DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This is in response to a letter for a patent filed 15 November 2024 in which claims 1-20 were presented for examination. Claims 1-20 are currently pending.
Claim Objections
Claim 17 is objected to because of the following informalities:
Claim 17 line 3, after “a” is incomplete. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 17, the Examiner is unable to determine what the second plan entails as the claim is incomplete. Appropriate correction is required.
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 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-9 are directed to a method (i.e., a process). Claims 10-18 are directed to a system (i.e., a machine); and Claims 19-20 are directed to a non-transitory computer-readable medium (i.e., a manufacture). Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1, 10, and 19 substantially recite:
predicting, using a [ ] model, daily data usage for a predetermined subsequent period for each of a plurality of subscribers [ ];
constructing, using a [ ] model, a plan grid and a cost grid for each of the plurality of subscribers based on their respective predicted daily data usage for the predetermined subsequent period, wherein the cost grid for each subscriber includes values indicating predicted costs for that subscriber for different time windows within the predetermined subsequent period;
allocating each subscriber to one of a first data plan and a second plan based on the plan grid;
reconstructing, using the second [ ] model, the plan grid and the cost grid for each of the plurality of subscribers based on actual data usage of each individual subscriber and total actual data usage of the plurality of subscribers on an immediately preceding day during each day of remaining days of the predetermined subsequent period; and
reallocating one or more of the subscribers to a different data plan based on the reconstructed plan grids and the reconstructed cost grids during each day of the remaining days of the predetermined subsequent period. These limitations recite a method of organizing human activity. Allocating subscribers to different data plans for cost minimization is the “Managing Personal Behavior or Relationships or Interactions Between People” which includes social activities, teaching, and following rules or instructions and/or “Commercial Interactions” which includes agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations and/or a “Mental Process” (concepts performed in the human mind) which includes observations, evaluations, judgments, and opinions.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: “a wireless network,” “ a first machine learning model,” “a second machine learning model,” and “a plurality of processing nodes”; claim 10 recites the additional elements: “a system,” “a wireless network,” “one or more processors,” “one or more memories,” “instructions,” “a first machine learning model,” and “a second machine learning model”; and claim 19 recites the additional elements: “non transitory computer-readable medium,” “instructions,” “one or more processors,” “a system,” “a wireless network,” “a first machine learning model,” and “a second machine learning model,” and “a plurality of processing nodes” to perform the “predicting,” “constructing,” “allocating,” “reconstructing,” and “reallocating” steps. The claimed computer components in the steps of claims 1, 10, and 19 are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “a wireless network,” “ a first machine learning model,” “a second machine learning model,” and “a plurality of processing nodes” in claim 1; “a system,” “a wireless network,” “one or more processors,” “one or more memories,” “instructions,” “a first machine learning model,” and “a second machine learning model” in claim 10; and “a non-transitory computer-readable medium,” “instructions,” “one or more processors,” “a system,” “a wireless network,” “a first machine learning model,” and “a second machine learning model,” and “a plurality of processing nodes” in claim 19 to perform the generic functions of “predicting,” “constructing,” “allocating,” “reconstructing,” and “reallocating”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations is no more than mere instructions to apply the exception using the generic computer components recited above. The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component as recited above. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are not patent eligible.
Step 2B
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the (“wireless network,” “first machine learning model,” “second machine learning model,” and the “plurality of processing nodes” in claim 1; “system,” “wireless network,” “one or more processors,” “one or more memories,” “instructions,” “first machine learning model,” and “second machine learning model” in claim 10; and “a non-transitory computer-readable medium,” “instructions,” “one or more processors,” “a system,” “wireless network,” “first machine learning model,” “second machine learning model,” and “a plurality of processing nodes” in claim 19 to perform the “predicting,” “constructing,” “allocating,” “reconstructing,” and “reallocating”) steps amount to no more than mere instructions to apply the exception using a generic computer component. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are patent ineligible.
As per dependent claims 2-4, 7, 11-13, 15-17 and 20, the limitations merely narrow the previously recited abstract idea limitations. Dependent claims 2 and 11 recite each of the time windows in the plan grid is a window of days, wherein the time windows cover each combination of days in the predetermined subsequent period. Dependent claims 3, 12, and 20 recite the first data plan is a metered plan, and the second data plan is a pooled plan, and both plans are wholesale plans. Dependent claims 4 and 13 recite wherein each cost grid contains one or more cost-reduction time windows, wherein each of the cost-reduction time window has a predicted cost for the subscriber if the subscriber stays in one of the first data plan and the second data plan. Dependent claims 7 and 15 recite wherein input parameters of the machine learning model includes one or more of: a subscriber’s daily data usages in a past period of time, a retail plan of the subscriber, a geographic location of the subscriber, and payment information of the subscriber. Dependent claim 16 recites wherein the first data plan is a metered plan, and the second data plan is a hybrid metered and pooled plan based on a tiered usage system. Dependent claim 17 recites wherein the first data plan is an individual subscriber metered plan and the second data plan is one of: a family plan that is based on pooled data usage for each individual subscriber that is part of the family plan and a for an allocating one or more of the subscribers to a different data plan includes. For the reasons described above with respect to claims 2-4, 7, 11-13, 15-17, and 20, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent claims 5 and 14, recitation of “… finding a best cost-reduction time window using a predetermined gradient descent algorithm” is further directed to a method of organizing human activity as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 6, recitation of “an N-beats” is another computer components recited at a high-level of generality and is merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claim 8, the recitation of “a logistic regression model, a decision tree and random forests model, a gradient boosting model, a deep learning model and a reinforce learning model” are other computer components recited at a high-level of generality and is merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 9 and 18, the recitations “declare a plan allocation for each of the plurality of subscribers when that subscriber is initially allocated to one of the first data plan and the second data plan and declares a plan reallocation for each of the one or more subscribers” are further directed to a method of organizing human activity as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent Claims 2-9, 11-18 and 20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-9, 3-8 and 10-16, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Schrenkel et al. (US PG Pub. 20230087930 A1) in view of Sasaki (US PG Pub. 20150242783 A1).
As per claim 1, Schrenkel et al. discloses a method of dynamically allocating subscribers to different plans for cost minimization in a wireless network, comprising:
predicting, using a first machine learning model, daily data usage for a predetermined subsequent period for each of a plurality of subscribers of the wireless network (Schrenkel et al.: Abstract: A system and method incorporating Robotic Processing Automation and Machine Leaning to match telecom plans and features to the historical and predicted usage models of user devices associated with a company in order to provide a set of plans and features across one or more carriers that best fits the particular needs of the company); and (Schrenkel et al.: [0059] Using the known usage data 20 the RPA system 10 extrapolates patterns of usage and predicts usage for upcoming billing periods (months and years) in an expected usage data 70 area).
