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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “First, Applicant submits that amended independent claim 1 recites the features that improve a success possibility of a technology trade between a public technology and a technology consumer by using a machine-learning model, in a non-conventional way.,” Examiner respectfully asserts that improving the success possibility of a technology trade is not an improvement to any technology or technical field including machine learning. An improved trade prediction would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1, which is not an improvement to the machine learning model itself. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “Second, Applicant submits that the above features recited in amended independent claim 1 should be considered as an ordered combination, which are indicative of integrating the judicial exception or abstract idea of certain methods of organizing human activity into a practical application. Specifically, the ordered combination of the additional elements of performing extracting particular information (e.g., the keyword), processing the particular information in a particular way (e.g., reducing the magnitude of the vector), applying the processed information to the machine-learning model to acquire a particular value (e.g., the score), and providing a particular result (e.g., the public technology list) to the user device integrate the method steps into a practical application that improves the technology or technical field of providing the technology trade infrastructure, like in BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 2016 WL 3514158, (Fed. Cir. June 27, 2016).,” Examiner respectfully disagrees. The claims of Bascom “presented a "technology-based solution" of filtering content on the Internet that overcame the disadvantages of prior art filtering systems and that amounted to significantly more than the recited abstract idea, it also would be reasonable for an examiner to have found these claims eligible at Pathway A or B if the examiner had considered the technology-based solution to be an improvement to computer functionality (See MPEP 2106.06(b)).” The present claims do not provide an analogous improvement over prior art systems. The present claims, as drafted, are directed to use of machine learning that is nothing more than use of a computer as a tool. There is no particular arrangement within the claims such that the claims recite a technical improvement. Therefore, Examiner respectfully disagrees with Applicant’s assertions in view of Bascom.
Regarding Applicant’s assertion of “Further, in the court case, Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018),, the court held that "[a]lthough the generic idea of summarizing information certainly existed prior to the invention, these claims are directed to a particular manner of summarizing and presenting information in electronic devices." Similarly, in determining patent-eligibility of the claim 1, Applicant respectfully requests considering that the claimed features are directed to a particular manner of providing technology trade information by using the computer technology.,” Examiner respectfully disagrees. The present claims do not provide an analogous improvement to the computer to that of Core Wireless, specifically because the present claims do not improve the machine learning model itself. The improvements of Core Wireless provide a specific benefit the user interface for electronic devices, which is not the case with the instant claims. The present claims are directed to determine technology trade success possibilities using machine learning, which is not an improvement to the computer or machine learning itself. The claims do not improve a problem rooted in computer technology because the computer is being used as a tool to predict and display information.
Therefore, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Thus, Applicant submits that amended independent claim 1 distinguishes over the cited references.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Chitrapura and Margolin references to cure the deficiencies of the prior art combination of the record. Therefore, the 35 USC 102 rejection has been withdrawn; however, the present claims remain rejected under 35 USC 103.
Accordingly, the present claims are rejected under 35 USC 103.
Claim Objections
Claim 7 is objected to because of the following informalities: Examiner suggests amending the claim to correct a minor typographical error by reciting “wherein the third machine- learning model is configured to output the score indicating the success possibility of [[aa]]a technology trade between the consumer feature data and the technology feature data”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 7-8, 13, and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 7, and 13, the metes and bounds of the claim are rendered unclear due to the limitation of “reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, by flattening a tensor trained in a previous layer into a 1*N vector form.” The metes and bounds of the claim are rendered unclear because it is unclear what limitation the claim term “1*N” provides to the claim because the present claim does not limit the variable “N” such that it is clear what this variable is. Applicant has defined or described the claimed feature “N” such that it is clear what limitations this limitation imparts on the claim. Therefore, the metes and bounds of the claims are rendered unclear.
For the sake of compact prosecution, Examiner is interpreting the claim as though the dimension of the vector is reduced in size. Examiner suggests amending the claim in order to clarify the claim limitations.
Dependent claims 2, 8, and 15 are rejected due to dependency on rejected base claims 1, 7, and 13.
Accordingly, claims 1-2, 7-8, 13, and 15 are rejected under 35 USC 101.
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 1-2, 7-8, 13, and 15 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-2 are directed to a method, claims 7-8 are directed to a method, and claims 13-16 are directed to a method. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 7, and 13 are related to matching or associating data, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Independent claim 1 recites the limitations of “acquiring data about the public technology, wherein the data about the public technology includes at least one text; acquire technology feature data about the public technology, extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector, which is the technology feature data; matching the public technology with at least one technology consumer, based on the technology feature data and a plurality of consumer feature data; output the score indicating the success possibility of a technology trade between the technology feature data and the consumer feature data; extracting, from the plurality of consumer feature data, one or more consumer feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of consumer feature data; and determining at least one technology consumer matching the public technology, based on the extracted one or more consumer feature data; converting scores corresponding to the plurality of consumer feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of consumer feature data.” Independent claim 7 recites the limitations of “acquiring data about the technology consumer, wherein the data about the technology consumer includes at least one text; acquire consumer feature data about the technology consumer, extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector, which is the consumer feature data; and matching the technology consumer with one or more public technologies, based on the consumer feature data and a plurality of technology feature data by performing: applying the consumer feature data and the plurality of technology feature data to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of technology feature data, output the score indicating the success possibility of aa technology trade between the consumer feature data and the technology feature data; extracting, from the plurality of technology feature data, one or more technology feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of technology feature data; and determining the one or more public technologies matching the technology consumer, based on the extracted one or more technology feature data; converting scores corresponding to the plurality of technology feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of technology feature data.” Independent claim 13 recites the limitations of “acquiring data about the public technology, wherein the data about the public technology includes at least one text; applying the data about the public technology to acquire technology feature data about the public technology, wherein extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector, which is output the technology feature data based on the data about the public technology; and associating the technology feature data with a public technology identifier.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of the preamble, covers an abstract idea but for the recitation of generic computer components. That is, other than reciting the preamble language, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2, 8, and 15 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 7, and 13 do not integrate the judicial exception into a practical application. Independent claim 1 recites “a method for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 7 recites “a method for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 13 recites “a method for constructing a database for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 1 recites “transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.” Independent claim 7 recites “… based on the consumer feature data and a plurality of technology feature data stored in a database” and “transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.” Independent claim 13 recites “storing the association in a database.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claim 1 recites “applying the acquired data to a first machine-learning model to acquire technology feature data about the public technology, wherein the first machine-learning model is configured to…reduce a first magnitude vector…by flattening a tensor trained in a previous layer into a 1*N vector form,” “…based on the technology feature data and a plurality of consumer feature data stored in a database,” “applying the technology feature data and the plurality of consumer feature data to a third machine-learning model to acquire a score…wherein the third machine-learning model is configured to output the score.” Independent claim 7 recites “applying the acquired data to a first machine-learning model to acquire consumer feature data…wherein the first machine-learning model is configured to …reduce a first magnitude vector …by flattening a tensor trained in a previous layer into a 1*N vector form, and output the second magnitude vector,” “applying the consumer feature data and the plurality of technology feature data to a third machine-learning model to acquire a score…wherein the third machine- learning model is configured to output the score.” Independent claim 13 recites the limitations of “applying the data about the public technology to a first machine-learning model to acquire technology feature data a…wherein the first machine-learning model is configured to reduce a first magnitude vector…by flattening a tensor trained in a previous layer into a 1 *N vector form.” The limitations related to applying a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claim 2 recites the additional element of “applying the plurality of data respectively about the plurality of technology consumers to a second machine-learning model to acquire a plurality of consumer feature data respectively about the plurality of technology consumers, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer; and storing the acquired plurality of consumer feature data in the database.” Dependent claim 8 recites the additional element of “applying the plurality of data respectively about the plurality of public technologies to a second machine-learning model to acquire a plurality of technology feature data respectively about the plurality of public technologies, wherein the second machine-learning model is configured to output the technology feature data based on the data about the public technology; and storing the acquired plurality of technology feature data in the database.” Dependent claim 15 recites the additional element of “applying the data about the technology consumer to a second machine-learning model to acquire consumer feature data about the technology consumer, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer.” The limitations related to applying a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 7, and 13 do not comprise anything significantly more than the judicial exception. Independent claim 1 recites “a method for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 7 recites “a method for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 13 recites “a method for constructing a database for matching a public technology with a technology consumer, the method being performed by a computing system, the method comprising” within the preamble of the claim. Independent claim 1 recites “transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.” Independent claim 7 recites “… based on the consumer feature data and a plurality of technology feature data stored in a database” and “transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.” Independent claim 13 recites “storing the association in a database.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Independent claim 1 recites “applying the acquired data to a first machine-learning model to acquire technology feature data about the public technology, wherein the first machine-learning model is configured to…reduce a first magnitude vector…by flattening a tensor trained in a previous layer into a 1*N vector form,” “…based on the technology feature data and a plurality of consumer feature data stored in a database,” “applying the technology feature data and the plurality of consumer feature data to a third machine-learning model to acquire a score…wherein the third machine-learning model is configured to output the score.” Independent claim 7 recites “applying the acquired data to a first machine-learning model to acquire consumer feature data…wherein the first machine-learning model is configured to …reduce a first magnitude vector …by flattening a tensor trained in a previous layer into a 1*N vector form, and output the second magnitude vector,” “applying the consumer feature data and the plurality of technology feature data to a third machine-learning model to acquire a score…wherein the third machine- learning model is configured to output the score.” Independent claim 13 recites the limitations of “applying the data about the public technology to a first machine-learning model to acquire technology feature data a…wherein the first machine-learning model is configured to reduce a first magnitude vector…by flattening a tensor trained in a previous layer into a 1 *N vector form.” The limitations related to applying a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claim 2 recites the additional element of “applying the plurality of data respectively about the plurality of technology consumers to a second machine-learning model to acquire a plurality of consumer feature data respectively about the plurality of technology consumers, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer; and storing the acquired plurality of consumer feature data in the database.” Dependent claim 8 recites the additional element of “applying the plurality of data respectively about the plurality of public technologies to a second machine-learning model to acquire a plurality of technology feature data respectively about the plurality of public technologies, wherein the second machine-learning model is configured to output the technology feature data based on the data about the public technology; and storing the acquired plurality of technology feature data in the database.” Dependent claim 15 recites the additional element of “applying the data about the technology consumer to a second machine-learning model to acquire consumer feature data about the technology consumer, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer.” The limitations related to applying a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-2, 7-8, 13, and 15 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 7-8, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (US 20230125177 A1) in view of Chitrapura et al. (US 11295375 B1) in view of Margolin et al. (US 20210182878 A1).
Regarding claim 1, Cohen teaches a method for matching a public technology with a technology consumer, the method being performed by a computing system (Fig. 6 and [0059, 0072, 0100] teach an internal system including a server), the method comprising:
acquiring data about the public technology (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc., wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors; see also: [0041, 0060-0061]),
wherein the data about the public technology includes at least one text (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc., wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors; see also: [0041, 0060-0061]);
applying the acquired data to a first machine-learning model to acquire technology feature data about the public technology (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0060] teaches a text processor and machine learning text classifier can parse the data and identify distinct product features that comprise the product, wherein the NLP also extracts the name of the technological solution from within the text that describes what the solution does vis a vis the product feature, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc.; see also: [0041, 0061, 0072]),
wherein the first machine-learning model is configured to extract…the technology feature data (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0060] teaches a text processor and machine learning text classifier can parse the data and identify distinct product features that comprise the product, wherein the NLP also extracts the name of the technological solution from within the text that describes what the solution does vis a vis the product feature, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc; see also: [0041, 0061, 0072]);
matching the public technology with at least one technology consumer, based on the technology feature data and a plurality of consumer feature data stored in a database (Fig. 6 and [0069] teach interpreting a business/enterprise challenge to a single or a set of product features followed by a matching and optimization process, which results in a single or a set of technology solutions that may potentially be implemented as a product, wherein [0071-0072] teach the machine learning process may be interpret the business/enterprise challenge in order to generate one or more product features, wherein the machine learning classification model that was automatically trained can execute the process, wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors, as well as in [0074] teaches the matching and optimization process matches and optimizes a single or a set of technology solutions that include product features, which are overlapping with the product features that were resulted from the machine learning process of enterprise challenge interpretation, wherein the system may select technology solutions by finding matching product features, wherein [0099-0102] teach a database comprising the technology solution vendors and product features; see also: [0041, 0061, 0073]),
by performing: determining at least one technology consumer matching the public technology, based on the extracted one or more consumer feature data (Fig. 6 and [0069] teach interpreting a business/enterprise challenge to a single or a set of product features followed by a matching and optimization process, which results in a single or a set of technology solutions that may potentially be implemented as a product, wherein [0071-0072] teach the machine learning process may be interpret the business/enterprise challenge in order to generate one or more product features, wherein the machine learning classification model that was automatically trained can execute the process, wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors, as well as in [0074] teaches the matching and optimization process matches and optimizes a single or a set of technology solutions that include product features, which are overlapping with the product features that were resulted from the machine learning process of enterprise challenge interpretation, wherein the system may select technology solutions by finding matching product features, wherein [0099-0102] teach a database comprising the technology solution vendors and product features; see also: [0041, 0061, 0073]).
However, Cohen does not explicitly teach wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, by flattening a tensor trained in a previous layer into a 1*N vector form, and output the second magnitude vector; applying the technology feature data and the plurality of consumer feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of consumer feature data, wherein the third machine-learning model is configured to output the score indicating the success possibility of a technology trade between the technology feature data and the consumer feature data; extracting, from the plurality of consumer feature data, one or more consumer feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of consumer feature data; and converting scores corresponding to the plurality of consumer feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of consumer feature data; and transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.
From the same or similar field of endeavor, Chitrapura teaches wherein the first machine-learning model is configured to extract at least one keyword from the at least one text (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
which has a smaller magnitude than the first magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
and output the second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
applying the technology feature data and the plurality of consumer feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of consumer feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54),
wherein the third machine-learning model is configured to output the score indicating the success possibility of a technology trade between the technology feature data and the consumer feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54);
extracting, from the plurality of consumer feature data, one or more consumer feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of consumer feature data (Col 9 line 49 to Col 10 line 34 teach constructing the overall outcome of success likelihood of a software solution for a business problem from process-wise outcomes for software-context combinations, wherein Col 11 lines 1-62 teach the platform identifies positive mentions of a process for a software application and it is that there are more mentions at a higher level in the hierarchy and hence more confidence for the estimate at a higher level and lower confidence for mentions which are lower in the hierarchy and thus very rare, wherein the right fit can be estimated for each software application for a given set of processes, wherein the platform can predict the right fit software application program for business needs to help the user identify the software applicable program that is most suitable for their needs, wherein Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 14 lines 31-54, Col 17 lines 23-48); and
converting scores corresponding to the plurality of consumer feature data into normalized values within a predetermined range (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22),
wherein the normalized values indicates technology trade success possibilities of the plurality of consumer feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54); and
transmitting a public technology list generated based on a result of the matching, along with the normalized values (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22), to a user device (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Cohen to incorporate the teachings of Chitrapura to include wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector; applying the technology feature data and the plurality of consumer feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of consumer feature data, wherein the third machine-learning model is configured to output the score indicating the success possibility of a technology trade between the technology feature data and the consumer feature data; extracting, from the plurality of consumer feature data, one or more consumer feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of consumer feature data; and converting scores corresponding to the plurality of consumer feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of consumer feature data; and transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device. One would have been motivated to do so in order to improve overall customer satisfaction by utilizing feedback to improve matching of business requirements to software application programs or vendors (Chitrapura, Col 18 lines 26-46). By incorporating the teachings of Chitrapura, one would have been able to permit a multi-sided interaction that facilitates interaction between the demand side customers, supply side, thought leaders, and implementers to refine selection of a software program (Chitrapura, Col 4 lines 25-53).
However, the combination of Cohen and Chitrapura does not explicitly teach reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form.
From the same or similar field of endeavor, Margolin teaches reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form ([0080-0097] teach training a machine learning model on features, wherein the layers of the machine learning model include on output of a previous layer, the tensor is flattened into an output that inputs into a following layer with 15 neurons, or N=15, wherein [0030] teaches characteristics determining by a machine learning model include a prediction or probability that a website or consumer product page will integrate new technologies; see also: [0028, 0031, 0060]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cohen and Chitrapura to incorporate the teachings of Margolin to include reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form. One would have been motivated to do so in order to determine a better risk for financial investment based on the probability output by the machine learning model (Margolin, [0031]). By incorporating the teachings of Margolin, one would have been motivated to do so in order to allow the machine learning model to learn the relations between different technologies over time (Margolin, [0080]).
Regarding claim 2, the combination of Cohen, Chitrapura, and Margolin teaches all the limitations of claim 1 above.
However, Cohen fails to explicitly teach further comprising: before the acquiring of the data about the public technology, acquiring a plurality of data respectively about a plurality of technology consumers; applying the plurality of data respectively about the plurality of technology consumers to a second machine-learning model to acquire a plurality of consumer feature data respectively about the plurality of technology consumers, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer; and storing the acquired plurality of consumer feature data in the database.
From the same or similar field of endeavor, Chitrapura teaches further comprising: before the acquiring of the data about the public technology, acquiring a plurality of data respectively about a plurality of technology consumers (Fig. 7 and Col 19 lines 10-43 teach implementing a near-automated collection and creation of data related to any specific entity from a particular entity class, such as software/company/consultant from online aggregators, blogs, news pages, entity-specific authoritative pages, such as the company’s or person’s own website, social media content, and more, wherein the platform identifies a seed set of key attributes for entities, such as the company founding year, etc., and can search with each attribute, wherein the pages can be identified for each entity, and wherein Col 5 lines 4-42 teach crawlers and adapters fetch online web content, extract the relevant information, and stores it in the data dump which is a collection from various online sources; see also: Col 22 lines 26-52);
applying the plurality of data respectively about the plurality of technology consumers to a second machine-learning model to acquire a plurality of consumer feature data respectively about the plurality of technology consumers (Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Col 12 line 46 to Col 13 line 14 teach mapping using a word moves distance that maps phrases from text to the hierarchy of processes, wherein the WMD is performed using Word2vec, which is a shallow neural network based model that maps each word to a numeric vector such that the words that are closer in meaning are mapped to vectors that are closer to each other in Euclidean space, wherein the model is trained for business language; see also: Col 14 line 55 to Col 15 line 18),
wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer (Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Col 12 line 46 to Col 13 line 14 teach mapping using a word moves distance that maps phrases from text to the hierarchy of processes, wherein the WMD is performed using Word2vec, which is a shallow neural network based model that maps each word to a numeric vector such that the words that are closer in meaning are mapped to vectors that are closer to each other in Euclidean space, wherein the model is trained for business language; see also: Col 14 line 55 to Col 15 line 18); and
storing the acquired plurality of consumer feature data in the database (Fig. 7 and Col 19 lines 10-43 teach implementing a near-automated collection and creation of data related to any specific entity from a particular entity class, such as software/company/consultant from online aggregators, blogs, news pages, entity-specific authoritative pages, such as the company’s or person’s own website, social media content, and more, wherein the platform identifies a seed set of key attributes for entities, such as the company founding year, etc., and can search with each attribute, wherein the pages can be identified for each entity, and wherein Col 5 lines 4-42 teach crawlers and adapters fetch online web content, extract the relevant information, and stores it in the data dump which is a collection from various online sources; see also: Col 22 lines 26-52).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cohen, Chitrapura, and Margolin to incorporate the further teachings of Chitrapura to include further comprising: before the acquiring of the data about the public technology, acquiring a plurality of data respectively about a plurality of technology consumers; applying the plurality of data respectively about the plurality of technology consumers to a second machine-learning model to acquire a plurality of consumer feature data respectively about the plurality of technology consumers, wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer; and storing the acquired plurality of consumer feature data in the database. One would have been motivated to do so in order to improve overall customer satisfaction by utilizing feedback to improve matching of business requirements to software application programs or vendors (Chitrapura, Col 18 lines 26-46). By incorporating the teachings of Chitrapura, one would have been able to permit a multi-sided interaction that facilitates interaction between the demand side customers, supply side, thought leaders, and implementers to refine selection of a software program (Chitrapura, Col 4 lines 25-53).
Regarding claim 7, Cohen teaches a method for matching a public technology with a technology consumer (Fig. 6 and [0059, 0072, 0100] teach an internal system including a server), the method being performed by a computing system, the method comprising:
acquiring data about the technology consumer ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources; see also: [0011]),
wherein the data about the technology consumer includes at least one text ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources; see also: [0011]);
applying the acquired data to a first machine-learning model to acquire consumer feature data about the technology consumer ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources, wherein [0081] teaches the machine learning training process creates a business challenges to product features interpretation model; see also: [0011]),
wherein the first machine-learning model is configured to extract…which is the consumer feature data ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources, wherein [0081] teaches the machine learning training process creates a business challenges to product features interpretation model; see also: [0011]);
matching the technology consumer with one or more public technologies, based on the consumer feature data and a plurality of technology feature data stored in a database by performing (Fig. 6 and [0069] teach interpreting a business/enterprise challenge to a single or a set of product features followed by a matching and optimization process, which results in a single or a set of technology solutions that may potentially be implemented as a product, wherein [0071-0072] teach the machine learning process may be interpret the business/enterprise challenge in order to generate one or more product features, wherein the machine learning classification model that was automatically trained can execute the process, wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors, as well as in [0074] teaches the matching and optimization process matches and optimizes a single or a set of technology solutions that include product features, which are overlapping with the product features that were resulted from the machine learning process of enterprise challenge interpretation, wherein the system may select technology solutions by finding matching product features, wherein [0099-0102] teach a database comprising the technology solution vendors and product features; see also: [0041, 0061, 0073]):
determining the one or more public technologies matching the technology consumer, based on the extracted one or more technology feature data (Fig. 6 and [0069] teach interpreting a business/enterprise challenge to a single or a set of product features followed by a matching and optimization process, which results in a single or a set of technology solutions that may potentially be implemented as a product, wherein [0071-0072] teach the machine learning process may be interpret the business/enterprise challenge in order to generate one or more product features, wherein the machine learning classification model that was automatically trained can execute the process, wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors, as well as in [0074] teaches the matching and optimization process matches and optimizes a single or a set of technology solutions that include product features, which are overlapping with the product features that were resulted from the machine learning process of enterprise challenge interpretation, wherein the system may select technology solutions by finding matching product features, wherein [0099-0102] teach a database comprising the technology solution vendors and product features; see also: [0041, 0061, 0073]).
However, Cohen does not explicitly teach wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, by flattening a tensor trained in a previous layer into a 1*N vector form, and output the second magnitude vector, and applying the consumer feature data and the plurality of technology feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of technology feature data, wherein the third machine- learning model is configured to output the score indicating the success possibility of aa technology trade between the consumer feature data and the technology feature data; extracting, from the plurality of technology feature data, one or more technology feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of technology feature data; and converting scores corresponding to the plurality of technology feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of technology feature data; and transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.
From the same or similar field of endeavor, Chitrapura teaches wherein the first machine-learning model is configured to extract at least one keyword from the at least one text (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48), and
applying the consumer feature data and the plurality of technology feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of technology feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54),
wherein the third machine- learning model is configured to output the score indicating the success possibility of aa technology trade between the consumer feature data and the technology feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54);
extracting, from the plurality of technology feature data, one or more technology feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of technology feature data (Col 9 line 49 to Col 10 line 34 teach constructing the overall outcome of success likelihood of a software solution for a business problem from process-wise outcomes for software-context combinations, wherein Col 11 lines 1-62 teach the platform identifies positive mentions of a process for a software application and it is that there are more mentions at a higher level in the hierarchy and hence more confidence for the estimate at a higher level and lower confidence for mentions which are lower in the hierarchy and thus very rare, wherein the right fit can be estimated for each software application for a given set of processes, wherein the platform can predict the right fit software application program for business needs to help the user identify the software applicable program that is most suitable for their needs, wherein Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 14 lines 31-54, Col 17 lines 23-48); and
converting scores corresponding to the plurality of technology feature data into normalized values within a predetermined range (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22),
wherein the normalized values indicates technology trade success possibilities of the plurality of technology feature data (Col 6 lines 37-67 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the user may not be able to completely specify their needs as they may be unaware of potential software application programs that exist to solve their needs, wherein the system can determine a probability of affinity, as well as in Col 8 lines 44-23 teach estimating the satisfaction rate of a software application program and computing a satisfaction model based on context and sentiment on web pages, wherein the platform employs a machine learnt model that predicts satisfaction of a particular software application program given business needs and the business context of the user, wherein the machine learning model is based on processes, business contexts, and sentiments found on webpages to accurately predict how satisfied the business user will be by using a given software application program for their expressed business needs, wherein Col 17 lines 23-48 teach generating match scores that are estimated based on evidence from reviews and reference customer cases, wherein the match scores are based on evidence from aggregator software/vendor pages, complementary software/companies to a software vendor company, and more; see also: Col 11 lines 1-62, Col 14 lines 31-54); and
transmitting a public technology list generated based on a result of the matching, along with the normalized values (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22), to a user device (Col 6 line 37-63 teach the platform includes a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein given the need, the platform uses the data from webpages on the internet to show what other needs users from similar business context have solved, wherein techniques similar to collaborative filtering are employed, wherein the platform estimates finding the process by utilizing a normalized matrix in order to determine the probability of affinity, as well as in Col 13 lines 14-61 teach the platform uses data from web pages in order to show what other needs users from similar business contexts have been solved by available software application programs, wherein the scores can be generated based on a normalized matrix, wherein Col 14 lines 6-30 teach the engagement portal collects, from the business user, an identification of processes that the software application program needs to address to complete the business need of the user, and to estimate the right fit of the various matching software application programs, wherein the user can sign into the portal, wherein the platform can identify the problem statement entered by the user, wherein the platform identifies the problem statement entered by the user along with a plurality of other processes together with affinity and cost, wherein affinity is a score that determines how often a pair of processes occur together, wherein the platform provides additional processes identified by the platform that have impact across multiple functions and possibly influencing outcomes for review by the user, wherein the results of the query with the parameters entered at the data entry screen are shown at a results screen, wherein the matching software application programs are identified by name and are accompanied by a satisfaction score and a right fit score; see also: Col 16 line 21 to Col 17 line 22).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Cohen to incorporate the teachings of Chitrapura to include wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector, and applying the consumer feature data and the plurality of technology feature data to a third machine-learning model to acquire a score indicating a technology trade success possibility corresponding to each of the plurality of technology feature data, wherein the third machine- learning model is configured to output the score indicating the success possibility of aa technology trade between the consumer feature data and the technology feature data; extracting, from the plurality of technology feature data, one or more technology feature data corresponding to one or more scores falling within a predetermined rank threshold among scores corresponding to the plurality of technology feature data; and converting scores corresponding to the plurality of technology feature data into normalized values within a predetermined range, wherein the normalized values indicates technology trade success possibilities of the plurality of technology feature data; and transmitting a public technology list generated based on a result of the matching, along with the normalized values, to a user device.
However, the combination of Cohen and Chitrapura does not explicitly teach reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form.
From the same or similar field of endeavor, Margolin teaches reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form ([0080-0097] teach training a machine learning model on features, wherein the layers of the machine learning model include on output of a previous layer, the tensor is flattened into an output that inputs into a following layer with 15 neurons, or N=15, wherein [0030] teaches characteristics determining by a machine learning model include a prediction or probability that a website or consumer product page will integrate new technologies; see also: [0028, 0031, 0060]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cohen and Chitrapura to incorporate the teachings of Margolin to include reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1*N vector form. One would have been motivated to do so in order to determine a better risk for financial investment based on the probability output by the machine learning model (Margolin, [0031]). By incorporating the teachings of Margolin, one would have been motivated to do so in order to allow the machine learning model to learn the relations between different technologies over time (Margolin, [0080]).
Regarding claim 8, the combination of Cohen, Chitrapura, and Margolin teaches all the limitation of claim 7 above.
Cohen further teaches further comprising: before the acquiring of the data about the technology consumer, acquiring a plurality of data respectively about a plurality of public technologies (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc., wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors; see also: [0041, 0060-0061]);
applying the plurality of data respectively about the plurality of public technologies to a second machine-learning model to acquire a plurality of technology feature data respectively about the plurality of public technologies ([0010] teaches the challenge interpretation engine includes a machine learning classification model trained on descriptors of the product features in the repository of product features, wherein [0016] teaches a further artificial intelligence or machine learning being employed for associating technology solutions with the corresponding product features and storing the associations in a product features to technology solutions repository, wherein [0060] teaches a text processor and machine language text classifier that can parse the data and identify distinct product features that comprise the product; see also: [0061-0064]),
wherein the second machine-learning model is configured to output the technology feature data based on the data about the public technology ([0010] teaches the challenge interpretation engine includes a machine learning classification model trained on descriptors of the product features in the repository of product features, wherein [0016] teaches a further artificial intelligence or machine learning being employed for associating technology solutions with the corresponding product features and storing the associations in a product features to technology solutions repository, wherein [0060] teaches a text processor and machine language text classifier that can parse the data and identify distinct product features that comprise the product; see also: [0061-0064]); and
storing the acquired plurality of technology feature data in the database ([0010] teaches the challenge interpretation engine includes a machine learning classification model trained on descriptors of the product features in the repository of product features, wherein [0016] teaches a further artificial intelligence or machine learning being employed for associating technology solutions with the corresponding product features and storing the associations in a product features to technology solutions repository, wherein [0060] teaches a text processor and machine language text classifier that can parse the data and identify distinct product features that comprise the product, wherein [0062] teaches a product features repository for storing product features; see also: [0061]).
Regarding claim 13, Cohen teaches a method for constructing a database for matching a public technology with a technology consumer (Fig. 6 and [0059, 0072, 0100] teach an internal system including a server), the method being performed by a computing system, the method comprising:
acquiring data about the public technology (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc., wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors; see also: [0041, 0060-0061]),
wherein the data about the public technology includes at least one text (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc., wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors; see also: [0041, 0060-0061]);
applying the data about the public technology to a first machine-learning model to acquire technology feature data about the public technology (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0060] teaches a text processor and machine learning text classifier can parse the data and identify distinct product features that comprise the product, wherein the NLP also extracts the name of the technological solution from within the text that describes what the solution does vis a vis the product feature, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc; see also: [0041, 0061, 0072]),
wherein the first machine-learning model is configured to extract… which is the technology feature data based on the data about the public technology (Fig. 4 and [0056-0058] teach breaking down multiple products into their corresponding normalized product features and storing them as a distinct collection in a products features repository, wherein [0059] teaches an automated bot scans the descriptive literature related to the products to extract descriptive data regarding the products, wherein the descriptive data can be gathered from the company website or other websites that are not under the control of the company, wherein scanned copies of data into a digital format to allow the bot or web crawler to gather the data, wherein [0060] teaches a text processor and machine learning text classifier can parse the data and identify distinct product features that comprise the product, wherein the NLP also extracts the name of the technological solution from within the text that describes what the solution does vis a vis the product feature, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc; see also: [0041, 0061, 0072]);
associating the technology feature data with a public technology identifier, and storing the association in a database ([0043] teaches different products may share the same or similar product features, wherein the names of the product features must be distinct and normalized across all products, wherein [0044] teaches each vendor can provide may technology solutions that may have unique names that identify the solution with a brand and/or vendor, as well as in [0061] teaches the product features repository stores a distinct collection of all normalized product features, wherein an identifier of the product feature is stored in the repository, wherein the identifier may be a description of the product feature, a normalized name of the product feature and/or any other type of identifier, wherein [0003] teaches the service and manufacturing industries may include insurance companies, healthcare providers, appliance manufacturers, teaching instructions, etc, wherein the unique names identify the solution with a brand and/or vendor; see also: [0067-0068, 0081]).
However, Cohen does not explicitly teach wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, by flattening a tensor trained in a previous layer into a 1 *N vector form, and output the second magnitude vector.
From the same or similar field of endeavor, Chitrapura teaches wherein the first machine-learning model is configured to extract at least one keyword from the at least one text (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
which has a smaller magnitude than the first magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48),
and output the second magnitude vector (Col 5 line 43 to Col 6 line 13 teach the platform retrieves publicly available information and maps the retrieved data via natural language processing to topics maintained by the platform, as well as in Col 21 lines 17-59 teach the system can utilize a natural language generation model, wherein Fig. 2 and Col 12 lines 11-45 teach the back end-module can periodically crawl the web for customer feedback and contexts of business users who have commented on process or software solutions, wherein Fig. 2 and Col 12 line 46 to Col 13 line 61 teach mapping spotted processes to a process hierarchy using word movers distance (WMD), wherein the platform preferably employs word movers distance to map spotted process phrases from text to the hierarchy of processes, wherein the WMD allows assessment of distance between two documents in a meaningful way, even when they have no words in common, wherein the WMD is performed using Word2Vec which is a shallow neural network based model that maps each word to a numeric vector such that words that are closer in meaning, like semantics, are mapped to vectors that are closer to each other in the Euclidean space, wherein the model is trained to crawl sites that contain reviews about software application programs that solve business problems, wherein the platform can utilize k-nearest neighbor and WMD to map spotted process phrases in a node in the process hierarchy based on how close, by WMD distance, is the phrase to the process label on the hierarchy, wherein a formula can be used to map a spotted process phrase to the best node in the hierarchy for the lowest score, wherein a value between 0.3 and 0.5 is used in order to match the process hierarchy node directly, wherein the system can compute a process affinity matrix based on process co-occurrence, wherein the platform provides a machine learnt model that predicts other business needs for a business user given their expressed needs, wherein the platform can estimate the chance of finding the process in the same page by using a column normalized matrix, wherein there is a dampening factor that reduces the probability due to transitivity, which results in a reachability matrix that estimates the stationary probability of affinity; see also: Col 17 lines 23-48).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Cohen to incorporate the teachings of Chitrapura to include wherein the first machine-learning model is configured to extract at least one keyword from the at least one text, reduce a first magnitude vector based on the extracted at least one keyword into a second magnitude vector, which has a smaller magnitude than the first magnitude vector, and output the second magnitude vector. One would have been motivated to do so in order to improve overall customer satisfaction by utilizing feedback to improve matching of business requirements to software application programs or vendors (Chitrapura, Col 18 lines 26-46). By incorporating the teachings of Chitrapura, one would have been able to permit a multi-sided interaction that facilitates interaction between the demand side customers, supply side, thought leaders, and implementers to refine selection of a software program (Chitrapura, Col 4 lines 25-53).
However, the combination of Cohen and Chitrapura does not explicitly teach reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1 *N vector form.
From the same or similar field of endeavor, Margolin teaches reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1 *N vector form ([0080-0097] teach training a machine learning model on features, wherein the layers of the machine learning model include on output of a previous layer, the tensor is flattened into an output that inputs into a following layer with 15 neurons, or N=15, wherein [0030] teaches characteristics determining by a machine learning model include a prediction or probability that a website or consumer product page will integrate new technologies; see also: [0028, 0031, 0060]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cohen and Chitrapura to incorporate the teachings of Margolin to include reduce a first magnitude vector by flattening a tensor trained in a previous layer into a 1 *N vector form. One would have been motivated to do so in order to determine a better risk for financial investment based on the probability output by the machine learning model (Margolin, [0031]). By incorporating the teachings of Margolin, one would have been motivated to do so in order to allow the machine learning model to learn the relations between different technologies over time (Margolin, [0080]).
Regarding claim 15, the combination of Cohen, Chitrapura, and Margolin teaches all the limitation of claim 13 above.
Cohen further teaches acquiring data about the technology consumer ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources; see also: [0011]);
applying the data about the technology consumer to a second machine-learning model to acquire consumer feature data about the technology consumer ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources, wherein [0081] teaches the machine learning training process creates a business challenges to product features interpretation model; see also: [0011]),
wherein the second machine-learning model is configured to output the consumer feature data based on the data about the technology consumer ([0048] teaches receiving a request for a new product or improvement to an already exist product that is resolved by creating a new product or by upgrading an existing product, wherein the enterprise/business challenge may be presented as a detailed, free, unstructured text, wherein [0072] teaches the product features and solutions may include product features from other enterprises and from within the company/enterprise, wherein Fig. 6 and [0073] teach the product features are outputted from the machine learning AI process of the enterprise challenge interpretation process, as well as in [0080] teaches the business/enterprise challenges to product features dataset includes business challenges and a corresponding set of product features, wherein the product features are identified using AI and ML engine trained on descriptive data and associated product features from within the enterprise environment and external sources, wherein [0081] teaches the machine learning training process creates a business challenges to product features interpretation model; see also: [0011]); and
associating the consumer feature data with a technology consumer identifier and storing the association in the database (Fig. 6 and [0069] teach interpreting a business/enterprise challenge to a single or a set of product features followed by a matching and optimization process, which results in a single or a set of technology solutions that may potentially be implemented as a product, wherein [0071-0072] teach the machine learning process may be interpret the business/enterprise challenge in order to generate one or more product features, wherein the machine learning classification model that was automatically trained can execute the process, wherein [0072] teaches the product features and product solutions may be taken from outside the institution and enterprise, wherein the system may look outwards of the enterprise in order to search for products, product features, and technology solutions from other enterprises and vendors, as well as in [0074] teaches the matching and optimization process matches and optimizes a single or a set of technology solutions that include product features, which are overlapping with the product features that were resulted from the machine learning process of enterprise challenge interpretation, wherein the system may select technology solutions by finding matching product features, wherein [0099-0102] teach a database comprising the technology solution vendors and product features; see also: [0041, 0061, 0073]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Menet et al. (US 20250103863 A1) discloses flattening a layer into a one dimensional vector
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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