DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL action upon examination of application number 17/028,857, filed on 09/22/2020, in response to Applicant’s Request for Continued Examination (RCE) filed on October 15, 2024. Claims 1-2, 4-23, and 25-42 are currently pending, of which claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 17/028,857, filed 09/22/2020 claims Priority from Provisional Application 62/905,003, filed 09/24/2019.
Response to Amendment
4. In the response filed October 15, 2024, Applicant amended claims 1, 5, 9, 13, 18, 22, 30, 34, 36, and 39-41, and did not cancel any claims. No new claims were presented for examination.
5. In the Supplemental response filed December 31, 2024, Applicant amended claims 1, 9, 13, 15, 20, 22, 34, 36, and 41, and did not cancel any claims. No new claims were presented for examination.
6. Applicant's amendments to claims 1 and 22 [Claims dated 10/15/2024] are hereby acknowledged. The amendments are sufficient to address the previously issued claim objections. Accordingly, the objections have been removed.
7. Applicant's amendments to the claims [Claims dated 10/15/2024] are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejections under 35 U.S.C. 112(b); accordingly, the rejections of claims 5, 13, 18-21, 34, 36-37, 40-42 under 35 U.S.C. 112(b) have been withdrawn. However a new ground of rejection is applied to these claims under §112(b) in the instant Office action.
8. Applicant's amendments to the claims [Claims dated 10/15/2024 and Claims dated 12/31/2024] are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejections under 35 U.S.C. 101; accordingly, the rejection of claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 under 35 U.S.C. 101 has been maintained.
Response to Arguments
9. Applicant's arguments filed October 15, 2024 and December 31, 2024, have been fully considered.
10. Applicant submits “The Examiner further indicated that the claims are directed to an “abstract idea of calculating a business value at risk, which falls under the realm of ‘Mathematical Concepts’. See Office Action, pp 27-35. The Applicant respectfully disagrees with this classification of the invention.” [Applicant’s Remarks, 10/15/2024, page 19]
The Examiner respectfully disagrees. In response, it is noted that claim 1 recites steps and concepts falling under the “Mathematical Concepts” grouping of abstract ideas set forth in the MPEP since the claim sets forth steps covering using a computerized mathematical predictive model that utilizes a binomial regression analysis and determining an individual frustration level vulnerability score for each key frustration, and calculating a business value at risk. The Examiner maintains that when evaluated under Step 2A Prong One, the “(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a mathematical predictive model that utilizes a binominal logistic regression analysis based at least in part on the calculated frequency of the key frustration, a calculated determination of uniqueness of the key frustration in connection to other verified and actual customers of the company and customers of competing companies, and a calculated determination of how much the key frustration prompts switching from the company to competitors; (5) determining an individual frustration level vulnerability score for each processed key frustration; (6) for each processed key frustration, automatically determining company level vulnerability based on a segmentation of individual frustrations according to one or more segments of individuals and calculated segment level average revenue among the actual and verified company customers, wherein the segmentation relates to a likelihood of attrition for different segments of the actual and verified company customers; and (7) for each processed key frustration, calculating a predicted probability of attrition for each of said one or more segments of individuals and calculating a business value at risk, caused by the calculated probability of attrition for each of said one or more segments of individuals among the actual and verified company customers, wherein the calculation of the business value at risk includes a determination and quantification of the losses to competitors” are part of the abstract idea itself, i.e., are steps within the “mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations” group within the enumerated groupings of abstract ideas set forth in the 2019 PEG.” Utilizing a binominal logistic regression analysis, determining an individual frustration level vulnerability score for each key frustration, calculating a predicted probability of attrition for each of said one or more segments of individuals, and calculating a business value at risk require some sort of mathematical analysis. It is clear from Applicant’s claims that the method involves mathematical concepts such as mathematical algorithms, mathematical relationships, and calculations. Therefore, this necessarily suggests that the method includes concepts related to “mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations” group within the enumerated groupings of abstract ideas set forth in the MPEP. For the reasons above, Applicant’s argument is not persuasive.
11. Applicant submits “The applicant reiterates that the existing problem relates to a specific way and an improvement to the software-driven system and algorithm that models, evaluates and builds a model based on “each individual key frustration” of the company’s “actual and verified customers”. The computerized system and method of the present invention provides a specific software implemented and an automated software-driven model of how each frustration is determined to be a “key frustration”, and then how it is evaluated (with respect to different factors, other actual and verified customers of the company and their frustration, the overall industry trends, and the actual impact of the frustration on the change in customer behaviour). The system also evaluates how each expressed key frustration of the actual and verified customers may be quantified to estimate the actual financial loss to the company, in comparison to other similar companies and competitors in the same field. Moreover, among all steps that involve complex and very specific sequence of processing and application of the automated software.” [Applicant’s Remarks, 10/15/2024, page 20]
The Examiner respectfully disagrees. In response to Applicant’s argument that “the existing problem relates to a specific way and an improvement to the software-driven system and algorithm that models, evaluates and builds a model based on “each individual key frustration” of the company’s “actual and verified customers”. The computerized system and method of the present invention provides a specific software implemented and an automated software-driven model of how each frustration is determined to be a “key frustration”, and then how it is evaluated,” the Examiner notes that even assuming arguendo that the claim techniques were considered specific, inventive, novel, and/or non-obvious, such a finding by itself would nevertheless be insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
Furthermore, although Applicant suggests that the claims are directed to “complex” sequence of processing and application of the automated software, Applicant has not provided facts or evidence to show that any of the claim steps involve “complex sequence of processing and application of the automated software” that fall outside of the scope of the abstract ideas noted above. Accordingly, this argument is found unpersuasive.
