DETAILED 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 19, 2025, has been entered.
Claims 10, 22, and 23 are amended.
Claims 21 and 26 are canceled.
Claims 10, 12, 14, 15, 17, 18, 22-24, 27-29, and 32 are pending.
Response to Arguments
35 USC §101 Rejections
The Applicant traverses the rejection of the present claims as being directed to an ineligible abstract idea, contending that the claims are subject matter eligible because the claims recite specialized computational elements, such as natural language processing. See Remarks p. 9. In response, the Examiner submits that the natural language processing (NLP) does not provide a practical application or significantly more than the recited abstract idea. In the claims, the NLP functions purely as a computational analog to the language processing that a human being could do. No apparent improvement to NLP technology is recited in the claims. Lack of conventionality does not imply subject matter eligibility. However, the vectorization of words in NLP is typical. See https://en.wikipedia.org/wiki/Word_embedding. Moreover, a human being could implement the steps of applying a weighted feature vector and regression model to graph data. The claims do not perform a method that is rooted in computer technology. Instead, the claims merely use a computer as a tool to implement an abstract idea.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the prior art. Independent claims 1 and 23 now stand rejected as being obvious over Alexander in view of Adolphe, Chibon, and Lambroschini; as set forth, below. The Applicant’s arguments are moot in light of the newly cited Chibon reference, as well as the newly cited portions of Alexander and Adolphe.
The Applicant additionally contends that Lambroschini is deficient because, according to the Applicant, Lambroschini does not teach overlap and density of specialized skills. See Remarks p. 17. In cited ¶[0061]; Lambroschini discloses reduced skill redundancy, which appears to meet the broadest reasonable interpretation of “overlap and density of specialized skills.”
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 10, 12, 14, 15, 17, 18, 22-24, 27-29, and 32 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 10, 12, 14, 15, 17, 18, 22-24, 27-29, and 32 are all directed to one of the four statutory categories of invention, the claims are directed to generating proposed teams (as evidenced by exemplary independent claim 10; “generating a plurality of specific alternative proposed teams”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 10 include: “maintaining . . . a continuously updated database of teaming opportunities;” “maintaining . . . a continuously updated database of profile data;” “processing requirements data for an individual RFP;” “conducting multi-criteria best-fit matching of weighted feature vector data with profile data . . . to identify available personnel at the institution;” “generating a plurality of specific alternative proposed teams . . . and respectively evaluating said plurality of proposed teams;” “prioritizing the plurality of proposed teams based on projected team success;” and “transmitting individualized electronic notifications . . . to each of the identified team members [and an administrative user].” . The steps are all steps for managing personal behavior related to the abstract idea of generating proposed teams that, when considered alone and in combination, are part of the abstract idea of generating proposed teams. The dependent claims further recite steps for managing personal behavior related to the abstract idea of generating proposed teams that are part of the abstract idea of generating proposed teams. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes assigning a group of workers to a project.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a computer-implemented method in independent claim 1; and a system with a processor in independent claim 23). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of artificial intelligence and natural language processing, but the abstract idea of generating proposed teams is generally linked to an artificial intelligence and natural language processing environment for implementation. Therefore, the artificial intelligence and natural language processing does not provide a practical application or significantly more than the recited abstract idea in the claims. See MPEP §2106.05(h). The claims require no more than a generic computer (a computer-implemented method in independent claim 1; and a system with a processor in independent claim 23) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 10, 12, 15, 18, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to Adolphe et al. (hereinafter ‘ADOLPHE’), US 20210334367 A1 to Chibon et al. (hereinafter ‘CHIBON’), and US 20150088567 A1 to LAMBROSCHINI (hereinafter ‘LAMBROSCHINI’).
