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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/15/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 3, 12, and 18 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a computer-implemented method, therefore falling into the statutory category of process. Independent Claim 10 recites, in part, 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, therefore falling into the statutory category of manufacture. Independent Claim 16 recites, in part, an apparatus, therefore falling into the statutory category of machine.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“generating at least one resource-related forecast by processing resource-related data and user-related data associated with prior activity related to the resource within at least one predetermined temporal period”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses generating a resource forecast using data related to the resource.
“modifying the at least one resource-related forecast”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses modifying a resource forecast.
“predicting data associated with future activity related to the resource within the at least one predetermined temporal period”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses making a prediction based on resource data.
“generating at least one combined resource-related forecast, for at least a portion of the at least one predetermined temporal period, based at least in part on at least a portion of the at least one modified resource related forecast and at least a portion of the predicted data”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses generating a resource forecast using data related to the resource.
“performing one or more automated actions based at least in part on the at least one combined resource-related forecast”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses performing an action in response to a generated report.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“using at least a first set of one or more artificial intelligence techniques”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (artificial intelligence techniques) (See MPEP 2106.05(f)).
“using one or more temporal window regressors”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (regressor) (See MPEP 2106.05(f)).
“using at least a second set of one or more artificial intelligence techniques”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (artificial intelligence techniques) (See MPEP 2106.05(f)).
“wherein the method is performed by at least one processing device comprising a processor coupled to a memory”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor and memory) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “using at least a first set of one or more artificial intelligence techniques”, “using one or more temporal window regressors”, “using at least a second set of one or more artificial intelligence techniques”, and “wherein the method is performed by at least one processing device comprising a processor coupled to a memory” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (AI techniques, regressors, processor, and memory) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein modifying the at least one resource-related forecast using one or more temporal window regressors comprises using multiple rolling temporal window regressors, wherein a first temporal window regressor comprises a first predetermined amount of time and wherein at least a second temporal window regressor comprises at least a second predetermined amount of time longer than the first predetermined amount of time”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (temporal data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein modifying the at least one resource-related forecast using one or more temporal window regressors comprises using multiple rolling temporal window regressors, wherein a first temporal window regressor comprises a first predetermined amount of time and wherein at least a second temporal window regressor comprises at least a second predetermined amount of time longer than the first predetermined amount of time” is/are directed to particular field(s) of use (temporal data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“processing the resource-related data and the user-related data using one or more multi-variant time series forecasting models”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“processing data pertaining to one or more user behavior trends in connection with the resource”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“processing enterprise-related dependency information associated with the resource”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 6:
Step 2A: Prong 1 analysis:
Claim 6 recites in part:
“wherein performing one or more automated actions comprises automatically generating at least one communication to at least one user based at least in part on the at least one combined resource-related forecast”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining what information to communicate to a user based on the forecast.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein performing one or more automated actions comprises automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (artificial intelligence techniques) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein performing one or more automated actions comprises automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (AI techniques) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein performing one or more automated actions comprises automatically training at least a portion of the second set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (artificial intelligence techniques) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein performing one or more automated actions comprises automatically training at least a portion of the second set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (AI techniques) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 1 analysis:
Claim 9 recites in part:
“wherein processing resource- related data comprises processing dispute-related information associated with the resource to determine one or more temporal effects on the at least one resource-related forecast”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing resource related data to identify one or more temporal trends/effects.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 10:
Due to claim language similar to that of Claim 1, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“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”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor and storage) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “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” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (processor and storage) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Due to claim language similar to that of Claim 2, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 12:
Due to claim language similar to that of Claim 3, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 13:
Due to claim language similar to that of Claim 4, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 14:
Due to claim language similar to that of Claim 5, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 15:
Due to claim language similar to that of Claim 6, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 16:
Due to claim language similar to that of Claims 1 and 10, Claim 16 is rejected for the same reasons as presented above in the rejection of Claims 1 and 10.