12. Applicant submits “the present claims overcome the rejection under 35 USC 101 in view of McRo decision and the November 2, 2016, Memorandum from the USPTO.” [Applicant’s Remarks, 10/15/2024, page 21]. Applicant also submits “under the PTO Memorandum and McRo decision, Applicant’s independent claims 1 and 22, and their dependent claims, recite an improvement in the computer-related technology, and satisfy the requirements for patentability under 35 U.S.C. 101.” [Applicant’s Remarks, 12/31/2024, page 22]
In response to the Applicant’s arguments that “under the PTO Memorandum and McRo decision, Applicant’s independent claims 1 and 22, and their dependent claims, recite an improvement in the computer-related technology, and satisfy the requirements for patentability under 35 U.S.C. 101,” the Examiner respectfully disagrees. With respect to Applicant's comparison to McRO, Examiner points out that the claims in McRO involved a method for automatically animating lip synchronization and facial expression of three-dimensional characters comprising: obtaining a first set of rules that defines a morph weight set stream as a function of phoneme sequence and times associated with said phoneme sequence; obtaining a plurality of sub-sequences of timed phonemes corresponding to a desired audio sequence for said three-dimensional characters; generating an output morph weight set stream by applying said first set of rules to each sub-sequence of said plurality of sub-sequences of timed phonemes. The claims at issue are far different from the claims in McRO. The claims of the present case involve a method of evaluating attrition risk for a company and calculating a business value at risk. The claims of the instant application do not recite techniques for automatically generating three-dimensional facial expressions matching a prerecorded track of speech. Second, it is noted that the claims in McRO recited a specific asserted improvement in computer animation. In contrast, the claim here is not directed to any improvement in computer functionalities/capabilities - only to quantify monetary losses caused by a predicted attrition. The focus of the invention is on the algorithms that have been identified as abstract ideas (as opposed to an improvement to operations of the additional elements, improvement to another technology or technical field). The claims of the instant application thus cannot be characterized as an improvement in computer technology.
Further, contrary to the claims in the McRO decision, the additional elements of the instant application are not reliant on the programmed rules to improve intrinsic operations of the additional elements themselves. In McRO, the rules were deemed to allow the additional elements to accomplish a technical feature presumably not accomplished before. The Examiner points out that the claims in McRO did not simply provide a particular solution to a problem, but the claimed invention in McRO was deemed to provide an improvement in the technology. Again, Applicant’s claimed invention does not provide an improvement in the technology. Simply processing a mathematical algorithm alone does not necessarily mean that the underlying operations of a processor are improved. Further, in McRO the courts did not find an Abstract idea in the claims. As the instant claims do contain an Abstract idea, they are not similar to the claims in McRO.
It is further noted that the Court in McRO said an improvement in computer-related technology is not limited in the operation of a computer or a computer network per se, but may also be claimed as a set of “rules” basically mathematical relationships that improve computer-related technology by allowing the computer performance of a function not previously performed by a computer. The instant Specification and claims are devoid of any indication that the claimed invention is to a “set of rules (basically mathematical relationships) that improve computer-related technology". Therefore, the Office finds that the concept present in the McRO is not analogous to the instant claimed invention. Based on the foregoing explanation the Examiner finds that the claims are not like those of McRO Applicant contends, because there is no expressed or implied improvement to a computer-related technology. The claims are not “directed to a specific improvement to the way computers operate.” Accordingly, this argument is found unpersuasive.
Moreover, contrary to Applicant’s assertions the claims “recite an improvement in the computer-related technology.” Applicant makes these assertions, but does not explain how the underlying operations of the additional elements themselves are improved. The alleged improvement is yielded regardless of whether or not the algorithm is implemented using the additional elements or not. In other words, the purported usefulness of the algorithm itself is not intrinsically intertwined with the operations of the additional elements. The additional elements in the claims operate in routine, conventional, and well-understood manners. Their underlying operations are not improved because of the type of mathematical operations being processed, for example. Contrary to the claims in the McRO decision, the additional elements of the instant application are not reliant on the programmed rules to improve intrinsic operations of the additional elements themselves. In McRO, the rules were deemed to allow the additional elements to accomplish a technical feat presumably not accomplished before. The underlying algorithm in Applicant’s claimed invention would be deemed to be novel without the implementation on at least one processor. The intended purpose of the claimed algorithm was not established in the original disclosure as improving operations of any additional elements either. The Examiner points out that the claims in McRO did not simply provide a particular solution to a problem, but the claimed invention in McRO was deemed to provide an improvement in the technology.
Notably, exemplary claim 1 of the McRO ‘576 patent applied the inventive solution in the final limitation of the claim by “applying said final applying said final stream of output morph weight sets to a sequence of animated characters to produce lip synchronization and facial expression control of said animated characters,” which is clearly a technological result/solution that was recognized by the CAFC as “a specific asserted improvement in computer animation.” In contrast, Applicant’s claim 1 yields a result of “a business value at risk” for one or more segments of actual and verified company customers” which, in contrast to McRO’s solution, neither invokes nor improves upon any form of technology. Therefore, in contrast to the claims in McRO, Applicant’s claims have not been shown to improve any technical process, but instead have been found to be directed to mental steps and certain methods of organizing human activity applied, at most, with a generic computer. For the reasons above, this argument is found unpersuasive.