constructing, using a second machine learning model, a plan grid and a cost grid for each of the plurality of subscribers based on their respective predicted daily data usage for the predetermined subsequent period, wherein the cost grid for each subscriber includes values indicating predicted costs for that subscriber for different time windows within the predetermined subsequent period (Schrenkel et al.: [0087] When modeling, the system again uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where. It is impractical to run through all possibilities by brute force when so many options exist for so many users. In a simple example, imagine 4 plans being offered with 3 options each for voice, data, and SMS amounts. Add to this international roaming features, specific data applications, times of usage, and even device types such as mobile land line for calling, and we quickly grow the number of possible combinations. Now consider a company with 50,000 lines, and then further add the possibility of forming pooled groups. You can see how the combinations by brute force grow exponentially beyond what can be computed, never mind done manually); (Schrenkel et al.: [0093] The mapping of predicted usage into each of the plans is made for multiple months/billing periods and the cost is calculated to determine if making a change should be implemented); .and Schrenkel et al.: [0019] It is further desired to provide a system and method to dynamically discover the plans as they are made available through RPA (robotic process automation) bots and ETL (Extract Transform Load) components that will re-run the calculations based on currently available plans in real time). Further the Examiner interprets the system again using uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where to mean a second machine learning model is used.; also see [0075] for predictive analytics )
allocating each subscriber to one of a first data plan and a second plan based on the plan grid (Schrenkel et al.: [0020] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company)
reconstructing, using the second machine learning model running on a plurality of processing nodes, the plan grid and the cost grid for each of the plurality of subscribers based on actual data usage of each individual subscriber and total actual data usage of the plurality of subscribers on an immediately preceding day during each day Schrenkel et al.: [0018],[0074] Therefore, it would be desirable to provide a system and a method of dynamically mapping current usage of all individual users on a plan to one or more carrier plans to evaluate the fit and cost of a carrier's plans, including pooled plans and applicable features, to meet a company's needs); (Schrenkel et al.: [0020]-[0022] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company. It is also desired to provide a system that maps actual usage and generates an ideal plan(s) based on the mapped actual usage and automatically generates a request for the ideal plan(s)… It is contemplated that this information may be provided in real time or near real time allowing the system to operate with high granularity providing recommendations during a current billing cycle. This would include recognizing not just billing cycle usage (i.e., monthly usage and patterns), but also weekly, daily, or even more granular usage information. This greatly increased data granularity allows for greater accuracy in formulating a dynamic plan(s) needed by the company and provided to multiple different carriers); and (Schrenkel et al.: [0054] Based on the live data fed into software application 10, the telecom tool can dynamically predict changed usage and automatically make adjustments to fit the changing needs of the business unit. [0056]-[0057] The usage data can also be gathered from real time or near real time feeds 40 from the service providers or carriers); and
reallocating one or more of the subscribers to a different data plan based on the reconstructed plan grids and the reconstructed cost grids during each day of the remaining days of the predetermined subsequent period (Schrenkel et al.: [0031]-[0032] The system is intended to run as one or more RPA bot(s) and ETL components autonomously in the background to discover carrier plans and features. Additional RPA bot or bots run mapping of prior usage and project current month usage to propose or implement plan changes dynamically adjusting the allocated plans as needed to optimize cost.
Schrenkel et al. discloses determining usage in real-time in ([0019][0022]), but does not explicitly disclose, however, Sasaki discloses the total actual data usage of the plurality of subscribers on an immediately preceding day during each day of remaining days of the predetermined subsequent period (Sasaki: [0323] FIG. 19 is a diagram showing a third example of the used amount confirmation screen 1500. On the used amount confirmation screen 1500 according to the third example, a used energy amount of each day is displayed by characters colored in the first color or the second color instead of displaying the entire box 1504 in the first color or the second color). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. to include the calendar showing each day before a present day of a concerned month or a concerned week based on the managed schedule information as it relates to an amount of a consumed utility as taught by Sasaki to show the latest status of the consumed amount up to the previous day can be confirmed everyday using the calendar data, and a trend of the consumed amount of the concerned month or the concerned week can be recognized with respect to the entire concerned month (Sasaki: [0194].
