DETAILED ACTION
The following is a non-final, first office action in response to the application filed February 3, 2025. Claims 1-20 are currently pending and have been examined.
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 .
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 is directed to a judicial exception (abstract idea) without significantly more.
Step 1: Statutory Category
(MPEP § 2106)
Claims 1-20 are directed towards a method, a system, and a computer-readable medium. The claims are directed to a statutory category: a process, a machine, and article of manufacture as defined under 35 U.S.C. § 101.
Regarding Claim 1:
Step 2A, Prong One: Judicial Exception – Abstract Idea
(MPEP § 2106.04)
Claim 1 recites an abstract idea in the form of certain methods of organizing human activity, specifically commercial or business interactions involving customer relationship management, customer service selection, and recommending services to customers.
For example, the claim recites limitations directed to:
collecting customer data including customer characteristics, engagement status, and usage patterns;
analyzing the customer data to generate a baseline heuristics assessment including cyclicality and seasonality factors with associated probability scores;
applying a predictive heuristics model to identify or generate document management services applicable to the customer;
applying a service optimization model to select a document management service;
determining whether the selected service satisfies a threshold; and
recommending that the selected document management service be offered to the customer.
These limitations describe evaluating customer information, predicting customer needs, selecting an appropriate service, and recommending that service, which are activities that can be performed as part of managing customer relationships or making business decisions. Such activities fall within the grouping of certain methods of organizing human activity identified in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Step 2A, Prong Two: Integration into a Practical Application
(MPEP § 2106.04(d))
The additional elements do not integrate the abstract idea into a practical application.
The claim recites the abstract idea as being performed within a "customer service intelligence platform" and a "document management system." However, these elements merely identify the environment in which the abstract idea is implemented and do not impose any meaningful limit on the judicial exception.
The claim does not recite:
an improvement to the functioning of the document management system;
an improvement to computer technology or another technology;
a particular machine that is integral to performing the claimed invention;
a transformation of an article to a different state or thing; or
any other meaningful limitation beyond generally applying the abstract idea using generic computer functionality.
The recited heuristics model, predictive heuristics model, service optimization model, probability scores, and threshold merely describe mathematical or analytical techniques used to evaluate customer information and make a recommendation. The claim does not specify how these models are implemented or improved, nor does it recite any technological improvement resulting from their use.
Step 2B: Inventive Concept
(MPEP § 2106.05)
The additional elements, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception.
The claim merely employs generic computer components, including a document management system and customer service intelligence platform, to collect customer information, perform data analysis, generate scores, compare those scores to a threshold, and recommend a service.
These additional elements perform their well-understood, routine, and conventional functions of receiving data, processing information, storing information, and presenting recommendations. The claim does not recite any unconventional data structures, specialized hardware, or specific improvement to computer functionality.
Viewed as an ordered combination, the limitations merely automate the longstanding business practice of evaluating customer characteristics, predicting customer needs, selecting an appropriate service, and recommending that service using generic computer technology. The ordered combination therefore does not provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Therefore, the claim is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding Claims 8 and 15
Independent claims 8 and 15 are parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 1-7, 9-14 and 16-20
Dependent claims 1-7, 9-14, and 16-20 merely set forth further embellishments to the abstract idea, and therefore do not confer eligibility on the claimed invention and are ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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, 6-10, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Samareh et al (US 2023/0315537 A1) in view of Siebel (US 2017/0006135 A1).
Regarding claims 1, 8, and 15, Samareh discloses method for managing customers accessing a customer service intelligence platform of a document management system, the method comprising:
collecting customer data for a customer account within the document management system, wherein the customer data includes a set of customer characteristics, an engagement status of the customer, and at least one usage pattern of the customer; (Samareh: pars. 24, 27, 30, 32, 40, 45, 81, and Fig. 1: e.g., model associated with a given customer subscription ID is trained exclusively on historical usage data collected for the subscription over a recent period of time, such as over the past 90 days; receives inputs 204 associated with a particular customer subscription (subscription #3) to a web-based service that is provided by a tenant leasing compute resources from the resource allocation system. In this example, the tenant (e.g., Tenant #7) is a service provider in the food and beverage industry [customer characteristics]. The customer subscription may be, for example, an online account associated with a username and password that a customer uses [customer characteristics]; current usage metric 214 indicates an average of peak usage over a recent interval, such as the past 24 hours [engagement status]; upward usage trend has been detected for a set number of days, when a current usage metric [usage pattern of the customer]; training seasonality models on usage data for customer subscriptions that are associated with a same industry but that are also characterized by similar usage characteristics (e.g., a similar peak quantity of usage, similar usage patterns); receives a current usage metric 304 for the customer subscription (e.g., 48 cores);
analyzing the customer data to generate a baseline heuristics assessment that includes at least one cyclicality factor and at least one seasonality factor (Samareh: pars. 40-42 and 82: usage data is analyzed for trends and patterns that repeat in time [cyclicality factor]. Usage time series often have multi-period seasonality as a result of different business functions [seasonality factor]; spikes in customer subscription usage that is periodic on some other basis; seasonality models can be generated as periodic functions of time; identifying a period of time in which resource usage trends repeat [cyclicality factor]; historical time series of aggregated usage data for same-industry services can be deconstructed to identify seasonal peaks and valleys that periodically repeat [i.e., cyclicality factor]);
applying a predictive heuristics model to the baseline heuristic assessment, the at least one cyclicality factor, and the at least one seasonality factor to identify or generate a plurality of document management services applicable to the customer account, (Samareh: pars. 23, 28, 31-38, 45-46, 56, 59, 64-65, and 76: quota manager 112 is shown to include both a predictive usage modeling engine 114 and a quota adjuster 118. The predictive usage modeling engine 114 applies usage models trained on individual subscriptions and/or groups of subscriptions (discussed below with respect to “seasonality modeling”) to generate a predicted future resource usage metric; For example, the predicted future resource usage metric 116 estimates a usage for one of the individual customer subscriptions 108 over a near-future time interval, such as the next 24 hours; quota manager 300 makes three different quota recommendations including a lower-end quota adjustment 308, a higher-end quota adjustment 312, and a middle-ground adjustment; model can be trained…guarantee (based on repeated experimentation) a baseline level of confidence in the accuracy of a predictive output generated by a subscription-based historical usage model that is trained; predicted 24-hour peak resource usage is one example of the predictive future resource usage metric; model outputs a predictive future usage metric, such as a metric forecasting predicted usage for the customer subscription over the next 24 hours. A quota generation operation 622 generates one or more recommended quota adjustments based on the predictive future usage metric output by the pre-trained subscription-based historical usage mode);
applying a service optimization model to the plurality of document management services from the predictive heuristics model and the customer data collected within the document management system to identify or select a document management service to provide to the customer account, wherein the document management service is assigned a score by the service optimization model; (Samareh: pars. 42-47, 59, and 76: models provide a basis for intelligently predicting future increases and decreases in resource usage with respect to customer subscriptions; quota manager 300 makes three different quota recommendations including a lower-end quota adjustment 308, a higher-end quota adjustment 312, and a middle-ground adjustment 310, each of which can be discretionarily implemented in the alternative; cloud compute resource provider 110 includes a quota manager 112 that intelligently recommends quota adjustments for the individual customer subscriptions 108 on an as-needed (forward-looking) predictive basis [strategy identification model]; For example, the recommended adjusted quota may be selected to ensure that the customer subscription is allotted sufficient resources to support an actual usage consistent with the predicted usage and also to ensure that the predicted usage and the adjusted quota satisfy a target quota (e.g., a ratio of the predicted usage to adjusted quota is 40% or some other target) [i.e., ratio is a score assigned to action for implementing the new quota as a strategy ensure sufficient resources are available for the customer subscription in the future]);
determining whether the score for the document management service is equal to or greater than a defined threshold for the customer account; and (Samareh: pars. 27, 46, 59, and 76: quota manager 112 may initiate the aforementioned modeling when usage activity for a given customer subscription satisfies a threshold; quota manager 300 recommends quota adjustments that are designed to ensure usage consistency with a preselected target utilization. If, for example, a target utilization is 70%, the quota manager 300 recommends a new, adjusted quota representing a value that the predicted future resource usage metric is 70%; outputting a recommended adjusted resource quota for the individual customer subscription, where the future resource usage metric satisfies a target utilization of the recommended adjusted resource quota; ensure that the predicted usage and the adjusted quota satisfy a target quota (e.g., a ratio of the predicted usage to adjusted quota is 40% or some other target) [ratio meets/exceeds threshold for customer subscription/account]);
determining whether the score for the document management service is equal to or greater than a defined threshold for the customer account; and (Samareh: pars. 25, 46-47: recommended adjusted quota 120 is determined based on a predefined target utilization 122 of the allotted quota; quota manager 300 makes three different quota recommendations including a lower-end quota adjustment 308, a higher-end quota adjustment 312, and a middle-ground adjustment 310, each of which can be discretionarily implemented in the alternative; system manager discretionarily selects the adjusted quota from amount the different options (e.g., 308, 310, 312) output by the quota manager).
Samareh does not expressly disclose wherein each of the at least one cyclicality factor is assigned a probability score and each of the at least one seasonality factor is assigned a probability score; wherein each of the plurality of document management services is assigned a probability. Siebel discloses:
wherein each of the at least one cyclicality factor is assigned a probability score and each of the at least one seasonality factor is assigned a probability score; wherein each of the plurality of document management services is assigned a probability (Siebel: pars. 430 and 510: assign a probability score).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and apparatus of Samareh to have included wherein each of the at least one cyclicality factor is assigned a probability score and each of the at least one seasonality factor is assigned a probability score; wherein each of the plurality of document management services is assigned a probability, as taught by Siebel because it would provide predictive management (Siebel: paragraph [0478]).
Samareh and Siebel do not expressly disclose at least one cyclicality factor and at least one seasonality factor. However these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The steps would be performed the same regardless of the type of factor. This descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 70 F.2d 1381, 1385, 217 USPQ 401 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Regarding claims 2, 9, and 16, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above. Samareh further discloses wherein the document management service includes a price point for the customer account (Samareh: Figure 3 308 310 312).
Samareh and Siebel do not expressly disclose the document management service includes a price point. However these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The steps would be performed the same regardless of the type of point. This descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 70 F.2d 1381, 1385, 217 USPQ 401 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Regarding claims 3, 10, and 17, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above. Samareh further discloses wherein the document management service includes a subscription plan for the customer account (Samareh: Figure 3 308 310 312, abstract - and outputting a recommended adjusted resource quota for the individual subscription, the predicted future resource usage metric satisfying a target utilization of the recommended adjusted resource quota).
Regarding claims 6, 13, and 19, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above. Samareh further discloses using a cyclicality analysis module to generate the at least one cyclicality factor (Samareh: pars. 41-42, 45, 63, 73-75: e.g., may experience spikes in customer subscription usage that is periodic on some other basis, such as every four weeks, two weeks, etc.; historical time series of aggregated usage data for same-industry services can be deconstructed to identify seasonal peaks and valleys that periodically repeat; industry-wide usage data may reflect a weekly seasonality window that peaks on the weekend when more individuals watch online entertainment; quota manager 300 also receives a current usage metric 304 for the customer subscription (e.g., 48 cores). In one implementation, the current usage metric 304 indicates a 24 hour average of hourly peak usage; modules and services may be embodied by instructions stored in memory; software modules).
Regarding claims 7, 14, and 20, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above. Samareh further discloses using a seasonality analysis module to generate the at least one seasonality factor (Samareh: pars. 23, 26, 33, 38, 40-45, 60-65 and 81: e.g., predictive usage modeling engine 114 applies usage models trained on individual subscriptions and/or groups of subscriptions (discussed below with respect to “seasonality modeling”); seasonality trend models 232 that can be used to generate the predictive future resource usage metric; seasonality trend model may generate a predicted future resource usage metric that predicts an increase of 10-20% from the current peak utilization within the next 24 hours).
Claims 4-5, 11-12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Samareh et al (US 2023/0315537 A1) in view of Siebel (US 2017/0006135 A1), and further in view of Bessette (US 2013/0144642 A1).
Regarding claims 4, 11, and 18, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above.
The combination of Samareh and Siebel does not disclose wherein applying the predictive heuristics model includes assigning a weight to each of the at least one cyclicality factor and a weight to each of the at least one seasonality factor. However, Bessette teaches wherein applying the predictive heuristics model includes assigning a weight to each of the at least one cyclicality factor and a weight to each of the at least one seasonality factor (Bessette: pars. 46, 64, 76-80, 114, and 146: determination of effect may be accomplished using regression analysis, such as a linear regression; averaged values for all variables were utilized in a linear regression equation to develop a predictor; Probability curves were created relating the linear predictor (i.e., the weighted sum of predictor variables with weights that are the estimated coefficients from the logistic regression) to the probability; linear regression weighted values in FIG. 7; See also, Figs. 2-3: probability curve).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the combination of Samareh and Siebel, in the apparatus and method wherein applying the predictive heuristics model includes assigning a weight to each of the at least one cyclicality factor and a weight to each of the at least one seasonality factor, as taught by Bessette since the claimed invention is just a combination of old elements, and in the combination each element merely would have performed that same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so because it would take more factors into consideration (Bessette: paragraph [0014]).
Samareh, Siebel, and Bessette do not expressly disclose at least one cyclicality factor and at least one seasonality factor. However these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The steps would be performed the same regardless of the type of factor. This descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 70 F.2d 1381, 1385, 217 USPQ 401 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Regarding claims 5 and 12, Samareh and Siebel teach or suggest all the limitations of claims 1, 8, and 15 as noted above.
The combination of Samareh and Siebel does not disclose wherein the predictive heuristics model is a weighted linear regression model to generate a probability curve. However, Bessette teaches wherein the predictive heuristics model is a weighted linear regression model to generate a probability curve (Bessette: pars. 46, 64, 76-80, 114, and 146: determination of effect may be accomplished using regression analysis, such as a linear regression; averaged values for all variables were utilized in a linear regression equation to develop a predictor; Probability curves were created relating the linear predictor (i.e., the weighted sum of predictor variables with weights that are the estimated coefficients from the logistic regression) to the probability; linear regression weighted values in FIG. 7; See also, Figs. 2-3: probability curve).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the combination of Samareh and Siebel, in the apparatus and method wherein the predictive heuristics model is a weighted linear regression model to generate a probability curve, as taught by Bessette since the claimed invention is just a combination of old elements, and in the combination each element merely would have performed that same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so because it would take more factors into consideration (Bessette: paragraph [0014]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0036386 A1, Horesh et al discloses SUBSCRIPTION RENEWAL PREDICTION WITH A COOPERATIVE COMPONENT.
US 2020/0202379 A1, Yacoub et al discloses DETERMINING SUBSCRIPTION OFFERS THROUGH USER PURCHASE BEHAVIOR.
US 11,488,086 B2, Garrish et al discloses User interface and underlying data analytics for customer success management.
US 2018/0314761 A1, Lewin-Eytan et al discloses METHOD AND SYSTEM FOR PROVIDING SUBSCRIBE AND UNSUBSCRIBE RECOMMENDATIONS.
US 12,205,091 B2, Book et al discloses Intelligent subscription identification using transaction data.
US 8,504,408 B2, Banthia et al discloses Customer analytics solution for enterprises.
US 9,674,362 B2, Piaggio et al discloses Customer journey management.
US 2023/0101487 A1, Briancon et al discloses CUSTOMER JOURNEY MANAGEMENT ENGINE.
PTO-892 Reference U discloses Recommendation method for extending subscription periods.
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KATHLEEN GAGE PALAVECINO
Primary Examiner
Art Unit 3688
/KATHLEEN PALAVECINO/Primary Examiner, Art Unit 3688
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