13. Applicant submits “Finally, the calculation of the attrition risk on a per-frustration basis gives a more in-depth result and predictive measure for the company management to use in remedying the effects. In other words, the practical application of the above improved automated and computerized system allows the user, who may be the company management to utilize the computerized system and the “calculated business value at risk” analysis that is provided by the computerized model to achieve the following objectives” [Applicant’s Remarks, 10/15/2024, page 22]. Applicant further submits “The automated system and periodic re-runs create a more precise analysis, and also allow the business management to track and evaluate the remedial measures that the company may have taken to reduce its ‘business value at risk’ calculated by the computerized model and software. In other words, the practical application of the above improved automated and computerized system allows the user, who may be the company management to utilize the computerized system and the “calculated business value at risk” analysis that is provided by the computerized model to achieve the following objectives: “wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company or individuals who use a company product or service, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors, and to take and monitor progress of remedial measures that reduce a calculated and projected losses to the company.” Thus, the management can “implement” the remedial measures based on the more precise results of the specific software-based analysis and calculations that are performed by the computer software that operates as recited in amended claims.” [Applicant’s Remarks, 10/15/2024, pages 21-22]
In response to Applicant’s assertion that “the automated system and periodic re-runs create a more precise analysis, and also allow the business management to track and evaluate the remedial measures that the company may have taken to reduce its ‘business value at risk’ calculated by the computerized model and software. In other words, the practical application of the above improved automated and computerized system allows the user, who may be the company management to utilize the computerized system and the “calculated business value at risk” analysis that is provided by the computerized model to achieve the following objectives: “wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company or individuals who use a company product or service, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors, and to take and monitor progress of remedial measures that reduce a calculated and projected losses to the company”,” the Examiner respectfully disagrees. Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In this instance, the additional elements recited in exemplary claim 1 are: at least one processor executing a plurality of computer instructions stored in memory, a computerized mathematical model, a computerized mathematical predictive model, computerized quantitative vulnerability testing, and software. These elements have been considered individually and in combination, however these computing elements amount to using a generic computer programmed with computer-executable instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment, which is not sufficient to amount to a practical application, as noted in the 2019 PEG. See also MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Instead, the at least one processor executing a plurality of computer instructions stored in memory amounts to using generic computing devices as tools to implement the abstract idea, which does not amount to a technological improvement or otherwise indicate a practical application. See MPEP 2106.05(f). Notably, Applicant’s Specification acknowledges that the invention may be implement with generic computing devices. See Specification, paragraphs 0112, 0114.
Even assuming arguendo that an improvement was achieved, improving the method for calculating a business value at risk using only generic computing devices does not improve the computing devices or any technology, but instead any incidental improvement achieved by automating the claim steps would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Accordingly, the generic computing elements do not integrate the judicial exception into a practical application.
Moreover, allowing company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company or individuals who use a company product or service, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors, and to take and monitor progress of remedial measures” provides a benefit to the end user, not an improvement to the technology. For the reasons above, this argument is found unpersuasive.
14. Applicant submits “that the aforementioned “additional” elements of the amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well-known.” [Applicant’s Remarks, 10/15/2024, page 24 and Applicant’s Remarks, 12/31/2024, page 22]
The Examiner respectfully disagrees. Under Step 2B, Applicant submits “that the aforementioned “additional” elements of the amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well-known.” In response to Applicant’s assertions, it is first noted that only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f), no evidence is needed. Arguing abstract elements for Berkheimer is not persuasive. See BSG Tech, LLC v. Buyseasons, Inc., 899 F.3d 1281,1290 (Fed. Cir. 2018) states “Our precedent has consistently employed this same approach. If a claim’s only “inventive concept” is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea. Furthermore, it is not clear as to what “the aforementioned “additional” elements of the amended claims 1 and 22” refers to.
Second, the Examiner notes that Applicant’s argument lacks merit because neither §101 nor any controlling legal precedent requires a showing that the combination of elements is well-understood, routine and conventional to support a §101 rejection. The Examiner emphasizes that unconventionality of the entire claimed invention, by itself, is insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec). The Federal Circuit’s recent BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) plainly addressed this very argument, emphasizing that: “The relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine.” Therefore, Applicant’s suggestion that the entire claimed invention must be shown to be well-understood, routine and conventional to support a contention of patent ineligibility is not persuasive.
Third, it is noted that the Office Action did provide factual evidence to support a conclusion that the additional elements identified as conventional are well-understood, routine, conventional activity [See Non-Final Rejection mailed 10/03/2023 and Final Rejection mailed 06/13/2024 ]. For the reasons above, in addition to the reasons provided in the updated §101 rejection below, Applicant’s amendment and supporting arguments are not sufficient to overcome the §101 rejection.
15. Applicant submits “the recited “additional” elements are not some general idea with an instruction to “apply it”.” [Applicant’s Remarks, 10/15/2024, page 24]
The Examiner respectfully disagrees. The receiving, identifying, selecting, performing, combining, evaluating, determining, calculating, displaying, rerunning, and comparing steps fall under the scope of the abstract idea itself (as noted in the §101 rejection below), with the reliance on at least one processor in a manner similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment). See MPEP 2106.05(f) and 2106.05(h). Any incidental improvement achieved by automating the claim steps would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Accordingly, this argument is found unpersuasive.
16. Applicant submits “Third, the prior Office Actions recites at least three different prior art references against the independent claims, and as many as 10 different prior art references against other claims of the present application. If the present invention involved some known Mathematical Concept, the re-construction of the claimed terms with so many prior art references would simply not be necessary. Moreover, in the last Final Office Action, it is admitted that these numerous cited prior art references do not describe or suggest the invention recited in amended claims 1 and 22. Applicant again fully concedes that the novelty and non-obviousness under 35 U.S.C. §§ 102 and 103 are separate and apart from the analysis under 35 U.S.C. § 101. However, presence of many different prior art systems and novelty and non-obviousness of the present invention because of presence of specific steps and features also indicates that the present invention the “additional” elements of the amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well-known. In other words, it supports the conclusion that the present invention is a specific software-based technological solution, rather than an attempt to monopolize some general idea or a general concept.” [Applicant’s Remarks, 10/15/2024, page 26]
The Examiner respectfully disagrees. In response to Applicant’s argument that “the presence of many different prior art systems and novelty and non-obviousness of the present invention because of presence of specific steps and features also indicates that the present invention the “additional” elements of the amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well-known. In other words, it supports the conclusion that the present invention is a specific software-based technological solution, rather than an attempt to monopolize some general idea or a general concept,” it is noted that preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the two-step framework. Applicant’s attempt to show that the recited abstract idea is a specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim. See the July 2015 Update: Subject Matter Eligibility that explains that questions of preemption are inherent in the two-part framework from Alice Corp and Mayo and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more…the latter pose no comparable risk of preemption, and therefore remain eligible.” The absence of complete preemption does not guarantee the claim is eligible. Therefore, “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). See also OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015).
Moreover, in response to Applicant’s argument that “the prior Office Actions recites at least three different prior art references against the independent claims, and as many as 10 different prior art references against other claims of the present application. If the present invention involved some known Mathematical Concept, the re-construction of the claimed terms with so many prior art references would simply not be necessary. Moreover, in the last Final Office Action, it is admitted that these numerous cited prior art references do not describe or suggest the invention recited in amended claims 1 and 22. Applicant again fully concedes that the novelty and non-obviousness under 35 U.S.C. §§ 102 and 103 are separate and apart from the analysis under 35 U.S.C. § 101. However, presence of many different prior art systems and novelty and non-obviousness of the present invention because of presence of specific steps and features also indicates that the present invention the “additional” elements of the amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well-known,” the Examiner emphasizes that unconventionality of the entire claimed invention, by itself, is insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
For the reasons above, this argument is found unpersuasive.
17. Applicant submits “Fourth, the present invention and specific automated steps of the present invention can’t be performed in a person’s mind or done using a calculator or some generic device and generic software (as when applying a known Mathematical Formula or Concept).” [Applicant’s Remarks, 10/15/2024, page 27]
Applicant argues that “the present invention and specific automated steps of the present invention can’t be performed in a person’s mind or done using a calculator or some generic device and generic software (as when applying a known Mathematical Formula or Concept).” The Examiner respectfully disagrees. The Examiner emphasizes that neither Applicant's claims nor the Specification support Applicant’s assertion that specific automated steps are required to implement the invention. Notably, Applicant's Specification suggests that virtually any computing device under the sun can be used to implement the claims, including general purpose computers. See, e.g., paragraphs [0112]: “Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer...”), and paragraph [0114]: “Those skilled in the art will also appreciate that the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, networked PCs, minicomputers, mainframe computers, and the like.” Accordingly, the generic computing elements recited in the is similar to simply adding the words “apply it,” which is not sufficient to amount to a practical application or add significantly more, as noted in the 2019 PEG. See also, MPEP 2106.05(f). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Furthermore, in so far as Applicant is suggesting that the use of "specific automated steps” mandates a finding of eligibility, the Examiner respectfully points out that the Federal Circuit’s opinion in In re Alappat, 33 F.3d 1526 (Fed. Cir. 1994) (which once supported the notion that a special purpose computer is §101-eligible) has been superseded by at least the Supreme Court’s Bilski, Mayo, and Alice opinions, such that eligibility does not hinge on the presence or absence of a special purpose computer to perform the claims, but instead hinges on the outcome of the subject matter eligibility inquiry adhering to more recent and authoritative guidance gleaned from the Supreme Court’s Mayo/Alice decisions, and the 2019 PEG that sets forth the procedure for determining subject matter eligibility in compliance with the controlling law.
Accordingly, even assuming arguendo that the claimed general purpose computer that has programmed/configured to perform Applicant’s invention were considered as “specialized computer hardware and software,” this alone would not be sufficient to mandate a finding of §101 eligibility because, when an abstract idea is recited in the claims, evaluation of any additional elements, including a “special purpose computer,” must be conducted to determine whether the additional limitations (e.g., the alleged special purpose computer) amount to a practical application or add significantly more beyond the abstract idea. Although neither Applicant's claims nor Specification indicate that a special purpose computer is required to implement the system, even if a special purpose computer was required, this alone would not be sufficient to render the claims eligible. Furthermore, with respect to exemplary claim 1, none of the steps of receiving, identifying, performing, combining, determining, determining, calculating, or rerunning individually or in combination, have been shown to yield an improvement to a computer or to any technology. Notably, the claims have not been shown to modify, reconfigure, manipulate, or transform the computer, computer software, or any technology in any discernible manner, much less yield an improvement thereto. There is simply no support to show that implementing the claim steps with at least one processor, and a plurality of computer instructions stored in memory, amounts to an improvement to the computer or to any other technology. Therefore, regardless of whether or not the claimed generic computing elements amount to "specialized computer hardware and software," these additional elements have not been shown to integrate the abstract idea into a practical application or add significantly more to the claims. Furthermore, in response to Applicant’s argument that “the present invention can’t be performed in a person’s mind,” it is noted that the rejection did not assert that the claimed limitations fall under the “Mental Processes” abstract idea grouping.
18. Applicant submits “the sequence and the actual software logic in each recited step in amended claims 1 and 22 involve complex and very specific sequence of processing and application of the automated software; not some generic idea that can be done in one’s head.” [Applicant’s Remarks, 12/31/2024, page 19]
The Examiner respectfully disagrees. Although Applicant suggests that the claims are directed to a “complex sequence of processing and application of the automated software” for calculating a business value at risk, Applicant has not provided facts or evidence to show that any of the claim steps involve a “complex sequence” that fall outside of the scope of the abstract ideas noted above. Furthermore, even assuming arguendo that the claim techniques were considered specific, inventive, novel, and/or non-obvious, such a finding by itself would nevertheless be insufficient to render a claim as eligible under §101. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
19. Applicant submits “Dependent claims 12 and 34 further recite that the identification of the key frustrations “utilizes a principal component analysis (PCA) to reduce the plurality of frustration data and a plurality of other factors to a smaller subset.” Support for this additional amendment can be found at least in paragraph [0068] of the specification. This step requires a very specific process and evaluation; not some generic idea, with an “apply it” instruction.” [Applicant’s Remarks, 12/31/2024, pages 19-20]
In response to Applicant’s argument that the “utilizes a principal component analysis (PCA) to reduce the plurality of frustration data and a plurality of other factors to a smaller subset” step “requires a very specific process and evaluation; not some generic idea, with an “apply it” instruction,” it is noted that the “principal component analysis (PCA)” recited in dependent claims 12 and 34 is recited at a high level of generality and lacks any meaningful technical details in the Specification regarding its implementation or any type of technological improvement. The Specification only refers to “principal component analysis” in one instance in paragraph 0068, but does not actually describe any details regarding its implementation. Accordingly, this argument is found unpersuasive.
20. Applicant submits “the determination of novelty of the present invention over the many prior art references cited in the prior Office Actions supports the conclusion that the present invention is a specific software-based technological solution, rather than an attempt to monopolize some general idea or a general concept with a simple “apply it” instruction.” [Applicant’s Remarks, 12/31/2024, page 23]
Lastly, in response to Applicant’s argument that ““the determination of novelty of the present invention over the many prior art references cited in the prior Office Actions supports the conclusion that the present invention is a specific software- based technological solution, rather than an attempt to monopolize some general idea or a general concept with a simple “apply it” instruction,” the Examiner notes that "the absence of complete preemption does not demonstrate patent eligibility." Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). "Where a patent's claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot." Id.; see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13 62-63 (Fed. Cir. 2015) ("[T]hat the claims do not preempt all price optimization or may be limited to price optimization in the e-commerce setting do not make them any less abstract."). Accordingly, Applicant’s preemption-based argument is not persuasive.
For the reasons above, Applicant’s arguments concerning the §101 rejection are not persuasive.
21. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Objections
22. Claims 1, 22, and 36 are objected to because of the following informalities: typographical/grammatical errors.
23. Claim 1 was amended to recite “(d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination which other companies in the same industry as the company define an industry's role.” Claim 1 should read “(d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination of which other companies in the same industry as the company define an industry's role.” Appropriate correction is required.
24. Claim 22 was amended to recite “(d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination which other companies in the same industry as the company define an industry's role.” Claim 22 should read “(d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination of which other companies in the same industry as the company define an industry's role.” Appropriate correction is required.
25. Claim 36 was amended to recite “The method of claim 22, further comprises automatically calculating and assigning a Vulnerability Score for each individual frustration, for individual customer, for one or more company in a same industry and for an overall industry.” Claim 36 should read “The method of claim 22, further comprising automatically calculating and assigning a Vulnerability Score for each individual frustration, for an individual customer, for one or more company in a same industry and for an overall industry.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
26. 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.
27. Claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 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.
28. Claim 1 was amended to recite “and based on an evaluation of the calculated frequency of the frustration with an evaluation of at least one other factor comprising (a) evaluation of a strength of the company's current relationship with one or more of the actual and verified company customers; (b) evaluation of verified company customers' engagement with industry; (c) evaluation of the verified company customers' satisfaction with the company; (d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination which other companies in the same industry as the company define an industry's role; and(e) evaluation of an identity of a primary relationship owner, including identification of a primary company or a product manufacturer…” The phrase “the frustration” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “frustrations” and “a plurality of frustrations” (plural) claim 1 does not introduce “a frustration.” Independent claim 22 recites similar limitations (i.e., “and based on an evaluation of the calculated frequency of the frustration with an evaluation of at least one other factor comprising (a) evaluation of a strength of the company's current relationship…”) as those recited in claim 1 and therefore is found to be indefinite for the same reasons provided above. Appropriate correction is required.
29. Claim 1 recites “(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical predictive model...” The phrase “the actual and verified customer” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “actual and verified company customers” (plural), claim 1 does not introduce “an actual and verified customer.” Independent claim 22 recites similar limitations (i.e., “(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical predictive model”) as those recited in claim 1 and therefore is found to be indefinite for the same reasons provided above. Appropriate correction is required.
30. Claim 1 was amended to recite “(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical...” The phrase “each identified and selected key frustration” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “(2) automatically identifying and selecting as input to a computerized mathematical model one or more key frustrations from the received plurality of frustrations,” claim 1 does not introduce “identified and selected one or more key frustration.” Independent claim 22 recites similar limitations as those recited in claim 1 and therefore is found to be indefinite for the same reasons provided above. Appropriate correction is required.
31. Claim 1 was amended to recite “(4) for each key frustration processed in step (3), automatically combining and evaluating frustrations of other company customers with respect to the same frustration, wherein the frustrations of other company customers are also evaluated by performing said computerized quantitative vulnerability testing using the computerized mathematical predictive model of step (3).” The phrase “said computerized quantitative vulnerability testing” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “quantitative vulnerability testing,” claim 1 does not introduce “computerized quantitative vulnerability testing.” Independent claim 22 recites similar limitations as those recited in claim 1 and therefore is found to be indefinite for the same reasons provided above. Appropriate correction is required.
32. Claim 1 was amended to recite “wherein the calculation of the business value at risk includes a determination and quantification of the losses to competitors.” The phrase “the losses to competitors” lacks antecedent basis, and therefore renders the claim indefinite. Independent claim 22 recites similar limitations as those recited in claim 1 and therefore is found to be indefinite for the same reasons provided above. Appropriate correction is required.
33. Claim 9 was amended to recite “wherein the system further calculates as part of the computerized mathematical modeling at least one value creation factor for a plurality of competing companies…” The phrase “the computerized mathematical modeling” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “a computerized mathematical model” and “a computerized mathematical predictive model,” claims 1/9 do not introduce “computerized mathematical modeling.” Appropriate correction is required.
