DETAILED ACTION
This non-final Office action is responsive to the application preliminary amendment filed August 25th, 2025. Claims 1-20 have been cancelled. Claims 21-40 are presented for examination.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/09/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Paragraphs [0045] and [0055] recite item 105 as ‘monadic input data’ which is a typographical error that should recite item 115 as ‘monadic input data’;
Paragraphs [0045] and [0055] recite item 110 as ‘discrete choice input data’ which is a typographical error that should recite item 120;
Appropriate correction is required.
Claim Objections
Applicant is advised that should claim 30 be found allowable, claim 31 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 21 (apparatus), 32 (method), and 38 (one non-transitory machine-readable medium) and dependent claims 22-31, 33-37, and 39-40, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 21 is directed to an apparatus (i.e. machine), claim 32 is directed to a method (i.e. process), and claim 38 is directed to a one non-transitory machine-readable medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite reduce a signal-to-noise ratio (SNR) of monadic scores, the method comprising: generating a graphical user interface (GUI) to present market-available products on a display device; generating choice sets of the market-available products, the choice sets associated with a candidate product; retrieving, from the GUI, selection information corresponding to (a) the market- available products and (b) the candidate product; calculating a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: applying a Gumbel distribution to a deviation between (a) an observed outcome and (b) a true outcome; associating one or more random variables, with an ordered logit model, to a binomial random variable; and modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure based on (a) a probability density function and (b) a cumulative distribution function; calculating a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution, the discrete choice probability of selection and the monadic probability associated with a homogenous population of panelists; generating a combined likelihood of selection by joining the discrete choice probability of selection of the first one of the market-available products with the monadic probability of selecting the first one of the market-available products; generating a signal-to-noise ratio based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection; and causing one or more products to be released for public access by adjusting to a customer probability of purchasing the first one of the products (Certain Method of Organizing Human Activity, Mental Process, & Mathematical Processes), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating choice sets of the market-available products; generating a combined likelihood of selection of maker-available products; generating a signal-to-noise ratio based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection; and causing one or more products to be released for public access by adjusting to a customer probability of purchasing the first one of the products, which is commercial interactions in the form of marketing. The Applicant’s claimed limitations are causing one or more products to be released for public access based on a determined customer probability of selection, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are generating choice sets of the market-available products; calculating a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products; associating one or more random variables, with an ordered logit model, to a binomial random variable; modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure; calculating a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution, the discrete choice probability of selection and the monadic probability associated with a homogenous population of panelists; generating a combined likelihood of selection by joining the discrete choice probability of selection of the first one of the market-available products with the monadic probability of selecting the first one of the market-available products; generating a signal-to-noise ratio based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection; and causing one or more products to be released for public access by adjusting to a customer probability of purchasing the first one of the products, which are observations, judgements, evaluations, and opinions of the human mind. The Applicant’s claimed limitations are causing one or more products to be released for public access based on a determined customer probability of selection, which recite the abstract idea of Mental Process.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mathematical Concept because the claimed limitations are calculating a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: applying a Gumbel distribution to a deviation between (a) an observed outcome and (b) a true outcome; associating one or more random variables, with an ordered logit model, to a binomial random variable; and modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure based on (a) a probability density function and (b) a cumulative distribution function; calculating a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution, the discrete choice probability of selection and the monadic probability associated with a homogenous population of panelists; generating a combined likelihood of selection by joining the discrete choice probability of selection of the first one of the market-available products with the monadic probability of selecting the first one of the market-available products; generating a signal-to-noise ratio based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection, which are mathematical calculations. The Applicant’s claimed limitations are reducing a signal-to-noise ratio (SNR) of monadic scores, which recite the abstract idea of Mathematical Concepts.
In addition, dependent claims 22-31, 33-37, and 39-40 further narrow the abstract idea and recite further defining the calculation of the monadic probability corresponding to the first one of the products based on a cut-off point error; generating a combined likelihood of product selection based on at least one of a distribution function of utility or a probability of item selection; automatically selecting at least one of the distribution function of utility or the probability of item selection based on at least one of a K-point Likert scale or a discrete choice-based selection, respectively; reducing the SNR of the monadic probability using a scaling factor indicative of a type of correlation between the discrete choice probability and the monadic probability, the correlation being a positive correlation or a negative correlation; performing a post-estimation analysis of data inconsistencies on an individual level, a group level, or an aggregate level; the generated choice sets; the products released for public access; and the observed outcome. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as marketing, mental processes, and mathematical concepts. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “generating a graphical user interface (GUI) to present market-available products on a display device; retrieving, from the GUI, selection information corresponding to (a) the market- available products and (b) the candidate product” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “An apparatus, the apparatus comprising: interface circuitry; machine readable instructions; and at least one processor circuit to be programmed by the machine readable instructions; graphical user interface (GUI); display device; a data structure of a memory; one or more of the at least one processor circuit; A computer; At least one non-transitory machine-readable medium comprising machine- readable instructions to cause at least one processor circuit” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). In addition, dependent claims 22-31, 33-37, and 39-40 further narrow the abstract idea.
The claimed “An apparatus, the apparatus comprising: interface circuitry; machine readable instructions; and at least one processor circuit to be programmed by the machine readable instructions; graphical user interface (GUI); display device; a data structure of a memory; one or more of the at least one processor circuit; A computer; At least one non-transitory machine-readable medium comprising machine- readable instructions to cause at least one processor circuit” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 32-37; apparatus claims 21-31; and at least one non-transitory machine-readable medium claims 38-40 recite “An apparatus, the apparatus comprising: interface circuitry; machine readable instructions; and at least one processor circuit to be programmed by the machine readable instructions; graphical user interface (GUI); display device; a data structure of a memory; one or more of the at least one processor circuit; A computer; At least one non-transitory machine-readable medium comprising machine- readable instructions to cause at least one processor circuit”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0026 and 0047-48 and Figures 1-2 & 14-17. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “generating a graphical user interface (GUI) to present market-available products on a display device; retrieving, from the GUI, selection information corresponding to (a) the market- available products and (b) the candidate product” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When 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.
In addition, claims 22-31, 33-37, and 39-40 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). 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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 21, 24-32, and 35-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harrang (U.S 2015/0039601 A1) in view of Karty (U.S 2009/0307055 A1) in view of Hassine (U.S 2009/0234710 A1).
Claims 21, 32, and 38
Regarding Claim 21, Harrang discloses the following:
An apparatus, the apparatus comprising [see at least Paragraph 0023 for reference to technology can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor; Paragraph 0025 for reference to a suitable network environment 100 for the delivery of content to user devices, such as the pre-delivery or anticipated delivery of content to user devices; Figure 1 and related text regarding the network environment for the delivery of content to user devices]
interface circuitry [see at least Figure 1 and related text regarding item 112 ‘user interface’; Figure 10 and related text regarding an example user interface that presents information identifying content available for playback]
machine readable instructions [see at least Paragraph 0023 for reference to technology can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor]
at least one processor circuit to be programmed by the machine readable instructions to [see at least Paragraph 0063 for reference to content server 120a, which may include an interface 180, a processor 182, and many content files 187 located in storage 185 of the content server 120a, provides requested content files 196 to the user equipment; Figure 1A and related text regarding item 114 ‘processor’]
generate a graphical user interface (GUI) to present market-available products on a display device [see at least Paragraph 0101 for reference to whether a commercial goods purchase; Paragraph 0128 for reference to the content delivery system 150 presents a user interface that indicates a retrieval location for content items available for play back at a user device (e.g., locally from an onboard device cache or remotely from an off-device external server); Paragraph 0132 for reference to the content presentation module 830 is configured and/or programmed to present a user interface that includes user-selectable elements associated with the content items available for playback via an application resident on a user device, the user-selectable elements displaying information identifying the content items and information identifying the retrieval location for the con tent items; Figure 10 and related text regarding an example user interface that presents information identifying content available for playback]
generate choice sets of the market-available products, the choice sets associated with a candidate product [see at least Paragraph 0067 for reference to the content selection module 420 is configured and/or programmed to select a subset of content items, from the content items available for retrieval, to deliver to the user device based on content usage information associated with the user device; Figure 4 and related text regarding item 420 ‘content selection module’]
retrieve, from the GUI, selection information corresponding to (a) the market- available products and (b) the candidate product [see at least Paragraph 0068 for reference to the content selection module 420 may monitor or access the usage behavior, such as previous usage behavior of a user consuming content via the applications installed on the user device, wherein usage behavior include whether a content item is or was pre-delivered, whether the content item is or was consumed (e.g., listened to, played, experienced, watched, and so on), how frequently the user uses or has used the applications, which networks are used to deliver the content, user preference configurations, and so on]
calculate a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products [see at least Paragraph 0069 for reference to the content selection module 420 may determine a viewing probability for each of the content items available for retrieval that is based on the con tent usage information that reflects an over delivery ratio for content items, and select content items assigned a determined viewing probability that satisfies a threshold probability]
cause one or more products to be released for public access by adjusting to a customer probability of purchasing the first one of the products [see at least Paragraph 0127 for reference to the content delivery system 150 may identify multiple content items associated with an application resident on the user device, the application capable of presenting the content items via an interface of the user device, determine a selection probability for each of the identified content items, the selection probability reflecting a likelihood of selection of a content items by a user of the user device, and retrieve content items that are assigned selection probabilities that satisfy a predetermined selection probability threshold]
While Harrang discloses the limitations above, it does not disclose calculate a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: applying a Gumbel distribution to a deviation between (1) an observed outcome and (2) a true outcome; associating one or more random variables, with an ordered logit model, to a binomial random variable; and modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure based on (a) a probability density function and (b) a cumulative distribution function; calculate a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution, the discrete choice probability of selection and the monadic probability associated with a homogenous population of panelists; generate a combined likelihood of selection by joining the discrete choice probability of selection of the first one of the market-available products with the monadic probability of selecting the first one of the market-available products; and generate a signal-to-noise ratio (SNR) based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection.
However, Karty discloses the limitations:
disclose calculate a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: associating one or more random variables, with an ordered logit model, to a binomial random variable [see at least Paragraph 0009 for reference to a set of concepts being scoring using discrete choice data gathering techniques; Paragraph 0011 for reference to the method gathering and analyzing respondent data including gathering discrete choice data that may be used as input into the modeling approach; Paragraph 0013 for reference to several additional metrics may also be calculated for each concept and/or individual that describe aspects of the distribution beyond conventional metrics such as the mean of the parameter distribution (i.e., the average calibrated purchase interest); Paragraph 0016 for reference to gathering data representative of the respondents choices includes gathering discrete choice data along multiple dimensions for each set of concepts; Paragraph 0024 for reference to respondents are then pooled and all (or some large percentage) complete a discrete choice study that includes multiple concepts; Paragraph 0028 for reference to an ordered logit or probit threshold model in which the individual level utilities are treated as the latent score and the monadic outcome is assumed to be dependent on that score in relation to a set of cutoff points may be used in which these cutoff points are used in the MCMC using a conditional Dirichlet distribution; Paragraph 0029 for reference to the posterior distribution for all parameters can be estimated using a sequence of sufficiently-spaced draws once the chain has “burned in’; Figure 1 and related text regarding ‘allowing respondents to complete a discrete choice study with multiple concepts’]
calculate a monadic probability corresponding to the first one of the market- available products based on the discrete choice probability of selection and the monadic probability associated with a homogenous population of panelists [see at least Paragraph 0011 for reference to respondents are brought into a study, and either prior to or after a discrete choice component of the study (preferably, prior), are asked to rate a monadic concept along one or more dimensions; Paragraph 0012 for reference to data resulting from both monadic and discrete choice testing is combined by relating data for comparable questions in the monadic and discrete choice studies; Paragraph 0014 for reference to identify concepts that have a particular niche appeal in a specific market (or across markets), and as a result, facilitate the characterization of these preference based groups using demographic, attitudinal, and behavioral characteristics gathered, for example, in online surveys and/or other means (e.g., databases of purchasing data, marketing response data, panel membership data, etc.); Paragraph 0024 for reference to the respondents are then split into small groups (e.g., 50 individuals per group), and each group sees and rates a single monadic concept; Paragraph 0025 for reference to each concept having a monadic score for purchase intent and uniqueness (e.g. Top Box, Top Two Box, or Mean score)]
generate a combined likelihood of selection by joining the discrete choice probability of selection of the first one of the market-available products with the monadic probability of selecting the first one of the market-available products [see at least Paragraph 0009 for reference to the system creating a combined metric that is more accurate than currently existing metrics, and, in some cases, a model accommodating preference patterns across metrics as well as preference patterns across the marketplace; Paragraph 0012 for reference to data resulting from both monadic and discrete choice testing is combined by relating data for comparable questions in the monadic and discrete choice studies, and calibrating the parameters estimated in a discrete choice model with the scores from testing the monadic concepts; Paragraph 0025 for reference to the predicted monadic scores being more stable and precise (e.g., less noisy) than the original monadic scores; Paragraph 0027 for reference to calibrated, discrete choice concept scores may be combined with monadic test scores to arrive at individual respondent-level scores using imputation and/or a Monte-Carlo-Markov-Chain (MCMC) method; Figure 3 and related text regarding the process for determining responses to the presentation of one or more choices]
generate a signal-to-noise ratio (SNR) based on data inconsistencies corresponding to a likelihood of product selection associated with the combined likelihood of product selection [see at least Paragraph 0025 for reference to monadic scores or some derived metric from the monadic scores may then be regressed against discrete choice parameter estimates of some function of these estimates to yield predicted monadic scores, wherein these predicted monadic scores are more stable and precise (e.g., less noisy) than the original monadic scores; Paragraph 0031 for reference to various derived metrics exist that can be constructed from the core metrics being generated in a model uch as one of those described above, for example: subsets of scores for individuals who skew positive in the preference for one or more of the concepts]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the selection probability determination of Harrang to include the discrete choice probability, monadic probability, and combined likelihood determination of Karty. Doing so provides statistical models, techniques, and systems for screening concepts for new products and services that accurately evaluate their potential in the marketplace, as stated Karty (Paragraph 0009).
While the combination of Harrang and Karty disclose the limitations above, they do not disclose calculate a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: applying a Gumbel distribution to a deviation between (1) an observed outcome and (2) a true outcome; modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure based on (a) a probability density function and (b) a cumulative distribution function; calculate a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution.
However, Hassine discloses the following:
calculate a discrete choice probability of selection based on the selection information corresponding to a first one of the market-available products by: applying a Gumbel distribution to a deviation between (1) an observed outcome and (2) a true outcome [see at least Paragraph 0057 for reference to CCRM capitalizes on Discrete Choice Analysis to generate a customer choice model for each sale transaction and derive choice probabilities for each possible offer; Paragraph 0925 for reference to CCRM Modeler uses the Discrete Choice Analysis framework to derive a utility function based on customer characteristics and offer attributes; Paragraph 1049 for reference to Logit Model. This model relies on the assumption that the error terms ∈in are independent and identically Gumbel distributed; Paragraph 1049 for reference to The error terms {tilde over (∈)}in,{tilde over (∈)}Ω ion are assumed to be independent with different scale parameters μ and μm. The scale parameters are parameters defining the standard deviation of the Gumbel distributions]
associating one or more random variables, with an ordered logit model, to a binomial random variable [see at least Paragraph 0442 for reference to the Choice Models tables are accessed to find the model corresponding to the current customer segment and retrieve the formulation of the choice probabilities: The type of model (logit, nested, cross nested . . . ); Paragraph 1045 for reference to In order to calculate Probabilities of choice, CCRM models rely on assumptions related to the random term of the utility ∈in in order to ensure the computability of the model; Paragraph 1049 for reference to Logit Model. This model relies on the assumption that the error terms ∈in are independent and identically Gumbel distributed]
modifying a data structure of a memory with distributed ones of the one or more random variables based on the Gumbel distribution, the one or more random variables distributed in the data structure based on (a) a probability density function and (b) a cumulative distribution function [see at least Paragraph 1049 for reference to This model relies on the assumption that the error terms ∈in are independent and identically Gumbel distributed. The general formula of the probability density function of the Gumbel won't be described here but it permits to express the probabilities of choice by Equation 37; Figure 1 and related text regarding the structure of CCRM and its principal components]
calculate a monadic probability corresponding to the first one of the market- available products based on the Gumbel distribution [see at least Paragraph 0119 for reference to Choice Probability (Offer): probability of choice of a given offer by a given customer (when this offer is proposed to the customer alone or within an offer set/sequence); Paragraph 0172 for reference to Sale Probability: probability that an offer is chosen and not cancelled or modified later-on; Paragraph 1049 for reference to The general formula of the probability density function of the Gumbel won't be described here but it permits to express the probabilities of choice by Equation 37]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the discrete choice probability & monadic probability calculation of Karty to include the Gumbel Distribution of Harrine. Doing so enables an enterprise to optimize the transactions and contracts with consumers or business customers, as stated by Hassine (Paragraph 0046).
Regarding claims 32 and 38, the claims recite limitations already addressed by the rejection of claim 21. Regarding claim 32, Harrang teaches a computer-implemented method [Paragraph 0020 & Figure 5]. Regarding claim 38, Harrang teaches at least one non-transitory machine-readable medium comprising machine- readable instructions [Paragraph 0023]. Therefore, claims 32 and 38 are rejected as being unpatentable over the combination of Harrang, Karty, and Hassine.
Claims 24 and 35
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose wherein one or more of the at least one processor circuit is to generate a combined likelihood of product selection based on at least one of a distribution function of utility or a probability of item selection.
Regarding Claim 24, Karty discloses the following:
wherein one or more of the at least one processor circuit is to generate a combined likelihood of product selection based on at least one of a distribution function of utility or a probability of item selection [see at least Paragraph 0009 for reference to a set of concepts being scoring using discrete choice data gathering techniques; Paragraph 0011 for reference to the method gathering and analyzing respondent data including gathering discrete choice data that may be used as input into the modeling approach; Paragraph 0016 for reference to gathering data representative of the respondents choices includes gathering discrete choice data along multiple dimensions for each set of concepts; Paragraph 0024 for reference to respondents are then pooled and all (or some large percentage) complete a discrete choice study that includes multiple concepts; Figure 1 and related text regarding ‘allowing respondents to complete a discrete choice study with multiple concepts’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the selection probability determination of Harrang to include the discrete choice probability, monadic probability, and combined likelihood determination of Karty. Doing so provides statistical models, techniques, and systems for screening concepts for new products and services that accurately evaluate their potential in the marketplace, as stated Karty (Paragraph 0009).
Regarding claim 35, the claim recites limitations already addressed by the rejection of claim 24.
Claims 25 and 36
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose wherein one or more of the at least one processor circuit is to automatically select at least one of the distribution function of utility or the probability of item selection based on at least one of a K-point Likert scale or a discrete choice-based selection, respectively.
Regarding Claim 25, Karty discloses the following:
wherein one or more of the at least one processor circuit is to automatically select at least one of the distribution function of utility or the probability of item selection based on at least one of a K-point Likert scale or a discrete choice-based selection, respectively [see at least Paragraph 0016 for reference to the invention that facilitates the gathering of discrete choice preference data for concepts for new products and services involves using an online graphical user interface for selecting concepts from a set of concepts; Paragraph 0024 for reference to respondents are then pooled and all (or some large percentage) complete a discrete choice study that includes multiple concepts; Paragraph 0027 for reference to individual utilities are calculated, conditional on assumptions and other estimates using, for example, the Metropolis-Hastings method, wherein the accept/reject probability is conditional on its fit with observed data]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the selection probability determination of Harrang to include the discrete choice probability, monadic probability, and combined likelihood determination of Karty. Doing so provides statistical models, techniques, and systems for screening concepts for new products and services that accurately evaluate their potential in the marketplace, as stated Karty (Paragraph 0009).
Regarding claim 36, the claim recites limitations already addressed by the rejection of claim 25.
Claims 26 and 37
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose wherein one or more of the at least one processor circuit is to reduce the SNR of the monadic probability using a scaling factor indicative of a type of correlation between the discrete choice probability and the monadic probability, the correlation being a positive correlation or a negative correlation.
Regarding Claim 26, Karty discloses the following:
wherein one or more of the at least one processor circuit is to reduce the SNR of the monadic probability using a scaling factor indicative of a type of correlation between the discrete choice probability and the monadic probability, the correlation being a positive correlation or a negative correlation [see at least Paragraph 0010 for reference to similar approach can be applied using hierarchical Bayesian methods, in which Monte Carlo Markov chain methods are used to account for correlation patterns across respondent behavior; Paragraph 0012 for reference to a calibration factor can be estimated across all concepts or respondents; Paragraph 0027 for reference to calibrated, discrete choice concept scores may be combined with monadic test scores to arrive at individual respondent-level scores using imputation and/or a Monte-Carlo-Markov-Chain (MCMC) method; Figure 3 and related text regarding the process for determining responses to the presentation of one or more choices]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the selection probability determination of Harrang to include the discrete choice probability, monadic probability, and combined likelihood determination of Karty. Doing so provides statistical models, techniques, and systems for screening concepts for new products and services that accurately evaluate their potential in the marketplace, as stated Karty (Paragraph 0009).
Regarding claim 37, the claim recites limitations already addressed by the rejection of claim 26.
Claim 27
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose wherein one or more of the at least one processor circuit is to perform a post-estimation analysis of data inconsistencies on an individual level, a group level, or an aggregate level.
Regarding Claim 27, Karty discloses the following:
wherein one or more of the at least one processor circuit is to perform a post-estimation analysis of data inconsistencies on an individual level, a group level, or an aggregate level [see at least Abstract for reference to methods and apparatus for storing, organizing, and reporting input and output from this system; Paragraph 0012 for reference to all scores can be reported for all the concepts that are comparable to monadic scores from externally executed monadic concepts]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the selection probability determination of Harrang to include the discrete choice probability, monadic probability, and combined likelihood determination of Karty. Doing so provides statistical models, techniques, and systems for screening concepts for new products and services that accurately evaluate their potential in the marketplace, as stated Karty (Paragraph 0009).
