DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 17/028,857, filed on 09/22/2020. 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 September 19, 2025, Applicant amended claims 1, 9, 13, 18, 22, 34, 36 and 39, and did not cancel any claims. No new claims were presented for examination.
5. Applicant's amendments to the claims 1 and 22 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim objections; accordingly, these objections have been maintained.
6. Applicant's amendments to the claim 36 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim objection; accordingly, this objection has been removed.
7. Applicant's amendments to claims 1 and 22 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejections under 35 U.S.C. 112(b); accordingly, the rejections of claims 1, 4-7, 9, 11-13, 15-22, 25-28, 30, 32-34, and 36-42 under 35 U.S.C. 112(b) have been maintained. [Items 28, 29, 31 - Office Action, dated 03/20/2025]
8. Applicant's amendments to claims 1 and 22 [with respect to Items 30 and 32 of the Office Action, dated 03/20/2025] 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rejections under 35 U.S.C. 112(b); accordingly, the rejections of claims 1 and 22 under 35 U.S.C. 112(b) have been withdrawn. [Items 30 and 32 - Office Action, dated 03/20/2025]
9. Applicant's amendments to claim 9 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, the rejection of claim 9 under 35 U.S.C. 112(b) has been withdrawn. [Item 33 - Office Action, dated 03/20/2025]
10. Applicant's amendments to claims 13 and 34 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, the rejection of claims 13 and 34 under 35 U.S.C. 112(b) has been withdrawn. [Item 34 - Office Action, dated 03/20/2025]
11. Applicant's amendments to claim 18 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, the rejection of claim 18 under 35 U.S.C. 112(b) has been maintained. [Item 35 - Office Action, dated 03/20/2025]
12. Applicant's amendments to the claims 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
13. Applicant's arguments filed September 19, 2025, have been fully considered.
14. Applicant submits “the amended claims "transform" or "integrate" the resulting system in a "particular useful application" that constitutes a patentable application under Step 2A(ii) (Prongs 2) and Step 2B of the § 101 analysis, irrespective of the type of computer processor used for the execution of the computer instructions that embody and implement the recited novel and specific methodology. Thus, amended independent claims 1 and 22, and their respective dependent claims, amount to "significantly more" than an abstract idea. The applicant reiterates that the existing problem relates to a specific way and an improvement to the software-driven system and algorithm that, among other technical benefits and improvements, reduces computational load to the computer model that receives "a plurality of frustrations" as input data (element/step 1 in the claims 1 and 22). First, the computer software "automatically identifies and selects as input to a computerized mathematical model" only key frustrations from the received plurality of frustrations. The software receives as an initial dataset all possible frustrations and performs analysis on this very large dataset of all received frustrations to determine that the frustration is a "key frustration" based on (i) a calculation of frequency of said frustration, and (ii) based on 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. As clarified in the amended claims 1 and 22, "only the verified key frustrations that satisfy the calculated frequency requirement and satisfy the at least one other factor are processed to the next step" and are built into a predictive model. This initial step significantly reduces the overall load and improves processing time for the model analysis and other steps in the recited system and method. Moreover, it reduces complexity of the overall model by reducing the model to only to the specific "key frustrations" and filtering out other frustrations that do not pass the initial "identification and selection as key frustration" automated analysis in step (2).” [Applicant’s Remarks, 09/19/2025, pages 18-19]
The Examiner respectfully disagrees. In response, as best understood by the Examiner, Applicant argues that identifying and selectin key frustrations before performing the predictive modeling constitutes a technological improvement because it reduces computational load and improves processing time. However, the claims, as amended, merely recite receiving data, evaluating data according to business and statistical criteria, selecting a subset of that data, and applying a mathematical model. These steps amount to organizing, filtering, and analyzing information, which courts have consistently held do be abstract even when done automatically. The alleged improvement of reducing the amount data passed to the mathematical model does not constitute an improvement to the functioning of the computer or a particular technology. The claims do not recite any specific change to a database design, memory organization, processor operation, or any other technological implementation.
Moreover, although the claims refer to frequency values, relationship strength evaluation, and other evaluative factors, the mathematical operation recited in the claims are also abstract and do not, by themselves, constitute an improvement to computer technology. Accordingly, the claim does not integrate the abstract idea into a practical application nor does it recite additional elements amounting to significantly more. For the reasons above, this argument is found unpersuasive. Examiner suggests amending the claims to more clearly recite a specific technical improvement supported by the Specification.
