DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 have been reviewed and are under consideration by this office action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/14/2025 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
Claim Objections
Claims 1, 11, and 16 objected to because of the following informalities:
Claims 1, 11, and 16 recite analyzing, using one or more machine learning models, a history of parent office to decision making office mapping… Examiner notes that a decision making office (or any decision) is not introduced within the claim therefore the Examiner interprets “decision making office” to be “to a decision making office” which would include any office that makes decisions(i.e. any office).
Appropriate correction is required.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the claims are not directed towards a judicial exception as they solve problems that occur when techniques are used to implement supplemental processing of federal fund data.
Examiner respectfully disagrees. The steps of receiving data, augmenting data, mapping data, award linking operations are all concepts capable of being performed in the human mind. Further the claims are directed towards Certain methods of organizing human activity as described in greater detail below.
Applicant further contends the claims recite operation of a computerized system not well-understood, routine, or conventional.
Examiner respectfully disagrees. The additional elements of the claim are separated from the abstract ide as recited below and are determined to be
Applicant further contends the claimed approach amounts to significantly more than the judicial exception.
Examiner respectfully disagrees. The 101 Rejection below addresses each of the additional elements (computing system, search interface, etc.) and each of the additional elements performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Applicant contends that similar to Motio, Inc. v. BSP Software LLC providing an automated agent, that the judicial exception is integrated into a practical application.
Examiner respectfully disagrees. Examiner notes the independent claim only recite an interface prior to amendments which would be merely apply it on a general purpose computer. The amended claims now recite automapping via machine learning which is recited at a high level of generality and as such merely amounts to apply it on a general-purpose computer. See below for full analysis.
The 101 Rejection is updated and maintained below.
Response to Arguments - 35 USC § 103
Applicant’s arguments with respect to the 35 USC 103 rejections have been fully considered but are moot in view of the new line of rejections wherein the Kim prior art reference is introduced to teach the amended claims. As Roth is relied upon to teach the analyzing and mapping of data and missing data, while Kim is relied upon to teach a history of parent office to decision making office mapping.
The 103 rejection is updated and maintained below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1-20 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 11, and 16 recite a series of steps for determining credentials of service providers:
Regarding Claims 1, 11, and 16; (additional elements bolded)
A computer-implemented method, comprising:/ system, comprising:
at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform:/ A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
receiving, by a computing system from multiple data sources, federal spend data;
augmenting, by the computing system, the received federal spend data;
performing, by the computing system, one or more of contract mapping operations or opportunity mapping operations;
performing, by the computing system, one or more of award linking operations or opportunity linking operations;
analyzing, by the computing system using one or more machine learning models, a history of parent data office to decision making office mapping history, and metadata for missing data;
determining, by the computing system based on said analysis, a missing parent office to decision making office mapping for the missing data; and
providing, by the computing system, a search interface for searching among data resulting from one or more of said augmenting, said mapping operations, or said linking operations.
As drafted the claims are directed to receiving data (by a computing system, additional element), augmenting data, and award linking operations which under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion).
The claims further fall within the abstract idea grouping “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards determining receiving federal spend data, augmenting the data, and performing contract mapping. Examiner further points to the Applicant’s Specification to provide further evidence for this analysis. (See Specification, [pg. 4, lines 10-15]; the computerized approaches discussed herein can present federal spending data to a user such that federal spending actions are grouped together into awards, and the user is presented with a complete picture of the entire award process, including how and when money is actually obligated to be paid out to the winning vendor).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least a computer-implemented method, comprising:/ system, comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform:/ A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: receiving, by a computing system; providing, by the computing system; a computing system; analyzing, by the computing system using one or more machine learning models (recited at a high level of generality); a search interface. The additional elements are performing the steps that would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The remaining additional elements are rejected similarly and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the remaining additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235).
Regarding Claim(s) 2-3, 5-10, 12, 14-15, 17, and 19-20 the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Regarding Claim(s) 4, 13, and 18, the claim further recite the additional element(s) of machine learning (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Celhar et al. (US 20160071037 A1) in view of Roth et al. (US 20190361146 A1), and Kim et al (US 20170004426 A1).
Regarding Claim(s) 1, 11, and 16, Celhar teaches: A computer-implemented method, comprising: receiving, by a computing system from multiple data sources, federal spend data; (Celhar, [24]; the system may periodically scrape government websites or access government databases via application programming interfaces (APIs) and web crawling technology in order to retrieve documentation and information that directly relates to the procurement opportunity, such as procurement records (e.g., awards, solicitations, amendments, contracts, bid results, etc.) that are made publicly available by the government. The system may also retrieve documentation and information that is indirectly related to the procurement opportunity such as agency information (e.g., contacts, locations and agency names), as well as private company information (e.g., financial data, business units, etc.), such as for businesses, interested vendors, and pre-approved vendors). Examiner interprets the procurement data as federal spend data.
augmenting, by the computing system, the received federal spend data; (Celhar, [15]; A granting organization providing a contract or grant opportunity is referred to herein as the “customer.” Customers can include various federal and state agencies within a government, such as the Department of Sanitation, the Department of Defense, etc. In some embodiments, customers can also include local organizations, such as non-profits, educational institutions and/or other local government funded organizations and Celhar, [24-25]; The system may also retrieve documentation and information that is indirectly related to the procurement opportunity such as agency information (e.g., contacts, locations and agency names), as well as private company information (e.g., financial data, business units, etc.), such as for businesses, interested vendors, and pre-approved vendors… Once obtained by the system 100, the system assesses and categorizes each procurement opportunity. As will be described in additional detail herein, the procurement opportunity is assessed and categorized using information associated with the customer 110 from which it originated, the industry 115 to which it was assigned, but also based on various attributes of the information included in the opportunity itself. For example, the textual data in the procurement opportunity can be searched for keywords in order to determine characteristics of the opportunity and estimate the complexity of the opportunity. The system may score the procurement opportunity based on the analyzed characteristics of the opportunity and Celhar, [39]; Additional information known to the system, such as a link to a list of customer contacts 410 and links 412 to both open procurement opportunities and historical procurement opportunities are provided. In addition, the first window includes an indication of a number 414 of fulfilling organizations that have provided service to the customer and an organization score 416 that the system has calculated for the customer 402.).
performing, by the computing system, one or more of contract mapping operations or opportunity mapping operations; (Celhar, [43]; The second window 420 of the customer profile can include additional analytics summarizing the customer's position in the marketplace. For example, the second window 420 can include a customer contract analysis component 422, a customer spend analysis component 424 and a customer connections analysis component 426, including a customer contacts component 428. The customer contract analysis component 422 can include the data related to the opportunities associated with a particular customer. For example, as shown in FIG. 4, the customer has 1,600 open opportunities, including two types: 1,598 contracts and 2 grants). Examiner interprets the inclusion of customer contacts as a mapping operation.
performing, by the computing system, one or more of award linking operations or opportunity linking operations; and (Celhar, [17]; The system may also generate scores associated with the contracts and grants involved in the procurement activity and the corresponding awards given for the procurement activity. Awards refer to any agreements or contracts, as well as any payments or transactions pursuant to such agreements or contracts, between the fulfilling organization and the customers for opportunities won in the marketplace and Celhar, [33]; Similarly, the fulfilling organization scores are generated by the score generation module 214 and can be based on historical industry-specific data 230 and/or fulfilling organization data 224 including past opportunity bids, losses and awards. In some embodiments, registered users on the system can provide feedback for various customers on the system which can also be utilized to determine the customer score. For example, fulfilling organizations can provide feedback data regarding procurement opportunities awarded to those organizations. The feedback data can be assessed for keywords reflecting various sentiments, each of which can be utilized to weight the organization score.). Examiner notes the system of Celhar links the award information with further information such as agreements, payments, or other corresponding information.
providing, by the computing system, a search interface for searching among data resulting from one or more of said augmenting, said mapping operations, or said linking operations. (Celhar, [43-44]; The customer contacts component 428 includes the number of customer contacts for which the system maintains contact information (e.g., a name, email address, phone number). Each of the analytics provided in the second window can be representative of the customer as a whole, or may be filtered to reflect only those aspects of the customer that are pertinent to a search query of a fulfilling organization….For example, the second window includes tabs 430 that allow a user to quickly access the customer's historical spend analytics, the customer's organizational hierarchy describing what the customer's organization and parent/children organizations provide, the customer opportunities (current and past) provided in the marketplace, and the industry or industries profile(s) affiliated with that customer. The tabs 430 also include a search tab in which a user can enter specific search criteria to generate analytics associated with that customer that correspond to that search criteria, as described in the previous paragraph. Access to one or more of the features in the second window 420 may be on a paid (subscription or per-use) basis. For example, the lock on the “Spend” button indicates that a user must have a specific account with the system operator in order to access the customer spend data. and Celhar, [25]; Once obtained by the system 100, the system assesses and categorizes each procurement opportunity. As will be described in additional detail herein, the procurement opportunity is assessed and categorized using information associated with the customer 110 from which it originated, the industry 115 to which it was assigned, but also based on various attributes of the information included in the opportunity itself. For example, the textual data in the procurement opportunity can be searched for keywords in order to determine characteristics of the opportunity and estimate the complexity of the opportunity).
While Celhar teaches receiving data, augmenting data, mapping operations, and linking operations, Celhar does not appear to explicitly teach: analyzing, by the computing system using one or more machine learning models,… mapping and metadata; However, Celhar in view of the analogous art of Roth (i.e. data mapping) does teach the entirety of the limitation: Roth, [15]; a plurality of information storage sources, received information about a plurality of wells in a defined geographic area, the received information comprising well location data, fracking data, production test data, completion data, production data, and directional survey data; detecting, with the processor, gaps within the received information, each gap corresponding to a missing data point; generating, with the processor, a predicted data point corresponding to each missing data point using a mapping-set based machine learning technique; substituting, with the processor, the gaps with the corresponding predicted data points to yield quality-controlled received information and Roth, [129]; According to some embodiments of the present disclosure, mapping set-based machine learning is used to fill in missing data. Mapping set-based machine learning provides a method for using existing data to predict the value of the missing data. The mapping set-based machine learning method may be used, for example, to fill in missing well header data, engineering data, and frac chemical data and Roth, [154]; This data is required on all state and federal regulatory forms, so it is widely available in the public data. Even so, the data often lacks consistency as data entry errors, variability in naming conventions, and the existence of both parent and subsidiary companies can result in a variety of different names for the same operator. In step 1 of the process 300, the many different names that may be used for a single operator are consolidated under a standardized operator name. As just one example, the operator identifiers “Anadarko Austin Chal,” “Anadarko Austin Chalk Company,” “Anadarko E & P Co LP,” “Anadarko E & P Company Limited Prtnrship,” “Anadarko E & P Onshore LLC,” “Anadarko E&P Onshore,” “Anadarko Min Inc,” “Anadarko Minerals Incorporated,” “Anadarko Pet Corp,” and “Anadarko Petroleum Corporation” may all be standardized and consolidated as “Anadarko.”). Examiner interprets the received information used to predict gaps and missing data points as metadata.
determining, by the computing system based on said analysis, missing… mapping; (Roth, [15]; a plurality of information storage sources, received information about a plurality of wells in a defined geographic area, the received information comprising well location data, fracking data, production test data, completion data, production data, and directional survey data; detecting, with the processor, gaps within the received information, each gap corresponding to a missing data point; generating, with the processor, a predicted data point corresponding to each missing data point using a mapping-set based machine learning technique; substituting, with the processor, the gaps with the corresponding predicted data points to yield quality-controlled received information and Roth, [129]; According to some embodiments of the present disclosure, mapping set-based machine learning is used to fill in missing data. Mapping set-based machine learning provides a method for using existing data to predict the value of the missing data. The mapping set-based machine learning method may be used, for example, to fill in missing well header data, engineering data, and frac chemical data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Celhar including mapping operations and miscellaneous and unreported categories of opportunities with the teachings of Roth including the use of machine learning to analyze parent mapping history, metadata for missing data and determining mapping for missing data in order to improve predictions and ensure accuracy of data points. (Roth, [128]; the processor not only to make predictions based on available data, but also to utilize machine learning to improve future predictions based on a comparison of past predictions to actual results. In some embodiments, the machine learning may involve identifying one or more additional types of data or data points that should be taken into account to improve the accuracy of a prediction, or identifying one or more types of data or data points that were being used to make the prediction but that negatively affected the accuracy of the prediction, or adjusting the relative weight of one or more types of data or data points being used to make the prediction so as to improve the accuracy of the prediction).
While Celhar/Roth teaches analyzing using machine learning model, mapping, metadata, and mapping missing data points, neither appears to explicitly teach a history of parent to decision making office. However, Celhar/Roth in view of the analogous art of Kim (i.e. business data processing) does teach the entirety of the limitation: (Kim, [53]; the color in the main area 330 for indicating existence of main information and that outside of the main area 330 could be allowed to be displayed differently depending on information on hierarchy among property categories of histories or sub-organizations of the organization. For example, when histories in one or more main areas for indicating existence of main information are related to a task(s), colors outside of one or more main areas for indicating existence of main information are determined by industrial group where the organization belongs and colors in the one or more main areas for indicating existence of main information could be allowed to be determined by referring to property categories of the sub-organizations which performed the task(s)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Celhar/Roth including analyzing using machine learning model, mapping, metadata, and mapping missing data with the teachings of Kim including parent to decision making office history in order to provide a hierarchical representation of organizations with color coding to provide a representation of organizational structure. (Kim, [53]; the color in the main area 330 for indicating existence of main information and that outside of the main area 330 could be allowed to be displayed differently depending on information on hierarchy among property categories of histories or sub-organizations of the organization. For example, when histories in one or more main areas for indicating existence of main information are related to a task(s), colors outside of one or more main areas for indicating existence of main information are determined by industrial group where the organization belongs and colors in the one or more main areas for indicating existence of main information could be allowed to be determined by referring to property categories of the sub-organizations which performed the task(s).
Further regarding Claim(s) 11. Celhar teaches: A system, comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: (Celhar, [30]; The system 204 includes a non-transitory computer readable medium 208 on which computer readable instructions are encoded for performing an analysis of customer data, procurement opportunity data and fulfilling organization data. The computer readable medium 208 is coupled to one or more processors 206, which operate to execute the stored instructions in order to implement the functionality of each component within the system).
Further regarding Claim(s) 16. Celhar teaches: A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: (Celhar, [30]; The system 204 includes a non-transitory computer readable medium 208 on which computer readable instructions are encoded for performing an analysis of customer data, procurement opportunity data and fulfilling organization data. The computer readable medium 208 is coupled to one or more processors 206, which operate to execute the stored instructions in order to implement the functionality of each component within the system).
Regarding Claim(s) 2. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said augmenting includes performing mapping between the received federal spend data and independently compiled federal staff organizational data. (Celhar, [24-25]; The system may also retrieve documentation and information that is indirectly related to the procurement opportunity such as agency information (e.g., contacts, locations and agency names), as well as private company information (e.g., financial data, business units, etc.), such as for businesses, interested vendors, and pre-approved vendors… Once obtained by the system 100, the system assesses and categorizes each procurement opportunity. As will be described in additional detail herein, the procurement opportunity is assessed and categorized using information associated with the customer 110 from which it originated, the industry 115 to which it was assigned, but also based on various attributes of the information included in the opportunity itself. For example, the textual data in the procurement opportunity can be searched for keywords in order to determine characteristics of the opportunity and estimate the complexity of the opportunity. The system may score the procurement opportunity based on the analyzed characteristics of the opportunity and Celhar, [39]; Additional information known to the system, such as a link to a list of customer contacts 410 and links 412 to both open procurement opportunities and historical procurement opportunities are provided). Examiner interprets the contact information as the independently compiled federal staff organizational data. Examiner further notes the information is compiled by the system independent from the government data sources.
Regarding Claim(s) 3, 12, and 17; Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said performance of contract mapping operations includes mapping one or more of funding offices or awarding offices to respective federal offices. (Celhar, [40]; One or more of the elements within the first window 400 of the customer profile can be included in the calculation of the organization score 416, though the organization score can also be based solely on the awarded opportunities from that customer. The organization score 416 can be calculated based on weighted criteria using known analyzing techniques, such as a recency, frequency and monetary (RFM) analysis technique. Accordingly, the recency of the award given can be a primary factor in the score generated for that customer, which can allow opportunities that were offered by the customer in the previous 6 months to carry more weight in calculating the organization score than opportunities that were offered by the customer more than a year ago. Directly related to the recency is the frequency of procurement opportunities awarded. Customers providing many awards (e.g., 200 per year) may achieve higher scores than those providing few opportunities and subsequent awards in the marketplace (e.g., 2 per year). Examiner notes that the awards of the office are linked to the respective federal office. Examiner further notes that the awarding offices can be federal offices as indicated by the Applicant’s Specification (Specification, [pg. 6-7, lines 30-4]; The various data sources can include data sources which regard contract transactions, federal offices (e.g., awarding offices), and opportunity notices).
Regarding Claim(s) 4, 13, and 18; Celhar/Roth/Kim teaches the computer-implemented method of claim I, wherein said performance of contract mapping operations includes employing machine learning approaches to determine mappings for one or more missing offices. (Roth, Roth, [15]; a plurality of information storage sources, received information about a plurality of wells in a defined geographic area, the received information comprising well location data, fracking data, production test data, completion data, production data, and directional survey data; detecting, with the processor, gaps within the received information, each gap corresponding to a missing data point; generating, with the processor, a predicted data point corresponding to each missing data point using a mapping-set based machine learning technique; substituting, with the processor, the gaps with the corresponding predicted data points to yield quality-controlled received information and Roth, [129]; According to some embodiments of the present disclosure, mapping set-based machine learning is used to fill in missing data. Mapping set-based machine learning provides a method for using existing data to predict the value of the missing data. The mapping set-based machine learning method may be used, for example, to fill in missing well header data, engineering data, and frac chemical data and Roth, [154]; This data is required on all state and federal regulatory forms, so it is widely available in the public data. Even so, the data often lacks consistency as data entry errors, variability in naming conventions, and the existence of both parent and subsidiary companies can result in a variety of different names for the same operator. In step 1 of the process 300, the many different names that may be used for a single operator are consolidated under a standardized operator name. As just one example, the operator identifiers “Anadarko Austin Chal,” “Anadarko Austin Chalk Company,” “Anadarko E & P Co LP,” “Anadarko E & P Company Limited Prtnrship,” “Anadarko E & P Onshore LLC,” “Anadarko E&P Onshore,” “Anadarko Min Inc,” “Anadarko Minerals Incorporated,” “Anadarko Pet Corp,” and “Anadarko Petroleum Corporation” may all be standardized and consolidated as “Anadarko.”). Examiner interprets the identifiers as missing offices as the system of Roth standardizes information from the same company in order to map data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Celhar including mapping operations and miscellaneous and unreported categories of opportunities with the teachings of Roth including the use of machine learning to analyze parent mapping history, metadata for missing data and determining mapping for missing data in order to improve predictions and ensure accuracy of data points. (Roth, [128]; the processor not only to make predictions based on available data, but also to utilize machine learning to improve future predictions based on a comparison of past predictions to actual results. In some embodiments, the machine learning may involve identifying one or more additional types of data or data points that should be taken into account to improve the accuracy of a prediction, or identifying one or more types of data or data points that were being used to make the prediction but that negatively affected the accuracy of the prediction, or adjusting the relative weight of one or more types of data or data points being used to make the prediction so as to improve the accuracy of the prediction).
Regarding Claim(s) 5, 14, and 19. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said performance of opportunity mapping operations includes mapping opportunity notices to federal staff organizational data. (Celhar, [24-25]; The system may also retrieve documentation and information that is indirectly related to the procurement opportunity such as agency information (e.g., contacts, locations and agency names), as well as private company information (e.g., financial data, business units, etc.), such as for businesses, interested vendors, and pre-approved vendors… Once obtained by the system 100, the system assesses and categorizes each procurement opportunity. As will be described in additional detail herein, the procurement opportunity is assessed and categorized using information associated with the customer 110 from which it originated, the industry 115 to which it was assigned, but also based on various attributes of the information included in the opportunity itself. For example, the textual data in the procurement opportunity can be searched for keywords in order to determine characteristics of the opportunity and estimate the complexity of the opportunity. The system may score the procurement opportunity based on the analyzed characteristics of the opportunity and Celhar, [39]; Additional information known to the system, such as a link to a list of customer contacts 410 and links 412 to both open procurement opportunities and historical procurement opportunities are provided).
Regarding Claim(s) 6, 15, and 20. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said performance of opportunity mapping operations includes one or more of joining opportunity notices to contracts, or normalizing solicitation numbers and award identifiers. (Celhar, [17]; Awards refer to any agreements or contracts, as well as any payments or transactions pursuant to such agreements or contracts, between the fulfilling organization and the customers for opportunities won in the marketplace. Contract and grant opportunities may be analyzed by the system using various attributes of the contract or grant, such as size of the contract/grant description, monetary amount awarded, resource requirements, completion times, etc. Additionally, the industry associated with a particular contract or grant may be used by the system to calculate the score…. fulfilling organizations to determine whether or not to participate in that particular procurement opportunity. For example, if an opportunity includes a contract with a complexity score of 89, then a fulfilling organization that usually handles contracts with a complexity score of 20-40 may not wish to pursue that contract).
Regarding Claim(s) 7. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said performance of award linking operations comprises processing one or more of awards, modifications, transactions, organizations, terms, or sums. (Celhar, [58]; In other embodiments, if the system determines that fulfilling organization data has changed, e.g., an opportunity has been awarded, the system can prompt the user associated with that fulfilling organization for details regarding the awarded opportunity, e.g., via email request or other communication means. In yet a further embodiment, in the aforementioned example for an awarded opportunity, the system can automatically update the fulfilling organization data and provide the information relating to those predetermined attributes according to data received from the customer associated with the awarded opportunity).
Regarding Claim(s) 8. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, wherein said performance of opportunity linking operations comprises processing one or more of opportunities, notices, organizations, or terms. (Celhar, [24]; The eight industries are utilized by the system to categorize procurement opportunities from various customers. The procurement opportunities may be received by the system using push technology or pull technology. For example, the system may periodically scrape government websites or access government databases via application programming interfaces (APIs) and web crawling technology in order to retrieve documentation and information that directly relates to the procurement opportunity, such as procurement records (e.g., awards, solicitations, amendments, contracts, bid results, etc.) that are made publicly available by the government. The system may also retrieve documentation and information that is indirectly related to the procurement opportunity such as agency information (e.g., contacts, locations and agency names), as well as private company information (e.g., financial data, business units, etc.), such as for businesses, interested vendors, and pre-approved vendors). Examiner notes that Celhar discloses categorizing opportunities (i.e. linking) to industry and further augments the data with contacts, agencies, etc.
Regarding Claim(s) 9. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, further comprising: updating, by the computing system, one or more of federal spend contracts associated with said augmented federal spend data, or federal opportunity notices associated with said augmented federal spend data.(Celhar, [46]; After an opportunity is opened and until it is awarded, updates, amendments, or other supplemental information (e.g., additional documents, third-party data, news coverage, etc.) can be received by the system which affect the complexity score of the opportunity. Accordingly, the complexity score may change as more data associated with the opportunity is received by the system).
Regarding Claim(s) 10. Celhar/Roth/Kim teaches: The computer-implemented method of claim 1, further comprising: tracking, by the computing system, across multiple time periods, modifications of federal spend contracts. (Celhar, [47]; the contract analysis can include an assessment of the details related to a particular contract including a count and identification of all fulfilling organizations awarded (awardees) historically for the contract, fulfilling organizations currently interested in the contract (e.g. currently bidding or flagging the contract), contacts for the contract owner, documents associated with the contract, and modifications to the contract (e.g., addendums, etc.). A connections analysis 524 section provides a visual summary of the current awarded and interested competitors, or fulfilling organizations, and those organization's contacts. A contract history tab 526, when selected, provides a timeline of the opportunity actions (i.e., bidding history) and associated amendments, contracts, awards, etc. for that opportunity). Examiner notes that the system of Celhar tracks modifications, amendments, etc. of a contract over a timeline of the contract (i.e. multiple time periods).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEREMY L GUNN/Examiner, Art Unit 3624