Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendments and remarks filed on June 26, 2025.
Claims 1, 19, and 20 have been amended.
Claim 8 has been canceled.
Claims 1-7 and 9-20 are currently pending and have been examined.
Applicant’s remarks and arguments are addressed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 and 9-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Eidelman et al. (US 2017/0308975 A1, hereinafter “Eidelman”) in view of Bonica (US 2015/0106170 A1) and Strausfeld et al., US 2013/0173354 A1.
Claim 1. Eidelman teaches: A system for analyzing organizational interconnectedness, the system comprising:
at least one processor configured to (see at least ¶ 8; see also ¶s 118 and 132):
scrape a plurality of sources on the Internet using a web crawler and an extraction bot to access first data associated with a plurality of policymakers, wherein the web crawler is configured to perform functions of finding, indexing, and fetching information from the plurality of sources on the Internet, and wherein the extraction bot is configured to perform processing on the information from the plurality of sources to generate the first data, the first data being associated with a plurality of policymakers (see at least ¶s 124-126 teaching scraping the internet with a web scraper comprising a web crawler and extraction bot; see further, e.g., ¶ 336 teaching scraping and ingesting data associated with policy positions of policymakers; see additionally ¶ 351);
generate, using a machine trained model, one or more first nodes within an issue graph model based at least in part on the first data, the one or more first nodes representing the plurality of policymakers, the machine trained model having been trained to identify relationships between policymakers and organizations using a training data set, the training data set including unstructured data and labels indicating types of relationships indicated in the unstructured data (see, e.g., ¶ 208 teaching generating nodes, i.e., relationships, between policymakers, such as the number of times that the related policymakers voted together, sponsored together, received donations from similar organizations, etc.; see also, e.g., ¶ 214 teaching substantially the same; see ¶ 207 teaching that these nodes can be generated with machine learning using a trained model and ¶ 112 teaching the training set of documents; see further, e.g., ¶ 279 teaching providing unstructured data for training and ¶ 295 teaching performing machine learning on connections between lawmakers; regarding unstructured data, see, e.g., ¶s 231-233 teaching parsing scraped data and replacing certain terms with normalized terms);
generate, using the machine trained model, a second node within the issue graph model representing an organization (see, e.g., ¶s 341-350 teaching analyzing the second node for the issue graph model, specifically representing an organization’s interest in the issue);
store the one or more first nodes and the second node in a graph database (see, e.g., ¶ 346 teaching a memory 2100 that stores data for performing the issue-based analysis of policymaker alignment with an organization posture);
receive, via a user interface, a selection of at least one agenda issue of interest to the organization (see, e.g., at least ¶ 347 teaching receipt of a selection of an agenda issue from a user-selectable list);
receive user data via the user interface, the user data indicating a likelihood of at least one of an influence, an agreement, or an interest of at least one of the plurality of policymakers relative to the selected agenda issue (see, e.g., at least ¶ 347 teaching receipt of a selection of an agenda issue from a user-selectable list, which then determines the corresponding policymaker interest in the selected agenda item even if that data is not initially available, i.e., the scraper will then go out and scrape that data);
generate, using the machine trained model, links within the issue graph model representing relationships between the first nodes and the second node, the relationships being identified based at least in part on the first data, the user data, and the selected agenda issue, wherein the links are associated with one or more labels indicating a type of the relationships between the first nodes and the second nodes (see, e.g., ¶ 357 teaching creating a visual graph where one axis measures relationships, specifically an “alignment rating,” between a user and a legislator, an effectiveness of the legislator, how much money the user has contributed to the legislator, or favorability of the legislator to multiple issues; regarding that this is done using the machine trained model, see ¶ 113, 207, 233, 245, 295, 301, 306, and 309);
receive, via the user interface, a selection of a calculated metric from a plurality of calculated metrics (see ¶ 328 and Figure 19C teaching a GUI with selectable features such as “effectiveness as a primary sponsor” and “categorized by policy type;” see further, e.g., at least ¶s 199-203 teaching calculated metrics such as number of pieces of legislation or rules with a selected sub area; see further ¶s 347-350 teaching that the calculated metrics are based on selected issues and selected actors; see additionally ¶ 356 teaching a GUI displaying “scores ranking the policymakers” based on “present[ing] to the user [] a list, where the user can select an ordering” as well as “where a user may select items and their associated weights”);
