DETAILED ACTION
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 .
Status of Claims
This office action is in response to the RCE filed on 6/26/2026.
Claims 1, 9, and 17 have been amended.
Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 are directed to a method. Claims 9-16 are directed to a non-transitory, computer readable medium. Claims 17-20 are directed to a system. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
Claims 1, 9, 15 recite virtually identical limitations. Claim 1 will be used as representative. Each claims additional elements will be addressed individually. The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
identifying a set of baseline neurometrics for a first subset of an audience;
exposing the first subset of the audience to a collection of media and measuring neurometrics of the audience during exposure to the media;
identifying demographic data associated with the first subset of the audience, wherein the demographic data includes a plurality of parameters associated with individual members of the audience;
identifying demographic data associated with a second subset of the audience, the second subset of the audience comprising audience members that respond to at least a portion of the collection of media; and
generating a predictive model of the audience’s response to the collection of media by:
generating a network comprising three layers of nodes, each node in the first layer of nodes representing a particular neurometric, each node in the second layer of nodes representing a particular parameter of the demographic data, and each node in the third layer representing a response type associated with the collection of media;
generating a first group of edges between each node in the first layer of nodes and each node in the second layer of nodes;
generating a second group of edges between each node in the second layer of nodes, and each node in the third layer of nodes;
for each individual member of the first subset of the audience, adjusting a weight of each edge of the first group of edges; and
for each individual member of the second subset of the audience, adjusting a weight of each edge of the second group of edges;
analyzing the predictive model to identify at least one audience phenotype based on the weights in the generated predictive model, wherein a phenotype identifies a likely response type based on at least one neurometric and at least one demographic.
generating a prompt to cause a generative model to produce improved media based on the phenotype; and
sending the prompt to the generative model
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
2, 10, 18: wherein measuring neurometrics comprises analyzing brainwave data to identify a psychological state of each individual of the first subset of the audience, wherein each neurometric represents a particular psychological condition for each individual.
3, 11, 19: wherein the baseline neurometrics comprise brainwave measurements of the first subset of the audience during a period when the first subset of the audience is not exposed to the media.
4, 12, 20: comprising, determining based on the baseline neurometrics, whether the first subset of the audience is sufficiently representative of the audience by determining that a neurosynchrony between members of the first subset of the audience is above a predetermined threshold, wherein neurosynchrony is determined based on a statistical analysis of the baseline neurometrics.
5, 13: wherein audience response to the portion of the media comprises at least one of:
a view, a selection of a hyperlink associated with the media;
a sale associated with the media;
a change in market share associated with the media; or
a reported sentiment improvement associated with the media.
6, 14: wherein the demographic data comprises at least one of:
age; location; time; frequency of media exposure; or platform of media exposure.
7, 15: wherein adjusting the weight of each edge of the first group of edges and adjusting the weight of each edge of the second group of edges comprises adjusting the weights using a stochastic gradient descent algorithm.
8, 16: wherein analyzing the predictive model to identify the at least one phenotype comprises identifying a portion of the predictive model with comparatively higher weighted edges
The claims provide a manner of analyzing user reactions to media, generating a predictive model for predicting behavioral responses to media. Thus, when considered individually and as an ordered combination, the claims embody certain methods of organizing human activity. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors).
Additionally, but for the inclusion of generic computing components, the claimed operations of identifying a baseline, exposing audience to media and measuring neurometrics, identifying demographic data of a first subset, identify demographic data of a second subset, generate a predictive model by generating a network comprising 3 layers of nodes, generating first and second groups of edges, adjusting weights of the edges, and analyzing the model to identify at least one audience phenotype can be performed in the human mind or with pen and paper. Thus, the claims additionally fall within the mental process grouping of abstract ideas.
Additionally, the claims recite mathematical concepts as they describe a process of mathematically modeling behavior responses to media. The claims generate layers of nodes representing neurometric data, demographic data, and response type, generate edges between each node in each layer, adjust weights of the edges, and analyze the predictive model to identify at least one audience phenotype based on the weights in the generative predictive model,
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: using a wearable EEG device (claims 1, 9, 17); a non-transitory computer readable medium storing one or more instructions executable by a computer system (claim 9); one or more computers; and one or more memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine readable media storing one or more instructions (claim 17); generative AI model (claims 1, 9, 17);
The non-transitory computer readable medium storing one or more instructions executable by a computer system and one or more computers; and one or more memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine readable media storing one or more instructions are recited at high level of generality and amount to mere instructions to “apply it” (the abstract idea) using generic computing components ([0078]-[0079]). The computing devices are merely used to process data (identifying, exposing, generating, adjusting, analyzing, determining). Nothing in the claims improves upon computers, technology, or a technical field (See MPEP 2106.05(f)).
The wearable EEG device is insignificant extra solution activity. It is only tangentially related to the invention and amounts to mere data gathering. The EEG devices is merely used to acquire neurometric data. It is recited at a high level of generality and nothing in the claims improves upon EEG device technology or a technical field (See MPEP 2106.05(g)).
The generative AI model is recited at a high level of generality. The claim merely generates a prompt and thus the generative AI model itself operates outside the scope of the claim. Further, paragraph [0048] makes clear that the generative AI model is any of known generative AI models (i.e. chatgpt, gemeni, etc) and thus nothing in the claims or specification improves upon generative AI model technology or technical field. As a result, the generative AI models does not go beyond the “apply it” level of implementation (See MPEP 2106.05(f)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea and provide insignificant extra solution activity of data gathering.
Further, using an EEG to gather neurometric data is well-understood, routine, and conventional at the time of the claimed invention (See A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques – Khondakar et al – Jun 2024 – https://link.springer.com/article/10.1186/s40708-024-00229-8 - “Among the neurophysiological signals, EEG has become the most popular and is widely used in the marketing sector owing to its low price and high temporal resolution.”; A review on the use of eeg for the investigation of the factors that affect Consumer’s behavior – Panteli et al – May 2024 - A review on the use of eeg for the investigation of the factors that affect Consumer’s behavior - ScienceDirect – “. It has been proved that EEG is the most popular neuroimaging tool in neuromarketing research.”; Is EEG Suitable for Marketing Research? A Systematic Review - Bazzani et al – Dec. 2020 - https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.594566/full - “Functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) are the most frequently adopted neuroscientific techniques to address marketing questions.” “Electroencephalography (EEG) is one of the most frequently applied neuroscientific techniques for marketing studies, thanks to its low cost and high temporal resolution.”)
As a result, the claims are not patent eligible.
Allowable Subject Matter
Claims 1-20 are allowed over the prior art but remain rejected under 35 USC 101.
The examiner was unable to find a reasonable combination of references to teach each and every limitation in the context of the claimed invention. Specifically, the examiner was unable to find:
generating a predictive model of the audience’s response to the collection of media by:
generating a network comprising three layers of nodes, each node in the first layer of nodes representing a particular neurometric, each node in the second layer of nodes representing a particular parameter of the demographic data, and each node in the third layer representing a response type associated with the collection of media;
generating a first group of edges between each node in the first layer of nodes and each node in the second layer of nodes;
generating a second group of edges between each node in the second layer of nodes, and each node in the third layer of nodes;
for each individual member of the first subset of the audience, adjusting a weight of each edge of the first group of edges; and
for each individual member of the second subset of the audience, adjusting a weight of each edge of the second group of edges.
The specific network including the specific layer setup and the specific edge connections were unable to be found in the prior art.
The closest prior arts include:
Pradeep et al (US 10,937,051) teaches analyzing a group of users by evaluating neural responses to content (i.e. using an EEG). Baseline neural data is captured and the difference between the baseline and after being shown the content are measured. The responses are correlated with various demographic features of the users and used to predict user responses to content.
Chappell, III et al (US 2020/0297262) teaches using a machine learning model including parameters for neurometric data, demographics, and responses including purchases, subscriptions, etc.
Deep Learning Basics by Sasirekha Cota - 12/7/2023 teaches various machine learning techniques including multilayer perception and adjusting weights including using back propagation such as a stochastic gradient descent algorithm to learn complex relationships between input and output variables.
Geng at el (US 2023/0346221) teaches measuring metrics of a group and providing threshold for the group to have a certain level of synchrony to ensure that a group has at least a target level of synchrony to increase the probability of the group representing a particular trait.
Combining Neural Networks and Decision Trees by Yury Polyakovsky - 10/15/2022 teaches neural networks that the output for a first layer is used as input to a second layer. The output form the second layer is used as input to a third layer and so on.
Response to Arguments
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101.
With regard to prong 1, the examiner respectfully disagrees. The generic use of an EEG devices is insignificant extra solution activity of data gathering. Nothing in the claims or the specification provide anything that improves EEG devices. Further, it is clear that the invention is in no way concerned with improving EEG devices or the way in which data is gathered using EEG's. Additionally, evidence has been provided showing the use of an EEG to provide neurometric data was well-understood, routine, and conventional at the time of the invention. Further, the EEG device is an additional element and not considered part of the abstract idea.
With regard to machine learning, the examiner respectfully disagrees. The claims merely use machine learning at a high level on the applicant’s chosen set of data. Nothing in the claims improves machine learning technology or a technical field. Merely defining the nodes of the machine learning does not improve machine learning or move it beyond the "apply it" level of implementation.
Further, generating a prompt is merely part of the abstract idea. Nothing in the claims or spec provides any meaningful limitations or citations that improve generative AI models. The spec makes clear that the generative AI models are merely well known models ([0048]) and thus the disclosure provides no improvements to generative AI model technology or the technical field. Applicant’s alleged improvement is to the business concept itself and not underlying technology.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraff can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621
/CHRISTOPHER STROUD/Primary Examiner, Art Unit 3621
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