As per claim 3, Schrenkel et al. in view of Sasaki discloses the method of claim 1. Schrenkel et al. further discloses:
wherein the first data plan is a metered plan, and the second data plan is a pooled plan, and both plans are wholesale plans (Schrenkel et al.: [0028]-[0029], metered, pooled [0073]-[0074] pooled; also see FIG. 6)
As per claim 4, Schrenkel et al. in view of Sasaki discloses the method of claim 1. Schrenkel et al. further discloses:
wherein each cost grid contains one or more cost-reduction time windows, wherein each of the cost-reduction time window has a predicted cost for the subscriber if the subscriber stays in one of the first data plan and the second data plan (Schrenkel et al.: [0061] The RPA system 10 also captures the available plans from all sanctioned and configured service providers 90, 100, 110 and includes its knowledge of existing plans 60. This allows the RPA system 10 to determine if staying on the same plan 60 or moving to a new plan or set of plans from other telecom providers 90, 100, 110 is most beneficial for the group and/or company); and (Schrenkel et al.: [0063] The analysis and mapping are accomplished by taking the expected usage data 70 for each device, which was derived from the known usage data 20 and then mapping that to available plans 80 and existing plans 60. Factors such as any contractual cost penalties or fees for moving to a different carrier are also factored into the calculation whether these be by-line or in aggregate. A cost analysis is done, and the most economical plan set is proposed 120 as a grouping of plan selections across the existing plans 60 and available plans 80).
As per claim 7, Schrenkel et al. in view of Sasaki discloses the method of claim 6. Schrenkel et al. further discloses:
wherein input parameters of the machine learning model includes one or more of:
a subscriber’s daily data usages in a past period of time (Schrenkel et al.: [0022] The software, based on a selected time in a billing cycle for a telecommunications provider, determines an expected usage for the devices for the billing cycle based on usage which has occurred between a beginning of the billing cycle and the selected time),
a retail plan of the subscriber,
a geographic location of the subscriber, and
payment information of the subscriber.
As per claim 9, Schrenkel et al. in view of Sasaki discloses the method of claim 1. Schrenkel et al. further discloses:
wherein the wireless network declare a plan allocation for each of the plurality of subscribers when that subscriber is initially allocated to one of the first data plan and the second data plan and declares a plan reallocation for each of the one or more subscribers (Schrenkel et al.: [0020] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company).
Claims 10, 12-13, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schrenkel et al. (US PG Pub. 20230087930 A1) in view of Schultz et al. (US PG Pub. 2014/0179266 A1) and Sasaki (US PG Pub. 2015024783 A1).
As per claim 10, Schrenkel et al. discloses a system for dynamically allocating subscribers to different plans for cost minimization in a wireless network, comprising:
predicting, using a first machine learning model, daily data usage for a predetermined subsequent period for each of a plurality of subscribers of the wireless network (Schrenkel et al.: Abstract: A system and method incorporating Robotic Processing Automation and Machine Leaning to match telecom plans and features to the historical and predicted usage models of user devices associated with a company in order to provide a set of plans and features across one or more carriers that best fits the particular needs of the company); and (Schrenkel et al.: [0059] Using the known usage data 20 the RPA system 10 extrapolates patterns of usage and predicts usage for upcoming billing periods (months and years) in an expected usage data 70 area).
constructing, using a second machine learning model, a plan grid and a cost grid for each of the plurality of subscribers based on their respective predicted daily data usage for the predetermined subsequent period, wherein the cost grid for each subscriber includes values indicating predicted costs for that subscriber for different time windows within the predetermined subsequent period (Schrenkel et al.: [0087] When modeling, the system again uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where. It is impractical to run through all possibilities by brute force when so many options exist for so many users. In a simple example, imagine 4 plans being offered with 3 options each for voice, data, and SMS amounts. Add to this international roaming features, specific data applications, times of usage, and even device types such as mobile land line for calling, and we quickly grow the number of possible combinations. Now consider a company with 50,000 lines, and then further add the possibility of forming pooled groups. You can see how the combinations by brute force grow exponentially beyond what can be computed, never mind done manually); (Schrenkel et al.: [0093] The mapping of predicted usage into each of the plans is made for multiple months/billing periods and the cost is calculated to determine if making a change should be implemented); .and Schrenkel et al.: [0019] It is further desired to provide a system and method to dynamically discover the plans as they are made available through RPA (robotic process automation) bots and ETL (Extract Transform Load) components that will re-run the calculations based on currently available plans in real time). Further the Examiner interprets the system again using uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where to mean a second machine learning model is used.; also see [0075] for predictive analytics )
allocating each subscriber to one of a first data plan and a second plan based on the plan grid (Schrenkel et al.: [0020] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company)
reconstructing, using the second machine learning model running on a plurality of processing nodes, the plan grid and the cost grid for each of the plurality of subscribers based on actual data usage of each individual subscriber and total actual data usage of the plurality of subscribers on an immediately preceding day during each day Schrenkel et al.: [0018],[0074] Therefore, it would be desirable to provide a system and a method of dynamically mapping current usage of all individual users on a plan to one or more carrier plans to evaluate the fit and cost of a carrier's plans, including pooled plans and applicable features, to meet a company's needs); (Schrenkel et al.: [0020]-[0022] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company. It is also desired to provide a system that maps actual usage and generates an ideal plan(s) based on the mapped actual usage and automatically generates a request for the ideal plan(s)… It is contemplated that this information may be provided in real time or near real time allowing the system to operate with high granularity providing recommendations during a current billing cycle. This would include recognizing not just billing cycle usage (i.e., monthly usage and patterns), but also weekly, daily, or even more granular usage information. This greatly increased data granularity allows for greater accuracy in formulating a dynamic plan(s) needed by the company and provided to multiple different carriers); and (Schrenkel et al.: [0054] Based on the live data fed into software application 10, the telecom tool can dynamically predict changed usage and automatically make adjustments to fit the changing needs of the business unit. [0056]-[0057] The usage data can also be gathered from real time or near real time feeds 40 from the service providers or carriers); and
reallocating one or more of the subscribers to a different data plan based on the reconstructed plan grids and the reconstructed cost grids during each day of the remaining days of the predetermined subsequent period (Schrenkel et al.: [0031]-[0032] The system is intended to run as one or more RPA bot(s) and ETL components autonomously in the background to discover carrier plans and features. Additional RPA bot or bots run mapping of prior usage and project current month usage to propose or implement plan changes dynamically adjusting the allocated plans as needed to optimize cost.
Schrenkel et al. does not explicitly disclose, however, Schultz et al. discloses: one or more processors (Schultz et al.: [0036][0087] Processors); and
one or more memories coupled to the one or more processors and storing instructions, which, when executed by the one or more processors, cause the system to perform operations (Schultz et al.: [0037] Memory 330 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processing unit 320, a read only memory (ROM) or another type of static storage device that stores static information and instructions for the processing unit 320, and/or some other type of magnetic or optical recording medium and its corresponding drive for storing information and/or instructions). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. to include the processor and memory as taught by Schultz et al. in order to implement the allocation and/or reallocation data based on consumption allotments (Schultz et al.: [0194].
Schrenkel et al. discloses determining usage in real-time in ([0019][0022]). Schrenkel et al. in view of Schultz et al. but does not explicitly disclose, however, Sasaki discloses the total actual data usage of the plurality of subscribers on an immediately preceding day during each day of remaining days of the predetermined subsequent period (Sasaki: [0323] FIG. 19 is a diagram showing a third example of the used amount confirmation screen 1500. On the used amount confirmation screen 1500 according to the third example, a used energy amount of each day is displayed by characters colored in the first color or the second color instead of displaying the entire box 1504 in the first color or the second color). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. in view of Schultz et al. to include the calendar showing each day before a present day of a concerned month or a concerned week based on the managed schedule information as it relates to an amount of a consumed utility as taught by Sasaki to show the latest status of the consumed amount up to the previous day can be confirmed everyday using the calendar data, and a trend of the consumed amount of the concerned month or the concerned week can be recognized with respect to the entire concerned month (Sasaki: [0194].
