DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL Office action upon examination of application number 18/486,901 filed on 10/13/2023, in response to Applicant’s Request for Continued Examination (RCE) filed on February 11, 2026. Claims 1-20 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/486,901 filed 10/13/2023 claims Priority from Provisional Application 63/415,849, filed 10/13/2022.
Response to Amendment
4. In the response filed February 11, 202, Applicant amended claims 1, 19, and 20, and did not cancel any claims. No new claims were presented for examination.
5. Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
6. Applicant's arguments filed February 11, 2026, have been fully considered.
7. Applicant submits “the amended claims do not recite an abstract idea but instead recite a specific technological implementation: a layered network processing architecture with three distinct computational layers, each generated by dedicated layer-specific processing modules, with constrained candidate evaluation based on predetermined alignment thresholds, and continuous automated score regeneration without manual intervention. The zero layer is created by a zero layer creation module, which generates identity archetypes from seller zero party data. The first layer, referred to as the nuclear network, is created by a first layer creation module and is constructed through a nomination-acceptance protocol where peer devices receive nomination requests and transmit acceptances. The second layer, referred to as the extended network, is created by a second layer creation module that identifies aligned cohorts from third-party datasets. Each layer is a distinct data structure generated by specialized processing modules and stored in system server memory.” The claimed invention further requires constrained candidate evaluation. The system "select[s], by the at least one system server, from at least one of the first layer dataset and the second layer dataset, individuals meeting a predetermined alignment threshold based on the zero layer dataset and the buyer needs dataset, whereby the selecting step constrains a pool of candidates evaluated for matching." This architectural constraint reduces the candidate pool from all possible sellers to a constrained set meeting layer-specific criteria before scoring operations occur. This is computational optimization through structured network layers, not recitation of an abstract idea.” [Applicant’s Remarks, 02/11/2026, pages 14-15]
The Examiner respectfully disagrees. In response, the Examiner notes that the claim recites limitations related to receiving a seller zero party dataset, receiving a nomination dataset representing a plurality of peers proposed by the seller, communicating a nomination request to the nominate peers, receiving acceptance of the nomination request from at least one accepted peer, creating a first layer dataset including an identification of the accepted peer, obtaining third-party datasets representing cohorts, selecting individuals meeting a predetermined alignment thresholds, and calculating multiple matching scores that are merged to produce a matchings core between the buyer and at least one of the sellers, accepted peer, or cohort. These steps collectively describe collecting information about individuals and their relationships, evaluating that information according to defined criteria, and producing a compatibility or matching score to facilitate the interactions between buyers and sellers. These activities constitute commercial matchmaking and relationship management between people, which falls withing the “certain methods of organizing human activity” abstract idea grouping. The layers (i.e., zero layer dataset, first layer dataset identifying accepted peers, and second layer dataset representing cohorts) represent different groupings of information about individuals and their relationships used to perform matchmaking evaluation. Organizing and analyzing this information to generate a score remains an abstract idea. For the reasons above, this argument is found unpersuasive.
8. Applicant submits “The claimed invention additionally requires multi-device coordination across four device types, namely, one or more seller devices, one or more peer devices, one or more buyer devices, and one or more system servers, all communicating through a wide area network with specialized modules executing on the system server. The system also requires dynamic weight adjustment, where the merging algorithm calculates weighted averages and "weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics." This is not a static formula. The system adjusts algorithmic weighting factors based on performance data, allowing the matching algorithm to adapt and improve. The claimed invention further requires continuous automated operation. The "system automatically generates updated matching scores as new datasets are received without manual intervention." The system operates continuously, monitoring for new data inputs and automatically regenerating matching scores across all three layers without requiring manual triggering. This combination of elements demonstrates that the claims are not directed to "comparing information" or "organizing human activity" in the abstract. Rather, they recite a specific layered network processing architecture with specialized computational modules, constrained candidate evaluation, dynamic algorithmic adjustment, and continuous automated operation. It can therefore be seen that the claimed method cannot be performed in the human mind. No human could simultaneously monitor multiple peer devices across a network for acceptance of nomination requests while creating computational identity archetypes from zero party data using algorithmic processing. No human could construct nuclear network datasets from real-time peer acceptances while identifying aligned cohorts from third-party social media platforms forming extended networks. No human could apply predetermined alignment thresholds to select individuals from multiple layered datasets while calculating social matching scores across three distinct layers encompassing the seller, nuclear network, and extended network. No human could dynamically adjust algorithmic weights based on performance metrics while continuously regenerating matching scores automatically as new datasets arrive without manual intervention, all while coordinating these operations across seller devices, peer devices, buyer devices, and system servers through specialized processing modules. The scale, simultaneity, layered processing architecture, real-time network coordination, and continuous automated operation vastly exceed human mental capability.” [Applicant’s Remarks, 02/11/2026, pages 15-16]
The Examiner respectfully disagrees. In response, the Examiner notes that the claim recites limitations related to receiving datasets describing sellers, buyers, peers, and cohorts, selecting individuals meeting a predetermined alignment threshold, determining multiple matching scores from the datasets, and merging the scores using weighted values to produce a matching score. These steps correspond to evaluating and comparing information about individuals and their relationships according to defined criteria to determine compatibility, which is a from of analysis and decision making that can be performed in the human mind or with the aid of pen and paper. Examiner notes that implementing a mental evaluation process on a computer system does not change the character of the underlying concept. For the reasons above, this argument is found unpersuasive.
9. Applicant submits “Second, even if the maintains that an abstract idea is recited within the claims, the claims explicitly recite additional elements that integrate any abstract idea into a practical application. The claims improve computer technology through a layered network architecture, implement a particular machine with specialized layer-specific processing modules, transform data through multiple computational layers, and recite elements that are not well-understood, routine, or conventional. Each of these considerations independently supports patent eligibility under Step 2A Prong Two of the patent eligibility framework.” [Applicant’s Remarks, 02/11/2026, page 16]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues that “independent Claim 1 further recites additional elements that integrate any abstract idea into a “practical application” under the first step, 2A, prong 2 of the revised guidelines in the Memorandum. “The additional elements in exemplary claim 1 are: providing a matching system having a seller device having a seller device human interface seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; wherein the system automatically generates updated matching scores as new datasets are received without manual intervention, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
The Examiner further notes that the system is merely being used as a tool to implement the abstract idea which does not integrate the abstract idea into a practical application or amount to significantly more (See MPEP 2106.05). Lastly, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Further, in response to Applicant’s argument that “the claims improve computer technology through a layered network architecture, implement a particular machine with specialized layer-specific processing modules, transform data through multiple computational layers, and recite elements that are not well-understood, routine, or conventional,” the Examiner notes that the claimed layers and modules merely organize and process information about individuals and their relationships to generate a matchings score. The claims do not recite a specific technological improvement to the functioning of the computer itself, but instead implements the abstract idea using generic computing components in a conventional manner. For the reasons above, thus argument is found unpersuasive.
10. Applicant submits “The amended claims recite an improvement to matching system technology through a layered network processing architecture that constrains candidate evaluation and improves computational efficiency. This constitutes an improvement to computer technology itself under MPEP 2106.05(a). Prior matching systems evaluated all possible candidates against all criteria, requiring computationally expensive searches across entire candidate populations without architectural constraints. The claimed system improves upon this baseline by implementing a three-layer processing architecture where the zero layer establishes seller identity archetypes through algorithmic processing of zero party data, the nuclear network constructs peer-based networks through nomination-acceptance protocols, and the extended network identifies third- party aligned cohorts. The system then constrains candidate evaluation by "selecting from at least one of the first layer dataset and the second layer dataset, individuals meeting a predetermined alignment threshold," thereby "constrain[ing] a pool of candidates evaluated for matching." This layered architecture with constrained candidate evaluation represents a technical improvement to matching system technology. By structuring candidate data into computational layers and selecting from constrained pools based on alignment thresholds before performing scoring operations, the system reduces computational overhead while improving matching accuracy. Each layer serves a distinct technical function with specialized processing modules generating layer-specific datasets. Rather than evaluating all possible candidates, the system architecturally constrains the evaluation space through layered processing. The system processes candidates through these structured layers rather than conducting unconstrained searches across all possible matches. This architectural design represents a specific improvement to how matching systems organize and process candidate data, reducing computational load while maintaining or improving matching quality.” [Applicant’s Remarks, 02/11/2026, pages 16-17]
The Examiner respectfully disagrees. In response to Applicant’s argument that the layered network architecture with constrained candidate evaluation provides a technical improvement by reducing computational overhead and improving matching accuracy, it is noted that the claimed layers and selection of candidates based on alignment thresholds organize and process information about sellers, peers, and cohorts to generate a matching score. These operations are directed to analyzing relationships and evaluating alignment between individuals, which falls within the abstract idea category of “certain methods of organizing human activity.”
Although, Applicant asserts that the architecture improves computational efficiency compared to prior matching systems, the claimed limitations do not recite a technological solution to a problem in computer functionality itself. The layered processing, constrained candidate evaluation, and scoring limitations describe how the abstract idea is implemented, but does not constitute an improvement to the operation of the computer beyond performing the abstract idea. For the reasons above, thus argument is found unpersuasive.
11. Applicant submits “The claimed system further improves computer technology by implementing algorithmic adjustment rather than static processing. The merging algorithm does not use fixed, unchanging weights. Instead, "weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics." This dynamic adjustment capability allows the matching algorithm to adapt based on performance data rather than rigidly applying predetermined weighting factors regardless of outcomes.” [Applicant’s Remarks, 02/11/2026, page 17]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that the claim recites limitations relayed to adjusting weights applied to multiple scores when calculating a matching score, but does not recite a specific technological mechanism or algorithm for how the weights are determined, modified, or optimized. Instead, the claims broadly states that the weights are dynamically adjustable based on performance metrics, which describes an outcome of the analysis rather than a specific technological improvement to computer functionality. While Applicant asserts that dynamically adjustment improves matching accuracy over time, improving the quality of a business or analytical result does not constitute an improvement to computer technology itself. The recited weight adjustment remains part of the evaluation and scoring process used to determine alignment between individuals, and therefore reflects an implementation of the underlying abstract idea rather than a technological solution to a problem in computer operation. For the reasons above, thus argument is found unpersuasive.
12. Applicant submits “The system also "automatically generates updated matching scores as new datasets are received without manual intervention." Rather than requiring manual triggering, batch processing at scheduled intervals, or human-initiated recalculation, the system operates continuously and autonomously. The system monitors for new data inputs and automatically regenerates matching scores across all three layers whenever new datasets arrive, ensuring that matching scores reflect current information without requiring human intervention to trigger updates. This continuous automated operation improves system responsiveness and ensures matching scores remain current as circumstances change.” [Applicant’s Remarks, 02/11/2026, page 17]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that
the claim recites “wherein the system automatically generates updated matching scores as new datasets are received without manual intervention.” However, automating the recalculation of results when input data changes merely reflect the use of a computer to repeatedly perform the same analytical process. This describes automation of the underlying scoring and evaluation process rather than a technological improvement to the functioning of the computer itself. While Applicant asserts that continuous operation improves responsiveness and keeps score current, these alleged benefits relate to the timeliness or usefulness of the analytical results, not an improvement in computer technology. The claimed limitation represents automation of data processing and recalculation based on additional input, which does not integrate the abstract idea into a practical application. For the reasons above, thus argument is found unpersuasive.
