge archive 120 is based on an extract match or a similarly match, for example the linking of known entity “Steve Jobs” to “Tim Cook” .., based on similarity match …, paragraph [0049], discloses number of clusters to be generated paragraph [0053], discloses an entity learing unit and an entity enriching unit and paragraph [0057] disclose a mesh network structure having known entities Ek and Em and inferring entity En. Ek and Em are assigned with weight 0.2 and 0.1 respectively based on relational inference. En is newly inferred entity from the knowledge archive [ a new relationship relating to new] and paragraph [0068], discloses plurality of content source may supply new entities ); and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship(paragraph [0063], discloses newly identified named entity is utilized to incrementally update the multiple hierarchical entity cluster based profile of each user , paragraph [0066], discloses consequently, local user profile 1502is updated to new version 1506 that includes related entities from pre-constructed user profile 1504 and pre-constructed user profile 1504 is updated new version 1508 that includes related entities from local profile 1502) and information includes cluster nodes information, for example, the name of the entity donated by the cluster node and the initial weight of the entity .., cluster node donated as a linkage with between two cluster noted.. (Fig. 10 and paragraph [0060]). Li failed to explicitly teach two nodes is created a new relationship related to the new entity and wherein the new edge connects the new node to the existing node.
However, Akella teaches creating, by the processing system, a new relationship relating to the new entity and wherein the new edge connects the new node to the existing node (Col. 2, lines 6-20, discloses data received for entity is used to create a new node , a strength of relationship value is calculated for each relationship between the new entity and entity represented an existing node and assign to an edge is created to represent each relationship and cause new nod to be updated ..) and updating a new relationship with new node (Fig. 5A, 505, discloses update relationship graph). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for cluster nodes within each clusters, the graphic structure of each cluster of Li by adding edge with calculated weight of Akella in order factored into the path calculation (see Akella , Col. 3, lines 65-66).
With respect to claim 18, Li in view of Akella teaches elements of claim 4117, furthermore, Li teaches the system wherein the knowledge data structure stores event data relating to the plurality of entities, and wherein the event data is used to generate the personalized message (paragraph [0009], discloses clustering the set of augmented entities into a set of hierarchical cluster and generating a set of user profile bade on the set hierarch cluster to the user profile it to be used to personalize content recommendation).
With respect to claim 19, Li in view of Akella teaches elements of claim 17, furthermore, Li teaches the system wherein the digital documents include documents obtained from public internet websites(Fig. 2, 202 disclose obtain information related to activates of user and paragraph [0043], discloses search queries and webpage content that user visited) .
With respect to claim 20, Li teaches a computer-implemented method (paragraph [0008], discloses methods, systems, and programming for user profiling for content recommendation over the internet) comprising:
determining, by a processing system, a recipient list based on a recipient profile (paragraph [0009], discloses generating user profiles based on the set of hierarchal cluster so that the user profile is to be used to personalized content recommendation, paragraph [0014], discloses user interest are organized in a hierarchical structure to form the set of user profile and each of the set of user profiled defines an aspect of user interest and paragraph [0015], discloses generating user profile with semantic knowledge comprise a user activity analyzer configured to obtain first information assocted with a user ) and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships (paragraph [0010], discloses identifying at lease on related entity from the second information, that related to the one or more entities.., paragraphs [0011]-[0012], discloses estimating user interest with respect to the set of augmented entity and incorporating the user interest with respect to the set of augmented entities into the profile and paragraph [0056], discloses clustering unit w. r. t. entity name 706 may use the .., network structure having one known entity and top five inferred entities);
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data (paragraph [0044], discloses provide personalized content recommendation .., paragraph [0050], discloses generate a user profile 126 to be used to personalized content recommendation, and paragraph [0051] discloses content pool 150 may be a general content pool to serve all users or have personalized content pools with respect to different users.., content pool 150 may be constructed as a hierarchal structures with a top layer ),
extracting, by the processing system, a new entity from digital documents (Fig. 9, 902, 904, discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], paragraph [0015], discloses an entity extractor configured to identify one or more entities from the first information, paragraph [0044], discloses utilizing the related entities extracted from a knowledge based paragraph [0046], discloses entity extractor, paragraph [0051], discloses determines whether is a newly detected interest from user…, when there is a newly detected interest from the user, entity extractor may be triggered to obtain information from newly updated content pool and identifies newly discover entity related to the newly deterred user enters);
Li teaches the above elements including: creating, by the processing system a new relationship relating to new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node(Fig. 7, discloses entity clustering model .., Fig. 9, 902, 904, 906 discloses extract known entities [existing entity] and extract inferred entities from the augmented entity list [new entity], and clustering the known entities and the inferred entities based on entity name using selected clustering model [creating a new relationship relating to new entity], paragraph [0016], discloses the set of hierarchal cluster defines a hierarchical relationship among at least part of the set of augmented entities, paragraph [0047], discloses entity managing module 104 first links the extracted known entities to corresponding entities in knowledge graph[creating a new relationship relating to the new entity] .., knowledge archive 120 is based on an extract match or a similarly match, for example the linking of known entity “Steve Jobs” to “Tim Cook” .., based on similarity match …, paragraph [0049], discloses number of clusters to be generated paragraph [0053], discloses an entity learing unit and an entity enriching unit and paragraph [0057] disclose a mesh network structure having known entities Ek and Em and inferring entity En. Ek and Em are assigned with weight 0.2 and 0.1 respectively based on relational inference. En is newly inferred entity from the knowledge archive [ a new relationship relating to new] and paragraph [0068], discloses plurality of content source may supply new entities ); and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship(paragraph [0063], discloses newly identified named entity is utilized to incrementally update the multiple hierarchical entity cluster based profile of each user , paragraph [0066], discloses consequently, local user profile 1502is updated to new version 1506 that includes related entities from pre-constructed user profile 1504 and pre-constructed user profile 1504 is updated new version 1508 that includes related entities from local profile 1502) and information includes cluster nodes information, for example, the name of the entity donated by the cluster node and the initial weight of the entity .., cluster node donated as a linkage with between two cluster noted.. (Fig. 10 and paragraph [0060]). Li failed to explicitly teach two nodes is created a new relationship related to the new entity and wherein the new edge connects the new node to the existing node.
However, Akella teaches creating, by the processing system, a new relationship relating to the new entity and wherein the new edge connects the new node to the existing node (Col. 2, lines 6-20, discloses data received for entity is used to create a new node , a strength of relationship value is calculated for each relationship between the new entity and entity represented an existing node and assign to an edge is created to represent each relationship and cause new nod to be updated ..) and updating a new relationship with new node (Fig. 5A, 505, discloses update relationship graph). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for cluster nodes within each clusters, the graphic structure of each cluster of Li by adding edge with calculated weight of Akella in order factored into the path calculation (see Akella , Col. 3, lines 65-66).
The following prior arts are in the record:
Li et al (US Pub/. No., 2017/0255862 A1) discloses the present teaching relates to generating user profiles with semantic knowledge. A first information associated with a user is obtained. One or more entities are identified from the first information. The one or more entities are augmented based on second information to generate a set of augmented entities. The set of augmented entities are clustered into a set of hierarchical clusters .
Akella et al (US Patent No., 7, 539, 697 B1) discloses a relationship graph representing a social network connecting multiple entities is created and maintained as nodes and edges. Data received for an entity is used to create a new node. A strength of relationship value is calculated for each relationship between the new entity and an entity represented by an existing node and assigned to an edge is created to represent each relationship.
Conclusion
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/SABA DAGNEW/ Primary Examiner, Art Unit 3621
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