Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/082,465, filed on December 15, 2022. In response to Examiner’s Non-Final Office Action of August 6, 2025, Applicant, on December 3, 2025, amended claims 1, 12, and 18. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed December 3, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed December 3, 2025.
On Pgs. 11-13, regarding the 35 U.S.C. § 101 rejection, Applicant states amended claim 1 represent improvements to search/query engine technology. In particular, the claimed system of amended claim 1 is configured to (1) dynamically update an entity database comprising summaries of entities based on generating embeddings of "scraped" data associated with an identified entity and performing semantic analysis of the obtained data, (2) update an entity database by storing a summary of said entity, and (3) responsive to determining that a given user is subscribed to one or more attributes associated with the entity and that the newly stored summary integrates updates to at least one of said attributes, generating and transmitting a push notification identifying the updates to a computing device of the subscriber user. The above-identified elements of amended claim 1 provide a connection between (1) search query processing, and (2) dynamic management of an entity database storing entity information and push notifications to subscriber computing devices. Applicant further states amended claim 1 integrates the features of updating an entity database based on collection and semantic analysis of entity information and post-generation analysis of subscribed-to attributes of searched entities into a process for dynamically managing access to entity information to both searching and subscriber users. The combination of steps recited in the claim is not routine or conventional activity in the relevant field. In particular, the features of amended claim 1 are not considered to be insignificant. The totality of the operations act in concert to improve a technical field, specifically private database search engine technology. In response, the present claims amount to no more than utilizing computer components as tools to perform data analysis. Examiner finds the present claims improve an existing business process of semantic analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to divide and parse data are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
On Pgs. 12-13, regarding the 35 U.S.C. § 103 rejection, Applicant argues that the cited references do not teach or suggest these features of amended claim 1.In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Bailey is now applied for Claims 1, 12 and 18. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to collecting and processing of entity information.
Claim 1 recites a system for collecting and processing of entity information and Claim 12 recites a method for collecting and processing of entity information, and Claim 18 recites an article of manufacture for collecting and processing entity information which include receiving a search query comprising a request to search a dynamically maintained entity database; in response to receiving the search query, performing a search of the entity database, wherein performing the search includes: detecting a defined trigger event associated with a backend of the system; updating the entity database responsive to the detecting based on: obtaining at least one identifier of an entity by parsing message data of a message associated with the trigger event; In response to identifying the at least one identifier of the entity, obtaining, automatically using the at least one identifier and without user input, bulk data associated with the entity based on querying from at least two disparate external computing systems to extract an entity-related dataset; generating embeddings associated with at least a portion of the obtained data; and updating the entity database by storing the summary of the entity in the entity database; responsive to determining that a subscriber user is subscribed to one or more attributes associated with the entity and that the stored summary integrates updates to at least one of said attributes, generating a push notification identifying the at least one updated attribute; and transmitting the generated push notification to a computing device associated with the subscriber user.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “computing systems”; “client computing device”; “query engine”; “system”; “data crawler”; “data parser”; “semantic analysis engine ; “computer-readable medium”, “computer” and “database”, provide nothing in the claim elements to preclude the step from being “Mental Processes”-evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The of “computing systems”; “client computing device”; “query engine”; “system”; “data crawler”; “data parser”; “semantic analysis engine ; “computer-readable medium”, “computer” and “database” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 12 and claim 18 recite using one or more semantic analysis/ neural network techniques. The specification discloses the semantic analysis/neural network at a high-level of generality, providing examples of different techniques that may be applied. The general use of a semantic analysis/ machine learning does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the semantic analysis/ machine leaning is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing systems”; “client computing device”; “query engine”; “system”; “data crawler”; “data parser”; “semantic analysis engine ; “computer-readable medium”, “computer” and “database” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and the data crawler and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and the additional element of semantic analysis/ neural network - n to transform the abstract idea into a practical application. Therefore, currently, the semantic analysis/machine learning is solely used a tool to perform the instructions of the abstract idea
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-11, and 13-17 and 19-20 recite wherein the one or more attributes associated with the entity comprise a category of the entity indicating a product or a service provided by the entity; performing a topic classification of the obtained information; performing a sentiment analysis of the obtained information; and performing attribute classification of the obtained information; maintaining stakeholder attributes of a plurality of stakeholders, each stakeholder corresponding to one or more stakeholder attributes; determining that at least one of the one or more attributes associated with the entity corresponds to a stakeholder attribute of a stakeholder; and sending the summary of the entity to a computing device of the stakeholder; receiving a query request to query entities, wherein the query request comprises one or more keywords; determining that an attribute of a particular entity corresponds to the one or more keywords; and sending a summary of the particular entity in response to the query request; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 12 and 20. Regarding Claims, 5, 9, 16, and the additional elements of “database” and “computing device”- it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 2, 4, 13, 15 and 19 and the additional element of machine learning model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Claim Rejections - 35 USC § 103
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 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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Layton et al., US Publication No. 20220206993A1 [hereinafter Layton], in view of Bailey et al., US Publication No. 20220365998A1, [hereinafter Bailey], in further view of Renard et al., US Publication No. 20220358293A1, [hereinafter Renard].
Regarding Claim 1,
Layton teaches
A system comprising: at least one memory storing instructions; at least one hardware processor interoperably coupled with the at least one memory, wherein the instructions instruct the at least one hardware processor to perform operations including: receiving…a search query…(Layton Par. 6-“In some example embodiments, the created custom object may include at least one of a custom identification label, a custom object name, an object type, or a property of the custom object. In some example embodiments, at least one association may be created for the custom object with another second object based on the custom object information. In some example embodiments, the at least one association may include at least one of an association identification, an association type, a first object identification, a second object identification, or a timestamp. The custom object may be directed to the first object identification or the second object identification based on a defined relationship between the custom object and the other second object.”; Par. 9; Par. 63; Par. 66; Par. 77; Par. 87;Par. 188);
updating the entity database responsive to the detecting based on: obtaining at least one identifier of an entity by parsing message data of a message associated with the trigger event (Layton Par. 66 Par. 87; Par. 164-“ In embodiments, each respective personalized message may be sent with a message tracking mechanism to a respective recipient of the recipient list. A message tracking mechanism may be a software mechanism that causes transmission of feedback data 272 to the system 200 in response to a certain triggering action. For example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when the recipient opens the email. In another example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when a recipient downloads an attachment or clicks on a link in the personalized message.; Par. 6; Par. 317”)
in response to identifying the at least one identifier of the entity, obtaining, automatically by a data crawler using the at least one identifier and without user input, bulk data associated with the entity based on querying at least two disparate external computing systems to extract an entity-related dataset (Layton Par. 6; Par. -63- “For example, some services that may be applied include workflow automation (e.g., automate based on changes to core objects and based on added custom objects or changes to custom objects and/or core objects), “; Par. 115; Par. 137; Par.145-“Furthermore, as the system 200 (e.g., the event extraction module 226) operates to extract entities and relationships from information obtained by the crawling system 202, the machine learning system 212 may utilize the classification of said entities and relationships, the information used to make the entity classifications, and any outcome data resulting from the entity classifications to reinforce the entity classification model(s).;Par. 120- “In embodiments, the methods and systems disclosed herein include methods and systems for pulling information at scale from one or more information sources. In embodiments, the crawling system 202 may obtain information from external information sources 230 accessible via a communication network 280 (e.g., the Internet), a private network, the proprietary database 208 (such as a content management system, a customer relationship management database, a sales database, a marketing database, a service management database, or the like), or other suitable information sources. Such methods and systems may include one or more crawlers, spiders, clustering systems, proxies, services, brokers, extractors and the like,”);
generating, by a data parser, embeddings associated with at least a portion of the obtained data (Layton Par. 72-“The system and method are then capable of projection of the crawled, stored and processed information, using the present processing hardware, networking and computing infrastructure so as to generate specially formatted vectors, e.g., a single vector or multiple vectors. The vector or vectors may be generated according to a Word2vec model used to produce word embeddings in a multi-layer neural network or similar arrangement.”; Par. 66; Par. 87;Par. 90-“An objective of the various models 118 is to enable clustering of content, or “topic clusters 168” around relevant key phrases, where the topic clusters 168 include semantically similar words and phrases (rather than simply linking content elements that share exactly matching keywords). Semantic similarity can be determined by calculating vector similarity around key phrases appearing in two elements of content. In embodiments, topic clusters may be automatically clustered, such as by an auto-clustering engine 172 that manages a set of software jobs that take web pages from the primary online content object 102, use a model 118, such as the LSA model 124 to turn the primary online content object 102 into a vector representation, project the vector representation on to a space (e.g., a two-dimensional space), perform an affinity propagation that seeks to find natural groupings among the vectors (representing clusters of ideas within the content), and show the groupings as clusters of content. Once groups are created, a reviewer, such as a marketer or other content developer, can select one or more “centers” within the clusters, such as recognizing a core topic within the marketer's “pillar” content (such as a main web page), which may correspond to the primary online content object 102. Nodes in the cluster that are in close proximity to the identified centers may represent good additional topics about which to develop content or to which to establish links; for example, topic clusters can suggest an appropriate link structure among content objects managed by an enterprise and with external content objects, such as third-party objects, where the link structure is based on building an understanding of a semantic organization of a cluster of topics and mirroring the other content and architecture of links surrounding a primary online content object 102 based on the semantic organization.”);
and updating the entity database by storing the summary of the entity in the entity database; (Layton Par. 8; Par. 119;Par.162-“Once a set of recipients is known, the system 200 may assist with generation of relevant content, such as targeted emails, chats, text messages, and the like. In such cases, the system composes a personalized message 218 for each recipient in the recipient list by combining a selected message template with information about the recipient and the recipient's organization that is stored in the knowledge graph 210.; Par. 167-“ The platform may enable the user to review content from the knowledge graph as well as to search an information network, such as a CRM system or the Internet, to find additional relevant information. In embodiments, the CRM system may include access to data about recipients maintained in a sales database, a marketing database, a service database, or a combination thereof, such that individuals preparing output text”; Par. 305)
Layton teaches search query analysis and the feature is expounded upon by Bailey:
receiving, via a client computing device, a search query comprising a request to search a dynamically maintained entity database; (Bailey Par. 70- FIG. 3A illustrates example interface page 302 for defining filter criteria based on company attributes in accordance with some embodiments. Interface page 302 includes a search box allowing the user to search for different filters and also presents a list of filters, which are hierarchical in nature.; Par. 104 );
in response to receiving the search query, performing, by a query engine, a search of the entity database, wherein performing the search includes: detecting a defined trigger event associated with a backend of the system (Bailey Par. 52- “During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.”; Par. 72);
responsive to determining that a subscriber user is subscribed to one or more attributes associated with the entity and that the stored summary integrates updates to at least one of said attributes, generating a push notification identifying the at least one updated attribute; and transmitting the generated push notification to a computing device associated with the subscriber user. (Bailey Par. 39-40- During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.; Par. 86-“ Process 600 then returns a list of the remaining event items (operation 608). In some embodiments, data enrichment service 104 uses an API to push the list to application 118. In other embodiments, application 118 may periodically invoke an API to pull the list and subsequent updates to the list.”);
Layton and Bailey are directed to query analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton, as taught by Bailey, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton with the motivation crawling the web to extract and verify entity event information to enhance entity-based functionality in software applications and cloud services (Bailey Par. 2).