Claim 10 (Currently Amended)
ALEXANDER discloses a computer-implemented method (see col 113, ln 5-18; a computer-based device with a computer-based product) for assisting an institution to teaming assemble internally resourced proposal teams (see col 1, ln 20-col 2, ln 3; identify, aggregate, navigate, validate, recommend, and broker relevant experience and team member capabilities, which more particularly may be used to facilitate a proposal response to a Request for Proposals (RFP). The relevance management system may help a prime contractor identify, understand, and manage experience and capabilities it may draw upon, both from within its organization, as well as from external subcontractors, which may be helpful in responding to an RFP with a proposal. Identify relevant RFPs that represent work opportunities. See also col 29, ln 51-col 30, ln 17; use relational databases. See also col 29, ln 51-col 30, ln 17; use relational databases. Data is entered into a relevance management system 1140 and “processed” to update various data stores (which may be implemented as relational databases, or other types of data persistence technologies)) for responding to external funding opportunities (see again col 1, ln 20-col 2, ln 3; the relevance management system may help a prime contractor identify, understand, and manage experience and capabilities it may draw upon, both from within its organization, as well as from external subcontractors. See also col 8, ln 38-58’ a customer may be a government agency or other entity that may be a request for a quote or proposal), comprising electronically maintaining, by a computing system, a continuously updated database of teaming opportunities (see col 29, ln 51-col 30, ln 18; updated various data stores, which may be implemented as relational databases for match processing).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, comprising active research RFPs from grant-funding agencies (see again ¶[0001] and [0003]; grant and proposal professionals. Collaboration between government contractors) received in real time from external data feeds (see ¶[0074]-[0077] and [0095]; communications with external servers and external market intelligence systems).
ALEXANDER further discloses electronically maintaining, by the computing system, a continuously updated database of profile data of research personnel (see col 10, ln 33-col 11, ln 30; identify project descriptions that represent relevant experience with respect to an RFP. Use contractor and subcontractors with complimentary and overlapping capabilities. See also Figs. 11 and 20 & col 114, ln 1-44; available at the institution (see col 115, ln 12-25; a contractor or subcontractor);
electronically processing (see col 113, ln 29-50; a computer system) requirements data for an individual RFP from the continuously updated database of teaming opportunities (see col 8, ln 38-col 9, ln 10 and col 118, ln 66-col 119, ln 18; meet aggregated requirements. An RFP includes multiple sections, including a performance work statement that describes requirements of works to be performed. See also col 10, ln 33-col 11, ln 30; eliminate capability gaps by including subcontractors to identify potential teammates with relevant experience) via a Natural Language Processing (NLP) module which generates a weighted feature vector of technical and collaboration requirements (see col 48, ln 4-24; Regarding procedure Determine_Relevance_Matrix( ) that we disclose in reference to FIG. 35, the input argument that is received as a Content_Comparator( ) function may be implemented with any of a broad range of methods, now known or hereafter developed, for determining similarity of a first document to a second document, examples of which include, but are not limited to, the well-known Term Frequency-Inverse Document Frequency (TF-IDF) methodology, and more sophisticated Latent Semantic Analysis (LSA) techniques, both of which may incorporate further techniques, now known or hereafter developed, such as removal of stop words (i.e., unimportant words), stemming, and other techniques. See also col 46, ln 24-39; to assist match processing, it may be advantageous for a relevance management system 1140 to transform a representation of a PD, RFP, or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, into an array, vector, map, or other data structure. We disclose that such transformation may be accomplished by navigating a Project Descriptor, such as a PD, RFP or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, using a depth-first traversal, and associating each node in a Project Descriptor with an element of an array);
conducting multi-criteria best-fit matching of the weighted feature vector data with profile data of the continuously updated database of profile data of research personnel based on skills, (see col 1, ln 38-col 2, ln 3; identify subcontractors with complementary or overlapping capabilities. See again col 46, ln 24-39; match processing using a vector. See also col 56, ln 26-41 and Fig. 47; a weights matrix).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, availability (see ¶[0069]; experts are then ranked by their experience responding to bid invitations from specific Government bodies, prior successes or win rates, customer satisfaction, availability, criminal, credit, and client reference checks, record of on time completion of previous projects, security clearances, and industry certifications), and historical success factors (see ¶[0043] and Fig. 5b; increase win probability by employing R1-R4. See also ¶[0069]; Experts are then ranked by their experience responding to bid invitations from specific Government bodies, prior successes or win rates).
ALEXANDER further discloses to identify available personnel at the institution specifically matched for submitting on a given teaming opportunity (see col 16, ln 21-44; identify similarities between RFP and PD work elements See also col 1, ln 38-col 2, ln 3; determine RFP-to-PD relevance, PD-owner (company) capabilities, and overall team capabilities. See also col 12, ln 59-col 13, ln 7; Which of these RFPs represent the best opportunity to bid upon, that represent the best match with the contractor's experience, such as represented in a PD).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, generating a plurality of specific alternative proposed teams respectively comprised of at least two members of the specifically matched personnel and respectively evaluating said plurality of proposed teams as discrete units using an analytical optimization engine (see ¶[0084] and Figs. 4 & 5b; a rules engine to increase win probability) to comparatively assess and rank their likelihood of success for the given teaming opportunity (see ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked));
prioritizing the plurality of proposed teams based on relative projected team success, the prioritizing comprising (i) computationally estimating a team performance score for each proposed team (see again ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked).
The combination of ALEXANDER and ADOLPHE does not specifically disclose, but CHIBON discloses, based on applying a non-linear regression model (see ¶[0029]; include a machine language model 28 that is trained in conventional fashion using exemplary code fragments for each of the one or more security profiles 26(0)-26(S). The machine language model 28 may comprise, as non-limiting examples, one or more of a non-linear regression algorithm. Determine whether a code matches a profile).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, trained on historical collaboration graph data specific to the members of each respective proposed team as a collective unit (see again ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked).
The combination of ALEXANDER and ADOLPHE does not specifically disclose, but LAMBROSCHINI discloses, and (ii) optimizing the proposed teams to maximize winning success and minimize projected team redundancy as calculated by overlap and density of specialized skills (see ¶[0061]; an optimal team of candidates can be selected that satisfies all or a threshold number of required skills as well as represents reduced or increased skill redundancy, according to the needs and parameters of the associated project).
ALEXANDER further discloses automatically transmitting individualized electronic notifications via email and internal messaging systems to each of the identified team members (see col 26, ln 42-53 and col 27, ln 53-col 28, ln 45 & Figs 18 and 19; a notification interface showing relevant experience or capabilities. See also col 23, ln 21-28; a controller module to notify an end user of a match a relevance system has identified).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, to an administrative user at the institution, the notifications triggered by prioritization results and including team composition, ranking, and the associated teaming opportunity (see ¶[0076]-[0077], [0089], & [0094] and Fig. 9c; data may include push notifications data. Buyer reviews qualifications and cost proposals).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to ranked teams. It would have been obvious to include the ranked teams as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to increase the chances of success on a project.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to increase the chances of winning a proposal. It would have been obvious to include the calculation of chances of winning as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to increase the chances of success on a project.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to increase the chances of winning a proposal. CHIBON discloses a non-linear regression algorithm used for matching language objects to profiles. It would have been obvious to include the matching based on non-linear regression as taught by CHIBON in the system executing the method of ALEXANDER and ADOLPHE with the motivation to determine if a contractor meets a projects educational requirements.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. LAMBROSCHINI discloses building optimal project teams that satisfy required skills and reduce skill redundancy. It would have been obvious to build teams as taught by LAMBROSCHINI in the system executing the method of ALEXANER with the motivation to optimize teams.
Claim 12 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
ALEXANDER does not specifically disclose, but ADOLPHE discloses, further including estimating team budgets (see ¶[0086]-[0087]; in an embodiment, buyer may request open bids or provide a budget based on results of price estimator rules base 138. Open bids provides no limitations on the amount a seller bids for the project. In another embodiment, budgets may provide a maximum bid amount for the project).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where budgets are estimated by a buyer for developing a proposal. It would have been obvious to include the budget estimator as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to determine costs associated with a bid on a project (see ADOLPHE ¶[0086]-[0087]).
Claim 15 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
ALEXANDER does not specifically disclose, but ADOLPHE discloses, wherein said teaming opportunities comprise responding to one of proposals in product and services supply chains, expert teams for a medical procedure at a hospital, players for a match for team-based sports, crews for a flight or mission, and active research RFPs from grant-funding agencies (see ¶[0001] and [0003]; grant and proposal professionals. Collaboration between government contractors).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where teams are matched to research funding projects to obtain grants. It would have been obvious to include the matching of teams to projects as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to win grant bids (see ADOLPHE ¶[0066]-[0067]).
Claim 18 (Previously Presented)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
ALEXANDER does not specifically disclose, but ADOLPHE discloses, further including after notifications, obtaining from the identified team members their opt-in or opt-out feedback for the associated teaming opportunity (see ¶[0008] & claims 1 and 6; acceptance by the desired seller and payment to the desired seller.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to ranked teams that choose whether to accept a contract. It would have been obvious to include the acceptance of a contract as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to provide contracting services to buyers.
Claim 23 (Currently Amended)
ALEXANDER discloses a system for assisting an institution to assemble internally resourced proposal teams (see col 1, ln 20-col 2, ln 3; identify, aggregate, navigate, validate, recommend, and broker relevant experience and team member capabilities, which more particularly may be used to facilitate a proposal response to a Request for Proposals (RFP). The relevance management system may help a prime contractor identify, understand, and manage experience and capabilities it may draw upon, both from within its organization, as well as from external subcontractors, which may be helpful in responding to an RFP with a proposal. Identify relevant RFPs that represent work opportunities. See also col 29, ln 51-col 30, ln 17; use relational databases. See also col 29, ln 51-col 30, ln 17; use relational databases. Data is entered into a relevance management system 1140 and “processed” to update various data stores (which may be implemented as relational databases, or other types of data persistence technologies)) for responding to external funding opportunities (see again col 1, ln 20-col 2, ln 3; the relevance management system may help a prime contractor identify, understand, and manage experience and capabilities it may draw upon, both from within its organization, as well as from external subcontractors. See also col 8, ln 38-58’ a customer may be a government agency or other entity that may be a request for a quote or proposal) in research Requests for Proposals (RFPs) (see again col 1, ln 20-col 2, ln 3; identify, aggregate, navigate, validate, recommend, and broker relevant experience and team member capabilities, which more particularly may be used to facilitate a proposal response to a Request for Proposals (RFP)) comprising:
a continuously updated electronic RFP database of active research RFPs (see col 29, ln 51-col 30, ln 18; updated various data stores, which may be implemented as relational databases for match processing).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, from grant-funding agencies as received in real time from external data feeds (see ¶[0074]-[0077] and [0095]; communications with external servers and external market intelligence systems).
ALEXANDER further discloses, a continuously updated electronic personnel database of profile data of research personnel (see col 10, ln 33-col 11, ln 30; identify project descriptions that represent relevant experience with respect to an RFP. Use contractor and subcontractors with complimentary and overlapping capabilities. See also Figs. 11 and 20 & col 114, ln 1-44) available at a given institution (see col 115, ln 12-25; a contractor or subcontractor); and
one or more processors comprising at least in part an AI-based system using primarily natural language processing and analytical/optimization techniques (see col 48, ln 4-24; Regarding procedure Determine_Relevance_Matrix( ) that we disclose in reference to FIG. 35, the input argument that is received as a Content_Comparator( ) function may be implemented with any of a broad range of methods, now known or hereafter developed, for determining similarity of a first document to a second document, examples of which include, but are not limited to, the well-known Term Frequency-Inverse Document Frequency (TF-IDF) methodology, and more sophisticated Latent Semantic Analysis (LSA) techniques, both of which may incorporate further techniques, now known or hereafter developed, such as removal of stop words (i.e., unimportant words), stemming, and other techniques. See also col 46, ln 24-39; to assist match processing, it may be advantageous for a relevance management system 1140 to transform a representation of a PD, RFP, or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, into an array, vector, map, or other data structure. We disclose that such transformation may be accomplished by navigating a Project Descriptor, such as a PD, RFP or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, using a depth-first traversal, and associating each node in a Project Descriptor with an element of an array), and said one or more processors programmed (see col 113, ln 5-50; a computer-based device with a computer-based product. The computer system 12000 includes one or more processors, such as processor 12002, providing an execution platform for executing software. Commands and data from the processor 12002 are communicated over a communication bus 12006) for electronically processing see col 113, ln 29-50; a computer system) requirements data for an individual RFP from the continuously updated database of teaming opportunities of active research RFPs (see col 8, ln 38-col 9, ln 10 and col 118, ln 66-col 119, ln 18; meet aggregated requirements. An RFP includes multiple sections, including a performance work statement that describes requirements of works to be performed. See also col 10, ln 33-col 11, ln 30; eliminate capability gaps by including subcontractors to identify potential teammates with relevant experience) via a Natural Language Processing (NLP) module which generates a weighted feature vector of technical and collaboration requirements (see col 48, ln 4-24; Regarding procedure Determine_Relevance_Matrix( ) that we disclose in reference to FIG. 35, the input argument that is received as a Content_Comparator( ) function may be implemented with any of a broad range of methods, now known or hereafter developed, for determining similarity of a first document to a second document, examples of which include, but are not limited to, the well-known Term Frequency-Inverse Document Frequency (TF-IDF) methodology, and more sophisticated Latent Semantic Analysis (LSA) techniques, both of which may incorporate further techniques, now known or hereafter developed, such as removal of stop words (i.e., unimportant words), stemming, and other techniques. See also col 46, ln 24-39; to assist match processing, it may be advantageous for a relevance management system 1140 to transform a representation of a PD, RFP, or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, into an array, vector, map, or other data structure. We disclose that such transformation may be accomplished by navigating a Project Descriptor, such as a PD, RFP or ST, exemplars of which we disclose in reference to FIG. 25, FIG. 30, and FIG. 23, respectively, using a depth-first traversal, and associating each node in a Project Descriptor with an element of an array);
conducting multi-criteria best-fit matching of the weighted feature vector data with profile data of the continuously updated database of profile data of research personnel based on skills (see col 1, ln 38-col 2, ln 3; identify subcontractors with complementary or overlapping capabilities. See again col 46, ln 24-39; match processing using a vector. See also col 56, ln 26-41 and Fig. 47; a weights matrix).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, availability (see ¶[0069]; experts are then ranked by their experience responding to bid invitations from specific Government bodies, prior successes or win rates, customer satisfaction, availability, criminal, credit, and client reference checks, record of on time completion of previous projects, security clearances, and industry certifications), and historical success factors (see ¶[0043] and Fig. 5b; increase win probability by employing R1-R4. See also ¶[0069]; Experts are then ranked by their experience responding to bid invitations from specific Government bodies, prior successes or win rates).
ALEXANDER further discloses to identify available personnel at the institution specifically matched for submitting on a given teaming opportunity of an individual RFP (see col 16, ln 21-44; identify similarities between RFP and PD work elements See also col 1, ln 38-col 2, ln 3; determine RFP-to-PD relevance, PD-owner (company) capabilities, and overall team capabilities. See also col 12, ln 59-col 13, ln 7; Which of these RFPs represent the best opportunity to bid upon, that represent the best match with the contractor's experience, such as represented in a PD).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, generating a plurality of specific alternative proposed teams respectively comprised of at least two members of the specifically matched research personnel and respectively evaluating said plurality of proposed teams as discrete units using an analytical optimization engine (see ¶[0084] and Figs. 4 & 5b; a rules engine to increase win probability) to comparatively assess and rank their likelihood of success for the given teaming opportunity (see ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked));
prioritizing the plurality of proposed teams based on relative projected team success, the prioritizing comprising (i) computationally estimating a team performance score for each proposed team (see again ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked).
The combination of ALEXANDER and ADOLPHE does not specifically disclose, but CHIBON discloses, based on applying a non-linear regression model (see ¶[0029]; include a machine language model 28 that is trained in conventional fashion using exemplary code fragments for each of the one or more security profiles 26(0)-26(S). The machine language model 28 may comprise, as non-limiting examples, one or more of a non-linear regression algorithm. Determine whether a code matches a profile).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, trained on historical collaboration graph data specific to the members of each respective proposed team as a collective unit (see again ¶[0089] and Figs 9c-d; teams are ranked based on matching rules. High performance teams with a likelihood to be able to assist buyer are ranked).
The combination of ALEXANDER and ADOLPHE does not specifically disclose, but LAMBROSCHINI discloses, and (ii) optimizing the proposed teams to maximize winning success and minimize projected team redundancy as calculated by overlap and density of specialized skills (see ¶[0061]; an optimal team of candidates can be selected that satisfies all or a threshold number of required skills as well as represents reduced or increased skill redundancy, according to the needs and parameters of the associated project).
ALEXANDER further discloses, automatically transmitting individualized electronic notifications via email and internal messaging systems to each of the identified team members (see col 26, ln 42-53 and col 27, ln 53-col 28, ln 45 & Figs 18 and 19; a notification interface showing relevant experience or capabilities. See also col 23, ln 21-28; a controller module to notify an end user of a match a relevance system has identified).
ALEXANDER does not specifically disclose, but ADOLPHE discloses, to an administrative user at the institution, the notifications triggered by the prioritization results and including team composition, ranking, and the associated teaming opportunity (see ¶[0076]-[0077], [0089], & [0094] and Fig. 9c; data may include push notifications data. Buyer reviews qualifications and cost proposals).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to ranked teams. It would have been obvious to include the ranked teams as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to increase the chances of success on a project.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to increase the chances of winning a proposal. It would have been obvious to include the calculation of chances of winning as taught by ADOLPHE in the system executing the method of ALEXANDER with the motivation to increase the chances of success on a project.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. ADOLPHE discloses a marketplace for collaboration, where services and solutions are matched to increase the chances of winning a proposal. CHIBON discloses a non-linear regression algorithm used for matching language objects to profiles. It would have been obvious to include the matching based on non-linear regression as taught by CHIBON in the system executing the method of ALEXANDER and ADOLPHE with the motivation to determine if a contractor meets a projects educational requirements.
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. LAMBROSCHINI discloses building optimal project teams that satisfy required skills and reduce skill redundancy. It would have been obvious to build teams as taught by LAMBROSCHINI in the system executing the method of ALEXANER with the motivation to optimize teams.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 10 above, and further in view of US 20140358607 A1 to Gupta et al. (hereinafter ‘GUPTA’).
Claim 14 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but GUPTA discloses, wherein extracting requirements data from the updated database of teaming opportunities includes focus on pre-determined keywords, topics, and concepts of selected interest for an institution (see ¶[0017] & [0068] and Fig. 14; job title keyword information).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. GUPTA discloses a team member recommendation system that includes using keyword searching to find candidate team members that match needs for a project (see ¶[0002]). It would have been obvious to include the keyword matching as taught by GUPTA in the system executing the method of ALEXANDER with the motivation to match opportunities to team members.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 10 above, and further in view of US 20070016514 A1 to Al-Abdulqader et al. (hereinafter ‘AL-ABDULQADER’).
Claim 17 (Previously Presented)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but AL-ABUDLQADER discloses, wherein matching includes checking research personnel eligibility to participate in a given individual RFP (see ¶[0026] and [0370]; determine contractor eligibility for a procurement transaction. Evaluate eligibility for bidding on the pending contract).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. AL-ABDULQADER discloses managing contract procurement that determines contractor eligibility to bid on a contract. It would have been obvious to include the eligibility determination as taught by AL-ABDULQADER in the system executing the method of ALEXANDER with the motivation to match teams to projects.
Claim(s) 22 and 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 10 above, and further in view of US 20170154307 A1 to Maurya et al. (hereinafter MAURYA’).
Claim 22 (Currently Amended)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the methodology as set forth in claim 10.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but MAURYA discloses, wherein prioritizing includes optimizing the proposed teams to incorporate diversity preferences about teams (see ¶[0143]-[0144]; arrange buckets of skills having optimal skill set diversity).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. MAURYA discloses job listing data with skills and associated teams (see ¶[0043]) that matches members and jobs (see ¶[0107]) and includes buckets of skills having optimal diversity. It would have been obvious to include the diversity as taught by MAURYA in the system executing the method of ALEXANDER with the motivation to match members to teams.
Claim 32 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the system as in claim 23.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but MAURYA discloses, wherein the profile data of research personnel includes at least one of skillset, expertise, and experience for available research personnel (see ¶[0085] and Fig. 4; member profiles with skills 418, occupation 416, and experience 420).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. MAURYA discloses job listing data with skills and associated teams (see ¶[0043]) that matches members and jobs (see ¶[0107]). It would have been obvious to include the profile information as taught by MAURYA in the system executing the method of ALEXANDER with the motivation to match members to teams.
Claim(s) 24 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 23 above, and further in view of US 20120041769 A1 to Dalal et al. (hereinafter ‘DALAL’).
Claim 24 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the system as in claim 23.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but DALAL discloses, wherein said RFP database and said personnel database each comprise one or more network-based non-transitory storage devices (see abstract and ¶[0032] and [0052]; match researchers with relevant research projects as described in RFPs. Scan web-based databases. See also ¶[0121]-[0122]; any storage medium known in the art).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. DALAL discloses matching researchers with research projects based on information contained in a database that includes RFPs. It would have been obvious to include the database with RFPs as taught by DALAL in the system executing the method of ALEXANDER with the motivation to match opportunities to team members.
Claim 27 (Previously Presented)
The combination of ALEXANDER, ADOLPHE, CHIBON, LAMBROSCHINI, and DALAL discloses the system as set forth in claim 24.
ALEXANDER additionally discloses wherein said one or more processors are further programmed: for periodically updating said RFP database (see col 22, ln 51-col 23, ln 4; monitor for additional or updated RFPs entered into a relevance management system. See also col 87, ln 34-43; save updates to a team under construction).
ALEXANDER does not specifically disclose, but DALAL discloses, for collating and storing information from multiple sources for RFPs; and for extracting data from selected information fields in RFPs (see ¶[0065]; collect data from sources where funding opportunities are published. Collect data from internal databases and RFP databases. Data sources include multiple organizational databases); and
for extracting data from selected information fields in RFPs (see again ¶[0065]; download web pages from RFP sites that are based on templates that have formatted fields. Text is compared to text in applicant’s profiles to calculate similarity. See also abstract; match researchers with relevant projects. ).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s educational requirements defined in a request for proposal. DALAL discloses requests for proposals management that includes semantic processing the includes extracting text from fields to determine similarity between RFPs and applicant’s profiles. It would have been obvious to include the data extraction as taught by DALAL in the system executing the method of ALEXANDER with the motivation to identify a team of personnel that meet a project’s requirements.
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 23 above, and further in view of US 20140074645 A1 to Ingram (hereinafter ‘INGRAM’).
Claim 28 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the system as in claim 23.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose, but INGRAM discloses, wherein said system comprises one of a web- based application and of a stand-alone application running on a personal computer INGRAM discloses, wherein said system comprises one of a web-based application and of a stand-alone application running on a personal computer (see ¶[0020]; a web-based client or server application with terminal devices; e.g., a personal computer. Terminals may execute standalone client software).
ALEXANDER discloses a relevance system that is implemented on a computer (see Fig. 120) that identifies a team of personnel that meet a project’s educational requirements for a contractor or subcontractor. INGRAM discloses bid assessment analytics for requests for proposals that includes teaming (see ¶[0049]) and may be run on a web-based or standalone application. It would have been obvious to include the web-based or standalone applications as taught by INGRAM in the system executing the method of ALEXANDER with the motivation to implement a computer executed method.
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10089585 B1 to Alexander (hereinafter ‘ALEXANDER’) in view of US 20190026820 A1 to ADOLPHE et al., US 20210334367 A1 to CHIBON et al., and US 20150088567 A1 to LAMBROSCHINI as applied to claim 23 above, and further in view of https://web.archive.org/web/20210425211038/https://cdn.pfizer.com/pfizercom/RFPChronicPainCare-InterprofessionalTeams.pdf?VersionId=LUxrJI7Ou5AUA9MR7ex3QwsAVMQuhwGH (hereinafter ‘PFIZER’).
Claim 29 (Original)
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI discloses the system as in claim 23.
The combination of ALEXANDER, ADOLPHE, CHIBON, and LAMBROSCHINI does not specifically disclose but PFIZER discloses, wherein the individual RFP is multi- disciplinary (see Title and Background; a multi-disciplinary approach).
ALEXANDER further discloses and the proposed team members have complementary skills (see col 10, ln 33-col 11, ln 30; identify project descriptions that represent relevant experience with respect to an RFP. Use contractor and subcontractors with complimentary and overlapping capabilities. See also Figs. 11 and 20 & col 114, ln 1-44; available at the institution (see col 115, ln 12-25; a contractor or subcontractor).
ALEXANDER discloses a relevance system that identifies a team of personnel that meet a project’s requirements defined in an RFP. PFIZER discloses a response to an RFP that involves an interdisciplinary approach. It would have been obvious to include the interdisciplinary approach as taught by PFIZER in the system executing the method of ALEXANDER with the motivation to meet a project’s requirements.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624