Regarding Claim 17:
Due to claim language similar to that of Claims 2 and 11, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 2 and 11.
Regarding Claim 18:
Due to claim language similar to that of Claims 3 and 12, Claim 18 is rejected for the same reasons as presented above in the rejection of Claims 3 and 12.
Regarding Claim 19:
Due to claim language similar to that of Claims 4 and 13, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 4 and 13.
Regarding Claim 20:
Due to claim language similar to that of Claims 6 and 15, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 6 and 15.
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.
Claim(s) 1, 4, 6, 10, 13, 15, 16, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaleem et al (US 20240281723 A1, hereinafter Kaleem) and Krishnan et al (US 20220027744 A1, hereinafter Krishnan).
Regarding Claim 1:
Kaleem teaches
A computer-implemented method comprising: generating at least one resource-related forecast by processing, using at least a first set of one or more artificial intelligence techniques, resource-related data and user-related data associated with prior activity related to the resource within at least one predetermined temporal period (Kaleem [0018]: “a resource provider may also correspond to a group of wired and wireless towers in a region that provides an adequate level of energy utility in accordance with a predicted level of energy utility consumption of users”; [0021]: “With the ensemble models 130 for the respective resource providers 170 generated, the ensemble model based forecaster 140 predicts the resource need for each of the resource providers based on their respective ensemble models as well as their time series data recently collected.”; [0022]: “To adapt the ensemble models 130, the resource use data collector 160 may continuously collect the actual resource usage time series data from the resource providers 170. Such newly collected data may then be used as a part of the historic time series data 110 which may be used by the ensemble model generator 120 to update the ensemble models”; [0026]: “Furthermore, to optimize the integration of selected base forecast models to generate an ensemble model, parameters used to combine the base forecast models may also be determined automatically”)
generating at least one combined resource-related forecast, for at least a portion of the at least one predetermined temporal period, based at least in part on at least a portion of the at least one modified resource related forecast and at least a portion of the predicted data (Kaleem [0021]: “With the ensemble models 130 for the respective resource providers 170 generated, the ensemble model based forecaster 140 predicts the resource need for each of the resource providers based on their respective ensemble models as well as their time series data recently collected.”; [0022]: “To adapt the ensemble models 130, the resource use data collector 160 may continuously collect the actual resource usage time series data from the resource providers 170. Such newly collected data may then be used as a part of the historic time series data 110 which may be used by the ensemble model generator 120 to update the ensemble models”; [0026]: “Furthermore, to optimize the integration of selected base forecast models to generate an ensemble model, parameters used to combine the base forecast models may also be determined automatically”) ;
and performing one or more automated actions based at least in part on the at least one combined resource-related forecast (Kaleem [0021]: " Such forecasts of resource needs (output from the ensemble model based forecaster 140) for respective resource providers may then be used by the forecast-based resource allocator 150 to allocate the predicted levels of resources to the resource providers."; (EN): "performing one or more automated actions" is broad and can reasonably be inferred to mean to perform any action in response to the report generation, in this case, allocating the predicted amount of resources to the providers);
modifying the at least one resource-related forecast using one or more temporal window regressors (Kaleem [0038]: “To determine seasonality is to capture some repetitive patterns in the time series data. To do so, the window to be used for analyzing seasonality may be determined based on applications. For instance, using the example of forecasting customer usage demands at different cell towers, the data window may be selected as 24 months so that the seasonality arising from holidays may be observed. Different data processing may be applied to the historic time series data and the processed results may then be used for classification to detect seasonality. In some embodiments, auto-correlation or auto-regression may be applied to identify any repetitive patterns.”; (EN): a regressor in is any independent variable used in a regression model, so auto-regression utilizes regressors);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Kaleem [0046]: " Computer 600, for example, includes COM ports 650 connected to and from a network connected thereto to facilitate data communications. Computer 600 also includes a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions. The exemplary computer platform includes an internal communication bus 610, program storage and data storage of different forms (e.g., disk 670, read only memory (ROM) 630, or random-access memory (RAM) 640), for various data files to be processed and/or communicated by computer").
Kaleem does not distinctly disclose
predicting data associated with future activity related to the resource within the at least one predetermined temporal period using at least a second set of one or more artificial intelligence techniques;
However, Krishnan teaches
predicting data associated with future activity related to the resource within the at least one predetermined temporal period using at least a second set of one or more artificial intelligence techniques (Krishnan [0017]: “The forecasts can pertain to a task volume or the number of data processing tasks that can be expected to be received in a specified time period. The forecasting and simulation system can be configured to generate short-term forecasts to predict the task volume that can be expected in shorter time periods such as a day. The forecasting and simulation system can also be configured to generate long-term forecasts to predict the task volume that can be expected in longer time periods such as one or more weeks or one or more months with data aggregated at weekly levels”;);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the ensemble model based time series forecasting system of Kaleem with the resource data modeling, forecasting, and simulation system of Krishnan in order to provide a method for both short-term forecasts and long-term forecasts. The method presented in Krishnan is beneficial for Kaleem in that it allows for optimized forecasted task volumes and can generate “what-if” scenarios by varying a number of the operational parameters (Krishnan [Abstract]: “The forecasted task volumes are further optimized based on different factors to determine the resources required to handle the forecasted task volume. Various simulations of hypothetical what-if scenarios are also generated based on the forecasts and the resource requirements. The resource data modeling, forecasting and simulation system is based on multi-algorithmic ensemble models for forecasting, automated model selection and the unique simulation methodology based on multiple parameters.”).
Regarding Claim 4:
Kaleem teaches
The computer-implemented method of claim 1, wherein processing user-related data associated with prior activity related to the resource comprises processing data pertaining to one or more user behavior trends in connection with the resource (Kaleem [0018]: “In some applications, a resource provider may also correspond to a group of wired and wireless towers in a region that provides an adequate level of energy utility in accordance with a predicted level of energy utility consumption of users.”; (EN): predicting level of energy utility consumption of users relies on user trends in the resource consumption data).
Regarding Claim 6:
Kaleem teaches
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically generating at least one communication to at least one user based at least in part on the at least one combined resource-related forecast (Kaleem [0043]: "Based on the forecasts generated by the forecast generator 104, the data simulator 108 provides users options to view current resource requirements in terms of different parameters that may be obtained from the SLA and TAT relationship. The input receiver 412 can, therefore, receive user parameters to generate what-if scenarios based on the current long-term and/or short-term forecasts.").
Regarding Claim 10:
Due to claim language similar to that of Claim 1, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Kaleem teaches
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 (Kaleem [0047]: " Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Tangible non-transitory “storage” type media include any or all of the memory or other storage for the computers, processors or the like ")
Regarding Claim 13:
Due to claim language similar to that of Claim 4, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 15:
Due to claim language similar to that of Claim 6, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 16:
Due to claim language similar to that of Claims 1 and 10, Claim 16 is rejected for the same reasons as presented above in the rejection of Claims 1 and 10.
Regarding Claim 19:
Due to claim language similar to that of Claims 4 and 13, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 4 and 13.
Regarding Claim 20:
Due to claim language similar to that of Claims 6 and 15, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 6 and 15.
Claim Rejections - 35 USC § 103
Claim(s) 2, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaleem and Krishnan as applied to claims 1, 10 and 16 above, and further in view of Turner et al (US 20210118054 A1, hereinafter Turner).
Regarding Claim 2:
Kaleem + Krishnan does not distinctly disclose
The computer-implemented method of claim 1, wherein modifying the at least one resource-related forecast using one or more temporal window regressors comprises using multiple rolling temporal window regressors, wherein a first temporal window regressor comprises a first predetermined amount of time and wherein at least a second temporal window regressor comprises at least a second predetermined amount of time longer than the first predetermined amount of time.
However, Turner teaches
The computer-implemented method of claim 1, wherein modifying the at least one resource-related forecast using one or more temporal window regressors comprises using multiple rolling temporal window regressors, wherein a first temporal window regressor comprises a first predetermined amount of time and wherein at least a second temporal window regressor comprises at least a second predetermined amount of time longer than the first predetermined amount of time (Turner [0127]: "The company's projected resource balance 702 and the inflows and outflows 704 may be viewed in context with a time range selector 724. The time range selector 724 comprises viewing ranges from a five day rolling forecast, a thirteen week rolling forecast, and a twelve month rolling forecast. These forecast selections change the view of the company's projected resource balance 702 and the inflows and outflows 704 display different values for the selected interval."; (EN): the rolling time windows of Turner can be combined with the regression of Kaleem to provide multiple different time windows of various sizes that act as regressors for the regression calculation(s)).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the resource data modeling, forecasting, and simulation system of Kaleem + Krishnan with the method of operating a resource credit exchange for a resource of a particular type of Turner in order to provide a method for resource forecasting and resource flow that is applied to a resource exchange that is coupled to resource providers. The method presented in Turner is beneficial for Kaleem + Krishnan in that it allows for more accurate resource flow mappings that does not rely on disparate work flows that are often disintegrated (Turner [0006]: “Functions performed in enterprise resource tracking, planning, and allocation have many interdependencies. However they often operate separately from one another as the skills required to perform the duties of each function are different. As a result, the systems used by each function, and the many work flows to produce desired results for each, may be disparate, poorly integrated, and may utilize inefficient manual processes. For example, most companies today still rely heavily on spreadsheets for many core or critical resource management tasks.”).
Regarding Claim 11:
Due to claim language similar to that of Claim 2, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 17:
Due to claim language similar to that of Claims 2 and 11, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 2 and 11.
Claim Rejections - 35 USC § 103
Claim(s) 5, 7, 8, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaleem + Krishnan as applied to claims 1, 10, and 16 above, and further in view of Devnani et al (US 20230130825 A1, hereinafter Devnani).
Regarding Claim 5:
Kaleem + Krishnan does not distinctly disclose
The computer-implemented method of claim 1, wherein processing resource- related data comprises processing enterprise-related dependency information associated with the resource.
However, Devnani teaches
The computer-implemented method of claim 1, wherein processing resource- related data comprises processing enterprise-related dependency information associated with the resource (Devnani [0007]: "A demand forecast model can be trained using historical data indicating logistical resources used by multiple enterprises. Resources can include, for example, space on a vessel that can be used to transport material and/or personnel to a destination. A demand forecast model can receive input data that includes a type of resource to be reserved by an enterprise, and generate corresponding output data"; [0063]: “The historical data 102 can include booking data including a booking identifier, a reservation identifier, a user identifier, and/or a requestor identifier. In some examples, the booking data can be anonymized. The historical data 102 can include project data including a project name, operation, and/or business unit associated with the booking”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the resource data modeling, forecasting, and simulation system of Kaleem + Krishnan with the methods for secure logistical resource planning of Devnani in order to provide a method for resource cost based on forecasted demand and resource availability. The method presented in Devnani is beneficial for Kaleem + Krishnan in that it allows for improved efficiency in the transportation of material and/or personnel (Devnani [0003]: “Transportation logistics involve a highly complex nature of operations and numerous operational constraints. Logistical planning is impacted by dynamically changing needs of enterprises that are supported by resource providers. The traditional logistics operating model for processes-industries such as oil and gas, mining, construction, and utilities is challenged by poor asset utilization resulting in resource wastage and high carbon footprint across the value chain. This complexity results in inefficient operation of transportation vehicles, which results in undesirable expenditure of resources, such as energy (e.g., fuel, electricity). It is desirable to efficiently manage fuel consumption of transportation vehicles while also ensuring that shipping schedules meet user requirements of cost and service levels and balanced capacity requirements.”).