15. Applicant submits “ Claims 4 and 25 recite additional automatic software evaluations that further reduce the overall load and improve processing time for the model, as well as reduce the complexity of the overall model by limiting “key frustrations” and the quantitative vulnerability model that is built on these frustrations.” [Applicant’s Remarks, 09/19/2025, page 19]
The Examiner respectfully disagrees. The “reduction in overall load” results from excluding certain data based on business logic criteria, not from any technological improvement to ow the computer operates. Claim 4 does not recite a specific improvement in computer functionality. Accordingly, the limitations of claim 4 do not integrate the abstract idea into a practical application nor ad significantly more. Accordingly, this argument is found unpersuasive.
16. Applicant submits “ Second, the computerized system and method of the present invention then provides a specific software implemented and a software-driven model that evaluates each determined “key frustration” 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 key frustration of the actual and verified customers may be quantified and transformed to an estimate of the actual financial loss to the company, in comparison to other similar companies and competitors in the same field. Among other benefits, this computerized analysis improves the technical precision, accuracy and quality of the produced results. Moreover, 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, 09/19/2025, page 20]
The Examiner respectfully disagrees. The claimed steps related to evaluating key frustrations, comparing them to other customers, assessing industry trends, and estimating monetary loss constitute mathematical modeling and business analytics, which are abstract ideas. Stating that these evaluations “improve precision” or “cannot be done in one’s head” does not demonstrate a technological improvement to computer functionality. The sequence of steps reflect a logical workflow, not a specific software architecture or technical mechanism that alters how the computer operates. Accordingly, the claim does not integrate the abstract idea into a practical application nor does it recite additional elements amounting to significantly more. For the reasons above, this argument is found unpersuasive.
17. Applicant submits “The amended dependent claims 13 and 34 further recite that the identification of the key frustrations, which serve as input to the computerized mathematical model, operates software that “applies a principal component analysis (PCA) to reduce the received plurality of frustrations and other evaluated factors to a smaller subset.” Support for this 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. Moreover, it clarifies that the processing is made more efficient and the overall frustration data and evaluated factors are “reduced to a smaller subset,” further enhancing the throughput and efficiency of the process. Thus, this recites an additional technical improvement.” [Applicant’s Remarks, 09/19/2025, page 21]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that although claims 12 and 34 recite applying principal component analysis (PCA), PCA is a known mathematical dimensionality reduction technique, and the claim does not specify any unconventional computer implementation of PCA or any modification to how the computer performs PCA. Stating that PCA “reduces data to a smaller subset” describes the inherent mathematical purpose of PCA, not a technological improvement to computer functionality. Accordingly, these limitations do not integrate the abstract idea into a practical application nor add significantly more. For the reasons above, this argument is found unpersuasive.
18. Applicant submits “Third, “for each identified and selected key frustration” the system performs: “".. 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, as recited in amended claims 1 and 22, not only includes a specific process and modelling (i.e., using the binomial logistic regression), but also specifies four specific variable that are included as part of the binominal logistic regression model, built and executed by the computer software. This type of modelling and processing requires specific computer software, and can’t be done in one’s head. Moreover, as discussed by the inventor at the interview conducted on August 7, 2025, use of this particular type of modeling makes the results more accurate and reflect the actual logical connection to the company’s actions or inactions, rather than overall industry trends or common complaints by customers against the overall industry, which do not properly reflect the potential “switching to competitors” and actual company losses to competitors.” [Applicant’s Remarks, 09/19/2025, pages 21-22]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that while claims 1 and 22 recite performing quantitative vulnerability testing suing binomial logistic regression with specific variables, the claims describe applying a mathematical and statistical technique to the collected data. The steps of calculating frequency, uniqueness, and potential customer switching are abstract processes implemented on a computer, rather than an improvement to the computer o a technological process itself. The recited modeling and evaluation steps do not integrate the abstract idea into a practical application nor add significantly more. For the reasons above, this argument is found unpersuasive.
19. Applicant submits “As also discussed at the August 7, 2025 interview, the “customer segmentation” processing of the present software model provides a more accurate and more precise evaluation of the company’s actual customer that are likely to switch, such as key segments of the company’s customers that present (a) a higher risk of switching to a competitor; and/or (b) higher potential losses of revenue due to switching.” [Applicant’s Remarks, 09/19/2025, page 22]
The Examiner respectfully disagrees. In response, the Examiner notes that while the claims recite segmentation of individuals, identifying higher risk segments and potential monetary losses describes an abstract concept of analyzing customer data using mathematical and statistical techniques. Implementing this analysis on a computer does not transform the abstract idea into a patent-eligible invention, nor does it improve the functioning of the computer itself. For the reasons above, this argument is found unpersuasive.
