DETAILED ACTION
Status of Claims
This is a non-final action in reply to the application filed on June 17, 2024.
Claims 1-16 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statements filed on 6/17/2024 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Rejections- 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-16 falls within statutory class of a process. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception. If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
designing a survey questionnaire that includes at least one non-existent product presented alongside at least one real product;
collecting field responses to the survey questionnaire related to both the at least one non-existent product and the at least one real product;
creating a second-generation interval null hypothesis from the field responses related to the at least one non-existent product;
generating confidence intervals for the at least one real product from the field responses; calculating a second-generation p-value based on the overlap of the confidence intervals and the second-generation interval null hypothesis;
utilizing the second-generation p-value to determine if the field responses related to the at least one real product is noise, signal, or indeterminate;
categorizing the field responses of the at least one real product that is determined to be signal or noise to either conclude the at least one real product is or is not used in a widespread manner within the survey’s inference population, wherein the survey’s inference population is a set of items, events, or people from which the survey sample is selected; and
conducting further computer simulation using the at least one real product and the at least one non-existent product to more accurately quantify statistical estimates of use and related behaviours about the survey questionnaire’s inference population.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mathematical Concepts such as second-generation p-value calculations, Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations i.e., surveys. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The computer is recited at a high level of generality, i.e., as a generic computing and processing system. This computer is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022] is directed to Step 2B. Therein, per Step 2B the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a computer. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer type structure at paragraphs 0017: “All embodiments of this invention are only realistically feasible through the use of a computer”.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-16 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claim 2 further limit the abstract idea that the survey questionnaire includes elements selected from the group consisting of written questions and images (a more detailed abstract idea remains an abstract idea). Claim 3 further limit the abstract idea that the at least one non-existent product is a non-existent drug product and the at least one real product is a drug product (a more detailed abstract idea remains an abstract idea). Claim 4 further limit the abstract idea by creating a distribution from the field responses such that the distribution describes the at least one non-existent product (a more detailed abstract idea remains an abstract idea). Claim 5 further limit the abstract idea that the second-generation interval null hypothesis includes an upper bound and a lower bound created from the distribution (a more detailed abstract idea remains an abstract idea). Claim 6 further limit the abstract idea that the upper bound is created using a method selected from the group consisting of empirical bootstrap, Poisson, Gaussian, and Maximal methods (a more detailed abstract idea remains an abstract idea). Claim 7 further limit the abstract idea that the empirical bootstrap method includes the steps of using a computer to generate multiple fake distributions via bootstrap with replacement, calculating a mean number of fake responses for each bootstrap sample, calculating the mean and standard deviation of the mean number of fake responses, and setting the upper bound of the second-generation interval null hypothesis as mean plus one standard deviation (a more detailed abstract idea remains an abstract idea). Claim 8 further limit the abstract idea that the Poisson method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Poisson distribution assumptions and setting the upper bound as the observed mean plus one standard deviation (a more detailed abstract idea remains an abstract idea). Claim 9 further limit the abstract idea that the Gaussian method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Gaussian assumptions and setting the upper bound as the observed mean plus one standard deviation (a more detailed abstract idea remains an abstract idea). Claim 10 further limit the abstract idea that the Maximal method includes the step of setting the upper bound as the maximum observed number of non-existent products endorsed by a survey participant (a more detailed abstract idea remains an abstract idea). Claim 11 further limit the abstract idea that the lower bound is created using a method selected from the group consisting of minimal method and zero method (a more detailed abstract idea remains an abstract idea). Claim 12 further limit the abstract idea that the minimal method includes the step of setting the lower bound as the minimum number of observed non-existent products endorsed by a survey participant (a more detailed abstract idea remains an abstract idea). Claim 13 further limit the abstract idea that the zero method includes the step of setting the lower bound to zero (a more detailed abstract idea remains an abstract idea). Claim 14 further limit the abstract idea that the confidence intervals for the at least one real product are established via a method selected from the group consisting of empirical bootstrap, Poisson, and Gaussian (a more detailed abstract idea remains an abstract idea). Claim 15 further limit the abstract idea that the field responses are classified using a numerical overlap of the interval null hypothesis derived from the at least one fake product with the confidence interval of the at least one real product (a more detailed abstract idea remains an abstract idea). And claim 16 further limit the abstract idea b the numerical overlap is used in a computer simulation to determine whether field responses of the at least one real product should be probabilistically removed from further numerical calculations involving those field responses (a more detailed abstract idea remains an abstract idea). The identified recitation of the dependents claims falls within the Mathematical Concepts such as second-generation p-value calculations, empirical bootstrap, Poisson, Gaussian, Maximum method, Minimum method and zero method. Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations i.e., surveys. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Karty et al., (US 9,799,041 A1) hereinafter “Karty” in both view of Neil Stone, Concept Testing: Exploring Surveys and Best Practices To Improve Your Product Launch, published on September 19, 2023, https://www.smartsurvey.com/blog/concept-testing-exploring-surveys-and-best-practices-to-improve-your-product-launch hereinafter “Stone” and Blume et al., (2019). An Introduction to Second-Generation p-Values. The American Statistician, 73(sup1), 157–167, hereinafter “Blume”.
Claim 1:
Karty as shown discloses a method, the method comprising:
designing a survey questionnaire (Figures 7A-7C, 10-11 and 15-17 illustrates survey questionnaires);
collecting field responses to the survey questionnaire (col. 50, lines 2-5: “The voting window, also referred to as the focus window, is the window presented to each voter for the purpose of displaying a set of design candidates and collecting that voter's assessment of them.”);
Karty as explained above design a survey questionnaire and collect field response from the survey questionnaire. Karty also teaches in col. 8, lines 53-55: “gather input on concepts and products from respondents where the respondent is given flexibility while ensuring that the results will be useful.” Karty is silent with regard of using a microservices. However, Stone in an analogous art of survey management for the purpose of providing the following limitations as shown does:
that includes at least one non-existent product presented alongside at least one real product (pages 5-7 describes the concept testing survey design, page 7, Concept testing use cases, Product development “Concept testing is particularly popular among companies, who use it to help them make decisions during the development of new products. From identifying which features customers care about and which ones they have no interest in, to knowing what pain points customers are experiencing with existing features. Through concept testing and usability studies, you can better gage customer expectations, make adjustments and increase your chances of a successful product launch.”);
related to both the at least one non-existent product and the at least one real product; (page 5: “The survey will need to be designed to analyze respondents’ feelings about your concepts or ideas, with the data collected used to help determine what customers prefer or don’t like.”);
Both Karty and Stone teach survey management. Karty teaches in col. 11, lines 3-5: “The exemplary embodiments described above can be deployed on a stable website, or can be integrated with online survey mechanisms to solicit and direct traffic toward a target page running the process.” Stone teaches in page 3 “we’ll explore the benefits and different ways of concept testing, as well as the best practices for creating an effective concept testing survey.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Stone would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Stone to the teaching of Karty would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as that the survey includes at least one non-existent product presented alongside at least one real product and the answers are related to both the at least one non-existent product and the at least one real product into similar systems. Further, as noted by Stone “Only customers can determine whether an idea will succeed or fail. That’s why it’s essential to test out your ideas and concepts before you launch them to your customers, as the insights you gather during concept testing will enable you to launch more effective and successful products.” (Stone, page 3, Benefits of concept testing).
Karty teaches statistical analysis a least in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Col. 60, lines 58-61: “any number of other techniques can be used, borrowing from mathematical techniques to find groups in data and/or measure uniqueness of dimensionality (e.g. factor analysis).” And col. 60, lines 35-38: “More complex methods can be deployed that rely on Bayesian updating to assess confidence in item performance, or other algorithmic or statistical methodologies.” Karty in view of Stone teaches the survey questionnaire that includes non-existent products and one real product. Karty in view of Stone is silent with regard to the following limitations. However, Blume in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
creating a second-generation interval null hypothesis from the field responses related to the at least one non-existent product (page 157-158: “The second-generation p-value (SGPV) was developed with this need in mind (Blume et al. Citation2018). The idea was to improve on the p-value, rather than discard it. This meant keeping familiar characteristics, such as the bounds of zero and one, while also adding new capabilities, such as the ability to indicate when the data support the null hypothesis.”);
generating confidence intervals for the at least one real product from the field responses; calculating a second-generation p-value based on the overlap of the confidence intervals and the second-generation interval null hypothesis (page 160: “. 4. The Second-Generation p-Value. The SGPV seeks to measure the fraction of data-supported hypotheses that are also scientifically null hypotheses. We will denote the SGPV by pδ to signal its dependence on the interval null and distinguish it from the classical p-value (Blume et al.2018). To identify the collection of “data-supported hypotheses,” we use an interval estimate such as a confidence interval (CI), a likelihood support interval (SI), or a credible interval. Any type of interval may be used, but the choice impacts the frequency characteristics of the SGPV (see Remark B).”);
utilizing the second-generation p-value to determine if the field responses related to the at least one real product is noise, signal, or indeterminate; (page 157: Having a gross indicator for when a set of data are sufficient to separate signal from noise is not a bad idea. The problem is that p-values perform poorly in this role. […] The second-generation p-value (SGPV) was developed with this need in mind (Blume et al. 2018). The idea was to improve on the p-value, rather than discard it.” See also page 160 4. The Second-Generation p-Value and page 161 table 1);
categorizing the field responses of the at least one real product that is determined to be signal or noise to either conclude the at least one real product is or is not used in a widespread manner within the survey’s inference population, The SGPV can be viewed as a formalization of today’s standard practice of using CIs to assess the potential scientific impact of new findings. In our view, it is much better to make these judgment calls about scientific impact before looking at the data. SGPVs are intended as summary statistics that indicate when a study has yielded a CI that supports only the null premise or meaningful alternative hypotheses.” See also page 161 table 1);
Both Karty and Blume teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Blume teaches in page 1, Introduction Science looks to statistics fora global assessment—a single number summary—of whether the data favor the null hypothesis, the alternative hypothesis or whether the data are inconclusive.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Blume would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Blume to the teaching of Karty in view of Stone would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as creating a second-generation interval null hypothesis from the field responses related to the at least one non-existent product; generating confidence intervals for the at least one real product from the field responses; calculating a second-generation p-value based on the overlap of the confidence intervals and the second-generation interval null hypothesis; utilizing the second-generation p-value to determine if the field responses related to the at least one real product is noise, signal, or indeterminate; categorizing the field responses of the at least one real product that is determined to be signal or noise to either conclude the at least one real product is or is not used in a widespread manner within the survey’s inference population into similar systems. Further, as noted by Blume “SGPVs promote transparency, rigor and reproducibility of scientific results by a priori identifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with the candidate hypotheses or null hypotheses, or when the data are inconclusive.” (Blume, Abstract).
In addition, Karty teaches:
wherein the survey’s inference population is a set of items, events, or people from which the survey sample is selected; and (col. 4, lines 48-50: “Conjoint Analysis studies typically are conducted with more than one individual, and part-worths are typically obtained for a representative sample of consumers.”);
conducting further computer simulation using the at least one real product and the at least one non-existent product to more accurately quantify statistical estimates of use and related behaviours about the survey questionnaire’s inference population (col. 68, lines 24-41: “to estimate switching from one product to another, using a broad range of selectors who have indicated some preference for a product or concept, and projecting switching behavior down to a smaller group that likes a product most out of the larger simulated set. […] the likelihood that a user may switch from one product version to another version, such as a competitive version, in a game-theoretic or simulation-based approach to estimating market share, may be based on the extent of preference of one or more concepts as compared to others as evidenced in an evolutionary process involving selection among many variants of concept.”);
Claim 2:
Karty as shown discloses the following limitations:
wherein the survey questionnaire includes elements selected from the group consisting of written questions and images (Figure 16);
Claim 3:
Karty as shown discloses the following limitations:
wherein the at least one non-existent product is a non-existent drug product and the at least one real product is a drug product (col. 54, line 67 to col. 55, lines 1-4: “Specialized indexes may be specific to such domains as medical devices, Rx drugs, food products, consumer electronics, household products, and so on. The indexes may also be specialized for such areas of interest as product innovation or user experience.”);
Claim 4:
Karty teaches in col. 65, lines 28-31: “Statistical tests (such as a chi squared test) can be run on the distribution of the respondents' results to aid in identifying patterns within a certain level of statistical confidence.” Karty in view of Stone is silent with regard to the following limitations. However, Blume in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
further comprising creating a distribution from the field responses such that the distribution describes the at least one non-existent product (page 161, 5. Frequency Properties: “Blume et al. (2018) shows why the frequency properties of SGPVs can be controlled through sample size. For convenience, we mention a few key results here.” Explain an acceptable approximation to its sampling distribution);
Both Karty and Blume teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Blume teaches in page 1, Introduction Science looks to statistics fora global assessment—a single number summary—of whether the data favor the null hypothesis, the alternative hypothesis or whether the data are inconclusive.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Blume would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Blume to the teaching of Karty in view of Stone would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as creating a distribution from the field responses such that the distribution describes the at least one non-existent product into similar systems. Further, as noted by Blume “SGPVs promote transparency, rigor and reproducibility of scientific results by a priori identifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with the candidate hypotheses or null hypotheses, or when the data are inconclusive.” (Blume, Abstract).
Claim 5:
Karty teaches in col. 65, lines 28-31: “Statistical tests (such as a chi squared test) can be run on the distribution of the respondents' results to aid in identifying patterns within a certain level of statistical confidence.” Karty in view of Stone is silent with regard to the following limitations. However, Blume in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the second-generation interval null hypothesis includes an upper bound and a lower bound created from the distribution (page 157-158: “The second-generation p-value (SGPV) was developed with this need in mind (Blume et al. Citation2018). The idea was to improve on the p-value, rather than discard it. This meant keeping familiar characteristics, such as the bounds of zero and one, while also adding new capabilities, such as the ability to indicate when the data support the null hypothesis.”);
Both Karty and Blume teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Blume teaches in page 1, Introduction Science looks to statistics fora global assessment—a single number summary—of whether the data favor the null hypothesis, the alternative hypothesis or whether the data are inconclusive.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Blume would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Blume to the teaching of Karty in view of Stone would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the second-generation interval null hypothesis includes an upper bound and a lower bound created from the distribution into similar systems. Further, as noted by Blume “SGPVs promote transparency, rigor and reproducibility of scientific results by a priori identifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with the candidate hypotheses or null hypotheses, or when the data are inconclusive.” (Blume, Abstract).
Claims 6-10 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Karty et al., (US 9,799,041 A1) hereinafter “Karty”, Neil Stone, Concept Testing: Exploring Surveys and Best Practices To Improve Your Product Launch, published on September 19, 2023, https://www.smartsurvey.com/blog/concept-testing-exploring-surveys-and-best-practices-to-improve-your-product-launch hereinafter “Stone” and Blume et al., (2019). An Introduction to Second-Generation p-Values. The American Statistician, 73(sup1), 157–167, hereinafter “Blume” as applied to claims 1 and 5 above, further in view of Jim Frost, Statistics By Jim 2017-2023 (https://web.archive.org/web/20170421012606/https://statisticsbyjim.com/) hereinafter “Frost”.
Claim 6:
Karty teaches in col. 34, lines 14-16: “ as small amount of Gaussian noise is added. In one particular embodiment, the added noise has a mean of zero and standard deviation of 2.0.” Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the upper bound is created using a method selected from the group consisting of empirical bootstrap (pages 32-40 describe the method of empirical bootstrap);
Poisson (pages 159-164 describe the method of Poisson);
Gaussian (pages 80-89 describe the method of Gaussian);
and Maximal methods (page 6 describe “The largest number (maximum”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the upper bound is created using a method selected from the group consisting of empirical bootstrap, Poisson, Gaussian and Maximal methods into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 7:
Karty teaches Survey Fraud Detection in col. 65, lines 5-36. Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the empirical bootstrap method includes the steps of using a computer to generate multiple fake distributions via bootstrap with replacement, calculating a mean number of fake responses for each bootstrap sample, calculating the mean and standard deviation of the mean number of fake responses, and (page 32: “Bootstrapping is a statistical procedure that resamples a single dataset to create many simulated samples. This process allows you to calculate standard errors, construct confidence intervals, and perform hypothesis testing for numerous types of sample statistics. Bootstrap methods are alternative approaches to traditional hypothesis testing and are notable for being easier to understand and valid for more conditions.” And page 33: “From a single sample, you can calculate a variety of sample statistics, such as the mean, median, and standard deviation”);
setting the upper bound of the second-generation interval null hypothesis as mean plus one standard deviation (pages 25-26: “The empirical rule in statistics, also known as the 68 95 99 rule, states that for normal distributions, 68% of observed data points will lie inside one standard deviation of the mean, 95% will fall within two standard deviations, and 99.7% will occur within three standard deviations. […] Additionally, statisticians also refer to the empirical rule as the three-sigma rule because nearly all observations occur within three standard deviations. This rule sets a statistical control chart’s upper and lower limits at +/- three standard deviations. In general, this limit serves as a valuable way to identify outliers because 99.7% of all values should fall within it.”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the empirical bootstrap method includes the steps of using a computer to generate multiple fake distributions via bootstrap with replacement, calculating a mean number of fake responses for each bootstrap sample, calculating the mean and standard deviation of the mean number of fake responses, and setting the upper bound of the second-generation interval null hypothesis as mean plus one standard deviation into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 8:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the Poisson method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Poisson distribution assumptions and (pages 159-164 describe a Poisson distribution, mean, variance and standard deviation calculation);
setting the upper bound as the observed mean plus one standard deviation (pages 25-26: “The empirical rule in statistics, also known as the 68 95 99 rule, states that for normal distributions, 68% of observed data points will lie inside one standard deviation of the mean, 95% will fall within two standard deviations, and 99.7% will occur within three standard deviations. […] Additionally, statisticians also refer to the empirical rule as the three-sigma rule because nearly all observations occur within three standard deviations. This rule sets a statistical control chart’s upper and lower limits at +/- three standard deviations. In general, this limit serves as a valuable way to identify outliers because 99.7% of all values should fall within it.”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the Poisson method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Poisson distribution assumptions and setting the upper bound as the observed mean plus one standard deviation into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 9:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the Gaussian method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Gaussian assumptions and (pages 80-89 describe a Gaussian distribution, mean, variance and standard deviation calculation);
setting the upper bound as the observed mean plus one standard deviation (pages 25-26: “The empirical rule in statistics, also known as the 68 95 99 rule, states that for normal distributions, 68% of observed data points will lie inside one standard deviation of the mean, 95% will fall within two standard deviations, and 99.7% will occur within three standard deviations. […] Additionally, statisticians also refer to the empirical rule as the three-sigma rule because nearly all observations occur within three standard deviations. This rule sets a statistical control chart’s upper and lower limits at +/- three standard deviations. In general, this limit serves as a valuable way to identify outliers because 99.7% of all values should fall within it.”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the Gaussian method includes the steps of calculating the mean, variance, and standard deviation of the field responses related to the non-existent products using Gaussian assumptions and setting the upper bound as the observed mean plus one standard deviation into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 10:
Karty as explained above teaches the survey questionnaire. Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the Maximal method includes the step of setting the upper bound as the maximum observed number of non-existent products endorsed by a survey participant (page 6 describe “The largest number (maximum”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the Maximal method includes the step of setting the upper bound as the maximum observed number of non-existent products endorsed by a survey participant into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 14:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the confidence intervals for the at least one real product are established via a method selected from the group consisting of empirical bootstrap (pages 37-39, Example of Using Bootstrapping to Create Confidence Intervals
Poisson (page 14: “You’ll frequently use confidence intervals to bound the sample mean and standard deviation parameters. But you can also create them for regression coefficients, proportions, rates of occurrence (Poisson), and the differences between populations.”);
and Gaussian (pages 13-24 describe calculation of a confidence interval for a normal distribution i.e., Gaussian);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such wherein the confidence intervals for the at least one real product are established via a method selected from the group consisting of empirical bootstrap, Poisson, and Gaussian into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 15:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the field responses are classified using a numerical overlap of the interval null hypothesis derived from the at least one fake product with the confidence interval of the at least one real product (page 177: “Determining whether confidence intervals overlap is an overly conservative approach for identifying significant differences between groups. It's true that when confidence intervals don't overlap, the difference between groups is statistically significant. However, when there is some overlap, the difference might still be significant. […] This example shows how the CI overlapping method fails to reject the null hypothesis more frequently than the corresponding hypothesis test. Using this method decreases the statistical power of your assessment (higher type II error rate), potentially causing you to miss essential findings.”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the field responses are classified using a numerical overlap of the interval null hypothesis derived from the at least one fake product with the confidence interval of the at least one real product into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 16:
