DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendment filed on Dec. 02nd, 2025. The amendments are linked to the original application filed on Nov. 04th, 2022.
Response to Amendment
The Examiner thanks the applicant for the remarks, edits and arguments.
Regarding Drawing Objections
Applicant Remarks:
The applicant has made amendments to the drawings and the specification. This was to ensure proper labeling of reference figures and properly explain the figures in the specification. Applicant has amended figure 1, 4, 5 and 7 and has amended the corresponding sections in the specification. The applicant believes the amendments have corrected all the issues noted by the examiner and request the Drawing Objection be withdrawn.
Examiner Response:
The examiner has reviewed the amendments to the drawings and the specification. There were no issues noted, therefore the Drawing Objection has been withdrawn.
Regarding Claim Rejections – 35 U.S.C. 112(b)
Applicant Remarks:
The applicant states they have made amendments to claims 1, 8 and 15. These amendments were made to further clarify the terms “pre-qualification” pre-verified” users. The claims 7, 14 and 21 are dependent ants of the amended claims and therefore will use the definitions defined in the independent claims. After these amendments the applicant believes the claim rejection under 35 U.S.C. 112(b) should be withdrawn.
Examiner Response:
The examiner has reviewed the amended claims and terms to ensure they disclose defined terms and concepts. After this review the examiner believes the amendment have clarified the term “pre-qualification” and pre-verified user”. The amendments present a clear definition of these terms and allows for better interpretation of the claims. Therefore, the examiner has withdrawn the 112(b) rejection.
Regarding Claim Rejections – 35 U.S.C. 101
Applicant Remarks:
The applicant has amended the claims to clarify and narrow the claimed subject matter. The applicant argues, that after the amendments, the claims recite a technical improvement to an e-commerce platform. The improvements listed include a method to ingest and use multi-source data to control an e-commerce market buyer/seller participation.
Next, the applicant argues that the claims recite a specific improvement to a computer implemented pre-qualification system. According to the applicant the claims recite a novel approach of data analysis to generate and identify pre-verified users. Further the applicant states this system is able to handle and process data in a practical manner and is able to present the findings to a graphical interface.
Next, in accordance with the 2019 PEG the applicant argues that the claims recite use of specific data sources, concrete control actions, technical implementations which are not merely mental steps or processes.
Finally for these reasons the applicant believes the amended claims recite more than abstract ideas and integrate the claims into practical application with an improvement to a technical field of technology. Therefore, the applicant believes that the current amended claims overcome the rejection under 35 U.S.C. 101, and the rejection should be withdrawn.
Examiner Response:
The applicant argues that the limitation in the independent claims do not recite abstract ideas. The examiner would like to point to the MPEP 2106.04(a)(2)(III)(C) which states, “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”. When reviewing the amended independent claims with the broadest reasonable interpretation they appear to recite concepts which a human is able to perform. For example, the broadest reasonable interpretation the limitation, “weight the collected data from each data source based on the frequency of the data collection from the data source to produce frequency-weighted data stored in the database;” would be a process of applying numerical values, weights, to data based on frequency they occur and store that information in a database. A human is able to evaluate a dataset and locate patterns such as frequency and apply mathematical functions to that data. Next this process is also performed on a computing system to store it in a database, therefore it is reasonable to assume this limitation recites an abstract idea which is performed on a generic computing systems, meaning this limitation would still be considered an abstract idea per the MPEP. Other limitations in the independent claims recite abstract ideas, see 101 rejection below for further analysis.
Next, the applicant argues that the remaining limitations are directed to an improvement of a computer-implemented prequalification system. The examiner would like to point to the MPEP 2106.04(d)(1) which states, “in short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.”. To evaluate the claims the examiner must also see if an ordinary person of the art could recognize an improvement. To do this, the claims as a whole must be evaluated as well as the specification. After reading though the amended claims the examiner does not believe they recite a technical improvement of computer-implemented prequalification system. The claims recite a system for e-commerce which is able to generate information for a user to make business decision using machine learning. The processes disclosed appear to recite how a process is completed but does not disclose the improvements of that process and how it improves computer-implemented prequalification system. Further information from the specification needs to be brought into the claims to clearly recite the improvement to a technical field. The claims fail to properly recite the technical improvement; therefore, they would not integrate the claims into a practical application.
Finally, After the claims are amended the examiner must review all claims with the same Alice/Mayo test to ensure patent eligibility. After further review of these claims and for the reasons above, the examiner still believes the claims recite patent ineligible subject matter and is therefore still rejected under 35 U.S.C. 101, see 101 rejection below.
Regarding Claim Rejections – 35 U.S.C. 103
Applicant Remarks:
The applicant argues that Both Wu and Sukrat fail to disclose, “(1) computing per-source update frequency and applying a learned weighting proportional to that frequency before model training/inference; (2) producing a binary pre-verification state (not merely a score) used to (3) gate publication into a directory/workspace that orchestrates branding, product, logistics, and contract-price fields.”.
Next, the applicant argues that Wu fails to discloses, “identity/compliance verification, authoritative registries, policy thresholds, transactional gating of marketplace functions, or a cryptographically chained audit log.”.
Next, the applicant argues that Sukrat fails to disclose, “pre-qualifying suppliers for e-commerce transactions using regulatory compliance databases, certifications registries, public financial records, no per-source frequency-of-collection weighting, and provides no identification of "pre-verified" users with verification information for a purchasing platform.”. Further the applicant states that Sukrat's discloses, “[an] advisory for vendor capability/maturity, not supplier verification for buyer assurance.”.
Next, the applicant argues that both Wu and Sukrat fail to disclose, “cadence-proportional weighting as technically meaningful for onboarding reliability or teach platform-level gating based on a binary model output.”.
Next, the applicant argues that Wu and Surat fail to disclose, “pre-qualification / verification workflow, nor the frequency-of-collection-based source weighting tied to disparate compliance-oriented data sources, nor the pre-verified user identification and dossier output.”.
Finally, the applicant argues that it would not be able to combine the art suggested by the examiner because a person or ordinary skill in the art would not have the motivation to combine the art as stated by the examiner.
Examiner Response:
The applicant states that Wu and Sukrat fails to disclose data ingestion of patterns and frequency of a specific source. Examiner would like to point to the model in Wu. This model is able to identify user sessions and record timestamps of the sessions. The model is then able to send this information to an AI model for further processing. Using the broadest reasonable interpretation, this is a process of collecting data from a source, or user, based on sets of timestamped-sessions. This set of sessions would be interpreted as the frequency of a user sessions. Next the applicant argues Wu and Sukrat fails to disclose a “binary pre-verification state (not merely a score)”. The examiner would like to point out that Wu or Sukrat does not need to teach or disclose this concept because this concept is not stated in the claims. The claims do not recite a binary, Yes/no or 1/0, system to determine a pre-verification state. As it is interpreted by the examiner and as stated in the claims, the pre-verification state is determined based on threshold. The broadest reasonable interpretation of a threshold can include binary states, yes/no or 1/0, as well as value on a scale containing many different values i.e. -1, 0, 1, 2, or 5. Next the applicant states that Wu and Sukrat fails to disclose, “gate publication into a directory/workspace that orchestrates branding, product, logistics, and contract-price fields”. The examiner would like to point out that in the non-final rejection, the marketplace and ratings is disclosed in Sukrat. Sukrat discloses a system for E-commerce that provides recommendations to vendors including products and pricing.
The applicant argues that Wu fails to disclose “identity/compliance verification, authoritative registries, policy thresholds, transactional gating of marketplace functions, or a cryptographically chained audit log.”. The examiner would like to point out that Wu does not explicitly need to disclose these concepts because it is not stated in the claims. The claims do not recite: “identity/compliance verification”, “authoritative registries”, “policy thresholds”, “transactional gating of marketplace functions” or a “cryptographically chained audit log”. Further review of the amended claims and the examiner would like to point out that these concepts are still absent from the amened claims. Therefore, Wu or Sukrat do not need to teach or disclose these concepts.
Next, the applicant argues that Sukrat fails to disclose, “pre-qualifying suppliers for e-commerce transactions using regulatory compliance databases, certifications registries, public financial records, no per-source frequency-of-collection weighting, and provides no identification of "pre-verified" users with verification information for a purchasing platform.”. The examiner would like to point out that Sukrat and Wu does not need to teach most of these concepts because most of these concepts are not present in the claims. The claims fail to recite, “pre-qualifying suppliers for e-commerce transactions using regulatory compliance databases” and “certifications registries”. The examiner would like to point out that Wu does teach a process which uses financial records from sessions, as stated in the previous office action, so Sukrat is not required to teach or disclose the use of “public financial records”. Next as stated above, Wu does teach the use of timestamped session information as input training data for the AI model presented. This teaches the concept of frequency-based data collection and is used to adjust the network through training. Finally, the examiner would like to point out that the system in Sukrat does teach the use of verified users in a commerce setting for vendors, these would be the users who are on the platform.
Next. the applicant argues that the combination of Wu and Sukrat would not obvious. The examiner would like to point to the non-final rejection which states the reasons for combining Sukrat and Wu. Further the combination of art is considered using the framework for determining obviousness as set forth in Graham v. John Deere Co. Under this framework, art is examined to determine its scope and content and evaluate the difference between the claimed invention and proposed. Both prior arts use machine learning to produce a recommendation using user patterns and data. In Wu the user data is used to produce a recommendation based on own data. In Sukrat they similarly produce a recommendation for a vendor based on collected data. Neither of these arts are claim to produce an e-commerce platform similar to Amazon or Temu, however the concepts of the arts are used to produce results similar to the claimed subject matter. The claimed subject matter does cover a lot of different technical fields such as user verification and a recommendation system. Developing an e-commerce solution is complex and requires the use of different components in different technical fields. These components may not directly relate but are used in conjunction to produce a system as a whole. An ordinary person of the art may look at many different articles when creating an e-commerce platform, including articles that disclose certain features of an e-commerce platform, such as recommendation algorithms.
Finally, after each amendment, the examiner must perform a full and compete search in order to find new material missed in the initial search and/or to teach the amended claims. A full and complete search was performed and new art was found, Khashman, which teaches the claimed subject matter. For the reasons above and considering the new proposed art, the rejection under 35 U.S.C. 103 is upheld, see 103 rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1, recites “A computerized network system for facilitating a plurality of users in e-commerce, the system comprising: at least one server computer comprising: a database, an artificial intelligence (AI) module functionally coupled to the database, the Al module comprising a neural network, and a data input/output interface coupled to the Al module and the database; wherein the database, the Al module, and the data input/output interface are configured to:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“weight the collected data from each data source based on the frequency of the data collection from the data source to produce frequency-weighted data stored in the database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and apply judgement or weight to the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyze the collected data and the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide a judgement of that data and observe that data for patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyze the pre-qualification scores, the associated verification information, and associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide judgement based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identify users that satisfy a pre-qualification threshold as pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and is able to determine if a threshold is met. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “repeatedly collect data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“repeatedly train the neural network of the Al module using the frequency-weighted data for establishing and optimizing one or more data- analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“output to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “repeatedly collect data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“repeatedly train the neural network of the Al module using the frequency-weighted data for establishing and optimizing one or more data- analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“output to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein each of the one or more data- analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein each of the one or more data- analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the database, the Al module, and the data input/output interface are configured for: identifying demographic markets and online marketing vessels;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and identify different markets and business strategies. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“providing marketing strategies and campaign plans; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide judgments on market strategies and campaign information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the database, the Al module, and the data input/output interface are configured for: providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the database, the Al module, and the data input/output interface are configured for: providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the database, the Al module, and the data input/output interface are configured for: automatically identifying targeted content and targeted users based on said analyzing the collected data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data for specified target information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“ranking the one or more of the pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and rank it based on given data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the database, the Al module, and the data input/output interface are configured for: providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the database, the Al module, and the data input/output interface are configured for: providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 8, recites “A computerized method for facilitating a plurality of users in e- commerce using a database, an AI module, and a data input/output interface, the computerized method comprising:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and apply judgement or weight to the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyzing the collected data and the frequency-weighted data using the one or more data- analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide a judgement of that data and observe that data for patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide judgement based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying users that satisfy a pre-qualification threshold as pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and is able to determine if a threshold is met. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein each of the one or more data-analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein each of the one or more data-analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“identifying demographic markets and online marketing vessels;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and identify different markets and business strategies. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“providing marketing strategies and campaign plans; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide judgments on market strategies and campaign information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“automatically identifying targeted content and targeted users based on said analyzing the collected data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data for specified target information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“ranking the one or more of the pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and rank it based on given data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 15, recites “One or more non-transitory computer-readable storage devices comprising computer-executable instructions for facilitating a plurality of users in e-commerce using a database, an AI module, and a data input/output interface, wherein the instructions, when executed, cause a processing structure to perform actions comprising:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and apply judgement or weight to the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyzing the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide a judgement of that data and observe that data for patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and provide judgement based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying users that satisfy a pre-qualification threshold as pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and is able to determine if a threshold is met. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein each of the one or more data-analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein each of the one or more data-analysis models comprises: a structure for computing a prediction;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“weights of the collected data from each data source for said weighting the collected data from each data source; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“biases of the collected data from each data source.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“identifying demographic markets and online marketing vessels;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and identify different markets and business strategies. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“providing marketing strategies and campaign plans; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide judgments on market strategies and campaign information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generating marketing solutions based on the collected data and using the one or more data- analysis models.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “providing links to points-of-purchase and/or to online ordering forms.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“automatically identifying targeted content and targeted users based on said analyzing the collected data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data for specified target information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “automatically sending the identified targeted content to the identified targeted users.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 21
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“ranking the one or more of the pre-verified users; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and rank it based on given data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“functionally connecting the pre-verified users for completing e-commerce transactions.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
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.
Claims 1, 2, 4-6, 8, 9, 11-13, 15, 16, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al., Wu et al., “Personal Recommendation Using Deep Recurrent Neural Networks in NetEase”, 2016, hereinafter “Wu”) in view of Khashman, (Khashman, “Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes”, 2010, hereinafter “Khashman”).
Regarding Claim 1, Wu discloses, “A computerized network system for facilitating a plurality of users in e-commerce, the system comprising:” (Experiments, pp. 1225; "To evaluate the proposed DRNN model, we deploy it on the Kaola testing system. The system repeats the same web log of June 1st, 2015, which consists of 232,326 records and can be grouped into 27,985 sessions. There are 37667 unique users in the log, who bought 1584 different items. The log is streamed to a document database (MongoDB2 in current implementation) which is split into sessions and fed to the DRNN model. The DRNN model is trained on a GPU server equipped with two Xeon 2.6G 8-core CPUs, 64GB memory and one Ge Force GTX Titian Z GPU (12GB DDR5). Caffe is configured to run on the GPU mode." This article discloses a computing system which executes a method for e-commerce user recommendation system. This recommendation system is designed to provide user recommendation in an e-commerce environment.)
“at least one server computer comprising: a database,” (Overview of Recommendation Module, pp. 1220; "Figure 2 shows its architecture. User's request to a web page is sent to the Kaola web server, which is accumulated together to form a viewing session." The system proposed in this article uses Kaola Web server. This teaches the use of a server in the system.) and (Figure 2, pp. 1220; This figure discloses the workflow of the recommendation module. This teaches the use of a database, which is labeled the document database. This stores the session data generated by the user and other user data demographics.)
“an artificial intelligence (AI) module functionally coupled to the database, the Al module comprising a neural network, and” (Overview of Recommendation Module, pp. 1220; "Figure 2 shows its architecture. User's request to a web page is sent to the Kaola web server, which is accumulated together to form a viewing session. The viewing session is used as the input for the DRNN model to generate a real time prediction. DRN N model collaborates with a FNN model, which is used to simulate the CF algorithm." Figure 2 discloses the system used in this article. It consists of a deep recurrent neural network which is connected to a database to produce customer recommendations.)
“a data input/output interface coupled to the Al module and the database;” (Figure 2, pp. 1220; This figure discloses the workflow of the recommendation module. This discloses the input of data from the kaola web server. This data can be stored in a database and used to update the recommendation model on a real time bases. This will then output the recommendations back to the web server to then output to the user. This teaches a system that has input of data which is handled accordingly and then output to the user.)
“wherein the database, the Al module, and the data input/output interface are configured to: repeatedly collect data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” (Overview of Recommendation Module, pp. 1220; "On the other hand, after a session completes, we actually get a ground truth result for our prediction (whether the user buys our recommended item). We can adjust our model using the new training sample. In particular, the http access log is streamed to a document database. Each log entry is saved as a JSON document. We create indexes on user ID, session ID and timestamp for each document." The proposed system in this article will generate recommendations based on the user's webpage viewing history and purchase history. This system will use the user's viewing history to constantly update the recommendation model and send the user recommendations for similar websites.)
“repeatedly train the neural network of the Al module using the frequency-weighted data for establishing and optimizing one or more data- analysis models;” (Introduction, pp. 1219; "Finally, we can use the access log as our training set, and it also has a record of which items the user finally bought when his/her session ended. However, the training set is keeping updated, because as long as the system is on line, we can obtain new logs of more user sessions which can be further used to refine our model. In other words, our model is not the "train once" model.” This system uses a real time recommendation system. This model constantly updates and trains based on user data gathered, which includes viewing and purchase history.)
“output to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” (Introduction, pp. 1218; "As shown in Figure 1, pages are accessed in some specific orders, e.g., from a category page to a product page. When the user opens a new page, we will update our recommendations and use a widget in the page to show the new results." The user will receive the recommendations from the system using a web widget. Under the broadest reasonable interpretation this teaches the that the results are output in some form which is graphically visible to a user.)
Wu fails to explicitly disclose, “weight the collected data from each data source based on the frequency of the data collection from the data source to produce frequency-weighted data stored in the database;”, “analyze the collected data and the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;”, “analyze the pre-qualification scores, the associated verification information, and associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and ratings;”, and “identify users that satisfy a pre-qualification threshold as pre-verified users; and”.
However, Khashman discloses, “weight the collected data from each data source based on the frequency of the data collection from the data source to produce frequency-weighted data stored in the database;” (Dataset for credit evaluation, pp. 6236; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” Data from the dataset is normalized and weighted to values between 0 and 1. This data set is financial data containing different forms of records and transactions. The frequency of transactions is also taken into considerations and is weighted accordingly.)
“analyze the collected data and the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” (Neural network arbitration, pp. 6237; “We implement our investigation using three neural models: ANN-1 with h = 18, ANN-2 with h = 23 and ANN-3 with h = 27. The optimum number of hidden neurons h in all three models, which assures meaningful training while keeping the time cost to a minimum, was obtained after several experiments involving the adjustment of the number of hidden neurons from one to 50 neurons. The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application.” After the data is transformed, it is evaluated using a neural network. This network architecture is seen in figure 1. Each of the 24 attributes are input into the model and each layer evaluates the values. At the very end the model will output a final score on whether to reject or approve the application. The broadest reasonable interpretation of the arts model is a process which intakes financial data, manipulates the data for the model, evaluates the modified data and finally generates a score as a result.
“analyze the pre-qualification scores, the associated verification information, and associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and ratings;” (Neural network arbitration, pp. 6236; “The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application. Notice that the output classification in the German credit dataset uses ‘‘1” and ‘‘2” for ‘‘accept” and ‘‘reject”, respectively. We have simply re-coded the output classification to binary 0 and 1.” This score and final output is derived from the financial information input into the system. This score will be between 1 and 0. This score is evaluated to meet certain criteria; the model would decide that the user is approved for a credit application.)
“identify users that satisfy a pre-qualification threshold as pre-verified users; and” (Neural network arbitration, pp. 6237; “A simple thresholding scheme is then sufficient for the neural network’s single output neuron to divide the feature space into the two categories. A threshold value of 0.5 is used to distinguish between credit groups, good credit and bad credit. If the output result of the neural network is greater than or equal to 0.5, the presented case is assigned to one class (good, accept); otherwise, it is assigned to the other class (bad, reject).” The final output of the model will be between 0 and 1. This model will use a threshold to determine approval or rejection. The example given if the final score is above 0.5 then the user would be approved and if is below then they would be rejected. The broadest reasonable interpretation of this is that a user is approved, or verified, if the final score meets a given threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Wu and Khashman. Wu teaches a system that is able to evaluate user data and provide recommendations for future purchases. Khashman teaches an evaluation machine learning model that is able to evaluate financial data and verify or authorize a credit application. One of ordinary skill would have motivation to combine a machine learning system that is able to provide a user personalized recommendations for e-commerce with a system that is able to evaluate user financial data and determine if the user is qualified for a credit application, “More importantly is the accuracy rate of the credit evaluation system. At first impression, and looking at the overall accuracy rates, the third implementation (ANN-3, LS6) appears to have the highest overall rate of 85.9%. However, careful analysis of the obtained results shows that this overall rate can be misleading. A more appropriate comparison requires inspecting the accuracy rates of the training dataset (T-dataset) and the validation dataset (V-dataset) separately. This is because the V-dataset accuracy rate is obtained by exposing the trained neural model to unseen inputs or cases, thus reflecting the robustness of the trained model. The T-dataset accuracy rate is also significant in particular with the German credit dataset, which is considered as unbalanced and difficult to process by intelligent systems. Based on these considerations, further investigation of the obtained results, reveals that the highest accuracy rates amongst the successfully trained models belong to neural model ANN-2, under learning scheme LS4; achieving 99.25% T-dataset accuracy rate, and 73.17% V-dataset accuracy rate. When combining the observed results, we find out that the credit risk evaluation system can be successfully and efficiently implemented, with an optimum configuration when using neural network model ANN-2 trained under learning scheme LS4, i.e. with a training-to-validation ratio of 40%:60%.” (Khashman, Implementation and experimental results, pp. 6238).
Regarding Claim 2, Wu fails to explicitly disclose the limitations of this claims, however, Khashman discloses, “wherein each of the one or more data- analysis models comprises: a structure for computing a prediction;” (The Evaluation system, pp. 6235; “The neural network-based credit risk evaluation system consists of two phases: a data processing phase where each numerical value of the applicant’s attributes within the dataset is normalized separately; this is one of our objectives in this work. The output of this phase provides normalized numerical values representing a credit applicant’s case, which is used in the second phase; evaluating the applicant’s attributes and deciding whether to accept or reject the application using a neural network.” The model in this article uses machine learning to produce an outcome. This model will ingest data and determine whether or not an applicant should be approved or denied for a credit application. This model uses a neural network to output a final result.)
“weights of the collected data from each data source for said weighting the collected data from each data source; and” (Dataset for credit evaluation, pp. 6235; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” The model in this article uses a dataset containing financial information. This model will process this data into different categories and normalize these numbers. The model will sort the data and then apply a weight or bias to it so it is between 1 and 0. The system will then process that data using a neural network to produce an outcome.)
“biases of the collected data from each data source.” (Credit application data processing, pp. 6236; “This phase is a data preparation phase for neural network training and classification/evaluation. Here, the input data (attribute numerical values) are separately normalized to values between ‘‘0” and ‘‘1”.” The data as stated above is categorized and then normalized. This model is able to use this data to make a determination using a neural network.)
Regarding Claim 4, Wu discloses, “wherein the database, the Al module, and the data input/output interface are configured for: providing links to points-of-purchase and/or to online ordering forms.” (Introduction, pp. 1218; "Suppose the user is searching for men's wallets and kids' shoes. The next page to be opened is "men.html" and correspondingly, we may update our recommendations as Levis' jeans and Diesel's shirts. This is definitely not a correct recommendation, but it is the best guess given current viewing history. After the user opens pages list.html?cat=101"(directory for men's wallets) and "2915823401.html? (boy's clog), we update the recommendations as wallets and crocs clog, which perfectly hit the hot spot." This prediction system was designed to send recommendations to a web widget on the e-commerce platform. This will display different recommended items that are product recommendations to potential buyers. The recommendation would contain a hyperlink which would send the user to that webpage.)
Regarding Claim 5, Wu discloses, “wherein the database, the Al module, and the data input/output interface are configured for: automatically identifying targeted content and targeted users based on said analyzing the collected data; and” (Introduction, pp. 1219; "Third, we also build a feedforwarding neural network (FNN) to simulate the CF algorithm. Our FNN accepts the user's purchase history vector as input and generates the prediction for the probability of buying an item. FNN is forged with DRNN to produce the final results, while how the two networks are combined is learned from our training data." This system is designed to send product recommendations to users. This will take users data to train multiple neural networks. The neural networks use the user data to output a target recommendation to the user.)
“automatically sending the identified targeted content to the identified targeted users.” (Overview of Recommendation Module, pp. 1220; "Finally, Kaola server returns the requested web page to the user by demonstrating the recommendation results in a specific web widget. As the viewing session continues (more pages have been viewed), we gradually refine our prediction results. Users are expected to find their items from the recommendation results with a higher probability." This system will regularly update the user recommendations based on user viewing history. Every time the user accesses a new marketplace webpage the model will update and send the user a new product recommendation.)
Regarding Claim 6, Wu discloses, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” (Overview of Recommendation Module, pp. 1220; "Finally, Kaola server returns the requested web page to the user by demonstrating the recommendation results in a specific web widget. As the viewing session continues (more pages have been viewed), we gradually refine our prediction results. Users are expected to find their items from the recommendation results with a higher probability." This system will regularly update the user recommendations based on user viewing history. Every time the user accesses a new marketplace webpage the model will update and send the user a new recommendation. This teaches a defined frequency to send the automated product recommendations.)
Regarding Claim 8, Wu discloses, “A computerized method for facilitating a plurality of users in e- commerce using a database, an AI module, and a data input/output interface, the computerized method comprising:” (Overview of Recommendation Module, pp. 1219; "Personalized recommendation is a key feature in the ecommerce system (e.g., Kaola) to improve the user's experience. Previous in Kaola, we collected users' purchase history and applied the CF algorithm to generate a recommendation result for each user in an offline process. When a user logged in, we pushed the recommendation results to him/her." The article discloses a method for a recommendation system. This recommendation system is designed to provide user product recommendations in an e-commerce setting.)
“repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” (Overview of Recommendation Module, pp. 1220; "On the other hand, after a session completes, we actually get a ground truth result for our prediction (whether the user buys our recommended item). We can adjust our model using the new training sample. In particular, the http access log is streamed to a document database. Each log entry is saved as a JSON document. We create indexes on user ID, session ID and timestamp for each document." The proposed system in this article will generate recommendations based on the user's webpage viewing history and purchase history. This system will use the user's viewing history to constantly update the recommendation model and send the user recommendations for similar websites.)
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” (Introduction, pp. 1219; "Finally, we can use the access log as our training set, and it also has a record of which items the user finally bought when his/her session ended. However, the training set is keeping updated, because as long as the system is on line, we can obtain new logs of more user sessions which can be further used to refine our model. In other words, our model is not the "train once" model.) This system uses a real time recommendation system. This model constantly updates and trains based on user data gathered, which includes viewing and purchase history.)
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” (Introduction, pp. 1218; "As shown in Figure 1, pages are accessed in some specific orders, e.g., from a category page to a product page. When the user opens a new page, we will update our recommendations and use a widget in the page to show the new results." The user will receive the recommendations from the system using a web widget. Under the broadest reasonable interpretation this teaches the that the results are output in some form which is graphically visible to a user.)
Wu fails to explicitly disclose, “weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;”, “analyzing the collected data and the frequency-weighted data using the one or more data- analysis models to generate pre-qualification scores, associated verification information, and associated ratings;”, “analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;”, and “identifying users that satisfy a pre-qualification threshold as pre-verified users; and”.
However, Khashman discloses, “weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;” (Dataset for credit evaluation, pp. 6236; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” Data from the dataset is normalized and weighted to values between 0 and 1. This data set is financial data containing different forms of records and transactions. The frequency of transactions is also taken into considerations and is weighted accordingly.)
“analyzing the collected data and the frequency-weighted data using the one or more data- analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” (Neural network arbitration, pp. 6237; “We implement our investigation using three neural models: ANN-1 with h = 18, ANN-2 with h = 23 and ANN-3 with h = 27. The optimum number of hidden neurons h in all three models, which assures meaningful training while keeping the time cost to a minimum, was obtained after several experiments involving the adjustment of the number of hidden neurons from one to 50 neurons. The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application.” After the data is transformed, it is evaluated using a neural network. This network architecture is seen in figure 1. Each of the 24 attributes are input into the model and each layer evaluates the values. At the very end the model will output a final score on whether to reject or approve the application. The broadest reasonable interpretation of the arts model is a process which intakes financial data, manipulates the data for the model, evaluates the modified data and finally generates a score as a result.)
“analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;” (Neural network arbitration, pp. 6236; “The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application. Notice that the output classification in the German credit dataset uses ‘‘1” and ‘‘2” for ‘‘accept” and ‘‘reject”, respectively. We have simply re-coded the output classification to binary 0 and 1.” This score and final output is derived from the financial information input into the system. This score will be between 1 and 0. This score is evaluated to meet certain criteria; the model would decide that the user is approved for a credit application.)
“identifying users that satisfy a pre-qualification threshold as pre-verified users; and” (Neural network arbitration, pp. 6237; “A simple thresholding scheme is then sufficient for the neural network’s single output neuron to divide the feature space into the two categories. A threshold value of 0.5 is used to distinguish between credit groups, good credit and bad credit. If the output result of the neural network is greater than or equal to 0.5, the presented case is assigned to one class (good, accept); otherwise, it is assigned to the other class (bad, reject).” The final output of the model will be between 0 and 1. This model will use a threshold to determine approval or rejection. The example given if the final score is above 0.5 then the user would be approved and if is below then they would be rejected. The broadest reasonable interpretation of this is that a user is approved, or verified, if the final score meets a given threshold.)
Regarding Claim 9, Wu fails to explicitly disclose the limitations of this claim, however, Khashman discloses, “a structure for computing a prediction;” (The Evaluation system, pp. 6235; “The neural network-based credit risk evaluation system consists of two phases: a data processing phase where each numerical value of the applicant’s attributes within the dataset is normalized separately; this is one of our objectives in this work. The output of this phase provides normalized numerical values representing a credit applicant’s case, which is used in the second phase; evaluating the applicant’s attributes and deciding whether to accept or reject the application using a neural network.” The model in this article uses machine learning to produce an outcome. This model will ingest data and determine whether or not an applicant should be approved or denied for a credit application. This model uses a neural network to output a final result.)
“weights of the collected data from each data source for said weighting the collected data from each data source; and” (Dataset for credit evaluation, pp. 6235; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” The model in this article uses a dataset containing financial information. This model will process this data into different categories and normalize these numbers. The model will sort the data and then apply a weight or bias to it so it is between 1 and 0. The system will then process that data using a neural network to produce an outcome.)
“biases of the collected data from each data source.” (Credit application data processing, pp. 6236; “This phase is a data preparation phase for neural network training and classification/evaluation. Here, the input data (attribute numerical values) are separately normalized to values between ‘‘0” and ‘‘1”.” The data as stated above is categorized and then normalized. This model is able to use this data to make a determination using a neural network.)
Regarding Claim 11, Wu discloses, “providing links to points-of-purchase and/or to online ordering forms.” (Introduction, pp. 1218; "Suppose the user is searching for men's wallets and kids' shoes. The next page to be opened is "men.html" and correspondingly, we may update our recommendations as Levis' jeans and Diesel's shirts. This is definitely not a correct recommendation, but it is the best guess given current viewing history. After the user opens pages list.html?cat=101"(directory for men's wallets) and "2915823401.html? (boy's clog), we update the recommendations as wallets and crocs clog, which perfectly hit the hot spot." This prediction system was designed to send recommendations to a web widget on the e-commerce platform. This will display different recommended items that are product recommendations to potential buyers. The recommendation would contain a hyperlink which would send the user to that webpage.)
Regarding Claim 12, Wu discloses, “automatically identifying targeted content and targeted users based on said analyzing the collected data; and” (Introduction, pp. 1219; "Third, we also build a feedforwarding neural network (FNN) to simulate the CF algorithm. Our FNN accepts the user's purchase history vector as input and generates the prediction for the probability of buying an item. FNN is forged with DRNN to produce the final results, while how the two networks are combined is learned from our training data." This system is designed to send product recommendations to users. This will take users data to train multiple neural networks. The neural networks use the user data to output a target recommendation to the user.)
“automatically sending the identified targeted content to the identified targeted users.” (Overview of Recommendation Module, pp. 1220; "Finally, Kaola server returns the requested web page to the user by demonstrating the recommendation results in a specific web widget. As the viewing session continues (more pages have been viewed), we gradually refine our prediction results. Users are expected to find their items from the recommendation results with a higher probability." This system will regularly update the user recommendations based on user viewing history. Every time the user accesses a new marketplace webpage the model will update and send the user a new product recommendation.)
Regarding Claim 13, Wu discloses, “wherein said automatically sending the identified targeted content to the identified targeted users comprises: automatically sending the identified targeted content to the identified targeted users with a predefined frequency or a frequency adaptively determined based on said analyzing the collected data.” (Overview of Recommendation Module, pp. 1220; "Finally, Kaola server returns the requested web page to the user by demonstrating the recommendation results in a specific web widget. As the viewing session continues (more pages have been viewed), we gradually refine our prediction results. Users are expected to find their items from the recommendation results with a higher probability." This system will regularly update the user recommendations based on user viewing history. Every time the user accesses a new marketplace webpage the model will update and send the user a new recommendation. This teaches a defined frequency to send the automated product recommendations.)
Regarding Claim 15, Wu discloses, “One or more non-transitory computer-readable storage devices comprising computer-executable instructions for facilitating a plurality of users in e-commerce using a database, an AI module, and a data input/output interface, wherein the instructions, when executed, cause a processing structure to perform actions comprising:” (Experiments, pp. 1225; "To evaluate the proposed DRNN model, we deploy it on the Kaola testing system. The system repeats the same web log of June 1st, 2015, which consists of 232,326 records and can be grouped into 27,985 sessions. There are 37667 unique users in the log, who bought 1584 different items. The log is streamed to a document database (MongoDB2 in current implementation) which is split into sessions and fed to the DRNN model. The DRNN model is trained on a GPU server equipped with two Xeon 2.6G 8-core CPUs, 64GB memory and one Ge Force GTX Titian Z GPU (12GB DDR5). Caffe is configured to run on the GPU mode." This article discloses a computing system which contains computer readable instructions which when executed, produces an e-commerce user product recommendation system. This recommendation system is designed to provide user product recommendation in an e-commerce environment.)
“repeatedly collecting data related to the plurality of users from a plurality of data sources, the data comprising one or more of history, regulatory compliance, certifications, public financial records, pricing records, shipping records, import & export records, purchasing records, reputation, customer testimonials, legal history, credibility, warranty and service terms;” (Overview of Recommendation Module, pp. 1220; "On the other hand, after a session completes, we actually get a ground truth result for our prediction (whether the user buys our recommended item). We can adjust our model using the new training sample. In particular, the http access log is streamed to a document database. Each log entry is saved as a JSON document. We create indexes on user ID, session ID and timestamp for each document." The proposed system in this article will generate recommendations based on the user's webpage viewing history and purchase history. This system will use the user's viewing history to constantly update the recommendation model and send the user recommendations for similar websites.)
“repeatedly training a neural network of the AI module using the frequency-weighted data for establishing and optimizing one or more data-analysis models;” (Introduction, pp. 1219; "Finally, we can use the access log as our training set, and it also has a record of which items the user finally bought when his/her session ended. However, the training set is keeping updated, because as long as the system is on line, we can obtain new logs of more user sessions which can be further used to refine our model. In other words, our model is not the "train once" model.) This system uses a real time recommendation system. This model constantly updates and trains based on user data gathered, which includes viewing and purchase history.)
“outputting to a graphic user interface (GUI), the pre-qualification scores, the associated verification information, the associated ratings, and the identified pre-verified users.” (Introduction, pp. 1218; "As shown in Figure 1, pages are accessed in some specific orders, e.g., from a category page to a product page. When the user opens a new page, we will update our recommendations and use a widget in the page to show the new results." The user will receive the recommendations from the system using a web widget. Under the broadest reasonable interpretation this teaches the that the results are output in some form which is graphically visible to a user.)
Wu fails to explicitly disclose, “weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;”, “analyzing the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;”, “analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;”, and “identifying users that satisfy a pre-qualification threshold as pre-verified users; and”.
However, Khashman discloses, “weighting the collected data from each data source based on a frequency of collection from that data source to produce frequency-weighted data stored in the database;” (Dataset for credit evaluation, pp. 6236; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” Data from the dataset is normalized and weighted to values between 0 and 1. This data set is financial data containing different forms of records and transactions. The frequency of transactions is also taken into considerations and is weighted accordingly.)
“analyzing the frequency-weighted data using the one or more data-analysis models to generate pre-qualification scores, associated verification information, and associated ratings;” (Neural network arbitration, pp. 6237; “We implement our investigation using three neural models: ANN-1 with h = 18, ANN-2 with h = 23 and ANN-3 with h = 27. The optimum number of hidden neurons h in all three models, which assures meaningful training while keeping the time cost to a minimum, was obtained after several experiments involving the adjustment of the number of hidden neurons from one to 50 neurons. The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application.” After the data is transformed, it is evaluated using a neural network. This network architecture is seen in figure 1. Each of the 24 attributes are input into the model and each layer evaluates the values. At the very end the model will output a final score on whether to reject or approve the application. The broadest reasonable interpretation of the arts model is a process which intakes financial data, manipulates the data for the model, evaluates the modified data and finally generates a score as a result.)
“analyzing the pre-qualification scores, the associated verification information, and the associated ratings to generate a pre-qualification decision for users as suppliers, manufacturers, and products and service providers together with the associated verification information and the associated ratings;” (Neural network arbitration, pp. 6236; “The output layer has one single neuron, which uses binary output data representation; ‘0’ for accepting or ‘1’ for rejecting a credit application. Notice that the output classification in the German credit dataset uses ‘‘1” and ‘‘2” for ‘‘accept” and ‘‘reject”, respectively. We have simply re-coded the output classification to binary 0 and 1.” This score and final output is derived from the financial information input into the system. This score will be between 1 and 0. This score is evaluated to meet certain criteria; the model would decide that the user is approved for a credit application.)
“identifying users that satisfy a pre-qualification threshold as pre-verified users; and” (Neural network arbitration, pp. 6237; “A simple thresholding scheme is then sufficient for the neural network’s single output neuron to divide the feature space into the two categories. A threshold value of 0.5 is used to distinguish between credit groups, good credit and bad credit. If the output result of the neural network is greater than or equal to 0.5, the presented case is assigned to one class (good, accept); otherwise, it is assigned to the other class (bad, reject).” The final output of the model will be between 0 and 1. This model will use a threshold to determine approval or rejection. The example given if the final score is above 0.5 then the user would be approved and if is below then they would be rejected. The broadest reasonable interpretation of this is that a user is approved, or verified, if the final score meets a given threshold.)
Regarding Claim 16, Wu fails to explicitly disclose the limitations in this claim, however, Khashman discloses, “a structure for computing a prediction;” (The Evaluation system, pp. 6235; “The neural network-based credit risk evaluation system consists of two phases: a data processing phase where each numerical value of the applicant’s attributes within the dataset is normalized separately; this is one of our objectives in this work. The output of this phase provides normalized numerical values representing a credit applicant’s case, which is used in the second phase; evaluating the applicant’s attributes and deciding whether to accept or reject the application using a neural network.” The model in this article uses machine learning to produce an outcome. This model will ingest data and determine whether or not an applicant should be approved or denied for a credit application. This model uses a neural network to output a final result.)
“weights of the collected data from each data source for said weighting the collected data from each data source; and” (Dataset for credit evaluation, pp. 6235; “The German credit dataset contains 1000 instances or cases of loan applications. The original data has a mix of 20 categorical and numerical attributes (see Table 1); recording various financial and demographic information about the applicants. In the repository a numeric version of this dataset is also available where the categorical attributes are transformed into numerical ones and a few indicator variables are added, which increases the dimension to 24 input numerical values. The data instances are labeled as classes 1 (good, 700 instances) and 2 (bad, 300 instances). Table 2 shows examples of the dataset attributes’ numerical representation for the first 10 cases; these numerical values are not normalized. Once normalization to values between ‘‘0” and ‘‘1” is completed, the values are used as the input data to a neural network.” The model in this article uses a dataset containing financial information. This model will process this data into different categories and normalize these numbers. The model will sort the data and then apply a weight or bias to it so it is between 1 and 0. The system will then process that data using a neural network to produce an outcome.)
“biases of the collected data from each data source.” (Credit application data processing, pp. 6236; “This phase is a data preparation phase for neural network training and classification/evaluation. Here, the input data (attribute numerical values) are separately normalized to values between ‘‘0” and ‘‘1”.” The data as stated above is categorized and then normalized. This model is able to use this data to make a determination using a neural network.)
Regarding Claim 18, Wu discloses, “providing links to points-of-purchase and/or to online ordering forms.” (Introduction, pp. 1218; "Suppose the user is searching for men's wallets and kids' shoes. The next page to be opened is "men.html" and correspondingly, we may update our recommendations as Levis' jeans and Diesel's shirts. This is definitely not a correct recommendation, but it is the best guess given current viewing history. After the user opens pages list.html?cat=101"(directory for men's wallets) and "2915823401.html? (boy's clog), we update the recommendations as wallets and crocs clog, which perfectly hit the hot spot." This prediction system was designed to send recommendations to a web widget on the e-commerce platform. This will display different recommended items that are product recommendations to potential buyers. The recommendation would contain a hyperlink which would send the user to that webpage.)
Regarding Claim 19, Wu discloses, “automatically identifying targeted content and targeted users based on said analyzing the collected data; and” (Introduction, pp. 1219; "Third, we also build a feedforwarding neural network (FNN) to simulate the CF algorithm. Our FNN accepts the user's purchase history vector as input and generates the prediction for the probability of buying an item. FNN is forged with DRNN to produce the final results, while how the two networks are combined is learned from our training data." This system is designed to send product recommendations to users. This will take users data to train multiple neural networks. The neural networks use the user data to output a target recommendation to the user.)
“automatically sending the identified targeted content to the identified targeted users.” (Overview of Recommendation Module, pp. 1220; "Finally, Kaola server returns the requested web page to the user by demonstrating the recommendation results in a specific web widget. As the viewing session continues (more pages have been viewed), we gradually refine our prediction results. Users are expected to find their items from the recommendation results with a higher probability." This system will regularly update the user recommendations based on user viewing history. Every time the user accesses a new marketplace webpage the model will update and send the user a new product recommendation.)
Claims 3, 7, 10, 14, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wu and Khashman in view of Sukrat et al., (Sukrat et al., “An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce” 2018, hereinafter “Sukrat”).
Regarding Claim 3, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “wherein the database, the Al module, and the data input/output interface are configured for: identifying demographic markets and online marketing vessels;” (Recommendation engine layer, pp. 11; "First, the system extracts buyers' opinions and attitude on products from each posting in order to select the postings that make customers feel satisfied or liked at the sentiment analyzer. In this component, the system also analyzes the quality of Face book page and group based on user satisfaction in order to recommend high-quality pages or groups to join for business implementation. Next, at the content extractor, the system analyzes the product postings that are selected and collected from the sentiment analyzer component in order to find patterns of product postings using information extraction, image analysis, and video analysis." The system proposed in this article discloses a method to give vendor marketing recommendations on e-commerce sites. This will take in data such as products and types to determine the best sales tactics and methods.)
“providing marketing strategies and campaign plans; and” (Recommendation engine layer, pp. 11; "This component provides an appropriate time, channels, types of posting, marketing activities, and promotional activities that will be recommended in conjunction with a sample product posting. After collecting business transactions for a period of time, the financial statement analyzer will analyze business transactions to provide reports such as sales, financial, and business performance reports. The component also evaluates the current business performance and compares with a plan in order to provide the right recommendation to vendors." This system will provide vendors with marketing advice and strategies. This system will take in user data and other data related to sales to generate a market strategy for a vendor in an e-commerce setting.)
“generating marketing solutions based on the collected data and using the one or more data- analysis models.” (Recommendation engine layer, pp. 11; "The C2C s-commerce RS is an application which was developed for helping vendors systematically implement on line transactions through SNSs among members. The system leverages artificial intelligence techniques such as text mining and machine learning approaches to generate a recommendation to vendors for maturity improvement." This system will provide vendors with different market strategies. This system will take into account many different variables, data collected from users and the internet to output sales solutions and advice.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Wu, Khashman and Sukrat. Wu teaches a system that is able to evaluate user data and provide recommendations for future purchases. Khashman teaches an evaluation machine learning model that is able to evaluate financial data and verify or authorize a credit application. Sukrat teaches a system that is able to assist and give marking strategies to vendors who use consumer-to-consumer on line marketplaces. One of ordinary skill would have motivation to combine a machine learning system that is able to provide a user personalized recommendations for e-commerce with a system that is able to evaluate user financial data and determine if the user is qualified for a credit application with a system that provides vendor assistance to help improve product sales in an online ecommerce environment to generate a new e-commerce platform, "In practice, C2C s-commerce recommendation system can support vendors to systematically perform business. The application helps novice vendors evolve from the initial stage of maturity to reach maturity level 3 vendors by providing a systematic process as experienced vendors do. By doing so, vendors can reach the fourth maturity level through business performance measurement conducted by financial statement analysis. Then, the system will adapt the recommendations based on the results from business performance analysis. This activity leads to the continuous improvement as can be seen in maturity level 5. Additionally, knowing a vendor's maturity level may aid buyers to build trust and confidence in vendors before placing an order and leads to the growth of the digital economy in developing countries." (Sukrat, Conclusion and discussion, pp. 11-12).
Regarding Claim 7, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “wherein the database, the Al module, and the data input/output interface are configured for: providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” (C2C s-commerce, pp. 2; "C2C s-commerce is ones-commerce business model that utilizes the social and commercial functionalities of SNSs for on line interactions and purchase arrangements among consumers (Sukrat et al. 2016). The purchasing process can be implemented through various social networking platforms such as Face book, Line, and Instagram depending on the channel selection of vendors." The system in this article uses a method which is able to promote sales on different social media marketplace website. This will connect vendors to buyer from different markets. The vendors can connect with registered users and provide them different information such as websites to make purchases.)
“ranking the one or more of the pre-verified users; and” (Recommendation engine layer, pp. 11; "At the user profile manager, the system will analyze user profile data using supervised machine learning in order to predict current maturity level of a new user. This information supports the system to provide a recommendation that suits the vendor's products and experience." This system will first look at buyers and evaluate them. This will help the vender and the system better determine sales strategies. The evaluation under the broadest reasonable interpretation teaches a form of a ranking.)
“functionally connecting the pre-verified users for completing e-commerce transactions.” (Recommendation engine layer, pp. 11; "To increase the sales volume, Face book pages and groups that are investigated from the sentiment analyzer and are consistent with vendors' products will be recommended to vendors for making a decision to join. After joining any Face book group, the system will monitor new postings from the joined groups. When group members post their needed products (both text and image), the system will apply image recognition and information extraction for matching its users' selling products with the posted content. If the results match, the system will immediately send a push notification to the vendors using push-based alert. The vendors can post their product content under any buyers' postings using comment feature." This system is able to monitor different social groups on different social media sites and alert a vendor when a sale could be possible. Once the system determines that a sale is possible, an alert will be sent to the vendor where they can send that buyer directed product information about products and lead them to a purchase and make a sale.)
Regarding Claim 10, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “identifying demographic markets and online marketing vessels;” (Recommendation engine layer, pp. 11; "First, the system extracts buyers' opinions and attitude on products from each posting in order to select the postings that make customers feel satisfied or liked at the sentiment analyzer. In this component, the system also analyzes the quality of Face book page and group based on user satisfaction in order to recommend high quality pages or groups to join for business implementation. Next, at the content extractor, the system analyzes the product postings that are selected and collected from the sentiment analyzer component in order to find patterns of product postings using information extraction, image analysis, and video analysis." The system proposed in this article discloses a method to give vendor marketing recommendations on e-commerce sites. This will take in data such as products and types to determine the best sales tactics and methods.)
“providing marketing strategies and campaign plans; and” (Recommendation engine layer, pp. 11; "This component provides an appropriate time, channels, types of posting, marketing activities, and promotional activities that will be recommended in conjunction with a sample product posting. After collecting business transactions for a period of time, the financial statement analyzer will analyze business transactions to provide reports such as sales, financial, and business performance reports. The component a Isa evaluates the current business performance and compares with a plan in order to provide the right recommendation to vendors." This system will provide vendors with marketing advice and strategies. This system will take in user data and other data related to sales to generate a market strategy for a vendor in an e-commerce setting.)
“generating marketing solutions based on the collected data and using the one or more data- analysis models.” (Recommendation engine layer, pp. 11; "The C2C s-commerce RS is an application which was developed for helping vendors systematically implement on line transactions through SNSs among members. The system leverages artificial intelligence techniques such as text mining and machine learning approaches to generate a recommendation to vendors for maturity improvement." This system will provide vendors with different market strategies. This system will take into account many different variables, data collected from users and the internet to output sales solutions and advice.)
Regarding Claim 14, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” (C2C s-commerce, pp. 2; "C2C s-commerce is ones-commerce business model that utilizes the social and commercial functionalities of SNSs for on line interactions and purchase arrangements among consumers (Sukrat et al. 2016). The purchasing process can be implemented through various social networking platforms such as Face book, Line, and Instagram depending on the channel selection of vendors." The system in this article uses a method which is able to promote sales on different social media marketplace website. This will connect vendors to buyer from different markets. The vendors can connect with registered users and provide them different information such as websites to make purchases.)
“ranking the one or more of the pre-verified users; and” (Recommendation engine layer, pp. 11; "At the user profile manager, the system will analyze user profile data using supervised machine learning in order to predict current maturity level of a new user. This information supports the system to provide a recommendation that suits the vendor's products and experience." This system will first look at buyers and evaluate them. This will help the vender and the system better determine sales strategies. The evaluation under the broadest reasonable interpretation teaches a form of a ranking.)
“functionally connecting the pre-verified users for completing e-commerce transactions.” (Recommendation engine layer, pp. 11; "To increase the sales volume, Face book pages and groups that are investigated from the sentiment analyzer and are consistent with vendors' products will be recommended to vendors for making a decision to join. After joining any Face book group, the system will monitor new postings from the joined groups. When group members post their needed products (both text and image), the system will apply image recognition and information extraction for matching its users' selling products with the posted content. If the results match, the system will immediately send a push notification to the vendors using push-based alert. The vendors can post their product content under any buyers' postings using comment feature." This system is able to monitor different social groups on different social media sites and alert a vendor when a sale could be possible. Once the system determines that a sale is possible, an alert will be sent to the vendor where they can send that buyer directed product information about products and lead them to a purchase and make a sale.)
Regarding Claim 17, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “identifying demographic markets and online marketing vessels;” (Recommendation engine layer, pp. 11; "First, the system extracts buyers' opinions and attitude on products from each posting in order to select the postings that make customers feel satisfied or liked at the sentiment analyzer. In this component, the system also analyzes the quality of Face book page and group based on user satisfaction in order to recommend high quality pages or groups to join for business implementation. Next, at the content extractor, the system analyzes the product postings that are selected and collected from the sentiment analyzer component in order to find patterns of product postings using information extraction, image analysis, and video analysis." The system proposed in this article discloses a method to give vendor marketing recommendations on e-commerce sites. This will take in data such as products and types to determine the best sales tactics and methods.)
“providing marketing strategies and campaign plans; and” (Recommendation engine layer, pp. 11; "This component provides an appropriate time, channels, types of posting, marketing activities, and promotional activities that will be recommended in conjunction with a sample product posting. After collecting business transactions for a period of time, the financial statement analyzer will analyze business transactions to provide reports such as sales, financial, and business performance reports. The component a Isa evaluates the current business performance and compares with a plan in order to provide the right recommendation to vendors." This system will provide vendors with marketing advice and strategies. This system will take in user data and other data related to sales to generate a market strategy for a vendor in an e-commerce setting.)
“generating marketing solutions based on the collected data and using the one or more data- analysis models.” (Recommendation engine layer, pp. 11; "The C2C s-commerce RS is an application which was developed for helping vendors systematically implement on line transactions through SNSs among members. The system leverages artificial intelligence techniques such as text mining and machine learning approaches to generate a recommendation to vendors for maturity improvement." This system will provide vendors with different market strategies. This system will take into account many different variables, data collected from users and the internet to output sales solutions and advice.)
Regarding Claim 21, Wu and Khashman fail to explicitly disclose the limitations of this claim, however, Sukrat discloses, “providing one or more of the pre-verified users an online directory or online store for branding, product management, logistics, and contract prices;” (C2C s-commerce, pp. 2; "C2C s-commerce is ones-commerce business model that utilizes the social and commercial functionalities of SNSs for on line interactions and purchase arrangements among consumers (Sukrat et al. 2016). The purchasing process can be implemented through various social networking platforms such as Face book, Line, and Instagram depending on the channel selection of vendors." The system in this article uses a method which is able to promote sales on different social media marketplace website. This will connect vendors to buyer from different markets. The vendors can connect with registered users and provide them different information such as websites to make purchases.)
“ranking the one or more of the pre-verified users; and” (Recommendation engine layer, pp. 11; "At the user profile manager, the system will analyze user profile data using supervised machine learning in order to predict current maturity level of a new user. This information supports the system to provide a recommendation that suits the vendor's products and experience." This system will first look at buyers and evaluate them. This will help the vender and the system better determine sales strategies. The evaluation under the broadest reasonable interpretation teaches a form of a ranking.)
“functionally connecting the pre-verified users for completing e-commerce transactions.” (Recommendation engine layer, pp. 11; "To increase the sales volume, Face book pages and groups that are investigated from the sentiment analyzer and are consistent with vendors' products will be recommended to vendors for making a decision to join. After joining any Face book group, the system will monitor new postings from the joined groups. When group members post their needed products (both text and image), the system will apply image recognition and information extraction for matching its users' selling products with the posted content. If the results match, the system will immediately send a push notification to the vendors using push-based alert. The vendors can post their product content under any buyers' postings using comment feature." This system is able to monitor different social groups on different social media sites and alert a vendor when a sale could be possible. Once the system determines that a sale is possible, an alert will be sent to the vendor where they can send that buyer directed product information about products and lead them to a purchase and make a sale.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147