As per claim 12 and 20, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system and non-transitory computer-readable medium of claims 10 and 19, respectively. Schrenkel et al. further discloses:
wherein the first data plan is a metered plan, and the second data plan is a pooled plan, and both plans are wholesale plans ([0028]-[0029], metered, pooled [0073]-[0074] pooled; also see FIG. 6)
As per claim 13, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system of claim 10. Schrenkel et al. further discloses:
wherein each cost grid contains one or more cost-reduction time windows, wherein each of the cost-reduction time window has a predicted cost for the subscriber if the subscriber stays in one of the first data plan and the second data plan (Schrenkel et al.: [0061] The RPA system 10 also captures the available plans from all sanctioned and configured service providers 90, 100, 110 and includes its knowledge of existing plans 60. This allows the RPA system 10 to determine if staying on the same plan 60 or moving to a new plan or set of plans from other telecom providers 90, 100, 110 is most beneficial for the group and/or company); and (Schrenkel et al.: [0063] The analysis and mapping are accomplished by taking the expected usage data 70 for each device, which was derived from the known usage data 20 and then mapping that to available plans 80 and existing plans 60. Factors such as any contractual cost penalties or fees for moving to a different carrier are also factored into the calculation whether these be by-line or in aggregate. A cost analysis is done, and the most economical plan set is proposed 120 as a grouping of plan selections across the existing plans 60 and available plans 80).
As per claim 15, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system of claim 10. Schrenkel et al. further discloses:
wherein input parameters of the machine learning model includes one or more of:
a subscriber’s daily data usages in a past period of time ([0022] The software, based on a selected time in a billing cycle for a telecommunications provider, determines an expected usage for the devices for the billing cycle based on usage which has occurred between a beginning of the billing cycle and the selected time),
a retail plan of the subscriber,
a geographic location of the subscriber, and
payment information of the subscriber.
As per claim 16, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system of claim 10. Schrenkel et al. further discloses:
wherein the first data plan is a metered plan, and the second data plan is a hybrid metered and pooled plan based on a tiered usage system ([0028]-[0029], metered, pooled [0073]-[0074] pooled; also see FIG. 6).
As per claim 17, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system of claim 10. Schrenkel et al. further discloses: wherein the first data plan is an individual subscriber metered plan and the second plan is a pooled plan (Schrenkel et al. [0028]-[0029], metered, pooled [0073]-[0074]).
Schrenkel et al. in view of Sasaki does not explicitly disclose, however, Schultz et al. discloses the second data plan is one of: a family plan that is based on pooled data usage for each individual subscriber that is part of the family plan and a for an allocating one or more of the subscribers to a different data plan includes (Schultz et al.: [0051] Service management module 430 may provide service information on an account, shared data plan, or user as described below with respect to FIG. 6. Service management module 430 may provide information based on a relationship of the requesting individual/entity with regard to a family plan, a business share plan, etc. For example, service management module 430 may provide different levels of information to an administrator of the shared data plan, a user under the shared data plan, or a third party data provider); (Schultz et al.: [0066] Shared data plan accounts 620 may include information regarding the shared data plans, such as a service plan 622, and a social graph 624. The service plan 622 may identify an overall data allocation, billing, subscribed services, terms of service, etc., and the social graph 624 may identify relationships between users with regard to privacy, access to information, and individual allotment of data. For example, the shared data plan may be for an extended family in which people are treated differently based on social graph. The service plan 622 may also include access to administrative functions, such as described with respect to account administration table 800 below. Different data allotments and different responses may be provided based on the social graph when users cross various data consumption thresholds).also see FIGS. 7 and 8). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. in view of Sasaki’s calendar showing daily usage to include the family plan as taught by Schultz et al. as the family plan is a shared data plan of individuals in a family, such as, a business share plan of co-workers (Schultz et al.: [0051])
As per claim 18, Schrenkel et al. in view of Schultz et al. and Sasaki discloses the system of claim 10. Schrenkel et al. further discloses:
wherein the wireless network declare a plan allocation for each of the plurality of subscribers when that subscriber is initially allocated to one of the first data plan and the second data plan and declares a plan reallocation for each of the one or more subscribers (Schrenkel et al.: [0020] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company).
As per claim 19, Schrenkel et al. discloses dynamically allocating subscribers to different plans for cost minimization in a wireless network, cause the system to perform operations comprising:
predicting, using a first machine learning model, daily data usage for a predetermined subsequent period for each of a plurality of subscribers of the wireless network (Schrenkel et al.: Abstract: A system and method incorporating Robotic Processing Automation and Machine Leaning to match telecom plans and features to the historical and predicted usage models of user devices associated with a company in order to provide a set of plans and features across one or more carriers that best fits the particular needs of the company); and (Schrenkel et al.: [0059] Using the known usage data 20 the RPA system 10 extrapolates patterns of usage and predicts usage for upcoming billing periods (months and years) in an expected usage data 70 area).
constructing, using a second machine learning model, a plan grid and a cost grid for each of the plurality of subscribers based on their respective predicted daily data usage for the predetermined subsequent period, wherein the cost grid for each subscriber includes values indicating predicted costs for that subscriber for different time windows within the predetermined subsequent period (Schrenkel et al.: [0087] When modeling, the system again uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where. It is impractical to run through all possibilities by brute force when so many options exist for so many users. In a simple example, imagine 4 plans being offered with 3 options each for voice, data, and SMS amounts. Add to this international roaming features, specific data applications, times of usage, and even device types such as mobile land line for calling, and we quickly grow the number of possible combinations. Now consider a company with 50,000 lines, and then further add the possibility of forming pooled groups. You can see how the combinations by brute force grow exponentially beyond what can be computed, never mind done manually); (Schrenkel et al.: [0093] The mapping of predicted usage into each of the plans is made for multiple months/billing periods and the cost is calculated to determine if making a change should be implemented); .and Schrenkel et al.: [0019] It is further desired to provide a system and method to dynamically discover the plans as they are made available through RPA (robotic process automation) bots and ETL (Extract Transform Load) components that will re-run the calculations based on currently available plans in real time). Further the Examiner interprets the system again using uses machine learning and predictive analysis from prior models to make its selections on what to tweak and where to mean a second machine learning model is used.; also see [0075] for predictive analytics )
allocating each subscriber to one of a first data plan and a second plan based on the plan grid (Schrenkel et al.: [0020] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company)
reconstructing, using the second machine learning model running on a plurality of processing nodes, the plan grid and the cost grid for each of the plurality of subscribers based on actual data usage of each individual subscriber and total actual data usage of the plurality of subscribers on an immediately preceding day during each day Schrenkel et al.: [0018],[0074] Therefore, it would be desirable to provide a system and a method of dynamically mapping current usage of all individual users on a plan to one or more carrier plans to evaluate the fit and cost of a carrier's plans, including pooled plans and applicable features, to meet a company's needs); (Schrenkel et al.: [0020]-[0022] It is still further desired to provide a system and method to individually analyze all users and dynamically determine if allocating individual users into specific user groups spread across multiple different telecom carriers would provide a more cost-effective solution for the company. It is also desired to provide a system that maps actual usage and generates an ideal plan(s) based on the mapped actual usage and automatically generates a request for the ideal plan(s)… It is contemplated that this information may be provided in real time or near real time allowing the system to operate with high granularity providing recommendations during a current billing cycle. This would include recognizing not just billing cycle usage (i.e., monthly usage and patterns), but also weekly, daily, or even more granular usage information. This greatly increased data granularity allows for greater accuracy in formulating a dynamic plan(s) needed by the company and provided to multiple different carriers); and (Schrenkel et al.: [0054] Based on the live data fed into software application 10, the telecom tool can dynamically predict changed usage and automatically make adjustments to fit the changing needs of the business unit. [0056]-[0057] The usage data can also be gathered from real time or near real time feeds 40 from the service providers or carriers); and
reallocating one or more of the subscribers to a different data plan based on the reconstructed plan grids and the reconstructed cost grids during each day of the remaining days of the predetermined subsequent period (Schrenkel et al.: [0031]-[0032] The system is intended to run as one or more RPA bot(s) and ETL components autonomously in the background to discover carrier plans and features. Additional RPA bot or bots run mapping of prior usage and project current month usage to propose or implement plan changes dynamically adjusting the allocated plans as needed to optimize cost.
