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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. PGPUB (US20250321823A1)
18/634,251
US20250321823A1
Claim 1:
A computer-implemented method comprising: obtaining data pertaining to at least one resource-related activity involving at least one resource; predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity; wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
Claim 1:
A computer-implemented method comprising: obtaining data pertaining to at least one resource-related activity involving at least one resource and one or more users; predicting one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons; wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
Claim 2:
The computer-implemented method of claim 1, wherein predicting one or more values associated with the at least one resource comprises processing at least a portion of the obtained data using at least one artificial neural network-based multi-output regression model.
Claim 2:
The computer-implemented method of claim 1, wherein predicting one or more failures associated with the at least one resource-related activity comprises processing at least a portion of the obtained data using at least one multi-output neural network model.
Claim 3:
The computer-implemented method of claim 2, wherein predicting one or more values attributed to the at least one resource-related activity comprises processing the at least a portion of the obtained data using the at least one artificial neural network-based multi-output regression model.
Claim 3:
The computer-implemented method of claim 2, wherein predicting one or more reasons attributed to at least one of the one or more predicted failures comprises processing the at least a portion of the obtained data using the at least one multi-output neural network model.
Claim 4:
The computer-implemented method of claim 2, wherein using the at least one artificial neural network-based multi-output regression model comprises configuring the at least one artificial neural network-based multi-output regression model to include an input layer, two or more hidden layers, and two or more output layers.
Claim 4:
The computer-implemented method of claim 2, wherein using the at least one multi- output neural network model comprises configuring the at least one multi-output neural network model to include an input layer, two or more hidden layers, and two or more output layers.
Claim 5:
The computer-implemented method of claim 4, wherein configuring the at least one artificial neural network-based multi-output regression model comprises configuring the input layer to include a number of neurons that matches a number of input data variables, configuring the two or more hidden layers to include a number of neurons that is based at least in part on the number of neurons in the input layer, and configuring each of the two or more output layers to include a single neuron.
Claim 5:
The computer-implemented method of claim 4, wherein configuring the at least one multi-output neural network model comprises configuring the input layer to include a number of neurons that matches a number of input data variables, configuring the two or more hidden layers to include a number of neurons that is based at least in part on the number of neurons in the input layer, and configuring the two or more output layers to include a variable number of neurons across the two or more output layers based at least in part on a type of output associated with each of the two or more output layers.
Claim 6:
The computer-implemented method of claim 5, wherein a first one of the two or more output layers is configured to generate a prediction of the one or more values associated with the at least one resource, wherein a second one of the two or more output layers is configured to generate a prediction of the one or more values attributed to the at least one resource-related activity.
Claim 6:
The computer-implemented method of claim 5, wherein a first one of the two or more output layers is configured to generate a prediction of the one or more failures associated with the at least one resource-related activity, wherein a second one of the two or more output layers is configured to generate a prediction of the one or more reasons attributed to the at least one of the one or more predicted failures, and wherein the first one of the two or more output layers includes one neuron associated with a binary determination with respect to failure, and the second one of the two or more output layers includes multiple neurons associated with multiple predetermined classes of reasons associated with resource-related activity failures related to at least one of the at least one resource and the one or more users.
Claim 7,
The computer-implemented method of claim 1, wherein the at least one resource-related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity.
Claim 7,
The computer-implemented method of claim 1, wherein the at least one resource- related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more course correction activities directed at avoid the at least a portion of the one or more predicted failures and related to the at least a portion of the one or more predicted reasons.
Claim 8:
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity.
Claim 8
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the at least a portion of the one or more predicted failures and the at least a portion of the one or more predicted reasons
Claim 9:
The computer-implemented method of claim 1, wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of historical values associated with resources related to the at least one resource, historical values attributed to previous instances of resource-related activities related to the at least one resource-related activity, data related to one or more actions already performed as part of the at least one resource-related activity, temporal data associated with the at least one resource, and user-related data associated with the at least one resource-related activity.
Claim 9:
The computer-implemented method of claim 1, wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of user-related data attributed to the one or more users, resource-related data attributed to the at least one resource, data related to one or more actions already performed as part of the at least one resource-related activity, and temporal data associated with the at least one resource.
Claim 10:
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: to obtain data pertaining to at least one resource-related activity involving at least one resource; to predict one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques; to predict one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques; and to perform one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity.
Claim 10:
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: to obtain data pertaining to at least one resource-related activity involving at least one resource and one or more users; to predict one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; to predict one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and to perform one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.
Claim 11:
The non-transitory processor-readable storage medium of claim 10, wherein predicting one or more values associated with the at least one resource comprises processing at least a portion of the obtained data using at least one artificial neural network-based multi-output regression model.
Claim 11:
The non-transitory processor-readable storage medium of claim 10, wherein predicting one or more failures associated with the at least one resource-related activity comprises processing at least a portion of the obtained data using at least one multi-output neural network model.
Claim 12:
The non-transitory processor-readable storage medium of claim 11, wherein predicting one or more values attributed to the at least one resource-related activity comprises processing the at least a portion of the obtained data using the at least one artificial neural network-based multi-output regression model.
Claim 12:
The non-transitory processor-readable storage medium of claim 11, wherein predicting one or more reasons attributed to at least one of the one or more predicted failures comprises processing the at least a portion of the obtained data using the at least one multi-output neural network model.
Claim 13:
The non-transitory processor-readable storage medium of claim 11, wherein using the at least one artificial neural network-based multi-output regression model comprises configuring the at least one artificial neural network-based multi-output regression model to include an input layer, two or more hidden layers, and two or more output layers.
Claim 13:
The non-transitory processor-readable storage medium of claim 11, wherein using the at least one multi-output neural network model comprises configuring the at least one multi- output neural network model to include an input layer, two or more hidden layers, and two or more output layers.
Claim 14:
The non-transitory processor-readable storage medium of claim 10, wherein the at least one resource-related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity.
Claim 7:
The computer-implemented method of claim 1, wherein the at least one resource- related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more course correction activities directed at avoid the at least a portion of the one or more predicted failures and related to the at least a portion of the one or more predicted reasons.
Claim 15:
The non-transitory processor-readable storage medium of claim 10, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity
Claim 8:
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the at least a portion of the one or more predicted failures and the at least a portion of the one or more predicted reasons.
Claim 16:
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: to obtain data pertaining to at least one resource-related activity involving at least one resource; to predict one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques; to predict one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques; and to perform one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity.
Claim: 16
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: to obtain data pertaining to at least one resource-related activity involving at least one resource and one or more users; to predict one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; to predict one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and to perform one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.
Claim 17:
The apparatus of claim 16, wherein predicting one or more values associated with the at least one resource comprises processing at least a portion of the obtained data using at least one artificial neural network-based multi-output regression model.
Claim 17:
The apparatus of claim 16, wherein predicting one or more failures associated with the at least one resource-related activity comprises processing at least a portion of the obtained data using at least one multi-output neural network model.
Claim 18:
The apparatus of claim 17, wherein predicting one or more values attributed to the at least one resource-related activity comprises processing the at least a portion of the obtained data using the at least one artificial neural network-based multi-output regression model.
Claim 18:
The apparatus of claim 17, wherein predicting one or more reasons attributed to at least one of the one or more predicted failures comprises processing the at least a portion of the obtained data using the at least one multi-output neural network model.
Claim 19:
The apparatus of claim 16, wherein the at least one resource-related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity.
Claim 7:
The computer-implemented method of claim 1, wherein the at least one resource- related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more course correction activities directed at avoid the at least a portion of the one or more predicted failures and related to the at least a portion of the one or more predicted reasons.
Claim 20:
The apparatus of claim 16, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity.
Claim 8:
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the at least a portion of the one or more predicted failures and the at least a portion of the one or more predicted reasons.
Although the claims at issue are not identical they are not patentably distinct from each other. Therefore, claims 1-20 of application 18/634,251 are rejected on the ground of a non-statutory obvious-type of double patenting as being unpatentable over claims US20250321823A1 as the application is co-pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) significantly more. The subject matter eligibility test for products and process is describe below for claim 1 in view of dependent claims.
Regarding claim 1:
Step 1: Is the claim to a process machine manufacture or composition of matter?
Yes – Claim 1 recites a method, which is a method that falls under the statutory categories.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data [using one or more machine learning techniques];” - The limitations recites a mental process of predicting one or more values associated with one resource (see MPEP 2106.04(a)(2)III).
“predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data [using the one or more machine learning techniques;]”- The limitations of claim 1 recites a mental process of determining a prediction (see MPEP 2106.04(a)(2)III).
Step 2 Prong 2: Does the claim recite additional elements that integrate the judicial exception into a particular application? No –
The claim includes the additional element(s):
“A computer-implemented method comprising: obtaining data pertaining to at least one resource-related activity involving at least one resource;”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering by obtaining data pertaining to the resource-related activity. See MPEP 2106.5(g).
“[predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data] using one or more machine learning techniques;”
The additional elements fall under “apply it” as using a generic computer to implement machine learning techniques to predict one or more values. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“[predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data] using the one or more machine learning techniques;”
The additional elements fall under “apply it” as using a generic computer to implement machine learning techniques to predict one or more values using at least a portion of the obtained data. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“performing one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity;” - The additional elements fall under “apply it” as using a generic computer to perform an automated action. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“wherein the method is performed by at least one processing device comprising a processor coupled to a memory.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No - The claim does not include additional elements that are sufficient to amount to a significantly more than the judicial exemption. As a whole, the claim is directed towards predicting values with the obtain data. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of obtaining, using machine learning techniques and performing automated actions fall under using generic computer to apply an exemption and mere data gathering. The method does not improve on the function of a computer, transforms an article into another article, nor is it applied by a particular machine, making the claim not patent eligible.
Regarding claim 2:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 1, wherein predicting one or more values associated with the at least one resource comprises processing at least a portion of the obtained data using at least one artificial neural network-based multi-output regression model.”
The additional elements fall under “apply it” as using a generic computer to implement an artificial neural network-based multi-output regression model to predict one or more values using at least a portion of the obtained data. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Regarding claim 3:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 2, wherein predicting one or more values attributed to the at least one resource-related activity comprises processing the at least a portion of the obtained data using the at least one artificial neural network-based multi-output regression model.”
The additional elements fall under “apply it” as using a generic computer to implement an artificial neural network-based multi-output regression model to predict one or more values with a resource-related activity using at least a portion of the obtained data. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Regarding claim 4:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 2, wherein using the at least one artificial neural network-based multi-output regression model comprises configuring the at least one artificial neural network-based multi-output regression model to include an input layer, two or more hidden layers, and two or more output layers.”
The additional elements fall under “apply it” as using a generic computer to implement an artificial neural network-based on the configuration. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Regarding claim 5:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 4, wherein configuring the at least one artificial neural network-based multi-output regression model comprises configuring the input layer to include a number of neurons that matches a number of input data variables, configuring the two or more hidden layers to include a number of neurons that is based at least in part on the number of neurons in the input layer, and configuring each of the two or more output layers to include a single neuron.”
The additional elements fall under “apply it” as using a generic computer to implement an artificial neural network-based on the configuration. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Regarding claim 6:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 5, wherein a first one of the two or more output layers is configured to generate a prediction of the one or more values associated with the at least one resource, wherein a second one of the two or more output layers is configured to generate a prediction of the one or more values attributed to the at least one resource-related activity.”
The additional elements fall under “apply it” as using a generic computer to implement an artificial neural network-based on the configuration. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Regarding claim 7:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 1, wherein the at least one resource-related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity.”
The additional element falls under the “apply it” by using computers to operate while ongoing activity is occurring and perform one or more automated actions (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 8:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity.”
The additional element falls under the “apply it” by using computers to perform the action of training the neural network (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 9:
Step 2A Prong 2, Step 2B: The additional element(s):
“The computer-implemented method of claim 1, wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of historical values associated with resources related to the at least one resource, historical values attributed to previous instances of resource-related activities related to the at least one resource-related activity, data related to one or more actions already performed as part of the at least one resource-related activity, temporal data associated with the at least one resource, and user-related data associated with the at least one resource-related activity.”
The additional element falls under the “apply it” by using computers to perform the action of training the neural network using historical values (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Claims 10-15 recite a computer readable medium product and are analogous to the method of claims 1-4, 7 and 8. Therefore, the rejections of claim 1-4, 7 and 8 above applies to claims 10-15.
Claims 16-20 recite a system and are analogous to the method of claims 1-3, 7 and 8. Therefore, the rejections of claim 1-3, 7 and 8 above applies to claims 16-20.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-8, 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US20230401570A1) (“Rao”) in view of Welder et al. (US20240087033A1) (“Welder”).
Regarding claim 1 and analogous claim 10 and 16, Rao teaches A computer-implemented method comprising: obtaining data pertaining to at least one resource-related activity involving at least one resource (Rao para 0017 line 1-5, As shown in FIG. 1B, and by reference number 115, the f-NFT system may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content [obtaining data pertaining].
Para 0017 line 20-24, The parameter unification model may utilize a quantity of sequels, a quantity of prequels, a quantity of seasons, a quantity of episodes, a popularity, and/or the like associated with the content to generate a longevity index (e.g., a derived parameter) for the content. The parameter unification model may utilize theatrical revenue, advertising revenue, merchandising revenue, broadcast revenue, and/or the like to generate a revenue value (e.g., a derived parameter) for the content [at least one resource-related activity involving at least one resource]);
predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques (RAO Para 0016 para line 1-5, As further shown in FIG. 1A, and by reference number 110, the f-NFT system may identify standard parameters and real-time parameters associated with the content. For example, the content may be associated with standard parameters,
Para line 12-22, The f-NFT system may analyze metadata associated with the content and may identify the standard parameters based on analyzing the metadata. The content may be associated with real-time parameters, such as reviews of the content (e.g., social media reviews of the content), availability of the content (e.g., showtimes of movies, air dates of television shows, and/or the like), and/or the like. The f-NFT system may analyze web-based data associated with social media, content availability, and/or the like, and may identify the real-time parameters associated with the content based on analyzing the web-based data [at least a portion of the obtained data using one or more machine learning techniques;].
(Examiner Note: The system identifies data that will be processed by the machine learning models.)
para 0019, As shown in FIG. 1C, and by reference number 120, the f-NFT system may process the derived parameters and the content type, with a multi-level linear regression machine learning (ML) model, to calculate a content score for the content. For example, the f-NFT system may utilize the multi-level linear regression machine learning model to assign weights to the derived parameters so that the content score may be accurately calculated. In some implementations, the multi-level linear regression machine learning model may include a linear regression model using a deep neural network model (e.g., a rectified linear activation function (ReLU) activation) to calculate the content score according to the content type. As further shown in FIG. 1C, the deep neural network model may receive outputs from the parameter unification model ( e.g., a deep neural network model). The parameter unification model may utilize the content type, the standard parameters, and the real-time parameters ( e.g., character popularity, storyline continuity, carbon footprint, transparency, and/ or the like) as inputs and may generate the derived parameters (e.g., cast reputation index, longevity index, ratings, ESG index, theatrical revenues, and/or the like) as the outputs. The deep neural network model may utilize the outputs from the parameter unification model as inputs and may calculate the content score based on the inputs.
Para. 0020, As shown in FIG. 1D, and by reference number 125, the f-NFT system may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity off-NFTs to generate for the content and a divestment ratio for the content. For example, when processing the derived parameters and the content score, with the linear regression machine learning model, to calculate the quantity of f-NFTs and the divestment ratio, the f-NFT system may calculate the quantity of f-NFTs and the divestment ratio based on the content score and based on the derived parameters that influence a future capability of the content. [predicting one or more values attributed to the at least one resource-related activity].);
and performing one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity (Rao para 0020, In some implementations, the multi-level linear regression machine learning model may utilize forward propagation to propagate the derived parameters and the content type through the deep neural network model. The multi-level linear regression machine learning model may also utilize a cost function (e.g., a mean squared error), gradient descent (e.g., an iterative first-order optimization model used to find a local minimum/maximum), and an optimizer (e.g., an Adam optimizer with a particular learning rate (e.g., 0.001)) to calculate an optimal content score.
0021, As shown in FIG. 1D, and by reference number 125, the f-NFT system may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity off-NFTs to generate for the content and a divestment ratio for the content. For example, when processing the derived parameters and the content score, with the linear regression machine learning model, to calculate the quantity of f-NFTs and the divestment ratio, the f-NFT system may calculate the quantity of f-NFTs and the divestment ratio based on the content score and based on the derived parameters that influence a future capability of the content. In some implementations, the linear regression machine learning model may utilize a cost function (e.g., a mean squared error), gradient descent (e.g., an iterative first-order optimization model), and an optimizer ( e.g., an Adam optimizer with a particular learning rate ( e.g., 0.001)) to calculate an optimal quantity of f-NFTs and divestment ratio [and performing one or more automated actions ]. In one example, the linear regression machine learning model may utilize the content score and particular derived parameters ( e.g., a longevity index for the content, a reputation index for the content, a popularity of the content, a reputation of a creator of the content, a reputation of a cast of the content, and/or the like) to calculate the quantity of f-NFTs and the divestment ratio [based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity].);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory ((
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The computing hardware 303 includes hardware and corresponding resources from one or more computing devices. For example, the computing hardware 303 may include hardware from a single computing device ( e.g., a single server) or from multiple computing devices (e.g., multiple servers), such as multiple computing devices in one or more data centers. As shown, the computing hardware 303 may include one or more processors 307, one or more memories 308, one or more storage components 309, and/or one or more networking components 310. Examples of a processor, a memory, a storage component, and a networking component (e.g., a communication component) are described elsewhere herein [performed by at least one processing device comprising a processor coupled to a memory]).
Welder teaches predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques ((Welder para. 0011, In some embodiments of the invention, the method is executed by a programmed data processing device system and comprises acquiring a training data set including a plurality of input features that influence performance of the portfolio of financial assets and one or more output predictors of the performance of the portfolio of financial assets; training a multi-output machine learning model using the plurality of input features included in the training data set to generate the one or more output predictors; simulating a plurality of mixes of assets, to form the portfolio of financial assets, and generating the one or more output predictors of the performance of each of the plurality of mixes of assets; selecting a mix of assets of the plurality of mixes of assets based on the generated one or more output predictors to form the portfolio of financial assets; receiving a debt service payment from the portfolio of financial assets; and determining whether a fund benefitted by the portfolio of financial assets meets a target actuarial value [at least a portion of the obtained data using one or more machine learning techniques].
Para 0128, In some embodiments of the invention, a multioutput machine learning model is generated and deployed to set up the CMLO. The multi-output machine learning approach extends traditional machine learning methods that predict/generate an output (eg. a dependent variable in a regression machine learning analysis) based on a plurality of inputs (eg. multiple independent variables in a multi-variate regression machine learning method) to predict multiple dependent variables (targets) simultaneously. A multi-output machine learning approach is particularly useful when the dependent variables are interrelated or when making separate predictions for each target doesn't capture the underlying relationships in the data [predicting one or more values associated with the at least one resource]);
Rao and Welder are considered to be analogous to the claim invention because they are in the same field of machine learning using deep learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rao to incorporate the teachings of Welder to include predicting multiple values. Doing so to take advantage of multi-output machine learning approach as it is user when the dependent variable are interrelated or when making sperate predictions (Welder para 0127, In some embodiments of the invention, a multioutput machine learning model is generated and deployed to set up the CMLO. The multi-output machine learning approach extends traditional machine learning methods that predict/generate an output (eg. a dependent variable in a regression machine learning analysis) based on a plurality of inputs (eg. multiple independent variables in a multi-variate regression machine learning method) to predict multiple dependent variables (targets) simultaneously. A multi-output machine learning approach is particularly useful when the dependent variables are interrelated or when making separate predictions for each target doesn't capture the underlying relationships in the data.).
Regarding claim 2 and analogous claims 11 and 17, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder further teaches wherein predicting one or more values associated with the at least one resource comprises processing at least a portion of the obtained data using at least one artificial neural network-based multi-output regression model (Welder Para. 0138, Typically, the dataset is split between a training set and a testing set, with the loss minimization being performed using the training set and the performance of the model's predictive capability being evaluated on the testing set. When working with multiple dependent variables, the selected model type can either be trained for each target (dependent) variable separately or a multi-output regression model that can predict 2 or more output variables simultaneously (such as a neural network having more two or more neurons in the output layer) must be selected. Depending on the selected model, hyperparameter tuning may be performed, using techniques such as cross-validation and grid search [artificial neural network-based multi-output regression model].
Regarding claim 3 and analogous claims 12 and 18, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder further teaches wherein predicting one or more values attributed to the at least one resource-related activity comprises processing the at least a portion of the obtained data using the at least one artificial neural network-based multi-output regression model (Welder Para. 0138, Typically, the dataset is split between a training set and a testing set, with the loss minimization being performed using the training set and the performance of the model's predictive capability being evaluated on the testing set. When working with multiple dependent variables, the selected model type can either be trained for each target (dependent) variable separately or a multi-output regression model that can predict 2 or more output variables simultaneously (such as a neural network having more two or more neurons in the output layer) must be selected. Depending on the selected model, hyperparameter tuning may be performed, using techniques such as cross-validation and grid search [artificial neural network-based multi-output regression model].
Para. 0140 line 1-12, In some embodiments, validation and testing of the trained multi-output machine learning model is performed to ensure that the model is generalized (it is not overfitted to the training data and can provide similar performance on new data as on the training data). In some embodiments, a portion of the data is held back from the training set for validation and testing. The validation dataset is used to estimate the machine learning model's performance while tuning the model parameters (for example, the weights and biases in a neural network). The test dataset is used to give an unbiased estimate of the performance of the final tuned machine learning model [comprises processing at least a portion of the obtained data]).
Para. 0140 line 1-12, In some embodiments, validation and testing of the trained multi-output machine learning model is performed to ensure that the model is generalized (it is not overfitted to the training data and can provide similar performance on new data as on the training data). In some embodiments, a portion of the data is held back from the training set for validation and testing. The validation dataset is used to estimate the machine learning model's performance while tuning the model parameters (for example, the weights and biases in a neural network). The test dataset is used to give an unbiased estimate of the performance of the final tuned machine learning model [comprises processing at least a portion of the obtained data]).
Regarding claim 4 and analogous claim 13, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder teaches wherein using the at least one artificial neural network-based multi-output regression model comprises configuring the at least one artificial neural network-based multi-output regression model to include an input layer, two or more hidden layers, and two or more output layers (Welder para. 0139, Typically, the dataset is split between a training set and a testing set, with the loss minimization being performed using the training set and the performance of the model's predictive capability being evaluated on the testing set. When working with multiple dependent variables, the selected model type can either be trained for each target (dependent) variable separately or a multi-output regression model that can predict 2 or more output variables simultaneously (such as a neural network having more two or more neurons in the output layer) must be selected [and two or more output layers]. Depending on the selected model, hyperparameter tuning may be per formed, using techniques such as cross-validation and grid search.
para 0145, The purpose of k-fold cross validation is not to pick one of the trained models as the machine learning model but, rather, to help determine the model structure and the parameter training process for the machine learning model. For example, a neural network model can have one or more "hidden" layers of neurons between the input layer and the output layer. Further, different neural network models can be built with different numbers of neurons in the hidden layers [configuring the at least one artificial neural network-based multi-output regression model to include an input layer, two or more hidden layers,].
Para. 0150, Multi-Output Neural Networks: Deep learning architectures like feedforward neural networks, convolutional neural networks ( CNN s ), or recurrent neural networks (RNNs) can be adapted for multi-output regression by having multiple output neurons, each corresponding to a target variable [wherein using the at least one artificial neural network-based multi-output regression model].).
Regarding claim 5, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder teaches wherein configuring the at least one artificial neural network-based multi-output regression model comprises configuring the input layer to include a number of neurons that matches a number of input data variables, configuring the two or more hidden layers to include a number of neurons that is based at least in part on the number of neurons in the input layer, and configuring each of the two or more output layers to include a single neuron (Welder para 0139, Typically, the dataset is split between a training set and a testing set, with the loss minimization being performed using the training set and the performance of the model's predictive capability being evaluated on the testing set. When working with multiple dependent variables, the selected model type can either be trained for each target (dependent) variable separately or a multi-output regression model that can predict 2 or more output variables simultaneously (such as a neural network having more two or more neurons in the output layer) must be selected. Depending on the selected model, hyperparameter tuning may be per formed, using techniques such as cross-validation and grid search.
Para 0145 line 1-11, The purpose of k-fold cross validation is not to pick one of the trained models as the machine learning model but, rather, to help determine the model structure and the parameter training process for the machine learning model. For example, a neural network model can have one or more "hidden" layers of neurons between the input layer and the output layer. Further, different neural network models can be built with different numbers of neurons in the hidden layers. In some embodiments, in the training phase, a plurality of machine learning models having different structures and hyperparatemers are generated [wherein configuring the at least one artificial neural network-based multi-output regression model comprises configuring the input layer to include a number of neurons that matches a number of input data variables, configuring the two or more hidden layers to include a number of neurons that is based at least in part on the number of neurons in the input layer,]
Para 0150, Multi-Output Neural Networks: Deep learning architectures like feedforward neural networks, convolutional neural networks ( CNN s ), or recurrent neural networks. (RNNs) can be adapted for multi-output regression by having multiple output neurons, each corresponding to a target variable [and configuring each of the two or more output layers to include a single neuron.]).
Regarding claim 6, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder teaches wherein a first one of the two or more output layers is configured to generate a prediction of the one or more values associated with the at least one resource, wherein a second one of the two or more output layers is configured to generate a prediction of the one or more values attributed to the at least one resource-related activity ((Para. 0156, FIG. 8 shows a flowchart for a method 800 of training and using a machine learning model to generate the mix of assets in a CMLO system 300, according to some embodiments of the invention. In step 810, a training data set including a plurality of input features that influence performance of the portfolio of financial assets and one or more output predictors of the performance of the portfolio of financial assets is acquired. If the training data set is sparse, in step 820, Monte Carlo simulation can be performed to generate additional training samples to enhance the data set. In step 830, a multi-output machine learning model is trained using the plurality of input features included in the training data set to generate the one or more output predictors. The input features describe the various characteristics of the mix of assets in each training sample of the training data set. The output predictors describe the performance of the mix of assets in the respective training sample and include one or more of the likelihood of full redemption at maturity, the need for principal deflection before maturity, early redemption, etc. as discussed above [wherein a second one of the two or more output layers is configured to generate a prediction of the one or more values attributed to the at least one resource-related activity, wherein a second one of the two or more output layers is configured to generate a prediction of the one or more values attributed to the at least one resource-related activity]).
Regarding claim 7 and analogous claims 14 and 19, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Rao wherein the at least one resource-related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity (Rapo para 0037, As an example, the trained machine learning model 225 may predict a value of content score A for the target variable of the content score for the new observation, as shown by reference number 235. Based on this prediction, the machine learning system may provide a first recommendation, may provide output for determination of a first recommendation, may perform a first automated action, may cause a first automated action to be performed (e.g., by instructing another device to perform the automated action), and/or the like [and wherein performing one or more automated actions comprises automatically initiating one or more automated actions in connection with one or more additional systems in furtherance of completing the at least one resource-related activity].
Para 0038, In some implementations, the recommendation and/or the automated action associated with the new observation may be based on a target variable value having a particular label ( e.g., classification, categorization, and/or the like), may be based on whether a target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, falls within a range of threshold values, and/or the like), and/or the like [wherein the at least one resource-related activity is ongoing]).
Regarding claim 8 and analogous claim 15 and 20, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Welder teaches wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity (Welder para 0140, In some embodiments, validation and testing of the trained multi-output machine learning model is performed to ensure that the model is generalized (it is not overfitted to the training data and can provide similar performance on new data as on the training data). In some embodiments, a portion of the data is held back from the training set for validation and testing. The validation dataset is used to estimate the machine learning model's performance while tuning the model parameters (for example, the weights and biases in a neural network). The test dataset is used to give an unbiased estimate of the performance of the final tuned machine learning model. It is well known that evaluating the learned model using the training set would result in a biased score as the trained model is, by design, built to learn the biases in the training set. Thus, to evaluate the performance of a trained machine learning model, one needs to use data that has not been used for training [wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques].
para 0156, FIG. 8 shows a flowchart for a method 800 of training and using a machine learning model to generate the mix of assets in a CMLO system 300, according to some embodiments of the invention. In step 810, a training data set including a plurality of input features that influence performance of the portfolio of financial assets and one or more output predictors of the performance of the portfolio of financial assets is acquired. If the training data set is sparse, in step 820, Monte Carlo simulation can be performed to generate additional training samples to enhance the data set. In step 830, a multi-output machine learning model is trained using the plurality of input features included in the training data set to generate the one or more output predictors. The input features describe the various characteristics of the mix of assets in each training sample of the training data set. The output predictors describe the performance of the mix of assets in the respective training sample and include one or more of the likelihood of full redemption at maturity, the need for principal deflection before maturity, early redemption, etc. as discussed above [using feedback related to one or more of the one or more predicted values associated with the at least one resource and the one or more predicted values attributed to the at least one resource-related activity].).
Claim(s) 9 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Welder and further in view of Moorthy et al. (US11915174B2) (“Moorthy”).
Regarding claim 9, Rao in view of Welder teach the method as recited in claim 1.
Rao and Welder are combine in the same rational as set forth above with respect to claim 1 and analogous claims 10 and 16.
Rao teaches wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of historical values associated with resources related to the at least one resource (Rao para. 0030, As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained from historical data, such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations ( e.g., as input) from the f-NFT system, as described elsewhere herein.),
and user-related data associated with the at least one resource-related activity (Rao As shown in FIG. 1A, and by reference number 105, the f-NFT system may receive content associated with a content type and a movie, a television series, art, audio, and/or the like. For example, the f-NFT system may continuously receive the content from the user device and/or a data source (e.g., a content provider data structure), may periodically receive the content from the user device and/or the data source, may receive the content from the user device and/or the data source based upon providing a request for the content to the user device and/or the data source, and/or the like [and user-related data associated with the at least one resource-related activity].).
However Roa does not explicitly teach historical values attributed to previous instances of resource-related activities related to the at least one resource-related activity, data related to one or more actions already performed as part of the at least one resource-related activity, temporal data associated with the at least one resource,
Moorthy does teach historical values attributed to previous instances of resource-related activities related to the at least one resource-related activity, data related to one or more actions already performed as part of the at least one resource-related activity, temporal data associated with the at least one resource (Moorthy Col 46-47 line 63-67 and line 1-5, The resource offer generation input data sets may include a historical offer data set. The historical offer data set may include at least information associated with previous generated offer data objects, resource acquisition information associated with the previous generated offer data objects, sales information associated with said resources, or the like. The historical offer data set may be used to retrieve and/or generate an expected resource volume data set, which may be associated with particular resource set identifiers for a particular region-program identifier. sales information associated with said resources, or the like. The historical offer data set may be used to retrieve and/or generate an expected resource volume data set, which may be associated with particular resource set identifiers for a particular region-program identifier [historical values attributed to previous instances of resource-related activities related to the at least one resource-related activity, data related to one or more actions already performed as part of the at least one resource-related activity, ].
Col 71, In some embodiments, the untrusted third-party resource characteristic data set and the distributed resource characteristic data set are aligned based on a temporal alignment and a resource set identifier alignment. Continuing the example of the third-party resource pricing data set and the distributed resource pricing data set, each record in the third-party resource pricing data set and the distributed resource pricing data set may also include, or otherwise be associated with, a particular resource set identifier [temporal data associated with the at least one resource, ].),
Rao and Moorthy are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rao to incorporate the teachings of Moorthy to include information about previous historical information. Doing so to modify or adapt the mapping in response to historical data return form pervious iterations (Col 28 line 25-37, In some examples, such learning can occur offline, in a system startup phase, or could occur in real-time or near real-time during performing the methods shown in the described figures (e.g., predicting and modeling an optimum channel for the distribution of resources). The trained model may comprise the results of clustering algorithms, classifiers, neural networks, ensemble of trees in that the trained model is configured or otherwise trained to map an input value or input features to one of a set of predefined output scores or recommendations and modify or adapt the mapping in response to historical data returned from previous iterations ( e.g., measured distributions, such as those derived from available data).).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vijayaraghavan et al. (US20220357929A1) – teaches a neural network with multiple outputs.
Conclusion
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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