20. Applicant submits “the additional 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 “....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 accurate, enhanced, precise and efficient results of the specific software-based analysis recited in amended claims.” [Applicant’s Remarks, 09/19/2025, page 24]
The Examiner respectfully disagrees. In response to Applicant’s argument, it is noted that while the claim describe generating a “calculated business value at risk” and using it to guide management decisions regarding competitors and remedial measures, this amounts to an abstract idea related to organizing human activity. The claim limitations related to identifying potential losses, determining which competitors benefit, and taking steps to mitigate loss reflect fundamental concepts of managing and organizing business operations, which are non-technical human activity concepts. The inclusion of a computer performing calculations or providing outputs does not transform the abstract idea into a patent-eligible invention because the computer is used as a tool to implement the abstract concept, rather than providing a technical improvement or solving a technical problem. The use of the calculated data to inform management actions does not does not transform the underlying abstract idea into patent-eligible subject matter. For the reason above, this argument is found unpersuasive.
21. Applicant 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, 09/19/2025, page 24]
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.
22. Applicant submits “that the aforementioned claim elements of amended claims 1 and 22, whether individually or in an ordered combination, are not routine, conventional or well- known. Moreover, as discussed above, the recited “additional” elements are not some general idea with an instruction to “apply it”.” [Applicant’s Remarks, 09/19/2025, page 24]
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 ].
Last, in response to Applicant’s argument that “the recited “additional” elements are not some general idea with an instruction to “apply it”,” 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.
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.
23. 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
24. Claims 1 and 22 are objected to because of the following informalities: typographical/grammatical errors.
25. Claim 1 recites “(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.
26. Claim 22 recites “(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.
Claim Rejections - 35 USC § 112
27. 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.
29. 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.
30. Claim 1 was amended to recite “(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 each of said one or more key frustrations as being key frustration is at least partially based on a calculation of frequency of said frustration 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…” The phrases “said frustration” and “the frustration” lack antecedent basis, and therefore render the claim indefinite. While claim 1 introduces “a plurality of frustrations” (plural) claim 1 does not introduce “a 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 “(3) for each of the one or more identified and selected key frustrations of the actual and verified customers identified and selected in (2) performing quantitative vulnerability testing using a computerized mathematical predictive model...” The phrase “the actual and verified customers” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “actual and verified company customers”, claim 1 does not introduce “actual and verified customers.” It is unclear whether “the actual and verified customers” refer to the “actual and verified company customers” introduced in step (1) or to different customers, therefore rendering the claim indefinite. Independent claim 22 recites similar limitations (i.e., “(3) for each of the one or more identified and selected key frustrations of the actual and verified customers”) 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 recites “(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.
33. Claim 13 was amended to recite “The system of claim 1, wherein the automatically identifying and selecting as input to the computerized mathematical model said one or more key frustrations from the received plurality of frustrations the processor executes computer instructions that apply a principal component analysis (PCA) to reduce the received plurality of frustrations and other evaluated factors to a smaller subset.” The phrase “the processor” lacks antecedent basis, and therefore renders the claim indefinite. While claim 1 introduces “at least one processor”, claim 1 does not introduce “a processor.” Appropriate correction is required.
34. 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 of said verified company customers 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 of said verified company customers,” 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
35. 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.
36. 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.
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 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, 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 MPEP 2106 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 each of said one or more key frustrations as being key frustration is at least partially based on a calculation of frequency of said frustration 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 of the one or more identified and selected key frustrations of the actual and verified customers identified and selected in (2) 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 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 MPEP 2106, 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 MPEP 2106, 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,” “further calculates as part of the mathematical model 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 automatically identifying and selecting as input to the mathematical model said one or more key frustrations from the received plurality of frustrations and that apply a principal component analysis (PCA) to reduce the received plurality of frustrations and other evaluated 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 of said verified company customers 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 other dependent claims have been fully considered as well however, similar to claims 4-7, 9, 11-13, 15-21, the limitations set forth in these claims describe steps/details falling within the same “Certain Methods of Organizing Human Activity” and “Mathematical Concepts.” The dependent claims recite additional elements of: at least some of the executed computer instructions (claim 4), the computerized mathematical model (claim 5), the system and computerized mathematical model (claim 9), at least one additional processor that executes a computer program stored in computer memory (claim 11), the computer program and the system (claim 12), the computerized mathematical model and the processor executes computer instructions (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
38. 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 of the one or more identified and selected key frustrations of the actual and verified customers identified and selected in (2) 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; 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 each of said one or more key frustrations as being key frustration is at least partially based on a calculation of frequency of said frustration 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 of the one or more identified and selected key frustrations of the actual and verified customers identified and selected in (2) 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 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 and 22 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.
Dickerson, JR., Pub. No.: US 2003/0046137 A1 – describes a method and system for generating a value proposition for a company in an industry.
Coussement, Kristof, and Dirk Van den Poel. "Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers." Expert Systems with Applications 36.3 (2009): 6127-6134 – describes a study that focuses on two aspects in which churn prediction models could be improved
by relying on customer information type diversity and choosing the best performing classification technique.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights