DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 08/18/2025 regarding application 18/473,988. Claims 1-9, 11-15 and 17 have been amended. Claims 16 and 18-20 have been canceled on the record. Claims 1-15 and 17 are currently pending have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 08/18/2025 necessitated new grounds of rejection in this office action.
Response to Arguments
3. Applicant’s arguments, see page 8 filed on 08/18/2025, with respect to the Claim Objections for Claims 3-8, 11, 13, 15 and 18-19 have been fully considered and are found to be persuasive. Therefore, the Claim Objections for Claims 3-8, 11, 13, 15 and 18-19 have been withdrawn.
4. Applicant’s arguments, see page 8 filed on 08/18/2025, with respect to the 35 U.S.C. § 112 (b) Claim Rejections for Claim 16 has been fully considered and is found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claim 16 has been withdrawn.
5. Applicant’s arguments, see pages 13-14 filed on 08/18/2025, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-19 have been fully considered and are found to be not persuasive. Applicant’s arguments with respect to Claims 1-15 and 17 have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to 35 U.S.C. § 101 Arguments
6. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-15 and 17 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 9-12, dated 08/18/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-15 and 17 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-12, dated 08/18/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the amended claim limitations of Independent Claims 1, 9 and 17 when considered in combination, integrate the alleged abstract idea into a practical application that improves the functioning of a computer under step 2a prong 2 of the 35 U.S.C. § 101 analysis (see Applicant’s Remarks, Page 10, dated 08/18/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks here, Examiner notes that the claims essentially describe collecting data, analyzing it for patterns using AI, and suggesting business actions, which aligns with the performance of fundamental economic or business practices on a computer.
The first step of "automatically aggregating connection data from a plurality of data sources, the connection data being related to connections between a first enterprise and a second enterprise, wherein at least one of the plurality of data sources includes an automated agent that retrieves the connection data for professional networking connections" directed to the abstract idea of gathering information (specifically, business/professional connections). Data aggregation is a fundamental business practice and a method of organizing human activity that can be performed mentally or with basic tools. The use of an "automated agent" is a generic instruction for using a computer tool to perform this abstract task. The step lacks an inventive concept. Aggregating data from multiple sources via agents (web scrapers, APIs) using a computer is a conventional, routine activity. There are no details on a specific, non-generic technological improvement to the data collection process itself (e.g., improving data transmission speed using an unconventional protocol or a novel sensor technology).
Secondly, the second step of "utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, the connection graph being generated based on the connection data" describes the application of a mathematical concept (graph theory) to the aggregated data. Representing relationships as a graph is an abstract mathematical way of visualizing and organizing information. The step simply instructs a generic "connection graph generating engine" (a computer program) to apply a well-known mathematical tool (graph generation) to the data. This is a conventional application of a computer and does not transform the abstract idea into a patent-eligible invention.
The 3rd step of "providing the connection graph to a pattern identification model to identify connection patterns in the connection data between the first enterprise and the second enterprise, wherein the pattern identification model is trained using a first training dataset which includes label connection graph data and the connection patterns" is directed to the abstract idea of identifying patterns in data, a fundamental mental process and analytical method. The use of a "pattern identification model" and "training dataset" are references to mathematical algorithms and machine learning techniques, which are themselves considered abstract ideas under § 101. Merely using a computer model to perform a known analytical task is not an inventive concept. The claim does not specify a novel machine learning architecture or a specific, unconventional technological improvement to the model's operation that goes beyond its abstract function. The training mechanism is also a conventional aspect of ML models. The recent Federal Circuit decision in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea were not sufficient to confer eligibility) held that "claims that do no more than apply established methods of machine learning to a new data environment" are not patent eligible. Unless the claim specifies a specific improvement to the underlying machine learning technology itself (e.g., a novel algorithm or a non-conventional system integration), it is likely considered an abstract idea.
The 4th step of "generating recommended actions using an action identification model based on the connection patterns and at least one of a first enterprise context and a second enterprise context, wherein the action identification model is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions" involves the abstract idea of providing business advice or recommendations based on analysis. This is a method of organizing human activity and decision-making. The use of an "action identification model" applies mathematical algorithms to perform this advisory function. As with the previous step, using a conventional computer model to perform a business analysis function is not an inventive concept. The claim lacks any concrete technological improvements or specific hardware implementation that would lift it above the realm of an abstract business method performed on a generic computer.
The 5th step of "providing the recommended actions for display to a user via a graphical user interface (GUI) screen" is directed to the abstract idea of communication or presenting information. Displaying data is a fundamental activity. Providing output via a "graphical user interface (GUI) screen" is a purely conventional and generic use of a computer display technology. Merely requiring the use of a generic computer component (a screen) to perform an abstract idea does not transform it into a patent-eligible invention. This step does not add an inventive concept to the overall claim.
The sixth step of "updating the first training dataset and the second training dataset by a training mechanism as new connection data is received from the plurality of data sources, the training mechanism further providing ongoing training of the pattern identification model and the action identification model" is directed to the abstract idea of managing data and continuously refining a process. This describes the abstract concept of maintaining and improving data and models, which is an inherent part of data analysis and model management. It is a conceptual instruction. Automating the retraining of a model with new data is a conventional, routine aspect of modern data science and machine learning applications. There are no inventive technical features described that would transform this abstract concept into patent-eligible subject matter.
Accordingly it appears that what is argued here is an improvement in the business process itself, as an entrepreneurial objective, [argued here as connection management for productivity applications] not a technological solution improving the computer itself or another technological field (see comparison to the unpersuasive argument in “Versata Dev. Grp., Inc. v. SAP Am., Inc. U.S. Court of Appeals Federal Circuit, 115 USPQ2d 1681, U.S. Court of Appeals Federal Circuit No. 2014-1194 Decided July 9, 2015, 2015 BL 219054, 793 F.3d 1306” p.1701 3rd to last ¶).
Moreover, the claims follow a similar ineligibility path as those in “Elec. Power Grp”, because Appellant merely asserts computation advances to already abstract concepts to which existing computer capabilities could be put, yet do not present any clear, genuine technological improvement, in how computers carry out basic functions as scrutinized by the Federal Circuit in “Elec. Power Grp., LLC v. Alstom S.A. U.S. Court of Appeals Federal Circuit No. 2015-1778 August 1, 2016 2016 BL 247416 830 F.3d 1350, 119 USPQ2d 1739” hereinafter “Elec. Power Grp” page 1742 ¶3 citing “Enfish, 822 F.3d at 1335-36” and “Bascom, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360”. Simply said the claims’ focus is not on an improvement in computers as tools, but rather on independently identified abstract ideas that use computers as tools to aid the above abstract process” itself (similar to “Elec. Power Grp., LLC v. Alstom S.A. U.S. Court of Appeals Federal Circuit, 119 USPQ2d 1739 No. 2015-1778 August 1, 2016 2016 BL 247416 830 F.3d 1350”, p.1742 ¶3 last sentence), or used in conjunction with computer tools (similar to “Synopsys, Inc. v. Mentor Graphics Corp. , U.S. Court of Appeals Federal Circuit, No. 2015-1599, October 17, 2016, 2016 BL 344522, 839 F.3d 1138” supra p.1481 last 2 ¶).
When viewed as individual steps and as a whole, the described process is directed to the abstract idea of analyzing business connections between enterprises to provide recommendations using generic computing technology and conventional machine learning algorithms. Therefore, when considering the additional elements of (e.g., “training mechanism” & “automated agent” & “graphical user interface (GUI) screen”) along with conventional components such as (e.g., generating engine, “a processor” & “a memory”), when view with the claim limitations both individually and as an ordered combination, these steps lack an "inventive concept" sufficient to transform the abstract ideas into a patent-eligible application. The claims lack an "inventive concept" because they simply implement abstract ideas (data aggregation, graph theory, pattern identification, AI/ML models) using computer components (automated agents, generating engines, GUIs) without specifying a particular, non-generic technological solution to a technological problem. Therefore, these claims are ineligible for patent protection under 35 U.S.C. § 101 based on the Alice v. Mayo test.
Therefore, Examiner maintains that Claims 1-15 and 17 are "directed to" the judicial exception (an abstract idea) because it fails to integrate that idea into a practical application, and are ineligible at Step 2A, Prong 2 of the 35 U.S.C. § 101 analysis.
Argument #2:
(B). Applicant argues that Claims 1-15 and 17 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis and cites “Example 47 of the 2024 USPTO Guidelines” as analogous (see Applicant Remarks, Page 10, dated 08/18/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks here, Example 47, Claim 2 was found ineligible because it explicitly recited specific mathematical algorithms like "backpropagation algorithm and a gradient descent algorithm". The provided claims shown in Independent Claims 1, 9 and 17 use generic terms like "pattern identification model" and "action identification model" trained on "labeled data." While the specific algorithms are not named, these models inherently rely on underlying mathematical algorithms (like backpropagation) for training and execution. The claims are effectively directed to these underlying mathematical concepts, much like Example 47, Claim 2. Example 47, Claim 2's use of "by the computer" was afforded little patentable weight as it was considered generic. The provided claims describe typical computer functions ("aggregating connection data from a plurality of data sources," "utilizing a connection graph generating engine," "providing the connection graph to a pattern identification model," "generating recommended actions," and "display to a user via a graphical user interface (GUI) screen"). These are conventional, routine computer steps that do not provide an inventive concept beyond applying an abstract idea on a general-purpose computer. Lack of Tangible Technical Improvement: Both the claims in question and Example 47, Claim 2 focus on generating information or recommendations (anomaly detection in Example 47; business connection recommendations here). Neither set of claims describes a specific, tangible improvement to a technology or technical field beyond the abstract results themselves, unlike Example 47, Claim 3, which was eligible because it integrated the anomaly detection into an improved network security system that automatically dropped malicious packets. In summary, like Example 47, Claim 2, the provided claims are directed to an abstract idea (analyzing data using mathematical models) implemented on a generic computer with no "significantly more" or inventive concept that transforms the abstract idea into a patent-eligible application.
The use of an "automated agent," "connection graph generating engine," "pattern identification model," and "action identification model" are general, functional descriptions of software components that perform the abstract ideas. The steps of "display to a user via a graphical user interface (GUI) screen" and "updating the first training dataset" are routine, conventional computer activities or data management functions. The claims do not specify any particular, non-generic hardware implementation (e.g., an Application-Specific Integrated Circuit (ASIC) or a physical sensor array) that would integrate the abstract idea into a specific machine. The claims do not describe an improvement to the functioning of the computer itself or a new technological process, such as improved network security that drops malicious packets automatically. Therefore, Examiner maintains that Claims 1-15 and 17 are "directed to" the judicial exception (an abstract idea) because it fails to integrate that idea into a practical application, and are more analogous to Example 47 Claim 2 of the 35 U.S.C. § 101 Examples as deemed ineligible.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-15 and 17 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-15 and 17 are each focused to a statutory category namely, a “system” or a “apparatus” (Claims 1-8), a “process” or a “method” (Claims 9-15), and a “non-transitory computer readable medium” or an “article of manufacture” (Claim 17).
Step 2A Prong One: Independent Claims 1, 9 and 17 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 1);
“:” (see Independent Claim 1);
“” (see Independent Claim 17);
“automatically aggregating connection data from a plurality of data sources, the connection data being related to connections between a first enterprise and a second enterprise, wherein at least one of the plurality of data sources retrieves the connection data for professional networking connections” (see Independent Claims 1, 9 and 17);
“ generate a connection graph for the connections between the first enterprise and the second enterprise, the connection graph being generated based on the connection data” (see Independent Claims 1, 9 and 17);
“providing the connection graph to a pattern identification model to identify connection patterns in the connection data between the first enterprise and the second enterprise, wherein the pattern identification model is trained using a first training dataset which includes label connection graph data and the connection patterns” (see Independent Claims 1, 9 and 17);
“generating recommended actions using an action identification model based on the connection patterns and at least one of a first enterprise context and a second enterprise context, wherein the action identification model is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions” (see Independent Claims 1, 9 and 17);
“providing the recommended actions for display to a user ” (see Independent Claims 1, 9 and 17);
“updating the first training dataset and the second training dataset as new connection data is received from the plurality of data sources, further providing ongoing training of the pattern identification model and the action identification model” (see Independent Claims 1, 9 and 17).
These abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions).
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (2) concepts performed in the human mind (including observations or evaluations or judgments) or (3) using pen and paper as a physical aid, which in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
That is, other than reciting (e.g., “memory” & “processor” & “a programmable device” & “generating engine” & “training mechanism” & “automated agent” & “graphical user interface (GUI) screen” & “enterprise connection analysis system”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mental Processes” which pertains to (2) concepts performed in the human mind (including observations or evaluations or judgments or opinions) or (3) using pen and paper as a physical aid.
Therefore, at step 2a prong 1, Yes, Claims 1-15 and 17 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 9 and 17 recites additional elements directed to: (e.g., “memory” & “processor” & “a programmable device” & “generating engine” & “a data processing system”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 9 and 17: With respect to reliance on (e.g., “automated agent” & “training mechanism” & “graphical user interface (GUI) screen”) as additional elements shown in Independent Claims 1, 9 and 17, when considered in view of the claim limitations both individually and as an ordered combination (as a whole), these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 due to: (1) limiting a particular field of use or technological environment pertaining to utilizing a connection graph for generating connections between the first and second enterprises and improving the connections between the first and second enterprises based on connection patterns between the first and second enterprises using a computer in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Moreover, with respect to Independent Claims 1, 9 and 17, certain/particular limitations shown recite “mere data outputting” (e.g., “providing the recommended actions for display to a user via a graphical user interface (GUI) screen” (see Independent Claims 1, 9 and 17)) wherein which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-15 and 17 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 9 and 17 recites additional elements directed to: (e.g., “memory” & “processor” & “a programmable device” & “generating engine” & “a data processing system”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (e.g., see at least Applicant’s Specification ¶ [0073].).
Independent Claims 1, 9 and 17: With respect to reliance on (e.g., “automated agent” & “training mechanism” & “graphical user interface (GUI) screen”) as additional elements shown in Independent Claims 1, 9 and 17, when considered in view of the claim limitations both individually and as an ordered combination (as a whole), these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) limiting a particular field of use or technological environment pertaining to utilizing a connection graph for generating connections between the first and second enterprises and improving the connections between the first and second enterprises based on connection patterns between the first and second enterprises using a computer in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Moreover, with respect to Independent Claims 1, 9 and 17, certain/particular limitations shown recite “mere data outputting” (e.g., “providing the recommended actions for display to a user via a graphical user interface (GUI) screen” (see Independent Claims 1, 9 and 17)) wherein which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1, 9 and 17 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-8 and 10-15 recite substantially the same or similar additional elements as addressed above and when considered individually and as an ordered combination (as a whole) with these limitations recite the same abstract idea(s) as shown in Independent 1, 9 and 17 along with further steps/details pertaining to “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mental Processes” which pertains to (2) concepts performed in the human mind (including observations or evaluations or judgments or opinions) or (3) using pen and paper as a physical aid. Dependent Claims 2-8 and 10-15 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Step 2A Prong 2 and Step 2B for Independent Claims 1, 9 and 17.
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-15 and 17 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-15 and 17 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
9. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
10. 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.
11. Claims 1-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2016/0148222 A1) hereinafter Davar, et. al., and in view of US PG Pub (US 2019/0138653 A1) hereinafter Fama Roller, et. al.
Regarding Independent Claim 1, Davar enterprise connection analysis system teaches the following:
- a processor (see at least Davar: ¶ [0057] & Fig. 1.);
- a memory (see at least Davar: ¶ [0057] & Fig. 1.) in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the enterprise connection analysis system (see at least Davar: ¶ [0057] & Fig. 1.) to perform the functions of:
- automatically aggregating connection data (see at least Davar: ¶ [0134] & ¶ [0139-0141] & ¶ [0195-0196].) from a plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11.), the connection data related to connections between the first enterprise and the second enterprise (see at least Davar: ¶ [0008-0010]. Davar teaches that the method comprises using a computer processor, selecting, from a business database recording business relationships between organizations, first organizations that are connected by a business relationship to one or more second organizations. See also Davar at ¶ [0010]: Davar notes that the instructions when executed provide: a data access agent for retrieving first organizations and second organizations that are connected by a business relationship; a social network agent for retrieving a set of social contacts of a user, and associating each first organization with social contacts that have an employment connection with second organizations having a business relationship with that first organization.), wherein at least one of the plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11. Davar teaches that FIG. 11 is a diagram of a survey interface and the datasources for generating it. The system's data input agent provides one or more ways to input a business relationship to the database, such as a website form, receiving a data file, an API callable by third-party software, and a web crawler. Preferably the relationship is input by a user working on behalf of one of the organizations (asserting organization) and comprises details about the relationship and the other organization (unverified organization.) includes an automated agent (see at least Davar: ¶ [0059] & ¶ [0104]. Davar teaches that the database and methods will be accessed by a computer bot for displaying a relevant organization to an identified organization. For example, an ad-tech bot may determine using cookies or viewed content that a user is interested in a particular industry. The ad-tech bot could display one or more organizations or their ads to the user, depending on what organization is most relevant to the user's organization. See also Davar at ¶ [0104]: The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees.) that retrieves the connection data for professional networking connections (see at least Davar: ¶ [0104] & ¶ [0145]. Davar teaches that this assumption is truer for social contacts within professional networks and industry-specific media, particularly when several employees of one organization are connected to several employees of another organization. The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees. The similarity agent may further determine the industries of the organizations. See also Davar at ¶ [0145]: The social contacts are preferably associated with another organization (preferably one of the first or second organizations), typically as employees, consultants, or advisors. These employment connections may be current or past and may be determined from professional social networks. The social contacts may also be Thought Leaders/Opinion Leaders/LinkedIn™ Influencers or persons well regarded by the user, but not necessarily as employees of third, first or second organizations.)
- utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, the connection graph being generated based on the connection data (see at least Davar: ¶ [0031] & ¶ [0118] & ¶ [0139]. Davar teaches that the graph may comprise a second type of edge (similarity edge), which records the degree to which one organization is similar to another. The similarity edge may be non-directional or bidirectional to indicate that two organizations are mutual peers or the peer edge may be unidirectional to indicate that one organization is considered a peer of another organization but not vice versa, or at least not in the same way or degree. FIG. 6 illustrates a small portion of an example graph, focusing on the nodes and edges between a user's organization node (A) and vendor nodes (D, E, F) being sought. USER is associated with organization A. Solid arrows indicate relationship edges as the flow of services from a vendor node towards a client node. Dashed arrows indicate similarity edges from one organization towards an organization recorded as similar. Thus D, E and F are mutual peers and are, in this case, the target of the search. Nodes C and B are similar and both are similar to A. Node and edge values are shown separately in FIG. 6B. See also Davar at ¶ [0031]: Ranking the set of profiles may comprise identifying a set of second organizations having a business relationship with the first organization and determining which profiles' professional experience data include past or current work experience with any of the second organizations. See also Davar at ¶ [0118]: The engine then finds all relationships in the database for each vendor to create a set of clients. The engine may use the relationship direction attribute stored in the database to ignore connected organizations that are not actually clients to each vendor or use this knowledge to weight their relevance. Thus for recommending a vendor, the suppliers to that vendor are ignored or lowly weighted compared to clients of that vendor. The engine then determines which clients are peers of or similar to the user's organization to create a set of second organizations. See also Davar at ¶ [0139]: Certain quantifiable attribute data may be aggregated and displayed as graphs and charts. For example, for each first organization the total number of connected second organizations in each sector or location could be tallied and displayed. This allows a viewer to make a meaningful interpretation of the huge number of relationships, even when some of the organizations are hidden. See also Davar at ¶ [0180]: A user employed by a first organization inputs data into the business database about relationships with second organizations. The system identifies and displays to the user profiles of people with current or previous work experience with the second organization. See also Davar at Figs. 6A-6B & Fig. 8 & Fig. 14.).
Moreover, Davar system for analyzing connections between a first enterprise and a second enterprise does not explicitly disclose, but Fama Roller in the analogous art for the system for analyzing connections between a first enterprise and a second enterprise does disclose the following:
- providing the connection graph to a pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) to identify connection patterns in the connection data between the first enterprise and the second enterprise (see at least Fama Roller: ¶ [0034] & ¶ [0076]. Fama Roller notes that if the second user searches for a closest connection to the target, the distributed graph 225 may indicate the connection strength of the first user to the target and the connection strength of the second user to the first user. In some organizations (e.g., large organizations with hundreds or thousands of users), this two vectors of closeness may further modify the connection strength values. For example, even if the first user has the strongest connection with the target, the first user and second user may share limited or no communications or activities. In these cases, a third user (e.g., a user with a strong connection with both the second user and the target) may have a stronger two vector closeness than the first user, despite having a slightly lower connection strength with the target. See also Fama Roller at ¶ [0076]: For each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.), wherein the pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) is trained using a first training dataset which includes labeled connection graph data (see at least Fama Roller: Figs. 2-3 & ¶ [0046] & ¶ [0069-0071]. Fama Roller teaches that the feature engineering module 530 may determine additional symbols, words, or phrases not included in the labeled training data 525 that relate to symbols, words, or phrases that are included in this training data set. The feature engineering module 530 may train a model to search for these additional symbols, words, or phrases, despite not being included in the training data. See also Fama Roller at ¶ [0046]: The tenants may share connection information, node information, or some combination of these to expand the dataset for generating the distributed graph 225. See also Fama Roller at ¶ [0064]: In order to train the model, the NLP server 505 may process sets of historical data or training data, such as communication messages 510-a. See also Fama Roller at ¶ [0069]: Labeled communication messages 510 may be stored in a labeled training data 525 database or disk. This labeled training data 525 may be used by a feature engineering module 530 to generate a model for the binary classification process.) and the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.)
- generating recommended actions using an action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) based on the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.)
and at least one of a first enterprise context and a second enterprise context (see at least Fama Roller: ¶ [0014-0016] & ¶ [0028] & ¶ [0037]. Fama Roller teaches that for example, in a sales scenario, the users may be individual salespeople working to generate profits for a company (e.g., the organization or entity). These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. See also Fama Roller at ¶ [0016]: Having users with strong connections contact targets, as opposed to users with no communication history with the targets, may greatly increase the chances of a successful communication (e.g., receiving a reply in response, scheduling a meeting, making a sale, etc.). See also Fama Roller at ¶ [0028]: For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. See also Fama Roller at ¶ [0037]: The connection strengths (e.g., with any targets) may be modified based on the user-specific statistics. In one case, if a user has a high success rate on sales, the closest connections service may increase the connection strengths for that user.), wherein the action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0046] & ¶ [0072] & Fig. 5. Fama Roller teaches that the model training tool 535 may use this information to update the NLP and binary classification models. The NLP server 505 may send these updated models to a model evaluation process 540, which may test the updated models against a set of test messages. The model evaluation process 540 may further refine the models based on running one or more tests, and may return these updated models to the feature engineering module 530. In some cases, the model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.);
- providing the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) for display to a user via a graphical user interface (GUI) screen (see at least Fama Roller: Figs. 4A-4C.);