34. Claims 13 and 34 were amended to recite “r utilizes a principal component analysis (PCA) to reduce the plurality of frustration data and a plurality of other factors to a smaller subset.” The phrase “the plurality of frustration data” lacks antecedent basis, and therefore renders the claim indefinite. While claims 1/22 introduce “a later obtained frustration data,” claims 1/13/22/34 do not introduce “a plurality of frustration data.” Appropriate correction is required.
35. Claim 18 was amended to recite “The system of claim 1, wherein the processor executes instructions utilizing a binominal logistic regression analysis for translating one or more individual frustration level Vulnerability Scores calculated for at least one key frustration for at least one verified company customer into a probability of that verified customer discontinuing use of company products or services.” The phrase “that verified customer” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “verified company customers” and claim 18 introduces “at least one verified company customer,” claims 1/18 do not introduce “a verified customer.” Appropriate correction is required.
All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
36. 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.
37. Claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
38. Claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1, 4-7, 9, 11-13, 15-21), and method (claims 22, 25-28, 30, 32-34, and 36-42) are directed to at least one potentially eligible category of subject matter (i.e., machine, and process, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 is satisfied.
With respect to Step 2A Prong One of 2019 PEG, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in the 2019 PEG because the claims recite steps for collecting customer feedback data from regarding a plurality of frustrations related to a company, which encompasses activity for managing personal behavior or relationships or interactions, as well as commercial interactions (e.g., advertising, marketing or sales activities or behaviors), and also fall under the “Mathematical Concepts” abstract idea grouping by reciting mathematical relationships and calculations. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below:
(1) receiving and processing data comprising a plurality of frustrations, wherein said plurality of frustrations are frustrations of actual and verified company customers, who expressly conveyed to the company the one or more frustrations about a company service or product (This step is organizing human activity by managing interactions between people by following rules, or instructions, i.e., by collecting information about a plurality of frustrations from company customers.);
(2) automatically identifying and selecting as input to a computerized mathematical model one or more key frustrations from the received plurality of frustrations, wherein the identification and selection of said one or more key frustrations as being key frustrations is at least partially based on a calculation of frequency of said key frustrations in the received plurality of frustrations from the actual and verified company customers, conveyed to the company and based on an evaluation of the calculated frequency of the frustration with an evaluation of at least one other factor comprising (a) evaluation of a strength of the company's current relationship with one or more of the actual and verified company customers; (b) evaluation of verified company customers' engagement with industry; (c) evaluation of the verified company customers' satisfaction with the company; (d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination which other companies in the same industry as the company define an industry's role; and (e) evaluation of an identity of a primary relationship owner, including identification of a primary company or a product manufacturer (This step is organizing human activity for similar reasons as provided for step (1) above.);
(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical predictive model that utilizes a binominal logistic regression analysis based at least in part on the calculated frequency of the key frustration, a calculated determination of uniqueness of the key frustration in connection to other verified and actual customers of the company and customers of competing companies, and a calculated determination of how much the key frustration prompts switching from the company to competitors (This step is organizing human activity for similar reasons as provided for step (1) above. This step recites mathematical concepts, relationships, formulas or equations, or calculations.);
(4) for each key frustration processed in step (3), automatically combining and evaluating frustrations of other company customers with respect to the same frustration, wherein the frustrations of other company customers are also evaluated by performing said computerized quantitative vulnerability testing using the computerized mathematical predictive model of step (3) (This step is organizing human activity for similar reasons as provided for step (1) above.);
(5) determining an individual frustration level vulnerability score for each processed key frustration (This step is organizing human activity for similar reasons as provided for step (1) above. This step also recites mathematical concepts, relationships, formulas or equations, or calculations.);
(6) for each processed key frustration, automatically determining company level vulnerability based on a segmentation of individual frustrations according to one or more segments of individuals and calculated segment level average revenue among the actual and verified company customers, wherein the segmentation relates to a likelihood of attrition for different segments of the actual and verified company customers (This step is organizing human activity for similar reasons as provided for step (1) above. This step also recites mathematical concepts, relationships, formulas or equations, or calculations.); and
(7) for each processed key frustration, calculating a predicted probability of attrition for each of said one or more segments of individuals and calculating a business value at risk, caused by the calculated probability of attrition for each of said one or more segments of individuals among the actual and verified company customers, wherein the calculation of the business value at risk includes a determination and quantification of the losses to competitors (This step recites mathematical concepts, relationships, formulas or equations, or calculations.);
(8) displaying the business value at risk for each of said one or more segments of the actual and verified company customers in a software-generated report (This step is organizing human activity for similar reasons as provided for step (1) above); and
(9) periodically rerunning the analysis from step (1), using a later obtained frustration data and automatically comparing the company's calculated business value at risk to the company's calculated business value at risk from prior periods and to trends among competitors, wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors, and to take and monitor progress of remedial measures that reduce a calculated and projected losses to the company (This step is organizing human activity for similar reasons as provided for step (1) above).
Considered together, these steps set forth an abstract idea of managing personal behavior/relationships/interactions via rules or instructions that simply manage customer feedback, thus falling under the “Certain methods of organizing human activity” grouping set forth in the 2019 PEG, and also set forth an abstract idea of calculating a business value at risk, which falls under the realm of “Mathematical Concepts.” Independent claim 22 recites similar limitations as those recited in claim 1 and therefore is found to recite the same abstract idea(s) as claim 1.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mathematical Concepts” abstract idea grouping described in the 2019 PEG, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are: at least one processor executing a plurality of computer instructions stored in memory, a computerized mathematical model, a computerized mathematical predictive model, computerized quantitative vulnerability testing, and software-generated (claim 1), and a computer processor, executing computer instructions, a computerized mathematical model, a computerized mathematical predictive model, computerized quantitative vulnerability testing, and software-generated (claim 22). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “receiving” and “displaying” steps are evaluated as additional elements, these steps amount at most to insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are: at least one processor executing a plurality of computer instructions stored in memory, a computerized mathematical model, a computerized mathematical predictive model, computerized quantitative vulnerability testing, and software-generated (claim 1), and a computer processor, executing computer instructions, a computerized mathematical model, a computerized mathematical predictive model, computerized quantitative vulnerability testing, and software-generated (claim 22). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification acknowledges that the claimed invention relies on nothing more than a general purpose computer executing instructions to implement the invention (Specification at paragraph [0112]: e.g., “Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer...”). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the “receiving” step, it is noted that receiving data has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d). Similarly, the “displaying” step may also be considered insignificant extra-solution output activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g), is not enough to add significantly more since it is well-understood and conventional activity, as noted in MPEP 2106.05(d)).
Even if the computerized mathematical model and computerized mathematical predictive model were evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of a computerized mathematical model and a computerized mathematical predictive model is recited at a high level of generality, these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Adar et al., US 2005/0195966 (paragraph 0007: “Conventional predictive modeling has worked well in determining which actions to perform to achieve a single goal”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 4-7, 9, 11-13, 15-21, 25-28, 30, 32-34, and 36-42 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 4-7, 9, 11-13, 15-21 recite “utilize, quantify, model, and evaluate as part of the quantitative vulnerability testing, a plurality of factors comprising: (a) a determination of a frequency of at least one frustration; (b) a determination of a uniqueness of at least one frustration; (c) a determination whether at least one frustration is shared with other actual and verified customers of the company; (d) a determination of an impact of at least one frustration on a relationship with the company or company product; and (e) a determination how much the at least one frustration prompts switching from the company, company product or company service,” “wherein the computerized mathematical model quantifies and assigns a frustration level score for each of the key frustrations when automatically evaluating the key frustrations based on the computerized mathematical model and calculating an average frustration score for the company,” “wherein the frustration level scores for each of the key frustrations and the calculated average frustration score are assigned values in a range from 1 to 10,” “wherein the received data comprising the plurality of frustrations from the customers further includes an industry benchmarking data, a qualitative research data, a direct customer data, a social media compiled data, a media or news coverage pertaining to the company or the customers, and data about the customers who have recently switched from the company to another company or switched to another company's products or services,” “wherein the system further calculates as part of the computerized mathematical modeling at least one value creation factor for a plurality of competing companies, which comprises processing: (1) deals and financial benefits information of said plurality of competing companies; (2) data about said plurality of competing companies with strong customer service; (3) data about product upgrades for different products; (4) information about ease of access to a company support for at least one of said plurality of competing companies; (5) evaluations about knowledge of a company support staff for at least one of said plurality of competing companies; (6) timeliness of requests and services provided to customers; (7) data about convenience of services for customers; and (8) information about ethical conduct and honesty of said plurality of the competing companies and their management; wherein the system also evaluates and computationally assesses which companies of said plurality of competing companies benefit most from a business risk of others,” “access, review and automatically assess a plurality of public social media posts and media coverage posts on the Internet about one or more competing companies or competing products, wherein the computer program causes the system to assign a positive or negative sentiment value to each of the plurality of public social media posts and media coverage posts,” “wherein the automatic identifying and selecting as input to a mathematical model one or more key frustrations from the received plurality of frustrations utilizes a principal component analysis (PCA) to reduce the plurality of frustration data and a plurality of other factors to a smaller subset,” “wherein the evaluation of the key frustrations from the received plurality of frustrations comprises automatically assigning a Vulnerability Score for each individual frustration, for individual customer, for one or more company in a same industry and for an overall industry,” “wherein the evaluation of the key frustrations from the received plurality of frustrations comprises automatically calculating a Vulnerability Score as a weighted average of the automatically identified one or more key frustrations, with specific weights assigned to the evaluated key frustrations,” “wherein a sum of all weights for the evaluated key frustrations adds up to 10,” “utilizing a binominal logistic regression analysis for translating one or more individual frustration level Vulnerability Scores calculated for at least one key frustration for at least one verified company customer into a probability of that verified customer discontinuing use of company products or services,” “wherein the probability of attrition, resulting in discontinuing use of company products or services for the plurality of customers, is segmented into groups, based on one or more determined individual frustration level Vulnerability Scores for the segmented groups,” “wherein one or more determined individual frustration level Vulnerability Scores for different segmented groups is used to determine a Business Value at Risk for the company, including a calculation of revenue or value shift between the company and one or more of its competing companies or out of an overall industry,” “wherein the determined Business Value at Risk is used for implementing a set of remedial measures by the company in order to prevent the company value erosion or to capture a value shift from one or more competing companies”, however these limitations cover organizing human activity since they flow directly from the customer feedback involving human interaction, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. Furthermore, claims 5 and 16-21 recite abstract ideas that fall into the “Mathematical Concepts” such as mathematical relationships, formulas and calculations. The dependent claims recite additional elements of: at least some of the executed computer instructions (claim 4), the computerized mathematical model (claim 5), at least one additional processor that executes a computer program stored in computer memory (claim 11), the computer program and the system (claim 12), a computerized mathematical model (claim 13), and at least one processor and a computer software (claim 25). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable Subject Matter
39. Claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 are allowable over prior art. With respect to independent claims 1 and 22, the closest prior art, Nielsen et al., Pub. No.: US 2016/0253688 A1, Chen et al., Patent No.: US 10,635,987 B1, Terui, Pub. No.: US 2008/0147427 A1, collectively teach features for receiving and processing data comprising a plurality of frustrations, wherein said plurality of frustrations are frustrations of actual company customers, who expressly conveyed to the company the one or more frustrations about company services or products; automatically identifying one or more key frustrations from the received plurality of frustrations, wherein the identification of said one or more frustrations as being key frustrations is at least partially based on a calculation of frequency of said frustrations in the received plurality of frustrations from the actual company customers, conveyed to the company; for each key frustration, automatically combining and evaluating frustrations of other company customers with respect to the same frustration, determining an individual frustration level vulnerability score for each key frustration; for each key frustration, automatically determining company level vulnerability based on a segmentation of individual frustrations according to one or more segments of individuals among the actual company customers, wherein the segmentation relates to a likelihood of attrition for different segments of the actual company customers; and for each key frustration, calculating a predicted probability of attrition for each of said one or more segments of individuals and calculating a business value at risk, caused by the calculated probability of attrition for each of said one or more segments of individuals among the actual company customers; wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual customers of the company, and to take remedial measures that reduce the calculated and projected losses to the company [See Office Action mailed 10/03/2023 for prior art citations pertinent to the above-noted subject matter].
However, with respect to independent claims 1 and 22, Nielsen et al., Chen et al., Terui, and the other prior art of record does not teach for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical predictive model that utilizes a binominal logistic regression analysis based at least in part on the calculated frequency of the key frustration, a calculated determination of uniqueness of the key frustration in connection to other verified and actual customers of the company and customers of competing companies, and a calculated determination of how much the key frustration prompts switching from the company to competitor; displaying the business value at risk for each of said one or more segments of the actual and verified company customers in a software-generated report; and periodically rerunning the analysis from step (1), using a later obtained frustration data and automatically comparing the company's calculated business value at risk to the company's calculated business value at risk from prior periods and to trends among competitors, wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors.
The following is a statement of reasons for the indication of allowable subject matter: The claims are directed to allowable subject matter because the prior art of record either individually or in combination does not teach: “An automated computerized system for evaluating attrition risk for a company, comprising: at least one processor executing a plurality of computer instructions stored in memory, causing the at least one processor to perform:(1) receiving and processing data comprising a plurality of frustrations, wherein said plurality of frustrations are frustrations of actual and verified company customers, who expressly conveyed to the company the one or more frustrations about a company service or product; (2) automatically identifying and selecting as input to a computerized mathematical model one or more key frustrations from the received plurality of frustrations, wherein the identification and selection of said one or more key frustrations as being key frustrations is at least partially based on a calculation of frequency of said key frustrations in the received plurality of frustrations from the actual and verified company customers, conveyed to the company and based on an evaluation of the calculated frequency of the frustration with an evaluation of at least one other factor comprising (a) evaluation of a strength of the company's current relationship with one or more of the actual and verified company customers; (b) evaluation of verified company customers' engagement with industry; (c) evaluation of the verified company customers' satisfaction with the company; (d) evaluation of an out-of-category expectation setting, including evaluation of an income-based category engagement levels with other products, and determination which other companies in the same industry as the company define an industry's role; and(e) evaluation of an identity of a primary relationship owner, including identification of a primary company or a product manufacturer;(3) for each identified and selected key frustration of the actual and verified customer performing quantitative vulnerability testing using a computerized mathematical predictive model that utilizes a binominal logistic regression analysis based at least in part on the calculated frequency of the key frustration, a calculated determination of uniqueness of the key frustration in connection to other verified and actual customers of the company and customers of competing companies, and a calculated determination of how much the key frustration prompts switching from the company to competitors;(4) for each key frustration processed in step (3), automatically combining and evaluating frustrations of other company customers with respect to the same frustration, wherein the frustrations of other company customers are also evaluated by performing said computerized quantitative vulnerability testing using the computerized mathematical predictive model of step (3); (5) determining an individual frustration level vulnerability score for each processed key frustration;(6) for each processed key frustration, automatically determining company level vulnerability based on a segmentation of individual frustrations according to one or more segments of individuals and calculated segment level average revenue among the actual and verified company customers, wherein the segmentation relates to a likelihood of attrition for different segments of the actual and verified company customers; (7) for each processed key frustration, calculating a predicted probability of attrition for each of said one or more segments of individuals and calculating a business value at risk, caused by the calculated probability of attrition for each of said one or more segments of individuals among the actual and verified company customers, wherein the calculation of the business value at risk includes a determination and quantification of the losses to competitors; (8) displaying the business value at risk for each of said one or more segments of the actual and verified company customers in a software-generated report; and (9) periodically rerunning the analysis from step (1), using a later obtained frustration data and automatically comparing the company's calculated business value at risk to the company's calculated business value at risk from prior periods and to trends among competitors, wherein the calculated business value at risk is utilized by a company management to quantify monetary losses caused by a predicted attrition among the different segments of the actual and verified customers of the company, to determine which of the competing companies benefits from the predicted attrition, to determine an impact on the company and its competitors, and to take and monitor progress of remedial measures that reduce a calculated and projected losses to the company,” as recited in amended claim 1 (and as similarly encompassed by independent claim 22), thus rendering claims as allowable over prior art. However, these claims are not allowable because they remain rejected under 35 U.S.C. 101. Claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 remain rejected under 35 U.S.C. 112(b). Claims 1, 22, and 36 remain objected to due to typographical errors.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beaton et al., Pub. No.: US 2009/0187471 A1 – describes a method and system for evaluating one or more attributes of an organization.
Retna et al., Pub. No.: US 2020/0202268 A1 – describes utilizing artificial intelligence to predict risk and compliance actionable insights, predict remediation incidents, and accelerate a remediation process.
Xu et al., Pub. No.: US 2004/0030667 A1 – describes that conventional techniques may such as factor analysis, principal component and variable clustering are used for variable reduction.
Verbeke, Wouter, et al. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach." European journal of operational research 218.1 (2012): 211-229 – describes that customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625