Claim 28
While the combination of Harrang, Karty, and Hassine disclose the limitations above, regarding Claim 28, Harrang discloses the following:
wherein the choice sets are associated with a future product or a current in-market product [see at least Paragraph 0067 for reference to the content selection module 420 is configured and/or programmed to select a subset of content items, from the content items available for retrieval, to deliver to the user device based on content usage information associated with the user device; Paragraph 0101 for reference to whether a commercial goods purchase; Paragraph 0128 for reference to the content delivery system 150 presents a user interface that indicates a retrieval location for content items available for play back at a user device (e.g., locally from an onboard device cache or remotely from an off-device external server); Figure 4 and related text regarding item 420 ‘content selection module’]
Claim 29
While the combination of Harrang, Karty, and Hassine disclose the limitations above, regarding Claim 29, Harrang discloses the following:
wherein the products are released for public access by adjusting a volume of the one or more products a manufacturer sells in a post-product launch [see at least Paragraph 0127 for reference to the content delivery system 150 may identify multiple content items associated with an application resident on the user device, the application capable of presenting the content items via an interface of the user device, determine a selection probability for each of the identified content items, the selection probability reflecting a likelihood of selection of a content items by a user of the user device, and retrieve content items that are assigned selection probabilities that satisfy a predetermined selection probability threshold; Paragraph 0127 for reference to content delivery system 150 may also implement and utilize token-based content delivery algorithms to control a volume of content delivery to the user device 110 and/or content items and retention policies to manage the local cache of content items stored at the user device]
Claim 30
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose wherein the observed outcome corresponds to an expected utility of a selected product and the true outcome corresponds to an actual utility of the selected product.
Regarding Claim 30, Hassine discloses the following:
wherein the observed outcome corresponds to an expected utility of a selected product and the true outcome corresponds to an actual utility of the selected product [see at least Paragraph 925 for reference to CCRM Modeler uses the Discrete Choice Analysis framework to derive a utility function based on customer characteristics and offer attributes; Paragraph 974 for reference to CCRM choice models are based on the Utility Theory, which assumes that the decision-maker preference for an alternative is expressed by a value, called utility, and the decision-maker selects the alternative in the choice set with the highest utility wherein the utility is modeled as a random variable in order to reflect uncertainty; Paragraph 1045 for reference to CCRM models rely on assumptions related to the random term of the utility ∈in in order to ensure the computability of the model]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the discrete choice probability & monadic probability calculation of Karty to include the Gumbel Distribution of Harrine. Doing so enables an enterprise to optimize the transactions and contracts with consumers or business customers, as stated by Hassine (Paragraph 0046).
Claim 31
While the combination of Harrang, Karty, and Hassine disclose the limitations above, Harrang does not disclose the observed outcome corresponding to an expected utility of a selected product and the true outcome corresponding to an actual utility of the selected product.
Regarding Claim 31, Hassine discloses the following:
the observed outcome corresponding to an expected utility of a selected product and the true outcome corresponding to an actual utility of the selected product [see at least Paragraph 925 for reference to CCRM Modeler uses the Discrete Choice Analysis framework to derive a utility function based on customer characteristics and offer attributes; Paragraph 974 for reference to CCRM choice models are based on the Utility Theory, which assumes that the decision-maker preference for an alternative is expressed by a value, called utility, and the decision-maker selects the alternative in the choice set with the highest utility wherein the utility is modeled as a random variable in order to reflect uncertainty; Paragraph 1045 for reference to CCRM models rely on assumptions related to the random term of the utility ∈in in order to ensure the computability of the model]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the discrete choice probability & monadic probability calculation of Karty to include the Gumbel Distribution of Harrine. Doing so enables an enterprise to optimize the transactions and contracts with consumers or business customers, as stated by Hassine (Paragraph 0046).
Claim(s) 22-23, 33-34, and 39-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harrang (U.S 2015/0039601 A1) in view of Karty (U.S 2009/0307055 A1) in view of Hassine (U.S 2009/0234710 A1), as applied in claims 21, 32, and 38, in view of Wrobel (U.S 2005/0114284 A1).
Claims 22, 33, and 39
While Harrang, Karty, and Hassine disclose the limitations above, Harrang do not disclose wherein one or more of the at least one processor circuit is to calculate the monadic probability corresponding to the first one of the products based on the Gumbel distribution and a cut-off point error.
Regarding Claim 22, Hassine discloses the following:
wherein one or more of the at least one processor circuit is to calculate the monadic probability corresponding to the first one of the products based on the Gumbel distribution [see at least Paragraph 0119 for reference to Choice Probability (Offer): probability of choice of a given offer by a given customer (when this offer is proposed to the customer alone or within an offer set/sequence); Paragraph 0172 for reference to Sale Probability: probability that an offer is chosen and not cancelled or modified later-on; Paragraph 1049 for reference to The general formula of the probability density function of the Gumbel won't be described here but it permits to express the probabilities of choice by Equation 37]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the discrete choice probability & monadic probability calculation of Karty to include the Gumbel Distribution of Harrine. Doing so enables an enterprise to optimize the transactions and contracts with consumers or business customers, as stated by Hassine (Paragraph 0046).
While Hassine discloses the limitations above, it does not disclose wherein one or more of the at least one processor circuit is to calculate the monadic probability corresponding to the first one of the products based on a cut-off point error.
However, Wrobel discloses the following:
wherein one or more of the at least one processor circuit is to calculate the monadic probability corresponding to the first one of the products based on a cut-off point error [see at least Paragraph 0007 for reference to the system solving the problem of finding groups of customers who are particularly likely (or unlikely) to buy a certain product; Paragraph 0018 for reference to a confidence interval f that bounds the possible difference between true utility (on the whole database) and estimated utility (on the sample) with a certain confidence; Paragraph 0172 for reference to the method refer to utility confidence bounds which makes it possible to, handle all utility functions that can be estimated with bounded error; Examiner notes ‘bounded error’ as analogous to ‘cut-off point error’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the monadic probability calculation of Karty to include the bounded error and average utility distribution of Wrobel. Doing so would provide an algorithm that works for any utility functions that can be estimated with bounded error at all, as stated by Wrobel.
Regarding claims 33 and 39, the claims recite limitations already addressed by the rejection of claim 22.
Claims 23, 34, and 40
While Harrang, Karty, and Hassine disclose the limitations above, Harrang do not disclose wherein the cut-off point error is associated with an average item utility value distribution.
Regarding Claim 23, Wrobel discloses the following:
wherein the cut-off point error is associated with an average item utility value distribution [see at least Paragraph 0007 for reference to the system solving the problem of finding groups of customers who are particularly likely (or unlikely) to buy a certain product; Paragraph 0018 for reference to a confidence interval f that bounds the possible difference between true utility (on the whole database) and estimated utility (on the sample) with a certain confidence; Paragraph 0074 for reference to the utility function average over all example instances; Paragraph 0172 for reference to the method refer to utility confidence bounds which makes it possible to, handle all utility functions that can be estimated with bounded error; Paragraph 0173 for reference to allows for utility criteria which are a function (with bounded derivative) of an average over the instances; Examiner notes ‘bounded error’ as analogous to ‘cut-off point error’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the monadic probability calculation of Karty to include the bounded error and average utility distribution of Wrobel. Doing so would provide an algorithm that works for any utility functions that can be estimated with bounded error at all, as stated by Wrobel.
Regarding claims 34 and 40, the claims recite limitations already addressed by the rejection of claim 23.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jikar, Vivek K., and Kenneth M. Ragsdell. "Signal-to-Noise Ratio Based Algorithm for Stock Screening." IISE Annual Conference. Proceedings. Institute of Industrial and Systems Engineers (IISE), 2009.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2020/0043029 A1
Li, Hongmin
SYSTEMS AND METHODS FOR COMPUTER-IMPLEMENTED OPTIMIZED PRICING UNDER DIFFUSION-CHOICE MODELS
US 2014/0122173 A1
Wang et al.
ESTIMATING SEMI-PARAMETRIC PRODUCT DEMAND MODELS
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIN ELIZABETH GAVIN whose telephone number is (571)270-7019. The examiner can normally be reached M-F 7:30-4:30 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
Prosecution Insights