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 . The following is a Final Office Action. Claims 1-20 are rejected.
Applicant’s Amendments
Applicant’s amendments are acknowledged.
Applicant’s Arguments
Applicant’s arguments with respect to 101 Rejections have been fully considered but are non-persuasive.
Applicant argues the machine learning models (ie. HTC model, causal model) extend beyond the abstract idea.
Examiner responds if interpreting the (HTC) model and casual model as machine learning models they generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Applicant argues the claims provides an improvement to known technical problems in the technical field of probabilistic models by providing the reasons for the churn, determining a market action, and deploying a treatment.
Examiner responds these functions (ie. determining reasons for churn, determining a market action, and deploying a treatment) are types of business processes and simply part of the abstract idea.
Applicant argues the claim is tied to a practical application ie. utilizing machine learning to improve network operation, customer retention, software diagnosis, and health care.
Examiner responds if interpreting the (HTC) model and casual model as machine learning models, these generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Applicant argues the claims was not considered as an ordered combination and as a whole, nor the additional elements have been considered in combination with non-additional elements.
Examiner responds the steps of the abstract idea (collecting information associated with services provided...generating, via a hyper targeted churn (HTC) model, HTC segments...generating, via a casual model, casual segments...estimating, based on the information, the HTC segments, and the casual segments, at least one casual reason...recommending a market action...and executing the market action) are simply managing human behavior and mental processes. Each of the additional elements, when viewed individually and when viewed as an ordered combination, are computing elements recited at a high level of generality implementing the abstract idea on a computer and no more than applying the abstract idea with generic computer components, and they further generally link the abstract idea to a field of use, namely a generic computing environment sending generic commands to generic devices, which is not sufficient to amount to significantly more than an abstract idea.
Applicant’s arguments with respect to the newly added amendments, “generating, via a hyper targeted churn (HTC) model, HTC segments based on disengagement features, risk features, and intent features included in the collected information” and “generating, via a causal model, causal segments based on causal features, the risk features, and the intent features included in the collected information” have been fully considered but are moot in view of reevaluation of Han (See Prior Art Sections Limitations).
Claim Rejections - 35 USC § 112(b) or Pre-AIA , 112 2nd Paragraph
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5, 10, 12, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
In Claims 3, 5, 10, 12, and 18, the limitation “the past churning” lacks antecedent basis. 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 is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically Claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-20 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claims 1 8 and 15 include limitations reciting identifying casual reasons for determining appropriate treatments, including steps:
Collecting information associated with services provided...
Generating, via a hyper targeted churn (HTC) model, HTC segments...
Generating, via a casual model, casual segments...
With respect to each of some of the plurality of customers, estimating, based on the information, the HTC segments, and the casual segments, at least one casual reason...
Recommending a market action...
Executing the market action...(under BRI this is initiating the processing of a market action)
which is an abstract idea reasonably categorized as
Certain methods of organizing human activity – managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), and
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 2-7, 9-14, and 16-20 further describe making determinations and descriptive data that further narrow the abstract idea.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 8, and 15 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include machine-readable medium, machine, system, processor. However, individually and when viewed as an ordered combination and pursuant to the broadest reasonable interpretation, Examiner submits that each of the additional elements do not integrate the abstract idea into a practical application because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
To the extent the use of the (HTC) model and casual model are machine learning models, these models generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Claim 2-7, 9-14, and 16-20 do not include additional elements above and beyond claims 1, 8, and 15.
As a result, Claims 1-20 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1, 8, and 15 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include machine-readable medium, machine, system, processor. However, individually and when viewed as an ordered combination and pursuant to the broadest reasonable interpretation, Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
To the extent the use of the (HTC) model and casual model are machine learning models, these models generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Claim 2-7, 9-14, and 16-20 do not include additional elements above and beyond claims 1, 8, and 15 and thus do not provide significantly more to the abstract idea.
Thus, Claims 1-20 do not provide significantly more to the abstract idea.
Accordingly, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-12, and 15-18 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Han (20170061343).
Regarding Claim 1,
A method, comprising (0020-0022 – computer, code, storage medium)
collecting information associated with services provided to a plurality of
customers; (0055, 0087 - company features 224, spending features 226, usage features 228, and account features 230 may be obtained from a number of data sources....)
generating, via a hyper targeted churn (HTC) model, HTC segments based on disengagement features, risk features, and intent features included in the collected
information, wherein each of the multiple HTC segments corresponds to a level of risk to churn and includes one or more of the plurality of customers estimated to be at a corresponding level of rick to churn; (0038-0042, 0046, 0063, Figure 3A(304) – the customers filtered by churn risk level (e.g., all levels, high and medium-high, high, medium-high, medium, low) (HTC segments) were placed in their churn risk level by a first statistical model (HTC model) from their company/spending/usage /account features; company/spending/usage/account features may be considered “risk features” because they predict churn risk level; spending trend over time features may be interpreted as intent features: usage engagement score features may be interpreted as disengagement features;
[0039] Company features 224 may include attributes and/or metrics associated with a customer that is a company (or other type of organization). Company features 224 may include demographic attributes such as a location, an industry, a company type (e.g., corporate, staffing, etc.), an age, and/or a size (e.g., small business, medium/enterprise, global/large, etc.) of the company.....
[0040] Spending features 226 may relate to the customer's spending behavior or spending history with the product.... Spending features 226 may also include metrics such as the customer's previous spending amounts, discount rates associated with the customer's spending amount, and/or spending growth that tracks a trend in the customer's spending amounts over time....(intent features)
[0041] Usage features 228 may identify the customer's usage of the online professional network through which the product is purchased or used. For example, usage features 228 may include metrics related to the customer's level of activity on the online professional network, such as a number of searches, messages sent, profile views, profile updates, company updates, and/or visits to the online professional network by the customer. The metrics may be aggregated into an engagement score for the company that is included in usage features 228 with the metrics or as a substitute for the metrics... (disengagement features)
[0042] Account features 230 may characterize the customer from a sales perspective. For example, account features 230 may include a potential spending amount that represents the most the company can spend on the product, given the company's size and needs. Account features 230 may also identify the stage of the sales renewal cycle occupied by the customer....(intent features)
Account features 230 may also include attributes and/or metrics that are relevant to the product. For example, account features 230 for predicting the customer's churn risk 216 for a recruiting solution may include the number of recruiters, number of talent professionals (e.g., human resources staff), and/or size of the staffing department in the company.
generating, via a causal model, causal segments based on causal features, the risk features, and the intent features included in the collected information, wherein each of the one or more causal segments corresponds to a causal reason to drive a customer to churn and includes at least one of the plurality of customers estimated to have an underlying causal reason corresponding to that of the causal segment; (
[0077] A second statistical model (causal model) is then used to obtain one or more risk factors (causal features) associated with the churn risk (operation 412), independently of the churn risk level of the customer. For example, the statistical model may use one or more decision trees to compare a subset of the features (ie. causal, risk, intent) for the customer to a number of thresholds. When a feature does not meet a given threshold, the corresponding risk factor is identified in the customer.
Figure 3A (304), 0047–0048- the displayed risk factors (casual segments) for customers associated with churn risk level)
with respect to each of some of the plurality of customers, estimating, based on the information, the HTC segments, and the causal segments, at least one causal reason, each of which corresponds to an underlying cause that potentially drives the customer to churn (Figure 3A (304) – the display of the customers associated with a filtered churn risk level (ie. high) associated to each of the risk factor causes (ie. prod usage, prod performance))
recommending a market action directed to each of the at least one causal reason, [0053] Finally, management apparatus 206 may provide one or more recommendations 240 for reducing high customer churn risk levels in GUI 204. For example, GUI 204 may identify one or more risk types associated with risk factors 232 and include information for engaging with customers to mitigate high churn risk levels based on the risk types.
Figure 3C, 0071-0072- the GUI may show a set of user-interface elements 330-336 related to risk factors associated with churn risk in customers of a product such as a recruiting solution....User-interface elements 330-332 may summarize different types of customer churn risk. For example, user-interface elements 330-332 may include names of the churn risk types, symptoms of the churn risk types, and/or prescriptions for addressing the churn risk types. User-interface elements 334-336 may provide detailed information for managing customers associated with the risk types identified in user-interface elements 330-332. For example, user-interface elements 334-336 may include slide decks that describe techniques for engaging with the customers and/or otherwise addressing issues associated with the risk types. In other words, the GUI of FIG. 3C may provide recommendations for reducing churn risk associated based on the risk types, which may be used by a sales professional to manage customer relationships and improve his/her sales performance.
executing the market action to address the corresponding underlying cause to churn to prevent the customer to churn. (0078-a communication containing content for reducing the churn risk is optionally transmitted to the customer (operation 416). For example, a risk factor associated with sub-optimal results experienced by the customer with the product may be mitigated by engaging the customer with marketing content that addresses a number of potential sources of the sub-optimal results.)
Regarding Claim 2, Han discloses: The method of claim 1, wherein the generating the HTC segments comprises:
with respect to each of the plurality of customers and based on the information relevant to the user, extracting the disengagement features of the customer,
[0041] Usage features 228 may identify the customer's usage of the online professional network through which the product is purchased or used. For example, usage features 228 may include metrics related to the customer's level of activity on the online professional network, such as a number of searches, messages sent, profile views, profile updates, company updates, and/or visits to the online professional network by the customer. The metrics may be aggregated into an engagement score for the company that is included in usage features 228 with the metrics or as a substitute for the metrics... (disengagement features)
determining an intent of the customer to churn based on the intent features,
[0042] Account features 230 may characterize the customer from a sales perspective. For example, account features 230 may include a potential spending amount that represents the most the company can spend on the product, given the company's size and needs. Account features 230 may also identify the stage of the sales renewal cycle occupied by the customer....(intent features)
estimating a level of risk to churn associated with the customer, and (0046, 0063, Figure 3A(304) - customers churn risk level (e.g., all levels, high and medium-high, high, medium-high, medium, low) (HTC segments) determined by the first statistical model (HTC model))
identifying whether the customer corresponds to a churner in accordance with the HTC model; and creating the HTC segments at different levels of risk to churn, wherein each of the HTC segments associated with a level of risk to churn includes those of the plurality of customers identified as a churner and having an estimated level of risk to churn corresponding to the associated level of risk to churn. [0044] As a result, churn risk 216 may be predicted for the customer by selecting a first statistical model (HTC model) that matches the company segment, its stage in the sales renewal cycle, and/or other features of the customer, and then inputting one or more company features 224, spending features 226, usage features 228, and/or account features 230 into the statistical model. In turn, the first statistical model may generate a prediction of churn risk 216 and one or more thresholds 218 associated with churn risk 216.
[0045] As described above, churn risk 216 may be a numeric score or value that represents the customer's propensity for fully or partially churning from the product. Because churn risk 216 may be assessed in relation to other values of churn risk 216 for other customers, thresholds 218 may represent values that indicate certain levels of churn risk 216, such as medium, medium-high, or high. For example, thresholds 218 may be set to values that represent certain percentiles of churn risk 216 for the company segment and stage in the sales renewal cycle of the customer. (corresponds to a churner)
Regarding Claim 3, Han discloses: The method of claim 2, wherein the HTC model is previously trained via machine learning to capture characteristics of a churner based on data associated with the past churning. (0043- Analysis apparatus 202 and/or another component of the system may create and maintain a set of statistical models 208 that predict churn risk 216 for different subsets of customers. Each statistical model may be trained and/or updated on a periodic basis (e.g., daily) using data associated with the corresponding subset of customers from data repository 134.
[0056] Finally, statistical models 208 may be implemented using different techniques and/or used to generate churn risk 216, thresholds 218, churn risk level 234, and/or risk factors 232 in different ways. For example, churn risk 216 and/or thresholds 218 may be generated using a gradient tree boosting technique, while risk factors 232 may be identified using one or more additional decision trees. Other types of statistical models, such as artificial neural networks, Bayesian networks, support vector machines, and/or clustering techniques, may also be used with or in lieu of the gradient tree boosting technique and/or decision trees to provide the functionality of analysis apparatus 202. Alternatively, churn risk 216, thresholds 218, churn risk level 234, and/or risk factors 232 may be generated using the same statistical model instead of separate statistical models.
Regarding Claim 4, Han discloses: The method of claim 1, wherein the generating the causal segments comprises:
with respect to each of the plurality of customers and based on the information relevant to the user, extracting the causal features of the customer, determining an intent of the customer to churn based on the intent features, estimating propensity of the customer to churn, and (0055, 0087, Figure 3A (308) – after receiving company features 224, spending features 226 (contains intent to churn), usage features 228, and account features 230, filtering customers associated to risk levels)
predicting a causal reason associated with the customer in accordance with the causal model; (Figure 3A (304), 0047–0048- displayed risk factors (casual segments) for customers associated with churn risk level)
and creating the causal segments with corresponding causal reasons, wherein each of the causal segments associated with a causal reason includes those of the plurality of customers predicted to have a causal reason corresponding to the associated causal reason. (Figure 3A (304) – list of customers associated to particular risk factors)
Regarding Claim 5, Han discloses: The method of claim 4, wherein the causal model is previously trained via machine learning to capture characteristics of a customer with a causal reason based on data associated with the past churning. (0043- ... Each statistical model may be trained and/or updated on a periodic basis (e.g., daily) using data associated with the corresponding subset of customers from data repository 134)
Claims 8-12 and 15-18 stand rejected based on the same citations and rationale as the Claims 1-5.
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.
Claims 6, 13, and 19 is rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Tuckfield (US Patent 11651314)
Regarding Claim 6, Han discloses: The method of claim 1, wherein the estimating the at least one causal reason comprises: obtaining one of the HTC segments that includes the customer; identifying one or more of the multiple causal segments that include the customer; and obtaining the at least one causal reason associated with the one or more causal segments associated with the customer.
(Figure 3A (304) – the customers associated to the filtered risk level (ie. high) (HTC segment) and risk factors (casual reasons associated with casual segment))
Han does not explicitly state: Tuckfield in analogous art discloses: determining an intensity of each of the causal reasons associated with the causal segments; ranking the causal reasons according to the respective intensities associated therewith; (3(3-7) - the system can evaluate the factors contributing to the customer's attrition risk, and rank them. Put another way, the system can evaluate the matters most important in explaining the likelihood of the customer's terminating the contract or the relationship. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate Tuckfield’s intensities and rankings to Han’s casual reasons, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the 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.
Claim 13 and 19 stand rejected based on the same citations and rationale as applied to Claim 6.
Claims 7, 14, and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Johnson (20100223099)
Regarding Claim 7, Han discloses: The method of claim 1. Han does not explicitly state: Johnson analogous art discloses: wherein the recommending a market action directed to each of the at least one causal reason comprises:
with respect to each of the at least one causal reason, accessing a driver/action mapping model, and mapping, via the driver/action mapping model, the causal action to a corresponding market action. (Figure 2 – V-Factors that influence churn (casual reasons) feed into (map to) a MDOO process/model determining offers;
Abstract - A Multi-Dimensional Offer Optimization. TM. (MDOO) process is provided that may be defined generally as an offer simulation engine that matches an offer most likely to be accepted to the customer most likely to accept it.
[0026] The analysis of V-Factors begins with the investigation to determine the drivers of churn and their level of influence (positively or negatively) in the churn model. If specific variables contribute significantly to churn (positively or negatively), then offers built using those variables can be used to influence churn behavior. The superset of V-Factors identified defines the characteristics that influence churn. These characteristics drive the identification of offers to be given to the subscribers who are likely to leave and terminate service.
[0028] MDOO process 20 of FIG. 2 is a computer implemented offer simulation engine which matches the offer most likely to be accepted by a customer. In the illustrated example, the purpose of MDOO process 20 is to literally ask the question, "Which offer would be most effective in saving this particular subscriber?" The effect of the offer is positive if the resulting churn score is lowered and vice versa. The same model that is used to generate predictions is used to predict the effect on churn of various interventions. In one embodiment, an aspect of the invention explores all possible interventions as determined by quantitative analysis. For each intervention, a reduction in churn probability may be computed, and then an intervention is chosen that leads to the greatest reduction in probability. If different interventions have different costs, decision-theoretic techniques may be used to choose the intervention that yields the greatest savings to the carrier.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate Johnson’s mapping via driver/action mapping model to Han’s recommended market action, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the 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.
Claim 14 and 20 stand rejected based on the same citations and rationale as applied to Claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [AltContent: rect]
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT ROSS whose telephone number is (571) 270-1555. The examiner can normally be reached on Monday-Friday 8:00 AM - 5:00 PM E.S.T..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu, can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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