determine, using a graph algorithm associated with the selected calculated metric, a gravitas score based on the issue graph model (see ¶ 210 teaching calculating a gravitas score “based on the number of connections and the closeness of those connections within the network,” i.e., based on an issue graph model; see also ¶ 357 teaching a visual graph with one axis being an “effectiveness” of each legislator, i.e., gravitas score; see further ¶ 356 teaching a GUI where the user may select items to affect the weight of the score);
cause display of a graphical user interface including a network, the network including graphical representations of the first nodes, the second node, and the links, the graphical user interface further including a representation of the gravitas score and an identification of the selected calculated metric (see ¶ 210 teaching outputting a gravitas score; see further Figures 22C and 22D teaching a display of a weighted score for each legislator; see further ¶ 357 teaching providing a three-dimensional presentation, specifically “a visual graph” that “may be displayed to a user” with one axis constituting an effectiveness (i.e., gravitas) of the legislator, noting that in at least ¶s 352-356 the graphs are for a specific issue, which could be an issue selected by the user as one of interest and for which the invention may go out and scrape data as taught in ¶ 347 as well as that in 356 the weights and items can be selected by the user to constitute the selected metric);
receive, via the graphical user interface, a user input including at least: a selection of a line corresponding to a link of the plurality of links in the network, the line connecting the second node representing the organization with one of the first nodes representing a policymaker of the plurality of policymakers, and a modification to a label associated with the link represented by the line, the modification including a change in a type of relationship between the organization represented by the second node and the policymaker represented by the at least one of the first nodes (see ¶ 363 and Figure 22D teaching that a user can modify by input different weights to change alignment coordinates between a legislator and the organization, i.e., the sector or company of the user, noting that there are weight lines as shown in Figure 22D regarding different sector weights; see further ¶ 367 teaching substantially the same; see also ¶s 306 and 309 teaching that there can be a second and third calculation regarding a likelihood that a legislator will vote for a bill or a likelihood that a bill will pass, including based on things like “the network of the co-sponsor” or the “strength of the bill sponsor;” see further ¶s 416-417 teaching actions that change the first and second nodes, such as a policymaker receiving a large number of letters from constituents; regarding connections, see ¶ 208 teaching each node representing a policymaker and the connections being based on weights; Examiner notes that this is further addressed below); and
update the network and the graph database based on the user input to reflect the change in the type of relationship represented by the label (see ¶ 363 and Figure 22D teaching that a user can modify by input different weights to change alignment coordinates for a legislator; see also ¶s 306 and 309 teaching that there can be a second and third calculation regarding a likelihood that a legislator will vote for a bill or a likelihood that a bill will pass, including based on things like “the network of the co-sponsor” or the “strength of the bill sponsor,” noting that GUI 1640 includes the change in the likelihood of the legislator voting for the bill).
Regarding cause display of a network including graphical representations of the first nodes, the second node, and the links, the display further including a representation of the gravitas score, Examiner notes that Eidelman teaches this limitation as described above. Nevertheless, for the purpose of compact prosecution, even allowing for a narrower construction of this limitation where a graphical representation is of the links between the first node (legislature member) and the second node (an organization), such a feature is taught in analogous prior art. Bonica, for example, teaches such graphical representations of links between legislature members and outside organizations (see, e.g., at least Figure 9B and ¶s 85-92 teaching displaying links between legislatures and, e.g., groups that have donated to them). Examiner notes that Bonica also teaches training a machine learning model with unstructured data to perform its analysis of legislator viewpoint (see, e.g., at least ¶s 56-60). Bonica is similar to the instant application and Eidelman because it displays information to a user about a legislator including voting patterns, issues that are priority, and other ways of scoring the legislator.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of graphing links between a legislator and an organization as lines or links between the nodes (as disclosed by Bonica) to the known method and system of analyzing and scoring legislators (as disclosed by Eidelman). One of ordinary skill in the art would have been motivated to apply the known technique of graphing links between a legislator and an organization because it would allow a user to see which donors gave to that legislator as well as see what other legislators certain donors gave to (see Bonica ¶ 88).
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of graphing links between a legislator and an organization (as disclosed by Bonica) to the known method and system of analyzing and scoring legislators (as disclosed by Eidelman), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of graphing links between a legislator and an organization and allowing those links to be user-selectable to the known method and system of analyzing and scoring legislators, because predictably such graphs from Bonica can be included along with the rest of the data displayed by Eidelman without changing how either operates). See also MPEP § 2143(I)(D).
Regarding receive, via the graphical user interface, a user input including at least: a selection of a line corresponding to a link of the plurality of links in the network, the line connecting the second node representing the organization with one of the first nodes representing a policymaker of the plurality of policymakers, and a modification to a label associated with the link represented by the line, the modification including a change in a type of relationship between the organization represented by the second node and the policymaker represented by the at least one of the first nodes, Examiner additionally notes that this is addressed by Eidelman above (see, e.g., Figure 22D and ¶ 363) or by the combination of Eidelmen and Bonica (as Bonica teaches graphical representations of links, i.e., lines, between legislature members and outside organizations, see, e.g., at least Figure 9B and ¶s 85-92). Nevertheless, for the purpose of compact prosecution, to the extent that neither reference expressly states that the line itself is user-selectable (Bonica discusses “selecting the donor,” i.e., organization, in ¶ 88 that is connected to the policymaker in Figure 9B), Examiner asserts that analogous reference Strausfeld teaches this limitation. Specifically, Strausfeld teaches that trendlines of changes for a policymaker or group of policymakers can be hovered over and selected to obtain more information (see at least Strausfeld ¶s 79-81 and Figure 4M). Strausfeld is similar to the instant application, Eidelman, and Bonica because it relates to data analysis of political entities, in particular to providing an issue-based analysis and graphical visualizations of the entities’ political orientation on various issues (see Strausfeld ¶ 3).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of using the line itself as the user-selectable point for providing changes (as disclosed by Strausfeld) to the known method and system of analyzing and scoring legislators and their links to organizations (as disclosed by Eidelman and Bonica). One of ordinary skill in the art would have been motivated to apply the known technique of graphing links between a legislator and an organization because it would allow a user to see which donors gave to that legislator as well as see which other legislators those donors gave to (see Bonica ¶ 88).
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of using the line itself as the user-selectable point for providing changes (as disclosed by Strausfeld) to the known method and system of analyzing and scoring legislators and their links to organizations (as disclosed by Eidelman and Bonica), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by using the line itself as the user-selectable point for providing changes to the known method and system of analyzing and scoring legislators and their links to organizations, because predictably such data visualizations can be combined or modified as any operator sees fit when they have been demonstrated to provide utility in providing visualizations regarding policymaker positions). See also MPEP § 2143(I)(D).
Regarding Claims 19 and 20, these claims recite the same steps as Claim 1 above yet recite different statutory categories. The rejection of Claim 1 above is incorporated herein. Claim 19 recites a “computer-implemented method.” As noted above, Eidelman teaches that a computer implements the method steps of the invention (see at least ¶ 8; see also ¶s 118 and 132). Claim 20 recites a non-transitory computer readable medium with instructions that, when executed by the processor, cause the processor to perform the method steps. Eidelman teaches such a feature (see at least ¶ 28). Thus, with these additional teachings, the combination of Eidelman, Bonica, and Strausfeld teaches Claims 19 and 20.
Claim 2. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the displayed network is specific to the at least one selected agenda issue (see at least ¶ 352-357 teaching developing a network that demonstrates alignment between a user and a legislator specific to one “agenda issue”).
Claim 3. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the user data comprises an identity of at least one non-policymaker (see ¶ 352 teaching that a user profile of a non-policymaker may establish a position or posture on an issue to identify bills on which the non-policymaker user and the legislator may agree).
Claim 4. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 3. Eideleman further teaches: The system of claim 3, wherein the non-policymaker includes a user of an electronic system that has a position or posture on an issue (see ¶ 352).