Layton in view of Bailey teaches semantic analysis and the feature is expounded upon by Renard:
performing, by a semantic analysis engine using an array of neural networks that operate in series, a semantic analysis of the obtained data to generate a summary of the entity comprising one or more attributes associated with the entity, wherein the semantic analysis of the text objects comprises quantifying the one or more attributes associated with the entity; (Renard Par. 4 –“Systems and methods are disclosed to determine alignment between first and second entities by collecting data from a plurality of sources including web search, social media, newspaper, and official sources of data; extracting entities and values; providing the entities and values text through multiple neural network text processing pipelines to an ensemblist density processing to generate the entities alignment values. ; Par. 54)
Layton, Bailey and Renard are directed to semantic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton in view of Bailey, as taught by Renard, by utilizing additional neural network analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton in view of Bailey with the motivation of determining and aligning values and opinions (Renard Par. 1).
Regarding Claim 2 and Claim 13 and Claim 19,
wherein generating the embeddings comprises: determining, by executing a machine learning model, the one or more attributes associated with the entity, wherein the machine learning model is trained using sample information of a plurality of sample entities and a plurality of sample attributes associated with the plurality of sample entities. (Layton Par. 94- In embodiments, the parser 110 uses a parsing machine learning system 140 to parse the crawled content. In embodiments, the parsing machine learning system 140 iteratively applies a set of weights to input data, wherein the weights are adjusted based on a parameter of success, wherein the parameter of success is based on the success of suggested topics 138 in the online presence of an enterprise. In embodiments, the machine learning system is provided with a parser training data set 142 that is created based on human analysis of the crawled content.”; Par. 119; Par. 37-142;”)
Regarding Claim 3 and Claim 14 and Claim 20,
wherein the one or more attributes associated with the entity comprise a category of the entity indicating a product or a service provided by the entity. (Layton Par. 195- Referring now to FIG. 16, an environment of a multi-client service platform 1600 is shown. A multi-client service platform 1600 may refer to a computing system that provides customer service solutions for any number of different clients. As used herein, a client may refer to an organization (e.g., a business, a government agency, a non-profit, and the like) that engages in some form of commercial or service-related activity, whereby the multi-client service platform 1600 may provide a customized or semi-customized customer service solution to service the customers of the client… As used herein, except where context indicates otherwise, a customer may refer to an entity or individual that engages with the client (e.g., a purchaser of a product or service of the client, a user of the client's software platform, and the like), and the term “customer” should be understood to encompass individuals at different stages of a relationship with a client, such as individuals/organizations who are being targeted with marketing and promotional efforts, prospects who are engaged in negotiations with sales people, customers who have purchased a product, and users of the product or service (such as individuals within a customer organization).”;
Regarding Claim 4 and Claim 15,
wherein executing the machine learning model and determining the one or more attributes comprises: performing a topic classification of the obtained information; performing a sentiment analysis of the obtained information; and performing attribute classification of the obtained information. (Layton Par. 119- a machine learning system 212 that learns/trains classification models that are used to extract events, entities, and/or relationships, scoring models that are used to identify intended recipients of directed content, and/or models that are used to generate directed models;” Par. 170-“ In another example, sentiment can be varied by learning positive and negative sentiment on a training set of reviews or other content that has a known positive or negative sentiment, such as numerically numbered or “starred” reviews on e-commerce sites like Amazon™ or review sites like Yelp™. In another example, tone can be varied by learning on text that has been identified by curators as angry, non-angry, happy, and the like. Thus, variations of text having different length, tense, sentiment, and tone can be provided and tracked to determine combinations that produce favorable outcomes, such that the content generation system 216 progressively improves in its ability to produce effecting content for communications.”; Par. 180-“ The system 200 may look at various attributes of generated language in optimizing generation, including the number of words used, average word length, average phrase length, average sentence length, grammatical complexity, tense, voice, word entropy, tone, sentiment, and the like. In embodiments, the system 200 may track outcomes based on differences (such as a calculated edit distance based on the number and type of changes) between a generated email (or one prepared by a worker) and a template email, such as to determine the extent of positive or negative contribution of customizing an email from a template for a recipient.”);
Regarding Claim 5 and Claim 16,
maintaining stakeholder attributes of a plurality of stakeholders, each stakeholder corresponding to one or more stakeholder attributes; determining that at least one of the one or more attributes associated with the entity corresponds to a stakeholder attribute of a stakeholder; and sending the summary of the entity to a computing device of the stakeholder. (Layton Par. 61; Par. 129- in embodiments, the entity extraction system 224 parses and derives entities, entity types, entity attributes, and entity relationships into a structured representation based on the received documents. The entity extraction system 224 may employ natural language processing, entity recognition, inference engines, and/or entity classification models to extract entities, entity types, entity attributes, entity relationships, and relationship metadata (e.g., dates on which the relationship was created). In embodiments, the event extraction system 226 may use known parsing techniques to parse a document into individual words and sequences of words. The event extraction system 226 may employ natural language processing and entity recognition techniques to determine whether any known entities are described in a document. In embodiments, the event extraction system 226 may utilize a classification model in combination with natural language processing and/or an inference engine to discover new entities, the entities' respective types, relationships between entities, and the respective types of relationships. In embodiments, the entity extraction system 224 may extract entity attributes from the documents using natural language processing and/or an inference engine. In embodiments, the inference engine may comprise conditional logic that identifies entity attributes (e.g., If/Then statements that correspond to different entity attributes and relationships). For example, the conditional logic may define rules for inferring an employer of an individual, a position of an individual, a university of an individual, a customer of an organization, a location of an individual or organization, a product sold by an organization, and the like. The entity extraction system 224 may employ additional or alternative strategies to identify and classify entities and their respective relationships.; Par. 305)
Regarding Claim 6 and Claim 17,
receiving a query request to query entities, wherein the query request comprises one or more keywords; determining that an attribute of a particular entity corresponds to the one or more keywords; and sending a summary of the particular entity in response to the query request. (Layton Par. 7- “The computing system may include a customization system for receiving the user request. The customization system may be configured to interpret and convert the custom object information into custom object metadata. The customization system may be configured to convert the custom object metadata into language-independent data creating the custom object. The customization system may be configured to send the custom object in language-independent data form to, at least one of the user device or one or more services of a multi-service business platform,”; Par. 90; Par. 99; Par. 116; Par. 129- in embodiments, the entity extraction system 224 parses and derives entities, entity types, entity attributes, and entity relationships into a structured representation based on the received documents. The entity extraction system 224 may employ natural language processing, entity recognition, inference engines, and/or entity classification models to extract; Par. 237)
Regarding Claim 7,
wherein determining that an attribute of a particular entity corresponds to the one or more keywords comprises determining that an attribute of two or more particular entities correspond to the one or more keywords, the operations further comprising: generating a comparison between the two or more particular entities based on a generated relevance score between the one or more keywords and the determined attribute; and sending the generated comparison of the two or more particular entities in response to the query request. (Layton Par. 72-“ The similarity may be translated into a corresponding score in some embodiments. In other aspects, said score may be used as an input to another process or another optional part of the present system. In yet other aspects, the output may be presented in a user interface presented to a human or machine. The score can further be presented as a “relevance” metric. Human-readable suggestions may be automatically generated by the system and method and provided as outputs, output data, or output signals in a processor-driven environment such as a modern computing architecture. The suggestions may in some aspects provide a content context model for guiding promoters (e.g., marketers) towards a best choice of topical content to prepare and put up on their websites, including suitable and relevant recommendations for work products such as articles and blog posts and social media materials that would promote the promoters' main topics or subjects of interest or sell the products and services of the marketers using the system and method.”; Par. 92; Par. 98;Par. 156- “In embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event. For example, in response to an event where Company A has acquired Company B, the attributes of an individual that may match to the event may be “works for Company B,” “Is a C-level or VP level executive,” and “Lives in New York City.” In this example, a person having these attributes may be receptive to a personalized message 218 from an executive headhunter. In this way, the lead scoring system 214 may generate the list of intended users based on the matches of attributes of the individual to the extracted event. In some embodiments, the lead scoring system 214 may generate the list of the intended users based on the matches of attributes of the individual to the extracted event given the subject matter and/or objective of the message.; Par. 234)
Regarding Claim 8,
wherein the message is an email, wherein a subject of the email contains the at least one identifier, and wherein the email triggers the data crawler to obtain the information about the entity. (Layton Par. 137; Par. 145-146; Par. 180; Par. 186- “In embodiments, the processing system 300 may execute the crawling system 202, the information extraction system 204, the machine learning system(s) 212, the lead scoring system 214, and the content generation system 216. The processing system 300 may execute additional systems not shown.”; Par. 158-“In embodiments, the content generation system 216 may utilize certain topics of information when generating directed content that can be included in an email subject line, opening greeting, and/or other portions of a message to make a more personal message for a recipient.”; Par. 167-“ As noted above, the system 200 may be used to help salespeople and marketers, such as to send automatically generated emails promoting an offering, where the emails are enriched with information that shows awareness of context and includes information of interest to recipients who match a given profile. The emails are in particular enriched with text generated from the knowledge graph 210 in which relevant information about organizations and individuals, such as event information, is stored. In embodiments the system 200 generates subject lines, blog post titles, content for emails, content for blog posts, or the like, such as phrases of a few words, e.g., about 5 words, about 6-8 words, about 10 words, about 15 words, a paragraph, or more, up to an including a whole article of content. In embodiments, where large amounts of reference data are available (such as where there are many articles about a company), it is possible to generate full articles. In other cases, shorter content may be generated, as noted above, by training a system to generate phrases based on training on a set of input-output pairs that include event content as inputs and summary words and phrases as outputs. As an example, company descriptions have been taken from LinkedIn™ and used to generate conversational descriptions of companies. Inputs varied by company, as the nature of the data was quite diverse. Outputs were configured to include a noun-phrase and a verb-phrase, where the verb phrase was constrained to be in a given tense. In embodiments, a platform and interface are provided herein in which one or more individuals (e.g., curators), may review input text (such as of company websites, news articles, job postings, and other content from the knowledge graph 210) and the individuals can enter output text summarizing the content of the inputs in the desired form (noun phrase, verb phrase). The platform may enable the user to review content from the knowledge graph as well as to search an information network, such as a CRM system or the Internet, to find additional relevant information. In embodiments, the CRM system may include access to data about recipients maintained in a sales database, a marketing database, a service database, or a combination thereof, such that individuals preparing output text (which in turn is used to train the natural language generation system) have access to private information, such as about conversations between salespeople and individuals in the recipient list, past communications, and the like.”)
Regarding Claim 9,
wherein a body of the email comprises human-generated insights associated with the entity, and wherein the human-generated insights are stored with the summary in the database. (Layton Par. 167-“ As noted above, the system 200 may be used to help salespeople and marketers, such as to send automatically generated emails promoting an offering, where the emails are enriched with information that shows awareness of context and includes information of interest to recipients who match a given profile. … Inputs varied by company, as the nature of the data was quite diverse. Outputs were configured to include a noun-phrase and a verb-phrase, where the verb phrase was constrained to be in a given tense. In embodiments, a platform and interface are provided herein in which one or more individuals (e.g., curators), may review input text (such as of company websites, news articles, job postings, and other content from the knowledge graph 210) and the individuals can enter output text summarizing the content of the inputs in the desired form (noun phrase, verb phrase). The platform may enable the user to review content from the knowledge graph as well as to search an information network, such as a CRM system or the Internet, to find additional relevant information. In embodiments, the CRM system may include access to data about recipients maintained in a sales database, a marketing database, a service database, or a combination thereof, such that individuals preparing output text (which in turn is used to train the natural language generation system) have access to private information, such as about conversations between salespeople and individuals in the recipient list, past communications, and the like.”)
Regarding Claim 10,
wherein the human-generated insights comprise a score of the entity that is associated with a category of the entity. (Layton Par. 150; Par. 154-155;Par. -“ In embodiments, the lead scoring system 214 is configured to identify a list of one or more intended recipients of a message based on the recipient profile 264 (whether specified by a user or generated by machine learning). In embodiments, the lead scoring system 214 may filter the most relevant entities in the knowledge graph 210 using the ideal recipient profile and create a recipient list. The recipient list may indicate a list of people that fit the recipient profile. In some embodiments, the lead scoring system 214 may determine a lead score for each person in the recipient list, whereby the lead score indicates a likelihood that a personalized message 218 sent to that person will lead to a successful outcome. In embodiments, the lead scoring system 214 may use historical and current data provided by the user and/or other users of the system to assess the probability that each recipient in the recipient list will respond to the user's message, and/or will be interested in knowing more about the user's offering, and/or will purchase the user's offering. The historical and current data used to evaluate this likelihood Regarding Claim 11,
wherein performing the semantic analysis comprises: identifying an additional source and extracting additional information about the entity from the additional source; and performing an additional semantic analysis of the additional source. (Layton Par. 91-93; Par.97; Par. 130-“ In embodiments, the event extraction system 226 is configured to parse and derive information relating to events and how those events relate to particular entities. For example, news articles, press releases, and/or other documents obtained from other information sources may be fed to the event extraction system 226, which may identify entities referenced in the documents based on the information contained in the news articles and other documents.”)
Regarding Claim 12,
Layton teaches
A computer-implemented method comprising: receiving …a search query… (Layton Par. 137-Examples of tasks that can be performed by machine learned models can include, but are not limited to, classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, and generating text. ; Par. 145;Par 146- “In embodiments, the machine learning system 212 is configured to train generative models. A generative model may refer to a machine learned models (e.g., neural networks, deep neural networks, recurrent neural networks, and the like) that are configured to output text given an objective (e.g., a message to generate a lead, a message to follow up a call, and the like) and one or more features/attributes of an intended recipient. In some embodiments, a generative model outputs sequences of three to ten words at a time. The machine learning system 212 may train a generative model using a corpus of text. For example, the machine learning system 212 may train a generative model used to generate professional messages may be trained using a corpus of messages, professional articles, emails, text messages, and the like. For example, the machine learning system 212 may be provided messages drafted by users for intended objective. The machine learning system 212 may receive the messages, the intended objectives of the messages, and outcome data indicating whether the message was successful (e.g., generated a lead, elicited a response, was read by the recipient, and the like). As the directed content system 200 generates and sends messages and obtains outcome data relating to those messages, the machine learning system 212 may reinforce a generative model based on the text of the messages and the outcome data.”);
updating the entity database responsive to the detecting based on: obtaining at least one identifier of an entity by parsing message data of a message associated with the trigger event (Layton Par. 66 Par. 87; Par. 164-“ In embodiments, each respective personalized message may be sent with a message tracking mechanism to a respective recipient of the recipient list. A message tracking mechanism may be a software mechanism that causes transmission of feedback data 272 to the system 200 in response to a certain triggering action. For example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when the recipient opens the email. In another example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when a recipient downloads an attachment or clicks on a link in the personalized message.; Par. 6; Par. 317”)
automatically parsing the received message to identify at least one identifier of an entity (Layton Par. 137; Par. 145- “In embodiments, the machine learning system 212 is configured to train entity classification models. An entity classification model may be a machine learned model that receives one or more documents (or features thereof) and identifies entities indicated in the documents and/or relationships between the documents. An entity classification model may further utilize the knowledge graph as input to determine entities or relationships that may be defined in the one or more documents.”; Par 146-147 “The machine learning system 212 may receive the messages, the intended objectives of the messages, and outcome data indicating whether the message was successful (e.g., generated a lead, elicited a response, was read by the recipient, and the like)… machine learning system 212 may train the lead scoring model 212 in a supervised, semi-supervised, or unsupervised manner. In some embodiments, the machine learning system 212 is fed a training data set that includes sets of triplets, where a triplet may include recipient attributes (e.g., employer, role, recent events, location, and the like), a message objective (e.g., start a conversation, make a sale, generate a website click), and a lead score assigned (e.g., by a human) to the triplet given the attributes and the message objective. The machine learning system 212 may initially train a model based on the training data. As the lead scoring model is deployed and personalized messages are sent to users, the machine learning system 212 may receive feedback data 272 from a recipient device 270 (e.g., message was ignored, message was read, message was read and responded to) to further train the scoring model..”);
in response to identifying the at least one identifier of the entity, obtaining, automatically by a data crawler using the at least one identifier and without user input, bulk data associated with the entity based on querying at least two disparate external computing systems to extract an entity-related dataset (Layton Par. 6; Par. -63- “For example, some services that may be applied include workflow automation (e.g., automate based on changes to core objects and based on added custom objects or changes to custom objects and/or core objects), “; Par. 115; Par. 137; Par.145-“Furthermore, as the system 200 (e.g., the event extraction module 226) operates to extract entities and relationships from information obtained by the crawling system 202, the machine learning system 212 may utilize the classification of said entities and relationships, the information used to make the entity classifications, and any outcome data resulting from the entity classifications to reinforce the entity classification model(s).;Par. 120- “In embodiments, the methods and systems disclosed herein include methods and systems for pulling information at scale from one or more information sources. In embodiments, the crawling system 202 may obtain information from external information sources 230 accessible via a communication network 280 (e.g., the Internet), a private network, the proprietary database 208 (such as a content management system, a customer relationship management database, a sales database, a marketing database, a service management database, or the like), or other suitable information sources. Such methods and systems may include one or more crawlers, spiders, clustering systems, proxies, services, brokers, extractors and the like,”);
generating, by a data parser, embeddings associated with at least a portion of the obtained data (Layton Par. 72-“The system and method are then capable of projection of the crawled, stored and processed information, using the present processing hardware, networking and computing infrastructure so as to generate specially formatted vectors, e.g., a single vector or multiple vectors. The vector or vectors may be generated according to a Word2vec model used to produce word embeddings in a multi-layer neural network or similar arrangement.”; Par. 66; Par. 87;Par. 90-“An objective of the various models 118 is to enable clustering of content, or “topic clusters 168” around relevant key phrases, where the topic clusters 168 include semantically similar words and phrases (rather than simply linking content elements that share exactly matching keywords). Semantic similarity can be determined by calculating vector similarity around key phrases appearing in two elements of content. In embodiments, topic clusters may be automatically clustered, such as by an auto-clustering engine 172 that manages a set of software jobs that take web pages from the primary online content object 102, use a model 118, such as the LSA model 124 to turn the primary online content object 102 into a vector representation, project the vector representation on to a space (e.g., a two-dimensional space), perform an affinity propagation that seeks to find natural groupings among the vectors (representing clusters of ideas within the content), and show the groupings as clusters of content. Once groups are created, a reviewer, such as a marketer or other content developer, can select one or more “centers” within the clusters, such as recognizing a core topic within the marketer's “pillar” content (such as a main web page), which may correspond to the primary online content object 102. Nodes in the cluster that are in close proximity to the identified centers may represent good additional topics about which to develop content or to which to establish links; for example, topic clusters can suggest an appropriate link structure among content objects managed by an enterprise and with external content objects, such as third-party objects, where the link structure is based on building an understanding of a semantic organization of a cluster of topics and mirroring the other content and architecture of links surrounding a primary online content object 102 based on the semantic organization.”);
and updating the entity database by storing the summary of the entity in the entity database; (Layton Par. 8; Par. 119;Par.162-“Once a set of recipients is known, the system 200 may assist with generation of relevant content, such as targeted emails, chats, text messages, and the like. In such cases, the system composes a personalized message 218 for each recipient in the recipient list by combining a selected message template with information about the recipient and the recipient's organization that is stored in the knowledge graph 210.; Par. 167-“ The platform may enable the user to review content from the knowledge graph as well as to search an information network, such as a CRM system or the Internet, to find additional relevant information. In embodiments, the CRM system may include access to data about recipients maintained in a sales database, a marketing database, a service database, or a combination thereof, such that individuals preparing output text”; Par. 305)
Layton teaches search query analysis and the feature is expounded upon by Bailey:
receiving, via a client computing device, a search query comprising a request to search a dynamically maintained entity database; (Bailey Par. 70- FIG. 3A illustrates example interface page 302 for defining filter criteria based on company attributes in accordance with some embodiments. Interface page 302 includes a search box allowing the user to search for different filters and also presents a list of filters, which are hierarchical in nature.; Par. 104 );
in response to receiving the search query, performing, by a query engine, a search of the entity database, wherein performing the search includes: detecting a defined trigger event associated with a backend of the system (Bailey Par. 52- “During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.”; Par. 72);
responsive to determining that a subscriber user is subscribed to one or more attributes associated with the entity and that the stored summary integrates updates to at least one of said attributes, generating a push notification identifying the at least one updated attribute; and transmitting the generated push notification to a computing device associated with the subscriber user. (Bailey Par. 39-40- During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.; Par. 86-“ Process 600 then returns a list of the remaining event items (operation 608). In some embodiments, data enrichment service 104 uses an API to push the list to application 118. In other embodiments, application 118 may periodically invoke an API to pull the list and subsequent updates to the list.”);
Layton and Bailey are directed to query analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton, as taught by Bailey, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton with the motivation crawling the web to extract and verify entity event information to enhance entity-based functionality in software applications and cloud services (Bailey Par. 2).
Layton in view of Bailey teaches semantic analysis and the feature is expounded upon by Renard:
performing, by a semantic analysis engine, using an array of neural networks operating in series, a semantic analysis of the obtained data to generate a summary of the entity comprising one or more attributes associated with the entity, wherein the semantic analysis comprises quantifying the one or more attributes associated with the entity in a unified format ; (Renard Par. 4 –“Systems and methods are disclosed to determine alignment between first and second entities by collecting data from a plurality of sources including web search, social media, newspaper, and official sources of data; extracting entities and values; providing the entities and values text through multiple neural network text processing pipelines to an ensemblist density processing to generate the entities alignment values.; Par. 54 )
Layton, Bailey and Renard are directed to semantic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton in view of Bailey, as taught by Renard, by utilizing additional neural network analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton in view of Bailey with the motivation of determining and aligning values and opinions (Renard Par. 1).
Regarding Claim 18,
Layton teaches
A non-transitory, computer-readable medium storing computer-readable instructions executable by a computer and configured to perform operations comprising: receiving … a search query… (Layton Par. 6-“In some example embodiments, the created custom object may include at least one of a custom identification label, a custom object name, an object type, or a property of the custom object. In some example embodiments, at least one association may be created for the custom object with another second object based on the custom object information. In some example embodiments, the at least one association may include at least one of an association identification, an association type, a first object identification, a second object identification, or a timestamp. The custom object may be directed to the first object identification or the second object identification based on a defined relationship between the custom object and the other second object.”; Par. 9; Par. 63; Par. 66; Par. 137’Par. 188);
updating the entity database responsive to the detecting based on: obtaining at least one identifier of an entity by parsing message data of a message associated with the trigger event (Layton Par. 66 Par. 87; Par. 164-“ In embodiments, each respective personalized message may be sent with a message tracking mechanism to a respective recipient of the recipient list. A message tracking mechanism may be a software mechanism that causes transmission of feedback data 272 to the system 200 in response to a certain triggering action. For example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when the recipient opens the email. In another example, a message tracking mechanism may transmit a packet indicating an identifier of the personalized message and a timestamp when a recipient downloads an attachment or clicks on a link in the personalized message.; Par. 6; Par. 317”)
automatically parsing the received message to identify at least one identifier of an entity (Layton Par. 137; Par. 145- “In embodiments, the machine learning system 212 is configured to train entity classification models. An entity classification model may be a machine learned model that receives one or more documents (or features thereof) and identifies entities indicated in the documents and/or relationships between the documents. An entity classification model may further utilize the knowledge graph as input to determine entities or relationships that may be defined in the one or more documents.”; Par 146-147 “The machine learning system 212 may receive the messages, the intended objectives of the messages, and outcome data indicating whether the message was successful (e.g., generated a lead, elicited a response, was read by the recipient, and the like)… machine learning system 212 may train the lead scoring model 212 in a supervised, semi-supervised, or unsupervised manner. In some embodiments, the machine learning system 212 is fed a training data set that includes sets of triplets, where a triplet may include recipient attributes (e.g., employer, role, recent events, location, and the like), a message objective (e.g., start a conversation, make a sale, generate a website click), and a lead score assigned (e.g., by a human) to the triplet given the attributes and the message objective. The machine learning system 212 may initially train a model based on the training data. As the lead scoring model is deployed and personalized messages are sent to users, the machine learning system 212 may receive feedback data 272 from a recipient device 270 (e.g., message was ignored, message was read, message was read and responded to) to further train the scoring model..”);
in response to identifying the at least one identifier of the entity, obtaining, automatically by a data crawler using the at least one identifier and without user input, bulk data associated with the entity based on querying at least two disparate external computing systems to extract an entity-related dataset (Layton Par. 6; Par. -63- “For example, some services that may be applied include workflow automation (e.g., automate based on changes to core objects and based on added custom objects or changes to custom objects and/or core objects), “; Par. 115; Par. 137; Par.145-“Furthermore, as the system 200 (e.g., the event extraction module 226) operates to extract entities and relationships from information obtained by the crawling system 202, the machine learning system 212 may utilize the classification of said entities and relationships, the information used to make the entity classifications, and any outcome data resulting from the entity classifications to reinforce the entity classification model(s).;Par. 120- “In embodiments, the methods and systems disclosed herein include methods and systems for pulling information at scale from one or more information sources. In embodiments, the crawling system 202 may obtain information from external information sources 230 accessible via a communication network 280 (e.g., the Internet), a private network, the proprietary database 208 (such as a content management system, a customer relationship management database, a sales database, a marketing database, a service management database, or the like), or other suitable information sources. Such methods and systems may include one or more crawlers, spiders, clustering systems, proxies, services, brokers, extractors and the like,”);
generating, by a data parser, embeddings associated with at least a portion of the obtained data (Layton Par. 72-“The system and method are then capable of projection of the crawled, stored and processed information, using the present processing hardware, networking and computing infrastructure so as to generate specially formatted vectors, e.g., a single vector or multiple vectors. The vector or vectors may be generated according to a Word2vec model used to produce word embeddings in a multi-layer neural network or similar arrangement.”; Par. 66; Par. 87;Par. 90-“An objective of the various models 118 is to enable clustering of content, or “topic clusters 168” around relevant key phrases, where the topic clusters 168 include semantically similar words and phrases (rather than simply linking content elements that share exactly matching keywords). Semantic similarity can be determined by calculating vector similarity around key phrases appearing in two elements of content. In embodiments, topic clusters may be automatically clustered, such as by an auto-clustering engine 172 that manages a set of software jobs that take web pages from the primary online content object 102, use a model 118, such as the LSA model 124 to turn the primary online content object 102 into a vector representation, project the vector representation on to a space (e.g., a two-dimensional space), perform an affinity propagation that seeks to find natural groupings among the vectors (representing clusters of ideas within the content), and show the groupings as clusters of content. Once groups are created, a reviewer, such as a marketer or other content developer, can select one or more “centers” within the clusters, such as recognizing a core topic within the marketer's “pillar” content (such as a main web page), which may correspond to the primary online content object 102. Nodes in the cluster that are in close proximity to the identified centers may represent good additional topics about which to develop content or to which to establish links; for example, topic clusters can suggest an appropriate link structure among content objects managed by an enterprise and with external content objects, such as third-party objects, where the link structure is based on building an understanding of a semantic organization of a cluster of topics and mirroring the other content and architecture of links surrounding a primary online content object 102 based on the semantic organization.”);
and updating the entity database by storing the summary of the entity in the entity database; (Layton Par. 8; Par. 119;Par.162-“Once a set of recipients is known, the system 200 may assist with generation of relevant content, such as targeted emails, chats, text messages, and the like. In such cases, the system composes a personalized message 218 for each recipient in the recipient list by combining a selected message template with information about the recipient and the recipient's organization that is stored in the knowledge graph 210.; Par. 167-“ The platform may enable the user to review content from the knowledge graph as well as to search an information network, such as a CRM system or the Internet, to find additional relevant information. In embodiments, the CRM system may include access to data about recipients maintained in a sales database, a marketing database, a service database, or a combination thereof, such that individuals preparing output text”; Par. 305)
Layton teaches search query analysis and the feature is expounded upon by Bailey:
receiving, via a client computing device, a search query comprising a request to search a dynamically maintained entity database; (Bailey Par. 70- FIG. 3A illustrates example interface page 302 for defining filter criteria based on company attributes in accordance with some embodiments. Interface page 302 includes a search box allowing the user to search for different filters and also presents a list of filters, which are hierarchical in nature.; Par. 104 );
in response to receiving the search query, performing, by a query engine, a search of the entity database, wherein performing the search includes: detecting a defined trigger event associated with a backend of the system (Bailey Par. 52- “During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.”; Par. 72);
responsive to determining that a subscriber user is subscribed to one or more attributes associated with the entity and that the stored summary integrates updates to at least one of said attributes, generating a push notification identifying the at least one updated attribute; and transmitting the generated push notification to a computing device associated with the subscriber user. (Bailey Par. 39-40- During runtime, application 118 may execute the application flow to determine if and when the conditions for making the social media post are satisfied, which social media platforms to target, what content, including images and/or text, to include in the social media post. In addition or as an alternative to triggering candidate posts, other actions may be triggered. Other examples include generating and sending analytic reports, modifying datafiles, and adjusting the configuration settings of a computing resource.; Par. 86-“ Process 600 then returns a list of the remaining event items (operation 608). In some embodiments, data enrichment service 104 uses an API to push the list to application 118. In other embodiments, application 118 may periodically invoke an API to pull the list and subsequent updates to the list.”);
Layton and Bailey are directed to query analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton, as taught by Bailey, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton with the motivation crawling the web to extract and verify entity event information to enhance entity-based functionality in software applications and cloud services (Bailey Par. 2).
Layton in view of Bailey teaches semantic analysis and the feature is expounded upon by Renard:
performing, by a semantic analysis engine, using an array of neural networks operating in series, a semantic analysis of the obtained data to generate a summary of the entity comprising one or more attributes associated with the entity, wherein the semantic analysis of the text objects comprises quantifying the one or more attributes associated with the entity in a unified format; (Renard Par. 4 –“Systems and methods are disclosed to determine alignment between first and second entities by collecting data from a plurality of sources including web search, social media, newspaper, and official sources of data; extracting entities and values; providing the entities and values text through multiple neural network text processing pipelines to an ensemblist density processing to generate the entities alignment values. ; Par. 54)
Layton, Bailey and Renard are directed to semantic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Layton in view of Bailey, as taught by Renard, by utilizing additional neural network analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Layton in view of Bailey with the motivation of determining and aligning values and opinions (Renard Par. 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230316186A1 to Miller et al.- Abstract-“ The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.”
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624