- updating the first training dataset and the second training dataset by a training mechanism (see at least Fama Roller: Fig. 5 & ¶ [0060-0061] & ¶ [0064] & ¶ [0072]. Fama Roller notes that the database server or user device may utilize a machine-learned model to generate the plaintext rationale on a user-by-user basis. In other cases, the brief connection rationale 425 may be boilerplate language based on the ranking of the connected user 410. For example, users with “high” connectivity to the target 405 may display the text “Higher volume of meetings and email exchange” as the connection rationale 425. In some cases, the brief connection rationale 425 for each connected user 410 may include a “view more rationale” button. See also Fama Roller at ¶ [0064]: Once the NLP server 505 determines a model, the NLP server 505 may utilize the model to analyze communication messages 510 in realtime or pseudo-realtime, and may or may not continue to update the model based on the incoming messages and user feedback. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.) as new connection data is received from the plurality of data sources (see at least Fama Roller: ¶ [0025] & ¶ [0038] & ¶ [0082] & ¶ [0097]. Fama Roller teaches that the target may be either a cloud client 105 or a contact 110 (e.g., a new contact 110 that the user has not previously interacted with). The user may send a request message to the cloud platform 115 running the closest connections service indicating a target and, in some cases, a number of connected users. The closest connections service may utilize the distributed graph to calculate closeness scores between the users within the organization and the target. See also Fama Roller at ¶ [0038]: These may include emails, calendar events, text messages, phone calls, personal address books, social media chats or messages, documents, files (e.g., Quip files), marketing campaigns, web forms, or any manually logged information, such as emails, events, or activities. See also Fama Roller at ¶ [0082] & Fig. 6: At 645, the database server 605 may receive a new communication message associated with the first and second user identifiers. The new communication message may be part of a realtime or pseudo-realtime communication stream. At 650, the database server 605 may extract metadata from the new communication message, analyze the new metadata, and update the distributed graph based on the new information. See also Fama Roller at ¶ [0097] & Fig. 8: Graph updating component 860 may receive a new communication message associated with the first user identifier and the second user identifier, extract new metadata from the new communication message, and update the distributed graph representation based on the extracted new metadata.), the training mechanism (see at least Fama Roller: ¶ [0060-0061] & ¶ [0069-0071] & Fig. 5.) further providing ongoing training of the pattern identification model and the action identification model (see at least Fama Roller: Figs. 5-6 & Fig. 11 & ¶ [0072-0076]. Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages. See also Fama Roller at Fig. 11 noting “updating the distributed graph representation based on the extracted new metadata.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar system for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: providing the connection graph to a pattern identification model to identify connection patterns in the connection data between the first enterprise and the second enterprise, wherein the pattern identification model is trained using a first training dataset which includes labeled connection graph data and the connection patterns & generating recommended actions using an action identification model based on the connection patterns and at least one of a first enterprise context and a second enterprise context, wherein the action identification model is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions & providing the recommended actions for a display to a user via a graphical user interface (GUI) screen & updating the first training dataset and the second training dataset by a training mechanism as new connection data is received from the plurality of data sources, the training mechanism further providing ongoing training of the pattern identification model and the action identification model, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Independent Claim 9, Davar method for analyzing connections between a first enterprise and a second enterprise teaches the following:
- automatically retrieving connection data related to connections between the first enterprise and the second enterprise (see at least Davar: ¶ [0008-0010]. Davar teaches that the method comprises using a computer processor, selecting, from a business database recording business relationships between organizations, first organizations that are connected by a business relationship to one or more second organizations. See also Davar at ¶ [0010]: Davar notes that the instructions when executed provide: a data access agent for retrieving first organizations and second organizations that are connected by a business relationship; a social network agent for retrieving a set of social contacts of a user, and associating each first organization with social contacts that have an employment connection with second organizations having a business relationship with that first organization.) from a plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11.), wherein at least one of the plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11. Davar teaches that FIG. 11 is a diagram of a survey interface and the datasources for generating it. The system's data input agent provides one or more ways to input a business relationship to the database, such as a website form, receiving a data file, an API callable by third-party software, and a web crawler. Preferably the relationship is input by a user working on behalf of one of the organizations (asserting organization) and comprises details about the relationship and the other organization (unverified organization.) includes an automated agent (see at least Davar: ¶ [0059] & ¶ [0104]. Davar teaches that the database and methods will be accessed by a computer bot for displaying a relevant organization to an identified organization. For example, an ad-tech bot may determine using cookies or viewed content that a user is interested in a particular industry. The ad-tech bot could display one or more organizations or their ads to the user, depending on what organization is most relevant to the user's organization. See also Davar at ¶ [0104]: The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees.) that retrieves the connection data for professional networking connections (see at least Davar: ¶ [0104] & ¶ [0145]. Davar teaches that this assumption is truer for social contacts within professional networks and industry-specific media, particularly when several employees of one organization are connected to several employees of another organization. The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees. The similarity agent may further determine the industries of the organizations. See also Davar at ¶ [0145]: The social contacts are preferably associated with another organization (preferably one of the first or second organizations), typically as employees, consultants, or advisors. These employment connections may be current or past and may be determined from professional social networks. The social contacts may also be Thought Leaders/Opinion Leaders/LinkedIn™ Influencers or persons well regarded by the user, but not necessarily as employees of third, first or second organizations.)
- utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, the connection graph being generated based on the connection data (see at least Davar: ¶ [0031] & ¶ [0118] & ¶ [0139]. Davar teaches that the graph may comprise a second type of edge (similarity edge), which records the degree to which one organization is similar to another. The similarity edge may be non-directional or bidirectional to indicate that two organizations are mutual peers or the peer edge may be unidirectional to indicate that one organization is considered a peer of another organization but not vice versa, or at least not in the same way or degree. FIG. 6 illustrates a small portion of an example graph, focusing on the nodes and edges between a user's organization node (A) and vendor nodes (D, E, F) being sought. USER is associated with organization A. Solid arrows indicate relationship edges as the flow of services from a vendor node towards a client node. Dashed arrows indicate similarity edges from one organization towards an organization recorded as similar. Thus D, E and F are mutual peers and are, in this case, the target of the search. Nodes C and B are similar and both are similar to A. Node and edge values are shown separately in FIG. 6B. See also Davar at ¶ [0031]: Ranking the set of profiles may comprise identifying a set of second organizations having a business relationship with the first organization and determining which profiles' professional experience data include past or current work experience with any of the second organizations. See also Davar at ¶ [0118]: The engine then finds all relationships in the database for each vendor to create a set of clients. The engine may use the relationship direction attribute stored in the database to ignore connected organizations that are not actually clients to each vendor or use this knowledge to weight their relevance. Thus for recommending a vendor, the suppliers to that vendor are ignored or lowly weighted compared to clients of that vendor. The engine then determines which clients are peers of or similar to the user's organization to create a set of second organizations. See also Davar at ¶ [0139]: Certain quantifiable attribute data may be aggregated and displayed as graphs and charts. For example, for each first organization the total number of connected second organizations in each sector or location could be tallied and displayed. This allows a viewer to make a meaningful interpretation of the huge number of relationships, even when some of the organizations are hidden. See also Davar at ¶ [0180]: A user employed by a first organization inputs data into the business database about relationships with second organizations. The system identifies and displays to the user profiles of people with current or previous work experience with the second organization. See also Davar at Figs. 6A-6B & Fig. 8 & Fig. 14.);
Moreover, Davar method for analyzing connections between a first enterprise and a second enterprise does not explicitly disclose, but Fama Roller in the analogous art for the method for analyzing connections between a first enterprise and a second enterprise does disclose the following:
- providing the connection graph to a pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) to identify connection patterns in the connection data between the first enterprise and the second enterprise (see at least Fama Roller: ¶ [0034] & ¶ [0076]. Fama Roller notes that if the second user searches for a closest connection to the target, the distributed graph 225 may indicate the connection strength of the first user to the target and the connection strength of the second user to the first user. In some organizations (e.g., large organizations with hundreds or thousands of users), this two vectors of closeness may further modify the connection strength values. For example, even if the first user has the strongest connection with the target, the first user and second user may share limited or no communications or activities. In these cases, a third user (e.g., a user with a strong connection with both the second user and the target) may have a stronger two vector closeness than the first user, despite having a slightly lower connection strength with the target. See also Fama Roller at ¶ [0076]: For each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.), wherein the pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) is trained using a first training dataset which includes labeled connection graph data (see at least Fama Roller: Figs. 2-3 & ¶ [0046] & ¶ [0069-0071]. Fama Roller teaches that the feature engineering module 530 may determine additional symbols, words, or phrases not included in the labeled training data 525 that relate to symbols, words, or phrases that are included in this training data set. The feature engineering module 530 may train a model to search for these additional symbols, words, or phrases, despite not being included in the training data. See also Fama Roller at ¶ [0046]: The tenants may share connection information, node information, or some combination of these to expand the dataset for generating the distributed graph 225. See also Fama Roller at ¶ [0064]: In order to train the model, the NLP server 505 may process sets of historical data or training data, such as communication messages 510-a. See also Fama Roller at ¶ [0069]: Labeled communication messages 510 may be stored in a labeled training data 525 database or disk. This labeled training data 525 may be used by a feature engineering module 530 to generate a model for the binary classification process.) and the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.)
- generating recommended actions using an action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) based on the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.) and at least one of a first enterprise context and a second enterprise context (see at least Fama Roller: ¶ [0014-0016] & ¶ [0028] & ¶ [0037]. Fama Roller teaches that for example, in a sales scenario, the users may be individual salespeople working to generate profits for a company (e.g., the organization or entity). These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. See also Fama Roller at ¶ [0016]: Having users with strong connections contact targets, as opposed to users with no communication history with the targets, may greatly increase the chances of a successful communication (e.g., receiving a reply in response, scheduling a meeting, making a sale, etc.). See also Fama Roller at ¶ [0028]: For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. See also Fama Roller at ¶ [0037]: The connection strengths (e.g., with any targets) may be modified based on the user-specific statistics. In one case, if a user has a high success rate on sales, the closest connections service may increase the connection strengths for that user.), wherein the action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0046] & ¶ [0072] & Fig. 5. Fama Roller teaches that the model training tool 535 may use this information to update the NLP and binary classification models. The NLP server 505 may send these updated models to a model evaluation process 540, which may test the updated models against a set of test messages. The model evaluation process 540 may further refine the models based on running one or more tests, and may return these updated models to the feature engineering module 530. In some cases, the model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.);
- providing the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) for display to a user via a graphical user interface (GUI) screen (see at least Fama Roller: Figs. 4A-4C.);
- updating the first training dataset and the second training dataset by a training mechanism (see at least Fama Roller: Fig. 5 & ¶ [0060-0061] & ¶ [0064] & ¶ [0072]. Fama Roller notes that the database server or user device may utilize a machine-learned model to generate the plaintext rationale on a user-by-user basis. In other cases, the brief connection rationale 425 may be boilerplate language based on the ranking of the connected user 410. For example, users with “high” connectivity to the target 405 may display the text “Higher volume of meetings and email exchange” as the connection rationale 425. In some cases, the brief connection rationale 425 for each connected user 410 may include a “view more rationale” button. See also Fama Roller at ¶ [0064]: Once the NLP server 505 determines a model, the NLP server 505 may utilize the model to analyze communication messages 510 in realtime or pseudo-realtime, and may or may not continue to update the model based on the incoming messages and user feedback. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.) as new connection data is received from the plurality of data sources (see at least Fama Roller: ¶ [0025] & ¶ [0038] & ¶ [0082] & ¶ [0097]. Fama Roller teaches that the target may be either a cloud client 105 or a contact 110 (e.g., a new contact 110 that the user has not previously interacted with). The user may send a request message to the cloud platform 115 running the closest connections service indicating a target and, in some cases, a number of connected users. The closest connections service may utilize the distributed graph to calculate closeness scores between the users within the organization and the target. See also Fama Roller at ¶ [0038]: These may include emails, calendar events, text messages, phone calls, personal address books, social media chats or messages, documents, files (e.g., Quip files), marketing campaigns, web forms, or any manually logged information, such as emails, events, or activities. See also Fama Roller at ¶ [0082] & Fig. 6: At 645, the database server 605 may receive a new communication message associated with the first and second user identifiers. The new communication message may be part of a realtime or pseudo-realtime communication stream. At 650, the database server 605 may extract metadata from the new communication message, analyze the new metadata, and update the distributed graph based on the new information. See also Fama Roller at ¶ [0097] & Fig. 8: Graph updating component 860 may receive a new communication message associated with the first user identifier and the second user identifier, extract new metadata from the new communication message, and update the distributed graph representation based on the extracted new metadata.), the training mechanism (see at least Fama Roller: ¶ [0060-0061] & ¶ [0069-0071] & Fig. 5.) further providing ongoing training of the pattern identification model and the action identification model (see at least Fama Roller: Figs. 5-6 & Fig. 11 & ¶ [0072-0076]. Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages. See also Fama Roller at Fig. 11 noting “updating the distributed graph representation based on the extracted new metadata.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: providing the connection graph to a pattern identification model to identify connection patterns in the connection data between the first enterprise and the second enterprise, wherein the pattern identification model is trained using a first training dataset which includes labeled connection graph data and the connection patterns & generating recommended actions using an action identification model based on the connection patterns and at least one of a first enterprise context and a second enterprise context, wherein the action identification model is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions & providing the recommended actions for a display to a user via a graphical user interface (GUI) screen & updating the first training dataset and the second training dataset by a training mechanism as new connection data is received from the plurality of data sources, the training mechanism further providing ongoing training of the pattern identification model and the action identification model, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Independent Claim 17, Davar non-transitory computer readable medium for analyzing connections between a first enterprise and a second enterprise teaches the following:
- automatically aggregating connection data (see at least Davar: ¶ [0134] & ¶ [0139-0141] & ¶ [0195-0196].) from a plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11.), the connection data related to connections between the first enterprise and the second enterprise (see at least Davar: ¶ [0008-0010]. Davar teaches that the method comprises using a computer processor, selecting, from a business database recording business relationships between organizations, first organizations that are connected by a business relationship to one or more second organizations. See also Davar at ¶ [0010]: Davar notes that the instructions when executed provide: a data access agent for retrieving first organizations and second organizations that are connected by a business relationship; a social network agent for retrieving a set of social contacts of a user, and associating each first organization with social contacts that have an employment connection with second organizations having a business relationship with that first organization.), wherein at least one of the plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11. Davar teaches that FIG. 11 is a diagram of a survey interface and the datasources for generating it. The system's data input agent provides one or more ways to input a business relationship to the database, such as a website form, receiving a data file, an API callable by third-party software, and a web crawler. Preferably the relationship is input by a user working on behalf of one of the organizations (asserting organization) and comprises details about the relationship and the other organization (unverified organization.) includes an automated agent (see at least Davar: ¶ [0059] & ¶ [0104]. Davar teaches that the database and methods will be accessed by a computer bot for displaying a relevant organization to an identified organization. For example, an ad-tech bot may determine using cookies or viewed content that a user is interested in a particular industry. The ad-tech bot could display one or more organizations or their ads to the user, depending on what organization is most relevant to the user's organization. See also Davar at ¶ [0104]: The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees.) that retrieves the connection data for professional networking connections (see at least Davar: ¶ [0104] & ¶ [0145]. Davar teaches that this assumption is truer for social contacts within professional networks and industry-specific media, particularly when several employees of one organization are connected to several employees of another organization. The similarity agent employs bots to determine a connectedness score between organizations based on social inter-connectivity of their employees. The similarity agent may further determine the industries of the organizations. See also Davar at ¶ [0145]: The social contacts are preferably associated with another organization (preferably one of the first or second organizations), typically as employees, consultants, or advisors. These employment connections may be current or past and may be determined from professional social networks. The social contacts may also be Thought Leaders/Opinion Leaders/LinkedIn™ Influencers or persons well regarded by the user, but not necessarily as employees of third, first or second organizations.)
- utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, the connection graph being generated based on the connection data (see at least Davar: ¶ [0031] & ¶ [0118] & ¶ [0139]. Davar teaches that the graph may comprise a second type of edge (similarity edge), which records the degree to which one organization is similar to another. The similarity edge may be non-directional or bidirectional to indicate that two organizations are mutual peers or the peer edge may be unidirectional to indicate that one organization is considered a peer of another organization but not vice versa, or at least not in the same way or degree. FIG. 6 illustrates a small portion of an example graph, focusing on the nodes and edges between a user's organization node (A) and vendor nodes (D, E, F) being sought. USER is associated with organization A. Solid arrows indicate relationship edges as the flow of services from a vendor node towards a client node. Dashed arrows indicate similarity edges from one organization towards an organization recorded as similar. Thus D, E and F are mutual peers and are, in this case, the target of the search. Nodes C and B are similar and both are similar to A. Node and edge values are shown separately in FIG. 6B. See also Davar at ¶ [0031]: Ranking the set of profiles may comprise identifying a set of second organizations having a business relationship with the first organization and determining which profiles' professional experience data include past or current work experience with any of the second organizations. See also Davar at ¶ [0118]: The engine then finds all relationships in the database for each vendor to create a set of clients. The engine may use the relationship direction attribute stored in the database to ignore connected organizations that are not actually clients to each vendor or use this knowledge to weight their relevance. Thus for recommending a vendor, the suppliers to that vendor are ignored or lowly weighted compared to clients of that vendor. The engine then determines which clients are peers of or similar to the user's organization to create a set of second organizations. See also Davar at ¶ [0139]: Certain quantifiable attribute data may be aggregated and displayed as graphs and charts. For example, for each first organization the total number of connected second organizations in each sector or location could be tallied and displayed. This allows a viewer to make a meaningful interpretation of the huge number of relationships, even when some of the organizations are hidden. See also Davar at ¶ [0180]: A user employed by a first organization inputs data into the business database about relationships with second organizations. The system identifies and displays to the user profiles of people with current or previous work experience with the second organization. See also Davar at Figs. 6A-6B & Fig. 8 & Fig. 14.).
Moreover, Davar non-transitory computer readable medium for analyzing connections between a first enterprise and a second enterprise does not explicitly disclose, but Fama Roller in the analogous art for the non-transitory computer readable medium for analyzing connections between a first enterprise and a second enterprise does disclose the following:
- providing the connection graph to a pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) to identify connection patterns in the connection data between the first enterprise and the second enterprise (see at least Fama Roller: ¶ [0034] & ¶ [0076]. Fama Roller notes that if the second user searches for a closest connection to the target, the distributed graph 225 may indicate the connection strength of the first user to the target and the connection strength of the second user to the first user. In some organizations (e.g., large organizations with hundreds or thousands of users), this two vectors of closeness may further modify the connection strength values. For example, even if the first user has the strongest connection with the target, the first user and second user may share limited or no communications or activities. In these cases, a third user (e.g., a user with a strong connection with both the second user and the target) may have a stronger two vector closeness than the first user, despite having a slightly lower connection strength with the target. See also Fama Roller at ¶ [0076]: For each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.), wherein the pattern identification model (see at least Fama Roller: Figs. 2-3 & ¶ [0061] & ¶ [0076]. Fama Roller notes that the database server or user device may implement a machine learned model to determine information specific to the connected user 410-a, and may display some or all of this information in the expanded connection rationale 430. See also Fama Roller at ¶ [0076]: Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at Fig. 2 noting “distributed graph 225” and Fig. 3.) is trained using a first training dataset which includes labeled connection graph data (see at least Fama Roller: Figs. 2-3 & ¶ [0046] & ¶ [0069-0071]. Fama Roller teaches that the feature engineering module 530 may determine additional symbols, words, or phrases not included in the labeled training data 525 that relate to symbols, words, or phrases that are included in this training data set. The feature engineering module 530 may train a model to search for these additional symbols, words, or phrases, despite not being included in the training data. See also Fama Roller at ¶ [0046]: The tenants may share connection information, node information, or some combination of these to expand the dataset for generating the distributed graph 225. See also Fama Roller at ¶ [0064]: In order to train the model, the NLP server 505 may process sets of historical data or training data, such as communication messages 510-a. See also Fama Roller at ¶ [0069]: Labeled communication messages 510 may be stored in a labeled training data 525 database or disk. This labeled training data 525 may be used by a feature engineering module 530 to generate a model for the binary classification process.) and the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.)
- generating recommended actions using an action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) based on the connection patterns (see also Fama Roller: ¶ [0076]: Fama Roller notes that for each communication message, the extracted metadata may include a timestamp associated with the message, user identifiers (e.g., the first user identifier, the second user identifier, or additional user identifiers) associated with the message, or businesses or organizations mentioned in the message. Additionally, or alternatively, the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier.) and at least one of a first enterprise context and a second enterprise context (see at least Fama Roller: ¶ [0014-0016] & ¶ [0028] & ¶ [0037]. Fama Roller teaches that for example, in a sales scenario, the users may be individual salespeople working to generate profits for a company (e.g., the organization or entity). These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. See also Fama Roller at ¶ [0016]: Having users with strong connections contact targets, as opposed to users with no communication history with the targets, may greatly increase the chances of a successful communication (e.g., receiving a reply in response, scheduling a meeting, making a sale, etc.). See also Fama Roller at ¶ [0028]: For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. See also Fama Roller at ¶ [0037]: The connection strengths (e.g., with any targets) may be modified based on the user-specific statistics. In one case, if a user has a high success rate on sales, the closest connections service may increase the connection strengths for that user.), wherein the action identification model (see at least Fama Roller: ¶ [abstract] & ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. See also Fama Roller at ¶ [abstract]: “The server may send a suggestion for the determined users to initiate communication with the target.” See also Fama Roller at ¶ [0048]: The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0046] & ¶ [0072] & Fig. 5. Fama Roller teaches that the model training tool 535 may use this information to update the NLP and binary classification models. The NLP server 505 may send these updated models to a model evaluation process 540, which may test the updated models against a set of test messages. The model evaluation process 540 may further refine the models based on running one or more tests, and may return these updated models to the feature engineering module 530. In some cases, the model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.);
- providing the recommended actions (see at least Fama Roller: ¶ [0029] & ¶ [0048]. Fama Roller notes that these communication messages 205 may be examples of documents associated with multiple users, or may identify actions or activities performed by or corresponding to multiple users. The communication messages 205, or information contained within the communication messages 205, may alternatively be referred to as activity data. The distributed graph 225 may determine a likely candidate within this target organization, and may provide the information of the candidate in the target organization for a user to contact (e.g., information corresponding to a suggested recipient). In some cases, the closest connections service may still determine an intra-organization user to initiate the contact (e.g., a suggested sender) along with the suggested recipient within the target organization.) for display to a user via a graphical user interface (GUI) screen (see at least Fama Roller: Figs. 4A-4C.);
- updating the first training dataset and the second training dataset by a training mechanism (see at least Fama Roller: Fig. 5 & ¶ [0060-0061] & ¶ [0064] & ¶ [0072]. Fama Roller notes that the database server or user device may utilize a machine-learned model to generate the plaintext rationale on a user-by-user basis. In other cases, the brief connection rationale 425 may be boilerplate language based on the ranking of the connected user 410. For example, users with “high” connectivity to the target 405 may display the text “Higher volume of meetings and email exchange” as the connection rationale 425. In some cases, the brief connection rationale 425 for each connected user 410 may include a “view more rationale” button. See also Fama Roller at ¶ [0064]: Once the NLP server 505 determines a model, the NLP server 505 may utilize the model to analyze communication messages 510 in realtime or pseudo-realtime, and may or may not continue to update the model based on the incoming messages and user feedback. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages.) as new connection data is received from the plurality of data sources (see at least Fama Roller: ¶ [0025] & ¶ [0038] & ¶ [0082] & ¶ [0097]. Fama Roller teaches that the target may be either a cloud client 105 or a contact 110 (e.g., a new contact 110 that the user has not previously interacted with). The user may send a request message to the cloud platform 115 running the closest connections service indicating a target and, in some cases, a number of connected users. The closest connections service may utilize the distributed graph to calculate closeness scores between the users within the organization and the target. See also Fama Roller at ¶ [0038]: These may include emails, calendar events, text messages, phone calls, personal address books, social media chats or messages, documents, files (e.g., Quip files), marketing campaigns, web forms, or any manually logged information, such as emails, events, or activities. See also Fama Roller at ¶ [0082] & Fig. 6: At 645, the database server 605 may receive a new communication message associated with the first and second user identifiers. The new communication message may be part of a realtime or pseudo-realtime communication stream. At 650, the database server 605 may extract metadata from the new communication message, analyze the new metadata, and update the distributed graph based on the new information. See also Fama Roller at ¶ [0097] & Fig. 8: Graph updating component 860 may receive a new communication message associated with the first user identifier and the second user identifier, extract new metadata from the new communication message, and update the distributed graph representation based on the extracted new metadata.), the training mechanism (see at least Fama Roller: ¶ [0060-0061] & ¶ [0069-0071] & Fig. 5.) further providing ongoing training of the pattern identification model and the action identification model (see at least Fama Roller: Figs. 5-6 & Fig. 11 & ¶ [0072-0076]. Fama Roller teaches that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. See also Fama Roller at ¶ [0072]: The model evaluation process 540 may further include receiving user feedback, and updating the models based on the feedback. The updated models may be used on future messages sent to the feature engineering module 530—or, in some cases, the filtering/sampling process 515 or the labeling tool 520—to analyze the messages. See also Fama Roller at Fig. 11 noting “updating the distributed graph representation based on the extracted new metadata.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar non-transitory computer readable medium for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: providing the connection graph to a pattern identification model to identify connection patterns in the connection data between the first enterprise and the second enterprise, wherein the pattern identification model is trained using a first training dataset which includes labeled connection graph data and the connection patterns & generating recommended actions using an action identification model based on the connection patterns and at least one of a first enterprise context and a second enterprise context, wherein the action identification model is trained using a second training dataset that includes labeled data created for training models for generating the recommended actions & providing the recommended actions for a display to a user via a graphical user interface (GUI) screen & updating the first training dataset and the second training dataset by a training mechanism as new connection data is received from the plurality of data sources, the training mechanism further providing ongoing training of the pattern identification model and the action identification model, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 2, Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 1 above, and Davar further teaches the system for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the plurality of data sources (see at least Davar: ¶ [0049] & ¶ [0088-0089] & Fig. 11.) includes at least one of: a communications data source, a social network data source, a professional network data source, a calendar data source and an organizational graph data source (see at least Davar: ¶ [0088-0090] & ¶ [0104] & ¶ [0163].)