Claim 5. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the user data comprises at least one activity performed by a non-policymaker (see ¶ 352 teaching that a user profile of a non-policymaker may establish a position or posture on an issue to identify bills on which the non-policymaker user and the legislator may agree).
Claim 6. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the calculated metric includes at least one of: an influence metric, an interest metric, an agreement metric, and an accessibility metric determined based on the issue graph network (see ¶ 210 teaching calculating a gravitas score “based on the number of connections and the closeness of those connections within the network,” i.e., based on influence; see also ¶ 357 teaching a visual graph with one axis being an “effectiveness” of each legislator, i.e., influence).
Claim 7. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 6. Eideleman further teaches: The system of claim 6, wherein the plurality of calculated metrics from which the calculated metric is selected includes the influence metric, the interest metric, the agreement metric, and the accessibility metric selected by the user (see ¶ 210 teaching calculating a gravitas score “based on the number of connections and the closeness of those connections within the network,” i.e., a metric based on influence; see also ¶ 357 teaching a visual graph with one axis being an “effectiveness” of each legislator, i.e., influence, noting that in ¶ 356 the user can select the particular items and weights that go into the calculated metric).
Claim 9. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one processor is further configured to parse and ingest data from a data source external to the system (see, e.g., ¶s 147 and 155; see also ¶ 336 teaching ingesting data from websites).
Claim 10. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 9. Eideleman further teaches: The system of claim 9, wherein the at least one processor is further configured to generate the issue graph model based on the ingested data (see ¶ 336 teaching ingesting data from websites and noting that the data are used to create the GUIs of Figures 19A-E, i.e., the issue graph models).
Claim 11. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one processor is further configured to generate the issue graph model to include at least one policymaker on at least one additional agenda issue not selected as being of interest to the organization (see ¶ 342 teaching outputting a relative alignment of legislators to the organization’s legislative posture; see also ¶ 347 teaching searching for policymaker data and scraping the Internet for more data if data is not originally available; see further Figure 19C teaching effectiveness of a selected policymaker on many different issues including issues not selected by the organization).
Claim 12. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one processor is further configured to determine the gravitas score based on a number of connections within the issue graph model (see ¶ 210 teaching calculating a gravitas score “based on the number of connections and the closeness of those connections within the network”).
Claim 13. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one processor is further configured to generate the gravitas score based on a closeness of connections within the issue graph model (see ¶ 210 teaching calculating a gravitas score “based on the number of connections and the closeness of those connections within the network”).
Claim 14. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one processor is further configured to calculate a weight for the one or more links within the issue graph model, the weight having a value indicating a relationship between nodes (see ¶ 208).
Claim 15. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 14. Eideleman further teaches: The system of claim 14, wherein the at least one processor is further configured to calculate the weights using a plurality of factors (see ¶ 208).
Claim 16. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 15. Eideleman further teaches: The system of claim 15, wherein the plurality of factors include one or more of: a number of times two or more policymakers have voted together, a number of times two or more policymakers have sponsored together, a number of times two or more policymakers have received donations from similar organizations, or whether two or more policymakers have attended the same school or schools (¶ 208).
Claim 17. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one selected agenda issue includes at least one of a legislative agenda issue or a regulator agenda issue (see ¶ 360).
Claim 18. The combination of Eidelman, Bonica, and Strausfeld teaches the limitations of Claim 1. Eideleman further teaches: The system of claim 1, wherein the at least one selected agenda issue is related to one or more government bodies (see, e.g., ¶ 160 teaching a selected agenda issue of “healthcare” for one or more governmental bodies).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant specifically addresses the following:
Claim Rejections - Prior Art:
Regarding the application of the prior art to the claims, Applicant argues that Eidelman and Bonica fail to teach the claims as amended (see Remarks pages 11-14). These arguments have been rendered moot because they relate to new claim language that is addressed by a new combination of references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Bloch, US 2018/0260928 A1.
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAN P MINCARELLI whose telephone number is (571)270-5909. The examiner can normally be reached on Monday through Friday, 8:00 AM to 4:30 PM Eastern Time.
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/JAN P MINCARELLI/Primary Examiner, Art Unit 3626