Schrenkel et al. does not explicitly disclose, however, Schultz et al. discloses:
a non-transitory computer readable medium storing instructions, which, when executed by one or more processors of a system for one or more processors (Schultz et al.: [0036][0087] Processors); (Schultz et al.: [0040] As described herein, device 300 may perform certain operations in response to processing unit 320 executing machine-readable instructions contained in a computer-readable medium, such as memory 330. A computer-readable medium may be defined as a non-transitory memory device. A memory device may include space within a single physical memory device or spread across multiple physical memory devices. The machine-readable instructions may be read into memory 330 from another computer-readable medium or from another device via communication interface 360. cause the system to perform operations). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. to include the processor and memory as taught by Schultz et al. in order to implement the allocation and/or reallocation data based on consumption allotments (Schultz et al.: [0194].
Schrenkel et al. discloses determining usage in real-time in (Schrenkel et al.: [0019][0022]). Schrenkel et al. in view of Schultz et al. but does not explicitly disclose, however, Sasaki discloses:
the total actual data usage of the plurality of subscribers on an immediately preceding day during each day of remaining days of the predetermined subsequent period (Sasaki: [0323] FIG. 19 is a diagram showing a third example of the used amount confirmation screen 1500. On the used amount confirmation screen 1500 according to the third example, a used energy amount of each day is displayed by characters colored in the first color or the second color instead of displaying the entire box 1504 in the first color or the second color). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. in view of Schultz et al. to include the calendar showing each day before a present day of a concerned month or a concerned week based on the managed schedule information as it relates to an amount of a consumed utility as taught by Sasaki to show the latest status of the consumed amount up to the previous day can be confirmed everyday using the calendar data, and a trend of the consumed amount of the concerned month or the concerned week can be recognized with respect to the entire concerned month (Sasaki: [0194].
Schrenkel et al. discloses determining usage in real-time in ([0019][0022]), but does not explicitly disclose, however, Sasaki discloses the total actual data usage of the plurality of subscribers on an immediately preceding day during each day of remaining days of the predetermined subsequent period (Sasaki: [0323] FIG. 19 is a diagram showing a third example of the used amount confirmation screen 1500. On the used amount confirmation screen 1500 according to the third example, a used energy amount of each day is displayed by characters colored in the first color or the second color instead of displaying the entire box 1504 in the first color or the second color). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Schrenkel et al. to include the calendar showing each day before a present day of a concerned month or a concerned week based on the managed schedule information as it relates to an amount of a consumed utility as taught by Sasaki to show the latest status of the consumed amount up to the previous day can be confirmed everyday using the calendar data, and a trend of the consumed amount of the concerned month or the concerned week can be recognized with respect to the entire concerned month (Sasaki: [0194]).
Prior Art Discussion
Claims 2, 5-6, 8, 11, and 14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Ahluwalia et al. (US PG Pub. 2013/0171959 A1) discloses a system and method for data network reassignment
2) Chiu et al. (US PG Pub. 2014/0087688 A1) discloses a system and method for corporate mobile subscription management
3) Ji et al. (US PG Pub. 2025/0094156 A1) discloses managing application updates
4) Kim et al. (US Patent No. 12,175,518 B2) discloses a method and system for automated assignment of devices to an optimal rate plan.
5) Mimaroglu et al. (US PG Pub. 20240405548 A1) discloses machine learning model selection for forecasting entity energy usage.
6) Sheikh Naziruddin et al. (US PG Pub. 2015/0065085 A1) discloses data sharing with mobile devices
7) Alabbasi et al. (WO 2024155216 A1) discloses enabling proactive analytics report delivery
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628