Regarding Dependent Claim 3, Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 1 above, and Fama Roller further teaches the system for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the identifying the connection patterns includes identifying changes in communication patterns over time (see at least Fama Roller: ¶ [0016] & ¶ [0033-0035] & ¶ [0076]. Fama Roller notes that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. If a communication message 205 or activity between the user and the target includes one or more other users, the relationship between the user and the target may not be as strong as if the user and the target often interact (e.g., send messages, meet, etc.) without any other users involved.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: wherein the identifying the connection patterns includes identifying changes in communication patterns over time, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 4, Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 1 above, and Fama Roller further teaches the system for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the identifying the connection patterns (see at least Fama Roller: ¶ [0033-0035] & ¶ [0076].) includes identifying at least one of weak connection areas between the first enterprise and the second enterprise and strong connection areas between the first enterprise and the second enterprise (see at least Fama Roller: ¶ [0025] & ¶ [0033-0035] & ¶ [0055]. Fama Roller notes that even if the first user has the strongest connection with the target, the first user and second user may share limited or no communications or activities. In these cases, a third user (e.g., a user with a strong connection with both the second user and the target) may have a stronger two vector closeness than the first user, despite having a slightly lower connection strength with the target. The closest connections service may weight messages and activities based on the recency of the corresponding timestamps. For example, an email between a user and a target sent one year ago may result in a lower connection strength than an email sent one week ago. Accordingly a smaller volume of messages sent more recently may result in a stronger connection strength than a larger volume of messages sent a long time ago. See also Fama Roller at ¶ [0016]: The closest connections service may utilize this distributed graph to provide a user of an organization with insights into which user or users within the organization have the strongest connections to a target. For example, a user may send a request message to the closest connections service to retrieve information about users connected to a specified target. See also Fama Roller at ¶ [0055]: The closeness scores may be normalized to values between 0 and 10, where 10 represents the strongest connection and 0 represents no connection.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: wherein the identifying the connection patterns includes identifying at least one of weak connection areas between the first enterprise and the second enterprise and strong connection areas between the first enterprise and the second enterprise, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 5, Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 1 above, and Fama Roller further teaches the system for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the identifying the connection patterns includes measuring a value for a measurement parameter (see at least Fama Roller: ¶ [0032] & ¶ [0037]. Fama Roller notes that the connection strength may be based on closeness, recency, calendar activity, email activity, additional activities, communication content, communication style, influencer measure, power measure, or any combination of these or other parameters associated with user activity. See also Fama Roller at ¶ [0037]: The system 200 may determine influencer or power measures associated with a user or user identifier. For example, the system 200 may calculate or track statistics associated with each user. The connection strengths (e.g., with any targets) may be modified based on the user-specific statistics. In one case, if a user has a high success rate on sales, the closest connections service may increase the connection strengths for that user.) that represents a strength of the connections between the first enterprise and the second enterprise (see at least Fama Roller: ¶ [0032-0034] & ¶ [0054] & ¶ [0076]. Fama Roller notes that the second user searches for a closest connection to the target, the distributed graph 225 may indicate the connection strength of the first user to the target and the connection strength of the second user to the first user. In some organizations (e.g., large organizations with hundreds or thousands of users), this two vectors of closeness may further modify the connection strength values.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: wherein the identifying the connection patterns includes measuring a value for a measurement parameter that represents a strength of the connections between the first enterprise and the second enterprise, and in view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 6, Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 1 above, and Davar further teaches the system for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the aggregating the connection data (see also Davar: ¶ [0134-0136] & ¶ [0139-0141].).
However, Davar method for analyzing connections between a first enterprise and a second enterprise does not explicitly disclose, but Fama Roller in the analogous art for the method for analyzing connections between a first enterprise and a second enterprise does disclose the following:
- categorizing the plurality of first users into a plurality of inside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.);
- categorizing the plurality of second users into a plurality of outside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: categorizing the plurality of first users into a plurality of inside groups and categorizing the plurality of second users into a plurality of outside groups, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 10, Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 9 above, and Davar further teaches the method for analyzing connections between a first enterprise and a second enterprise comprising:
- further comprising aggregating the connection data (see also Davar: ¶ [0134-0136] & ¶ [0139-0141].) from the plurality of data sources (see also Davar: ¶ [0049] & ¶ [0088] & Fig. 11.) for use in generating the connection graph (see at least Davar: Fig. 3 & Figs. 6A-6B & ¶ [0076-0079]. Davar teaches that the graph may comprise a second type of edge (similarity edge), which records the degree to which one organization is similar to another. The similarity edge may be non-directional or bidirectional to indicate that two organizations are mutual peers or the peer edge may be unidirectional to indicate that one organization is considered a peer of another organization but not vice versa, or at least not in the same way or degree. See also Davar at Fig. 14.).
Regarding Dependent Claim 11, Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Claims 9-10 above, and Davar further teaches the method for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the aggregating the connection data (see also Davar: ¶ [0134-0136] & ¶ [0139-0141].) includes:
- identifying a plurality of first users in the first enterprise (see also Davar: ¶ [0011-0012] & ¶ [0030-0032]. Davar teaches that identifying referees for a first organization comprising: identifying a business segment of the first organization; identifying business segment keywords for the identified business segment; identifying a set of professional profiles and retrieving their professional experience data. Also note identify a set of referees to the business segment of the organization. See also Davar at ¶ [0064-0067]. See also Davar at ¶ [0090]: “Preferably, a plurality of employees of an organization log in using their social network accounts to identify a plurality of referees for each relationship.” See also Davar at ¶ [0094]: “Identification data that enable the system to identify the organization. Identification data includes data such as legal name, parent company name, CEO's name, office address, IP address, logos, brand names, or company registration number.”)
- identifying a plurality of second users in the second enterprise (see also Davar: ¶ [0024] & [0030-0032]. Davar notes that the web content may further comprise identification data of second organizations that are marked as visible and identification data of second organizations that are marked as hidden if that second organization is an employer of one of the social contacts. Tanking the set of profiles may comprise identifying a set of second organizations having a business relationship with the first organization and determining which profiles' professional experience data include past or current work experience with any of the second organizations. Identifying the set of profiles may comprise identifying people in a social network recorded as Though Leaders or Opinion Leaders in an industry relevant to the business segment. See also Davar at ¶ [0046]: “FIG. 8 is a diagram of identified vendors and social connections.” See also Davar at ¶ [0064-0067]. See also Davar at ¶ [0090]: “Preferably, a plurality of employees of an organization log in using their social network accounts to identify a plurality of referees for each relationship.” See also Davar at ¶ [0094]: “Identification data that enable the system to identify the organization. Identification data includes data such as legal name, parent company name, CEO's name, office address, IP address, logos, brand names, or company registration number.”)
However, Davar method for analyzing connections between a first enterprise and a second enterprise does not explicitly disclose, but Fama Roller in the analogous art for the method for analyzing connections between a first enterprise and a second enterprise does disclose the following:
- categorizing the plurality of first users into a plurality of inside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.);
- categorizing the plurality of second users into a plurality of outside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: categorizing the plurality of first users into a plurality of inside groups and categorizing the plurality of second users into a plurality of outside groups, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claims 7 and 12, Davar / Fama Roller system / method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Claims 1, 6 and 9-11 above, and Fama Roller further teaches the system / method for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein identifying the connection patterns include identifying the connection patterns (see at least Fama Roller: ¶ [0016] & ¶ [0033-0035] & ¶ [0076]. Fama Roller notes that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. If a communication message 205 or activity between the user and the target includes one or more other users, the relationship between the user and the target may not be as strong as if the user and the target often interact (e.g., send messages, meet, etc.) without any other users involved.) between one or more of the plurality of inside groups and one or more of the plurality of outside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system / method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: wherein the identifying the connection patterns include identifying the connection patterns between one or more of the plurality of inside groups and one or more of the plurality of outside groups, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claims 8 and 13, Davar / Fama Roller system / method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Claims 1, 6 and 9-12 above, and Fama Roller further teaches the system / method for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the identifying the connection patterns (see at least Fama Roller: ¶ [0016] & ¶ [0033-0035] & ¶ [0076]. Fama Roller notes that the database server 605 may use the NLP to determine an intent of the message, a formality level of the message, an influence level of one or more users associated with the message, or any other metrics or patterns for the message that may help define the relationship between the first user identifier and the second user identifier. If a communication message 205 or activity between the user and the target includes one or more other users, the relationship between the user and the target may not be as strong as if the user and the target often interact (e.g., send messages, meet, etc.) without any other users involved.) further includes measuring a value for a measurement parameter (see at least Fama Roller: ¶ [0032] & ¶ [0037]. Fama Roller notes that the connection strength may be based on closeness, recency, calendar activity, email activity, additional activities, communication content, communication style, influencer measure, power measure, or any combination of these or other parameters associated with user activity. See also Fama Roller at ¶ [0037]: The system 200 may determine influencer or power measures associated with a user or user identifier. For example, the system 200 may calculate or track statistics associated with each user. The connection strengths (e.g., with any targets) may be modified based on the user-specific statistics. In one case, if a user has a high success rate on sales, the closest connections service may increase the connection strengths for that user.) that represents a strength of the connections (see at least Fama Roller: ¶ [0032-0034] & ¶ [0054] & ¶ [0076]. Fama Roller notes that the second user searches for a closest connection to the target, the distributed graph 225 may indicate the connection strength of the first user to the target and the connection strength of the second user to the first user. In some organizations (e.g., large organizations with hundreds or thousands of users), this two vectors of closeness may further modify the connection strength values.) between the one or more of the plurality of inside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.) and the one or more of the plurality of outside groups (see at least Fama Roller: ¶ [0028] & ¶ [0045] & ¶ [0078]. Fama Roller notes that users within a first organization may communicate with users outside of the organization. For example, in a sales scenario, a salesperson of a first company may interact via emails, voice calls, or texts with an outside client. In some cases, if one user in the organization desires to contact someone outside of the organization (e.g., referred to as a target user, or just a target), another user in the same organization may already have an established relationship (e.g., email exchanges, in-person meetings, etc.) with the desired target. Having the user with the previously established relationship contact the target—instead of having a user with no previous relationship cold-call the target—may increase the likelihood of a sale. Additionally, the strength of relationships between users and targets may be important in determining which user should initiate communication. See also Fama Roller at ¶ [0014]: These users may communicate and interact with other contacts—referred to as targets—who may be within the organization (and, accordingly, referred to as a user) or outside the organization. Each user and target may be differentiated based on a corresponding user identifier. See also Fama Roller at ¶ [0045]: The organization may use the information related to these internal activities to determine connection strengths between users internal to the organization and contacts or targets outside of the organization (e.g., inter-organization relationships). In some cases, the organization may additionally determine connection strengths between different users within the organization (e.g., intra-organization relationships). See also Fama Roller at ¶ [0078]: These additional operations may include inferring whether a contact is within or outside of a user organization, identifying emails to avoid creating nodes for (e.g., based on a non-human email address, a suspicious name or email address, a shared email account, etc.), condensing user to user edge communication to reduce the size of the distributed graph, or any combination of these or similar graph enhancement operations.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller system / method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: wherein the identifying the connection patterns further includes measuring a value for a measurement parameter that represents a strength of the connections between the one or more of the plurality of inside groups and the one or more of the plurality of outside groups, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Regarding Dependent Claim 14, Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Claims 9-13 above, and Fama Roller further teaches the method for analyzing connections between a first enterprise and a second enterprise comprising:
- further comprising the value for the measurement parameter for further display to the user (see at least Fama Roller: Fig. 4B & ¶ [0054-0055] & ¶ [0089]. Fama Roller notes that based on the algorithms and NLP analysis of the messages and activities, the distributed graph representation 300 may assign connection values to the connections 315. For example, connection 315-a may have a value of 7.1, connection 315-b a value of 3.5, connection 315-c a value of 4.0, connection 315-d a value of 5.2, and connection 315-e a value of 3.8. See also Fama Roller at ¶ [0079-0080]: “The weighted values may be based on when a communication occurred, whether the communication message represents a calendar invite or an email, how many users are associated with the communication, or any combination of these or other weighting criteria. Calculating the closeness score may involve aggregating the assigned weighted values for the connections between the first and second user identifiers. In some cases, calculating the closeness score may additionally involve normalizing the closeness score according to an organization size, a total number of communication messages in the organization, a transmission or reception rate of communication messages associated with the organization, or some combination of these normalizing factors.” See also Fama Roller at ¶ [0089]: “Transmission component 750 may transmit an indication of the closeness score.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: further comprising the value for the measurement parameter for further display to the user, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Moreover, even assuming arguendo that Fama Roller doesn’t teach or suggest: further comprising the value for the measurement parameter for further display to the user, however, Davar teaches or suggests: further comprising the value for the measurement parameter for further display to the user (see at least Davar: ¶ [0100] & ¶ [0153]. Davar notes that a similarity edge may comprise a value measuring the degree of similarity or relevance. Alternatively, or additionally, similarity edges may be recorded as either TRUE or FALSE. The similarity edge may comprise a text or code indicating the nature of the similarity (e.g. “small biotech peers”, “large banks”, “subsidiaries of XYZ Corp”). The nature of the similarity may be output to a user to indicate how organizations are similar. The Professional Relevance Score is a measure of the relevance of the social contact to the business segment of the organization being evaluated. A business segment may be the whole of an organization or its industry, specialism, product or service.)
Regarding Dependent Claim 15, Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise teaches the limitations of Independent Claim 9 above, and Fama Roller further teaches the method for analyzing connections between a first enterprise and a second enterprise comprising:
- wherein the first enterprise context includes at least one of an objective of the first enterprise, a first relevance of an individual or a group to the first enterprise, and a second relevance of a project or a product to the first enterprise (see at least Fama Roller: ¶ [0028] & ¶ [0096] & ¶ [0134]. Fama Roller notes that the relevancy component 855 may identify a relevancy value for each communication message of the set of communication messages based on the NLP analysis or a characteristic of the communication message, and, in some cases, may remove a communication message from the set of communication messages if an associated relevancy value is below a relevancy threshold. See also Fama Roller at [0039]: “The system 200 may implement a markoff process to sort through the communication messages 205 and identify the messages or activities with information relevant to the connections.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Davar / Fama Roller method for analyzing connections between a first enterprise and a second enterprise with the aforementioned teachings of: further comprising the value for the measurement parameter for further display to the user, and in further view of Fama Roller, whereby two or more tenants may share information across graphs in order to improve the available communication information. The tenants may not share intra-organization information or specific communication messages or metadata. Instead, the tenants may share contact information (e.g., email signature information) to improve edge density and granularity of the graphs for the tenants involved. The service may determine which user should reply to certain communication messages, at what time a user should send a communication message, or any other inferences related to improving communication with a target (see at least Fama Roller: ¶ [0046-0047].)
Further, the claimed invention is merely a combination of old elements in a similar field for analyzing connections between a first enterprise and a second enterprise and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Fama Roller, the results of the combination were predictable.
Moreover, even assuming arguendo that Fama Roller doesn’t teach or suggest: wherein the first enterprise context includes at least one of an objective of the first enterprise, a first relevance of an individual or a group to the first enterprise, and a second relevance of a project or a product to the first enterprise, however, Davar teaches or suggests: wherein the first enterprise context includes at least one of an objective of the first enterprise, a first relevance of an individual or a group to the first enterprise, and a second relevance of a project or a product to the first enterprise (see at least Davar: ¶ [abstract] & ¶ [0016] & ¶ [0022]. Davar notes that the relevancy of the social contacts to the organization is calculated and web content is displayed showing the organizations and social contacts. A method may retrieve professional experience data from the social network for each referee and calculating the social score using a relevance of said professional experience data to the associated first organization or a business segment thereof. See also Davar at Figs. 12-13.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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