Karty teaches in figure 1, a computer. Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the numerical overlap is used in a computer simulation to determine whether field responses of the at least one real product should be probabilistically removed from further numerical calculations involving those field responses (page 35 describe simulated datasets);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the numerical overlap is used in a computer simulation to determine whether field responses of the at least one real product should be probabilistically removed from further numerical calculations involving those field responses into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Karty et al., (US 9,799,041 A1) hereinafter “Karty”, Neil Stone, Concept Testing: Exploring Surveys and Best Practices To Improve Your Product Launch, published on September 19, 2023, https://www.smartsurvey.com/blog/concept-testing-exploring-surveys-and-best-practices-to-improve-your-product-launch hereinafter “Stone” and Blume et al., (2019). An Introduction to Second-Generation p-Values. The American Statistician, 73(sup1), 157–167, hereinafter “Blume” as applied to claim 5, further in both view of Jim Frost, Statistics By Jim 2017-2023 (https://web.archive.org/web/20170421012606/https://statisticsbyjim.com/) hereinafter “Frost” and Goroncy et al., Lower bounds on expectations of positive L-statistics from without-replacement models, Journal of Statistical Planning and Inference, Volume 138, Issue 12, 2008, Pages 3647-3659, hereinafter “Goroncy”.
Claim 11:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the lower bound is created using a method selected from the group consisting of minimal method and (page 6 describe “The smallest number (minimum”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the lower bound is created using a method selected from the group consisting of minimal method into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Karty in view of Stone, Blume and Frost is silent with regard to the following limitations. However, Goroncy in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
zero method (page 3649: “We provide the optimal upper non-positive bounds on the expectations of L-statistics in the case, […] and the projection method results with the zero bounds. We show that this zero bound can be improved”);
Both Karty and Goroncy teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Goroncy teaches in the Abstract “We establish optimal lower non-negative and upper non-positive bounds on the expectations of linear combinations of order statistics centered about the population mean in units generated by the population central absolute moments of various orders.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goroncy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goroncy to the teaching of Karty in view of Stone, Blume and Frost would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the lower bound is created using a method selected from the group consisting of zero method into similar systems. Further, as noted by Goroncy “We also specify the general results for important examples of sample extremes, Gini mean differences and sample range.” (Goroncy, Abstract).
Claim 12:
Karty in view of Stone and Blume is silent with regard to the following limitations. However, Frost in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the minimal method includes the step of setting the lower bound as the minimum number of observed non-existent products endorsed by a survey participant (page 6 describe “The smallest number (minimum”);
Both Karty and Frost teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Frost teaches in page 1 “Welcome to my website! If you want to learn statistics at a deeply intuitive level, you're at the right place!.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Frost would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Frost to the teaching of Karty in view of Stone and Blume would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the minimal method includes the step of setting the lower bound as the minimum number of observed non-existent products endorsed by a survey participant into similar systems. Further, as noted by Frost “My approach is to use plain English, concepts, and graphs to help you understand. Statistics does not have to be hard!.” (Frost, page 2).
Claim 13:
Karty in view of Stone, Blume and Frost is silent with regard to the following limitations. However, Goroncy in an analogous art of statistical data analysis for the purpose of providing the following limitations as shown does:
wherein the zero method includes the step of setting the lower bound to zero (page 3649: “We provide the optimal upper non-positive bounds on the expectations of L-statistics in the case, […] and the projection method results with the zero bounds.”);
Both Karty and Goroncy teach statistical data analysis. Karty teaches in col. 44, lines 51-55: “Where previously revealed preferences are estimated based on previous choices made by the participant(s), using statistical techniques that would be well-understood by practitioners in the field of conjoint analysis and choice modeling.” Goroncy teaches in the Abstract “We establish optimal lower non-negative and upper non-positive bounds on the expectations of linear combinations of order statistics centered about the population mean in units generated by the population central absolute moments of various orders.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goroncy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goroncy to the teaching of Karty in view of Stone, Blume and Frost would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the zero method includes the step of setting the lower bound to zero into similar systems. Further, as noted by Goroncy “We also specify the general results for important examples of sample extremes, Gini mean differences and sample range.” (Goroncy, Abstract).
Conclusion
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
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