Regarding Claim 7:
Kaleem + Krishnan does not distinctly disclose
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast.
However, Devnani teaches
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast (Devnani [0080]: "In some examples, the probabilistic forecast model 214 can generate a generic demand profile, and update the demand profile over time as additional data is stored in the database 104. For example, the probabilistic forecast model 214 can generate an initial demand profile for a particular type of project. As resource usage reservations are made for the particular type of project, the reservation data is stored in the database 104 as encrypted historical data. The encrypted historical data 105 is used to train and update the probabilistic forecast model 214. Over time, the probabilistic forecast model 214 can become more accurate for predicting demand for the particular type of project."; (EN): the constant updating of the model of Devnani is analogous to the automatic training of a model based on feedback about the model and can be applied to the combined forecast model demonstrated by Kaleem in Claim 1).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the resource data modeling, forecasting, and simulation system of Krishnan + Taylor with the methods for secure logistical resource planning of Devnani in order to provide a method for resource cost based on forecasted demand and resource availability. The method presented in Devnani is beneficial for Krishnan + Taylor in that it allows for improved efficiency in the transportation of material and/or personnel (Devnani [0003]: “Transportation logistics involve a highly complex nature of operations and numerous operational constraints. Logistical planning is impacted by dynamically changing needs of enterprises that are supported by resource providers. The traditional logistics operating model for processes-industries such as oil and gas, mining, construction, and utilities is challenged by poor asset utilization resulting in resource wastage and high carbon footprint across the value chain. This complexity results in inefficient operation of transportation vehicles, which results in undesirable expenditure of resources, such as energy (e.g., fuel, electricity). It is desirable to efficiently manage fuel consumption of transportation vehicles while also ensuring that shipping schedules meet user requirements of cost and service levels and balanced capacity requirements.”).
Regarding Claim 8:
Krishnan + Taylor does not distinctly disclose
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the second set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast.
However, Devnani teaches
The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the second set of one or more artificial intelligence techniques using feedback related to the at least one combined resource- related forecast (Devnani [0080]: "In some examples, the probabilistic forecast model 214 can generate a generic demand profile, and update the demand profile over time as additional data is stored in the database 104. For example, the probabilistic forecast model 214 can generate an initial demand profile for a particular type of project. As resource usage reservations are made for the particular type of project, the reservation data is stored in the database 104 as encrypted historical data. The encrypted historical data 105 is used to train and update the probabilistic forecast model 214. Over time, the probabilistic forecast model 214 can become more accurate for predicting demand for the particular type of project."; (EN): the constant updating of the model of Devnani is analogous to the automatic training of a model based on feedback about the model and can be applied to the combined forecast model demonstrated by Kaleem in Claim 1. It can also be reasonably understood that the same automatic training applied to the first set of AI techniques can be applied to the second set of AI techniques).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the resource data modeling, forecasting, and simulation system of Krishnan + Taylor with the methods for secure logistical resource planning of Devnani in order to provide a method for resource cost based on forecasted demand and resource availability. The method presented in Devnani is beneficial for Krishnan + Taylor in that it allows for improved efficiency in the transportation of material and/or personnel (Devnani [0003]: “Transportation logistics involve a highly complex nature of operations and numerous operational constraints. Logistical planning is impacted by dynamically changing needs of enterprises that are supported by resource providers. The traditional logistics operating model for processes-industries such as oil and gas, mining, construction, and utilities is challenged by poor asset utilization resulting in resource wastage and high carbon footprint across the value chain. This complexity results in inefficient operation of transportation vehicles, which results in undesirable expenditure of resources, such as energy (e.g., fuel, electricity). It is desirable to efficiently manage fuel consumption of transportation vehicles while also ensuring that shipping schedules meet user requirements of cost and service levels and balanced capacity requirements.”).
Regarding Claim 14:
Due to claim language similar to that of Claim 5, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128