13. Applicant submits “The continuous automated operation is analogous to the system found patent-eligible in Example 47, Claim 3 of the USPTO's Subject Matter Eligibility Examples (July 2024 Update). Example 47 was found patent-eligible because it "enhances network security by allowing for automatic, proactive remediation." Like Example 47's system that continuously monitors network packets, automatically analyzes them for malicious activity, and automatically takes remedial action by dropping packets and blocking sources without human intervention, the present invention continuously monitors for new datasets, automatically processes them through the layered architecture, and automatically regenerates matching scores without human intervention. Both systems perform continuous automated monitoring, automated analysis through specialized processing, and automated action without requiring human triggering or oversight. Both represent improvements to computer technology-network security in Example 47, and matching system technology in the present invention.” [Applicant’s Remarks, 02/11/2026, page 18]
The Examiner respectfully disagrees. In response to Applicant’s citation to Example 47, the Examiner first emphasizes that the claims in Example 47 share virtually no similarities with Applicant’s invention. The Examiner emphasizes that there are virtually no similarities in subject or fact pattern as between Applicant’s claims and claim 3 of Example 47 (Anomaly Detection). As compared to Example 47 (claim 3), Applicant’s claims recite no limitations remotely similar to the limitations for “A method of using an artificial neural network (ANN) to detect malicious network
packets comprising: (a) training, by a computer, the ANN based on input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a
backpropagation algorithm and a gradient descent algorithm; (b) detecting one or more anomalies in network traffic using the trained ANN; (c) determining at least one detected anomaly is associated with one or more malicious network packets; (d) detecting a source address associated with the one or more malicious network packets in real time; (e) dropping the one or more malicious network packets in real time; and (f) blocking future traffic from the source address.” The amended claim limitations do not reflect an improvement in the technical field of network intrusion detection by providing for improved network security using information from the detection (of a source address associated with the one or more malicious network packets in real time) to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. While claim 1 of Example 47 requires steps for “dropping the one or more malicious network packets in real time; and blocking future traffic from the source address,” Applicant’s claim as a whole, in contrast to claim 3 of Example 47 does not integrate the judicial exception into a practical application such that the claim is not directed to the judicial exception.
14. Applicant submits “The claims also recite a particular machine rather than a generic computer. The claimed system is a multi-device architecture with coordinated seller devices, peer devices, buyer devices, and system servers, where the system server contains specialized layer-specific processing modules that generate and process distinct computational layers. This specific
configuration of hardware and software components represents a particular machine under MPEP 2106.05(b).” [Applicant’s Remarks, 02/11/2026, page 18]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that the claim recites devices such as seller devices, peer devices, buyer devices, and system servers with different modules, however, these components are described at a high level of generality and perform their typical functions of receiving, transmitting, storing, and processing data. The recitation of multiple devices and software modules configured to execute the claimed steps does not impose a meaningful limitation on the abstract idea, but instead represents the use of generic computing components as tool to perform the data collection, analysis, and scoring operations describes in the claim. Accordingly, the claimed system does not constitute a particular machine that meaningfully limits the judicial exception, and therefore does not integrate the abstract idea into a practical application. For the reasons above, thus argument is found unpersuasive.
15. Applicant submits “The claims also effect a transformation of data through multiple processing layers, satisfying the machine-or-transformation test articulated in MPEP 2106.05(c).” [Applicant’s Remarks, 02/11/2026, page 20]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that the alleged transformations merely involve the manipulation, organization and analysis of information. Converting seller questionnaire responses into a “zero layer dataset,” identifying accepted peers to create a “first layer dataset,” converting third-party information into “a second layer dataset,” and calculating various matching scores all constitute changes in the forms or arrangement of data rather than a transformation of an article into a different state or thing. The creation of datasets, application of thresholds, and calculation of weighted averages represent data processing operations that do not meaningfully transform any physical article or technology.
Furthermore, the recited seller device, peer device, buyer device, system server, and associated software module are described at a high level of generality and perform routine functions such as receiving data, storing data, processing data, and transmitting results. The multi-stage layered processing described by Applicant amounts to organizing and analyzing information to produce a compatibility score, implemented on a generic computing components. Therefore, the claimed data processing does not constitute a transformation and does not integrate the abstract idea into a practical application. For the reasons above, thus argument is found unpersuasive.
16. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are
directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
17. 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.
18. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
19. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-18) and system (claim 19) is directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-19 is satisfied. Claim 20 is directed to “A non-transient computer-readable storage medium...” A “computer readable medium” does not fall within one of the four statutory categories of invention because, as currently recited, the claimed computer-readable storage medium could be embodied as software per se, a transitory signal, or any other non-tangible medium. Thus, Step 1 of the Subject Matter Eligibility test for claim 20 is not satisfied because the computer readable medium encompasses transitory embodiments.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 since the claims set forth steps for sales/marketing purposes, which amounts to sales or marketing activities or behaviors (under “commercial or legal interactions” in MPEP 2106) within the “Certain Methods of Organizing Human Activity” abstract idea grouping, and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: providing a matching system having a seller device having a seller device human interface seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for inputting a seller zero party dataset and a nomination dataset, the seller zero party dataset including at least one answer by the seller to at least one seller questionnaire, the nomination dataset representing a plurality of peers proposed by the seller as forming a nuclear network of the seller, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for receiving a nomination request from at least one nominated peer of the plurality of peers and for transmitting an acceptance of the nomination request, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for inputting a buyer zero party dataset, the buyer zero party dataset including at least one answer by the buyer to at least one buyer questionnaire, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a buyer needs dataset for the buyer and a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; creating, by the zero layer creation module of the at least one system server, a zero layer dataset based on the seller zero party dataset, the zero layer dataset representing an identity archetype of the seller; receiving, by the at least one system server from the seller device, the nomination party dataset; communicating, by the at least one system server to the peer device, the nomination request to the at least one nominated peer of the plurality of peers; receiving, by the at least one system server from the peer device, the acceptance of the nomination request from at least one accepted peer, whereby the at least one accepted peer forms the nuclear network of the seller; creating, by the first layer creation module of the at least one system server, a first layer dataset including an identification of the at least one accepted peer; receiving, by the at least one system server from the buyer device, the buyer zero party dataset; obtaining, by the at least one system server from the at least one third party server, the at least one third party dataset representing at least one cohort aligned with the seller, the at least one cohort forming an extended network of the seller; creating, by the second layer creation module of the at least one system server, a second layer dataset based on the at least one third party dataset, the second layer dataset representing the extended network; selecting, by the at least one system server, from at least one of the first layer dataset and the second layer dataset, individuals meeting a predetermined alignment threshold based on the zero layer dataset and the buyer needs dataset, whereby the selecting step constrains a pool of candidates evaluated for matching; calculating, by the social matching score module of the at least one system server, a social matching score from the at least one third party dataset and the buyer needs dataset, the social matching score associated with the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort; determining, by the textual matching score module of the at least one system server, a textual matching score from the seller zero party dataset and the buyer zero party dataset; processing, by the presets matching score module of the at least one system server, the at least one answer by the seller to the at least one seller questionnaire and the at least one answer by the buyer to the at least one buyer questionnaire with the artificial intelligence module to provide a presets matching score; merging, by the merging module of the at least one system server, the social matching score, the textual matching score, and the presets matching score with a merging algorithm that calculates a weighted average of the social matching score, the textual matching score, and the presets matching score to provide the matching score between the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort, wherein weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics, whereby the matching score is an indicator of a degree of alignment of the buyer and at least one of the seller, the nuclear network, and the extended network; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device, wherein the system automatically generates updated matching scores as new datasets are received without manual intervention. These steps describe managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and are part of the abstract idea falling under “Certain Methods of Organizing Human Activity” and steps that can be performed in the human mind, and therefore fall under the “Mental Processes” abstract idea grouping.
Because the above-noted limitations recite steps falling within the “Certain methods of organizing human activity” abstract idea grouping and the “Mental Processes” abstract idea grouping, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry. Independent claims 19 and 20 recite similar limitations as those recited in claim 1 and therefore are found to recite the same abstract idea(s) as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claims 1, 19, and 20, the additional elements are: providing a matching system having a seller device having a seller device human interface seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; wherein the system automatically generates updated matching scores as new datasets are received without manual intervention (claim 1); a seller device having a seller device human interface, a seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device, human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; wherein the system automatically generates received without manual intervention (claim 19); instructions being executable by one or more processors, providing a matching system, a seller device having a seller device human interface, a seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; the system automatically generates without manual intervention (claim 20). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the step for transmitting is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which is not sufficient to amount to a practical application. In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claims 1, 19, and 20, the additional elements are: providing a matching system having a seller device having a seller device human interface seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; wherein the system automatically generates updated matching scores as new datasets are received without manual intervention (claim 1); a seller device having a seller device human interface, a seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device , human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; wherein the system automatically generates received without manual intervention (claim 19); instructions being executable by one or more processors, providing a matching system, a seller device having a seller device human interface, a seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the system server memory storing a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; the system automatically generates without manual intervention (claim 20). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes that generic computer devices that may be used to implement the invention, which cover virtually any computing device under the sun (Specification at paragraph [0065]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.). Next, the step for transmitting is considered insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-18 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-14 and 17-18 recite “wherein at least one of seller zero party dataset and buyer zero party data includes at least one of personal, demographic, behavioral, financial, geographic, tracking, educational, public life, and professional information, information relating to religious and philosophical beliefs, political affiliations, physical characteristics, online activity and social networking, opinions, interests, preferences, affinities, affiliations, needs, likes and dislikes, passions, and personal identifier information,” “wherein third party dataset includes at least one dataset received,” “wherein the matching score is a numerical value between 0 and 100, and wherein 0 indicates no match and 100 indicates a perfect match,” “wherein the social matching score includes a social reach value, and the social reach value is an indication of fame,” “wherein the social matching score is calculated using seller social information including at least one of a number of followers, a trending score, and a daily engagement rate,” “wherein the social matching score is calculated using the at least one third party dataset, the buyer needs dataset, and at least one of the seller zero party dataset and the buyer zero party dataset,” “wherein the method further includes a step of analyzing at least one seller textual description and at least one buyer textual description,” “provides a seller third party dataset and a buyer third party dataset, and the textual matching score is calculated using the seller zero party dataset, the buyer zero party dataset, the seller third party dataset, and the buyer third party dataset,” “provides at least one of i) at least one question and ii) at least one answer choice for at least one of the at least one seller questionnaire and the at least one buyer questionnaire,” “wherein the at least one question may be a multiple choice question or a textual prompt,” “provides at least one seller question that is included in the at least one seller questionnaire, and provides at least one predetermined seller answer to the at least one seller question, and provides at least one buyer question that is included in the at least one buyer questionnaire, and provides at least one predetermined buyer answer to the at least one buyer question, the at least one buyer question corresponding with the at least one seller question, and provides at least one predetermined seller-buyer answer combination having a predetermined score associated with the predetermined seller-buyer answer combination, and the step of processing the at least one answer by the seller and the at least one answer by the buyer further includes calculating the presets matching score for an actual seller-buyer answer combination by assigning the predetermined score associated with the predetermined seller-buyer answer combination that is same as the actual seller-buyer answer combination,” “wherein the matching score is a weighted average of at least the social matching score, the textual matching score, and the presets matching score,” “wherein at least one of a seller additional dataset and a buyer additional dataset is provided by at least one of an administrator, at least one third party individual, and at least one third party organization,” “wherein the matching score is an indicator of the degree of alignment of the buyer and the seller with respect to at least one of a project, field, industry, opportunity, and arrangement,” “wherein the matching score is an indicator of the degree of alignment of the buyer and the seller and the degree of alignment of the buyer and of a nuclear network of the seller,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible. The additional elements recited in the dependent claims comprise “at least one of an outsourced website, an external database, and a SaaS platform” (claim 3), “wherein the artificial intelligence module is a curated artificial intelligence process” (claim 15), “ wherein the artificial intelligence module includes at least one of a supervised artificial intelligence process, an unsupervised artificial intelligence process, and a Saaty analytical hierarchy process” (claim 16). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements rely on generic computing elements or software for generally linking the judicial exception to a particular technological environment, which does not amount to a practical application. MPEP 2106.05(g)/(h). Under Step 2B, the use of such generic computing elements has been recognized by courts as insufficient to amount to significantly more than the abstract idea. See, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQe2d 1681, 1701 (Fed. Cir. 2015).
Even if the curated artificial intelligence process was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of Artificial Intelligence is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 0039: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”). See also, Muchkaev, US 2010/0287011 (paragraph 0047: “artificial intelligence algorithm such as a search algorithm, a learning algorithm, or any other artificial intelligence algorithm commonly known in the art”).
Similarly, even if the “at least one of a supervised artificial intelligence process, an unsupervised artificial intelligence process, and a Saaty analytical hierarchy process” was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of unsupervised artificial intelligence is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Yesudas et al., US 2022/0343244 (paragraph 0097: “The unsupervised machine learning model may be implemented by any conventional or other machine learning models”).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable Subject Matter
20. Claims 1-20 are allowable over prior art. With respect to independent claims 1, 19, and 20, the closest prior art, Horen et al., Pub. No.: US 2022/0051304 A1 and Balduzzi et al., Pub. No.: US 2014/0114965 A1, collectively teach features for providing a matching system having a seller device having a seller device human interface, a seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for inputting a seller zero party dataset, the seller zero party dataset including at least one answer by the seller to at least one seller questionnaire, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for inputting a buyer zero party dataset, the buyer zero party dataset including at least one answer by the buyer to at least one buyer questionnaire, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a buyer needs dataset for the buyer and a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; receiving, by the at least one system server from the buyer device, the buyer zero party dataset; obtaining, by the at least one system server from the at least one third party server, the at least one third party dataset; calculating, by the social matching score module of the at least one system server, a social matching score from the at least one third party dataset and the buyer needs dataset; determining, by the textual matching score module of the at least one system server, a textual matching score from the seller zero party dataset and the buyer zero party dataset; processing, by the presets matching score module of the at least one system server, the at least one answer by the seller to the at least one seller questionnaire and the at least one answer by the buyer to the at least one buyer questionnaire with the artificial intelligence module to provide a presets matching score; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device,” as recited in amended claim 1 (and as similarly encompassed by independent claims 19 and 20) [See Office Action mailed 06/10/2025 for prior art citations pertinent to the above-noted subject matter].
However, with respect to independent claim 1, Horen et al., Balduzzi et al., and the other prior art of record does not teach “merging, by the merging module of the at least one system server, the social matching score, the textual matching score, and the presets matching score with a merging algorithm that calculates a weighted average of the social matching score, the textual matching score, and the presets matching score to provide the matching score between the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort, wherein weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics, whereby the matching score is an indicator of a degree of alignment of the buyer and at least one of the seller, the nuclear network, and the extended network; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device, wherein the system automatically generates updated matching scores as new datasets are received without manual intervention.”
With respect to the newly added limitation “merging, by the merging module of the at least one system server, the social matching score, the textual matching score, and the presets matching score with a merging algorithm that calculates a weighted average of the social matching score, the textual matching score, and the presets matching score to provide the matching score between the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort, wherein weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics, whereby the matching score is an indicator of a degree of alignment of the buyer and at least one of the seller, the nuclear network, and the extended network; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device, wherein the system automatically generates updated matching scores as new datasets are received without manual intervention,” it is noted that the limitation introduces a specific dynamic weighting mechanism that utilizes performance metric to adjust the relative weighting of the social matching score, the textual matching score, and the presets matching score through an artificial intelligence module and/or administrator unput. Neither Horen nor Balduzzi, alone in combination, teaches or suggests dynamically adjusting weighting factors among multiple score component based on performance metrics nor do they disclose a feedback mechanism that allows such weights to be automatically or administratively tuned in response to performance outcomes. Horen discusses assigning relative weight to various inputs when calculating a final score (paragraph 0147) and generally refers to the use of machine learning algorithms for optimizing machine outputs (paragraph 0072), but it does not teach or suggest merging, by the merging module of the at least one system server, the social matching score, the textual matching score, and the presets matching score with a merging algorithm that calculates a weighted average of the social matching score, the textual matching score, and the presets matching score to provide the matching score between the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort, wherein weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics, whereby the matching score is an indicator of a degree of alignment of the buyer and at least one of the seller, the nuclear network, and the extended network; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device, wherein the system automatically generates updated matching scores as new datasets are received without manual intervention. Similarly, Balduzzi describes assigning relative values to matched attributes (paragraph 0020), but is silent as to dynamically modifying those values or weights over time based on observed performance.
The following is a statement of reasons for the indication of allowable subject matter: The claims are directed to allowable subject matter because the prior art of record either individually or in combination does not teach: “A method for creating a matching score between a seller and a buyer, the method comprising steps of: providing a matching system having a seller device having a seller device human interface seller device memory, a seller device processor, and a seller device display, the seller device human interface configured for inputting a seller zero party dataset and a nomination dataset, the seller zero party dataset including at least one answer by the seller to at least one seller questionnaire, the nomination dataset representing a plurality of peers proposed by the seller as forming a nuclear network of the seller, the seller device memory having machine-readable instructions stored on the seller device memory, and the seller device processor in communication with the seller device human interface and the seller device memory, a peer device having a peer device human interface, a peer device memory, a peer device processor, and a peer device display, the peer device human interface configured for receiving a nomination request from at least one nominated peer of the plurality of peers and for transmitting an acceptance of the nomination request, a buyer device having a buyer device human interface, a buyer device memory, a buyer device processor, and a buyer device display, the buyer device human interface configured for inputting a buyer zero party dataset, the buyer zero party dataset including at least one answer by the buyer to at least one buyer questionnaire, the buyer device memory having machine-readable instructions stored on the buyer device memory, and the buyer device processor in communication with the buyer device human interface and the buyer device memory, and at least one system server having a system server memory and a system server processor, the at least one system server being accessible by an administrator, the at least one system server in communication with the seller device, the peer device, the buyer device, and at least one third party server through a wide area network, the at least one third party server having at least one third party dataset, the system server memory storing a buyer needs dataset for the buyer and a plurality of modules including tangible, non-transitory processor executable instructions, the plurality of modules including a zero layer creation module, a first layer creation module, a second layer creation module, a social matching score module, a textual matching score module, a presets matching score module, a merging module, and an artificial intelligence module; creating, by the zero layer creation module of the at least one system server, a zero layer dataset based on the seller zero party dataset, the zero layer dataset representing an identity archetype of the seller; receiving, by the at least one system server from the seller device, the nomination dataset; communicating, by the at least one system server to the peer device, the nomination request to the at least one nominated peer of the plurality of peers; receiving, by the at least one system server from the peer device, the acceptance of the nomination request from at least one accepted peer, whereby the at least one accepted peer forms the nuclear network of the seller; creating, by the first layer creation module of the at least one system server, a first layer dataset including an identification of the at least one accepted peer; receiving, by the at least one system server from the buyer device, the buyer zero party dataset; obtaining, by the at least one system server from the at least one third party server, the at least one third party dataset representing at least one cohort aligned with the seller, the at least one cohort forming an extended network of the seller; creating, by the second layer creation module of the at least one system server, a second layer dataset based on the at least one third party dataset, the second layer dataset representing the extended network; selecting, by the at least one system server, from at least one of the first layer dataset and the second layer dataset, individuals meeting a predetermined alignment threshold based on the zero layer dataset and the buyer needs dataset, whereby the selecting step constrains a pool of candidates evaluated for matching; calculating, by the social matching score module of the at least one system server, a social matching score from the at least one third party dataset and the buyer needs dataset, the social matching score associated with the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort; determining, by the textual matching score module of the at least one system server, a textual matching score from the seller zero party dataset and the buyer zero party dataset; processing, by the presets matching score module of the at least one system server, the at least one answer by the seller to the at least one seller questionnaire and the at least one answer by the buyer to the at least one buyer questionnaire with the artificial intelligence module to provide a presets matching score; merging, by the merging module of the at least one system server, the social matching score, the textual matching score, and the presets matching score with a merging algorithm that calculates a weighted average of the social matching score, the textual matching score, and the presets matching score to provide the matching score between the buyer and at least one of the seller, the at least one accepted peer, and the at least one cohort, wherein weights applied to the social matching score, the textual matching score, and the presets matching score are dynamically adjustable by at least one of the artificial intelligence module and an administrator based on performance metrics, whereby the matching score is an indicator of a degree of alignment of the buyer and at least one of the seller, the nuclear network, and the extended network; and transmitting the matching score from the at least one system server to at least one of the seller device and the buyer device, wherein the system automatically generates updated matching scores as new datasets are received without manual intervention,” as recited in amended claim 1 (and as similarly encompassed by independent claims 19 and 20), thus rendering claims as allowable over prior art. However, these claims are not allowable because claims 1-20 remain rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stockwell, Pub. No.: US 2014/0149251 A1 – describes systems and methods for matching buyers and sellers based on business oriented parameters.
Ren, Jing, et al. "Matching algorithms: Fundamentals, applications and challenges." IEEE Transactions on Emerging Topics in Computational Intelligence 5.3 (2021): 332-350 – describes Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights