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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. SG10201907343S, filed on August 8, 2019.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 1, 2026 has been entered.
Response to Amendment
In the amendment filed on May 8, 2026, the following has occurred: claim(s) 46, 62-63 have been amended. Now, claim(s) 46-63 are pending.
Affidavit
The Examiner has reviewed the Affidavit, “Second Declaration of Jonathan Jacobs Pursuant to 37 C.F.R. § 1.132” from May 8, 2026. The Examiner has reviewed the Affidavit in light of the newly amended claims. The Examiner does not acknowledge that the Affidavit overcomes the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s) as described below in the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s) below, and further described in the Response to Argument(s) section below, as the Applicant’s Remarks point to the Affidavit.
Claim Objections
Claim 62 objected to because of the following informalities: “the predetermined olfactory stimulus” in p. 5, ll. 23. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “the predetermined olfactive stimulus”.
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.
Claim(s) 46-63 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 46-61: Step 2A Prong One
Claim 46 recite(s):
(I) predicting an individual's long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli, wherein predicting the individual's long-term liking comprises:
(a) exposing the individual to a predetermined sensory stimulus at least three times during an initial exposure period, following a defined exposure pattern;
(b) for each exposure, measuring the individual's physiological and/or psychometric response; and
(d) predict the individual's long- term liking of the predetermined sensory stimulus at a predetermined future time point, wherein the predetermined future time point is at least ten times later than the duration of the initial exposure period;
(II) repeating step (I) for a plurality of predetermined sensory stimuli;
(III) identifying a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined sensory stimuli; and
(IV) incorporating the identified predetermined sensory stimulus into the consumer product
These limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “using the artificial neural network” language, the “predicting” and “predict” functions in the context of this claim encompass a person following instructions to predict an individual’s long-term liking to a predetermined sensory stimulus and predict the individual's long- term liking of the predetermined sensory stimulus at a predetermined future time point. Similarly, “exposing” function in the context of this claim encompasses a person following instructions to expose the individual to a predetermined sensory stimulus at least three times during an initial exposure period, following a defined exposure pattern. Similarly, but for the “using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device” language, the “measuring” function in the context of this claim encompasses a person following instructions to measuring the individual's physiological and/or psychometric response. Similarly, the “repeating step (I)” function in the context of this claim encompasses a person following instructions to repeat step (I) for a plurality of predetermined sensory stimuli. Similarly, the “identifying” function in the context of this claim encompasses a person following instructions to identify a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined sensory stimuli. Finally, the “incorporating” function in the context of this claim encompasses a person following instructions to incorporating the identified predetermined sensory stimulus into the consumer product. But for the recitation of generic computer components, such steps encompass Certain Methods of Organizing Human Activity, as the steps could be accomplished by a physician, a nurse, an engineer, or a person in a similar position following rules or instructions.
Claims 47-61 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, claim 47 further describes the individual's physiological and psychometric responses. Similarly, claim 48 describes repeating the steps (a) to (d) for predicting a long-term hedonic response of an audience. Similarly, claims 49-51 further describe the artificial neural network. Similarly, claims 52-54 further describe the predetermined sensory stimulus. Similarly, claim 55 further describes the predicted long-term liking. Similarly, claims 56-57 further describe the physiological and/or psychometric response data. Similarly, claim 58 further describes the initial exposure period. Similarly, claim 59 further describes the predetermined future time point. Similarly, claim 60 further describes deriving the long-term hedonic response of the audience. Finally, claim 61 further describes the self-report data. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity.
Claims 46-61: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and generally linking the abstract idea to a particular technological environment.
This judicial exception is not integrated into a practical application because “using the artificial neural network to…” are recited at a high-level of generality. As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “…using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device”, “(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 46-61: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention".)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The claims are not patent eligible.
Claims 62-63: Step 2A Prong One
Claim 62 recite(s)
(I) predicting an individual's long-term liking to a predetermined olfactive stimulus, wherein predicting the individual's long-term liking comprises:
(a) exposing the individual to a predetermined olfactive stimulus at least three times during an initial exposure period, following a defined exposure pattern, wherein the initial exposure period is less than one week;
(b) for each exposure, measuring the individual's physiological and/or psychometric response; and
(d) predict the individual's long- term liking of the predetermined olfactive stimulus at a predetermined future time point, wherein the predetermined future time point is at least three months after the initial exposure period;
(II) repeating step (I) for a plurality of predetermined olfactive stimuli;
(III) identifying a predetermined olfactory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined olfactory stimuli; and
(IV) incorporating the identified predetermined olfactory stimulus into the consumer product
These limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “using the artificial neural network” language, the “predicting” and “predict” functions in the context of this claim encompass a person following instructions to predict an individual’s long-term liking to a predetermined olfactive stimulus and predict the individual's long- term liking of a predetermined olfactive stimulus at a predetermined future time point. Similarly, “exposing” function in the context of this claim encompasses a person following instructions to expose the individual to a predetermined olfactive stimulus at least three times during an initial exposure period, following a defined exposure pattern, wherein the initial exposure period is less than one week. Similarly, but for the “using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device” language, the “measuring” function in the context of this claim encompasses a person following instructions to measuring the individual's physiological and/or psychometric response. Similarly, the “repeating step (I)” function in the context of this claim encompasses a person following instructions to repeat step (I) for a plurality of predetermined olfactive stimuli. Similarly, the “identifying” function in the context of this claim encompasses a person following instructions to identify a predetermined olfactory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined olfactory stimuli. Finally, the “incorporating” function in the context of this claim encompasses a person following instructions to incorporating the identified predetermined olfactory stimulus into the consumer product. But for the recitation of generic computer components, such steps encompass Certain Methods of Organizing Human Activity, as the steps could be accomplished by a physician, a nurse, an engineer, or a person in a similar position following rules or instructions.
Claim 63 incorporates the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, claim 63 further describes the EEG device. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity.
Claims 62-63: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and generally linking the abstract idea to a particular technological environment.
This judicial exception is not integrated into a practical application because “using the artificial neural network to…” are recited at a high-level of generality. As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “…using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device”, “(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 62-63: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention".)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The claims are not patent eligible.
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.
Claims 46-63 are rejected under 35 U.S.C. 103 as being unpatentable over Parra et al. (U.S. Patent Pre-Grant Publication No. 2015/0248615) in view of Olsen et al. (U.S. Patent Pre-Grant Publication No. 2012/0010474).
As per independent claim 46, Parra discloses a method for making a consumer product, the method comprising:
(I) predicting an individual's long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli, wherein predicting the individual's long-term liking comprises:
(a) exposing the individual to a predetermined sensory stimulus at least three times during an initial exposure period, following a defined exposure pattern (See Fig. 1, Paragraphs [0053], [0056]-[0060], [0071]-[0079]: Reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response, which the Examiner is interpreting repeated exposures to encompass at least three times (Paragraph [0088]: The two data-sets can represent repeated exposures of the same subject to a stimulus));
(d) using the artificial neural network to predict the individual's long- term liking of the predetermined sensory stimulus at a predetermined future time point (See Fig. 2, Paragraphs [0053]-[0066], [0090], [0117]: A predictor of audience behavioral response is established based on historical data using the aggregated neural data, the predictor is used to predict audience behavioral response for future media exposure by repeating steps on a novel stimulus and using the predictor to generate a prediction of the future audience response to the novel stimulus, and the multivariate time series is fed into a learning algorithm which computes a set of parameters which optimally predict the ground-truth viewership or audience behavioral responses which the Examiner is interpreting a predictor to encompass predict the individual's long- term liking of the predetermined sensory stimulus (Paragraph [0117]: Long-term memories can be identified), interpreting the predict the ground-truth viewership or audience behavioral responses to encompass a predetermined future time point), wherein the predetermined future time point is at least ten times later than the duration of the initial exposure period (See Paragraphs [0122]-[0124]: For novel stimuli, the reliability of the sample population's neural signal is used to generate predictions of the future viewership or audience behavioral response, which the Examiner is interpreting the future to encompass the predetermined prediction future time point is at least ten times later than the duration of the initial exposure period when combined with Olsen’s disclosure of an algorithm for pattern recognition described below);
(II) repeating step (I) for a plurality of predetermined sensory stimuli (See Paragraphs [0053], [0056]-[0060], [0071]-[0079], [0098]-[0099]: A set of candidate stimuli can be used ([0098]-[0099], which the Examiner is interpreting to encompass a plurality of predetermined sensory stimuli), and a form of data aggregation which combines data from multiple subjects into a sample-wide measure of the neural response to the stimulus is performed, reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response);
(III) identifying a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined sensory stimuli (See Paragraphs [0120]-[0125]: Models are designed to mathematically optimize the match between neural responses and future consumption, and then the models are used to make predictions about consumption of unreleased products or services, which the Examiner is interpreting mathematically optimize the match between neural responses and future consumption to encompass identifying a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined sensory stimuli); and
(IV) incorporating the identified predetermined sensory stimulus into the consumer product (See Paragraph [0120]: Models are designed to mathematically optimize the match between neural responses and future consumption, and then the models are used to make predictions about consumption of unreleased products or services, which the Examiner is interpreting the predictions about consumption of unreleased products or services to encompass incorporating the identified predetermined sensory stimulus into the consumer product.)
While Parra discloses the method as described above, Parra may not explicitly teach (I) predicting an individual's long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli, wherein predicting the individual's long-term liking comprises:
(b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device;
(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking.
Olsen teaches a method for (I) predicting an individual's long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli (See Paragraphs [0037], [0047], [0076]-[0077], [0086]: system can continuously adapt to the updated response data and thereby improve the ability to predict and/or recognize response patterns from the test persons, which the Examiner is interpreting predict response patterns from the test persons to encompass predicting an individual's long-term liking to a predetermined sensory stimulus, and interpreting the stimuli to encompass olfactive and taste stimuli ([0047])), wherein predicting the individual's long-term liking comprises:
(b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device (See Paragraphs [0077]-[0080]: The test person is exposed to certain stimuli and that measurement(s) relating to activity in the nervous system(s), e.g. brain activity, of the test subject is made simultaneously, and the brain activity can for example be measured in the form of EEG measurements, but perspiration, respiration, heart rate, eye movements, blood pressure, muscle activity and/or body temperature can also be measured as an indication of the test person's response to the stimuli, which the Examiner is interpreting standard EEG equipment to encompass using an electroencephalography (EEG) device);
(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking (See Paragraphs [0086], [0090]-[0092]: The step of determining comprises the use of at least one algorithm, such as an algorithm for pattern recognition, which the Examiner is interpreting the determining step to encompass inputting the defined exposure pattern and the measured physiological and/or psychometric responses as [0090] describes the step of automatically determining may furthermore comprise a step of automatically interpreting the identified at least one induced response pattern, or at least one feature thereof, to provide the at least one response indicator, and the Examiner is interpreting the algorithm to encompass an artificial neural network trained to predict long-term liking as the central server unit which returns interpreted data, such as Attention, Liking, Memory and Action (ALMA) and other values determined by the central server unit.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include (I) predicting an individual's long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli, wherein predicting the individual's long-term liking comprises: (b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device; (c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of collecting data in such a way that synchronised data from several test persons can be analysed, interpreted, combined, averaged and displayed at the same time (See Summary of the Invention of Olsen in Paragraph [0031]).
As per claim 47, Parra/Olsen discloses the method of claim 46 as described above. Parra may not explicitly teach wherein both the individual's physiological and psychometric responses are measured, and the measurements are performed synchronously.
Olsen teaches a method wherein both the individual's physiological and psychometric responses are measured, and the measurements are performed synchronously (See Paragraphs [0073]-[0076]: An adaptive algorithm can also be provided to the data from the response data from the plurality of test persons, thereby also providing information of synchronisation of response signals and/or response patterns, which the Examiner is interpreting providing information of synchronisation of response signals and/or response patterns to encompass the measurements are performed synchronously, and the response signals to encompass the individual's physiological and psychometric responses.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include both the individual's physiological and psychometric responses are measured, and the measurements are performed synchronously as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of collecting data in such a way that synchronised data from several test persons can be analysed, interpreted, combined, averaged and displayed at the same time (See Summary of the Invention of Olsen in Paragraph [0031]).
As per claim 48, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches further comprising predicting a long-term hedonic response of an audience to the at least one predetermined sensory stimulus (See Fig. 1, Paragraphs [0053], [0056]- [0060], [0071]-[0079]: A form of data aggregation which combines data from multiple subjects into a sample-wide measure of the neural response to the stimulus is performed, reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response) by:
(i) repeating steps (a) to (d) of claim 46 for each individual of a group of individuals (See Fig. 1, Paragraphs [0053], [0056]- [0060], [0071]-[0079]: A form of data aggregation which combines data from multiple subjects into a sample-wide measure of the neural response to the stimulus is performed, reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response), wherein a number of the individuals in the group is smaller than a number of individuals in the audience ((See Paragraphs [0053]-[0060]: The reliability of these distributed patterns of neural activity across multiple subjects and within subjects are used as a key feature that carries predictive information as to the general audience's behavioral responses, predicting behavior of an audience from this reduced but more reliable neural signal which reflects consensus of a group now becomes manageable with traditional machine learning techniques, which the Examiner is interpreting to encompass a number of the individuals in the group of individuals is smaller than a number of individuals in the audience as the subjects measured in Parra is a sample size of the audience to predict audience behavior response); and
(ii) deriving the long-term hedonic response of the audience to the at least one predetermined sensory stimulus based on the long-term hedonic response of each individual in the group (See Fig. 2, Paragraphs [0053]-[0066], [0090], [0117]: A predictor of audience behavioral response is established based on historical data using the aggregated neural data, the predictor is used to predict audience behavioral response for future media exposure by repeating steps on a novel stimulus and using the predictor to generate a prediction of the future audience response to the novel stimulus, and the multivariate time series is fed into a learning algorithm which computes a set of parameters which optimally predict the ground-truth viewership or audience behavioral responses, which the Examiner is interpreting a predictor to encompass a control unit that predicts the individual's long-term hedonic response (Paragraph [0117]: Long-term memories can be identified).)
As per claim 49, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the artificial neural network of step (c) is trained using hedonic-response training data comprising EEG data, GSR data, eye-tracking data, or video data (See Paragraphs [0089]-[0090]: The parameters of the predictive model are tuned in a training procedure that employs historical viewership or audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli, which the Examiner is interpreting historical viewership or audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli to encompass the artificial neural network of step (c) is trained using hedonic-response training data comprising EEG data, GSR data, eye-tracking data, or video data (Paragraph [0150]) when combined with Olsen’s adaptive algorithm.)
As per claim 50, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the artificial neural network of step (c) is trained using self-report training data (See Paragraphs [0089]-[0090]: The parameters of the predictive model are tuned in a training procedure that employs historical viewership or audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli, which the Examiner is interpreting audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli to encompass self-report training data.)
As per claim 51, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the artificial neural network of step (c) is trained using purchase-behavior training data (See Paragraphs [0089]-[0090]: The parameters of the predictive model are tuned in a training procedure that employs historical viewership or audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli, which the Examiner is interpreting audience behavioral responses to encompass purchase-behavior training data.)
As per claim 52, Parra/Olsen discloses the method of claim 46 as described above. Parra may not explicitly teach wherein the predetermined sensory stimulus is an olfactive stimulus.
Olsen teaches a method wherein the predetermined sensory stimulus is an olfactive stimulus (See Paragraph [0047]: Stimuli refers to any sensory activation or combination of sensory activations be it audition, vision, gustation (taste), olfaction (smell), tactition (feel), chemoreceptive, photoreceptive, proprioreceptive, mechanoreceptive, thermoreceptive, nociceptive or other.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include the predetermined sensory stimulus is an olfactive stimulus as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of improve the ability to predict and/or recognize response patterns from the test persons (See Summary of the Invention of Olsen in Paragraph [0037]).
As per claim 53, Parra/Olsen discloses the method of claim 46 as described above. Parra may not explicitly teach wherein the predetermined sensory stimulus is a taste stimulus.
Olsen teaches a method wherein the predetermined sensory stimulus is a taste stimulus (See Paragraph [0047]: Stimuli refers to any sensory activation or combination of sensory activations be it audition, vision, gustation (taste), olfaction (smell), tactition (feel), chemoreceptive, photoreceptive, proprioreceptive, mechanoreceptive, thermoreceptive, nociceptive or other.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include the predetermined sensory stimulus is a taste stimulus as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of improve the ability to predict and/or recognize response patterns from the test persons (See Summary of the Invention of Olsen in Paragraph [0037]).
As per claim 54, Parra/Olsen discloses the method of claims 46 and 52 as described above. Parra may not explicitly teach wherein the olfactive stimulus is a fragrance.
Olsen teaches a method wherein the olfactive stimulus is a fragrance (See Paragraph [0101]: The means for providing at least one input stimulus comprise means for providing audio and/or video signals and/or other stimuli such as smell, taste and touch to the test person, which the Examiner is interpreting a smell to encompass a fragrance as the broadest reasonable interpretation of a fragrance is a type of scent.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include the olfactive stimulus is a fragrance as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of collecting data in such a way that synchronised data from several test persons can be analysed, interpreted, combined, averaged and displayed at the same time (See Summary of the Invention of Olsen in Paragraph [0031]).
As per claim 55, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the predicted long-term liking is expressed as a numerical value ranging from negative infinity, indicating a negative response, to positive infinity, indicating a positive response, with zero indicating indifference (See Paragraphs [0073]-[0074]: This aggregation can take a number of forms, for example, computing the mean across all individuals, or the range or variance of responses across individuals, or computing a measure of reproducibility or reliability of the neural response across individuals (e.g., CCA, as described below), to summarize: mean, range, standard deviation, correlation, or any other group statistic of the neural response reliability resolved in time, which the Examiner is interpreting range or variance of responses across individuals to encompass predicted long-term liking is expressed as a numerical value ranging from negative infinity, indicating a negative response, to positive infinity, indicating a positive response, with zero indicating indifference as the claimed portion recites a range of values from negative infinity to positive infinity.)
As per claim 56, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the physiological and/or psychometric response data includes EEG signals, and wherein the EEG signals are filtered by applying a low-pass filter to remove frequencies above 50 Hz and a high-pass filter to remove frequencies below 0.5 Hz (See Paragraphs [0150]: After extracting the EEG/EOG segments corresponding to the duration of each movie, the signals were high-pass filtered (0.5 Hz) and notch filtered (60 Hz), which the Examiner is interpreting to encompass the claimed portion as the notch filtered is for filtering out specific frequency interference, which could be reduced to 50 Hz.)
As per claim 57, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the physiological and/or psychometric response data includes EEG signals obtained using an electroencephalography (EEG) device (See Fig. 1, Paragraphs [0053]-[0054], [0056]-[0060], [0074], [0150]: Multiple sensors are used to compute measurements that reflect the contributions of multiple brain regions, which the Examiner is interpreting the multiple sensors are used to compute measurements to encompass the physiological and/or psychometric response data includes EEG signals obtained using an electroencephalography (EEG) device (Paragraph [0071] recites that the neural activity can be recorded by electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI)), and wherein the EEG signals are subjected to Independent Component Analysis (ICA) (See Paragraph [0182]: Both the Correlated Components Analysis (CCA) described herein and the classical canonical correlation analysis explicitly correlate two data sets; instead, one may also apply conventional source separation algorithms such as Independent Components Analysis (ICA) to a concatenated data matrix of the form [X.sub.1X.sub.2].)
As per claim 58, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the initial exposure period is less than one week (See Fig. 1, Paragraphs [0053], [0056]-[0060], [0071]-[0079]: Reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response, which the Examiner is interpreting multivariate time series to encompass an initial exposure period is less than one week.)
As per claim 59, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the predetermined future time point is at least three months after the initial exposure period (See Fig. 2, Paragraphs [0053]-[0066], [0090], [0117]: A predictor of audience behavioral response is established based on historical data using the aggregated neural data, the predictor is used to predict audience behavioral response for future media exposure by repeating steps on a novel stimulus and using the predictor to generate a prediction of the future audience response to the novel stimulus, and the multivariate time series is fed into a learning algorithm which computes a set of parameters which optimally predict the ground-truth viewership or audience behavioral responses which the Examiner is interpreting the multivariate time series to encompass the initial exposure period, interpreting predict the ground-truth viewership or audience behavioral responses to encompass the predetermined future time point is at least three months after the initial exposure period, and in Paragraphs [0122]-[0124]: For novel stimuli, the reliability of the sample population's neural signal is used to generate predictions of the future viewership or audience behavioral response, which the Examiner is interpreting the future to encompass a time point at least three months after the initial exposure period.)
As per claim 60, Parra/Olsen discloses the method of claims 46 and 48 as described above. Parra further teaches wherein deriving the long-term hedonic response of the audience comprises aggregating the responses of individuals in the group (See Paragraphs [0089]-[0090]: The parameters of predictive model are tuned in a training procedure that employs historical viewership or audience behavioral responses in conjunction with neural response reliability to the corresponding stimuli acquired from a group of individuals who had not previously viewed the stimuli, which the Examiner is interpreting the audience behavioral responses to encompass aggregating the responses of individuals in the group.)
As per claim 61, Parra/Olsen discloses the method of claim 46 as described above. Parra further teaches wherein the self-report data comprises liking scores, purchase intention ratings, or choice-based preference data (See Paragraph [0075]: The viewership or audience behavioral data consisted of NIELSEN ratings on a minute-by-minute basis, which the Examiner is interpreting NIELSEN ratings to encompass liking scores.)
As per independent claim 62, Parra discloses a method for making a consumer product, the method comprising:
(I) predicting an individual's long-term liking to a predetermined olfactive stimulus, wherein predicting the individual's long-term liking comprises:
(a) exposing the individual to a predetermined olfactive stimulus at least three times during an initial exposure period, following a defined exposure pattern (See Fig. 1, Paragraphs [0053], [0056]-[0060], [0071]-[0079]: Reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response, which the Examiner is interpreting repeated exposures to encompass at least three times (Paragraph [0088]: The two data-sets can represent repeated exposures of the same subject to a stimulus)), wherein the initial exposure period is less than one week (See Fig. 1, Paragraphs [0053], [0056]-[0060], [0071]-[0079]: Reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response, which the Examiner is interpreting multivariate time series to encompass an initial exposure period is less than one week);
(d) using the artificial neural network to predict the individual's long- term liking of the predetermined olfactive stimulus at a predetermined future time point (See Fig. 2, Paragraphs [0053]-[0066], [0090], [0117]: A predictor of audience behavioral response is established based on historical data using the aggregated neural data, the predictor is used to predict audience behavioral response for future media exposure by repeating steps on a novel stimulus and using the predictor to generate a prediction of the future audience response to the novel stimulus, and the multivariate time series is fed into a learning algorithm which computes a set of parameters which optimally predict the ground-truth viewership or audience behavioral responses which the Examiner is interpreting a predictor to encompass predict the individual's long- term liking of the predetermined olfactive stimulus (Paragraph [0117]: Long-term memories can be identified), interpreting the predict the ground-truth viewership or audience behavioral responses to encompass a predetermined future time point, when combined with Olsen’s disclosure in Paragraph [0047]), wherein the predetermined future time point is at least three months after the initial exposure period (See Fig. 2, Paragraphs [0053]-[0066], [0090], [0117]: A predictor of audience behavioral response is established based on historical data using the aggregated neural data, the predictor is used to predict audience behavioral response for future media exposure by repeating steps on a novel stimulus and using the predictor to generate a prediction of the future audience response to the novel stimulus, and the multivariate time series is fed into a learning algorithm which computes a set of parameters which optimally predict the ground-truth viewership or audience behavioral responses which the Examiner is interpreting the multivariate time series to encompass the initial exposure period, interpreting predict the ground-truth viewership or audience behavioral responses to encompass the predetermined future time point is at least three months after the initial exposure period, and in Paragraphs [0122]-[0124]: For novel stimuli, the reliability of the sample population's neural signal is used to generate predictions of the future viewership or audience behavioral response, which the Examiner is interpreting the future to encompass a time point at least three months after the initial exposure period);
(II) repeating step (I) for a plurality of predetermined olfactive stimuli (See Paragraphs [0053], [0056]-[0060], [0071]-[0079], [0098]-[0099]: A set of candidate stimuli can be used ([0098]-[0099], which the Examiner is interpreting to encompass a plurality of predetermined olfactive stimuli when combined with Olsen’s disclosure in Paragraph [0047]), and a form of data aggregation which combines data from multiple subjects into a sample-wide measure of the neural response to the stimulus is performed, reliability can capture how reproducible neural responses are for a given subject under repeated exposures to the same stimulus, and an aggregated multivariate time series can be determined, that captures neural response reliability and which can be utilized by the predictive model to generate estimates of the viewership or audience behavior response);
(III) identifying a predetermined olfactory stimulus predicted to have the highest long-term liking from amongst the plurality of predetermined olfactory stimuli (See Paragraphs [0120]-[0125]: Models are designed to mathematically optimize the match between neural responses and future consumption, and then the models are used to make predictions about consumption of unreleased products or services, which the Examiner is interpreting mathematically optimize the match between neural responses and future consumption to encompass identifying a predetermined olfactory stimulus predicted to have the highest long-term liking from amongst the plurality of olfactory stimuli combined with Olsen’s disclosure in Paragraph [0047]); and
(IV) incorporating the identified predetermined olfactory stimulus into the consumer product (See Paragraph [0120]: Models are designed to mathematically optimize the match between neural responses and future consumption, and then the models are used to make predictions about consumption of unreleased products or services, which the Examiner is interpreting the predictions about consumption of unreleased products or services to encompass incorporating the identified predetermined olfactory stimulus into the consumer product combined with Olsen’s disclosure in Paragraph [0047].)
While Parra discloses the method as described above, Parra may not explicitly teach (I) predicting an individual's long-term liking to a predetermined olfactive stimulus, wherein predicting the individual's long-term liking comprises:
(b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device;
(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking.
Olsen teaches a method for (I) predicting an individual's long-term liking to a predetermined olfactive stimulus (See Paragraphs [0037], [0047], [0076]-[0077], [0086]: system can continuously adapt to the updated response data and thereby improve the ability to predict and/or recognize response patterns from the test persons, which the Examiner is interpreting predict response patterns from the test persons to encompass predicting an individual's long-term liking to a predetermined sensory stimulus, and interpreting the stimuli to encompass olfactive stimulus), wherein predicting the individual's long-term liking comprises:
(b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device (See Paragraphs [0077]-[0080]: The test person is exposed to certain stimuli and that measurement(s) relating to activity in the nervous system(s), e.g. brain activity, of the test subject is made simultaneously, and the brain activity can for example be measured in the form of EEG measurements, but perspiration, respiration, heart rate, eye movements, blood pressure, muscle activity and/or body temperature can also be measured as an indication of the test person's response to the stimuli, which the Examiner is interpreting standard EEG equipment to encompass using an electroencephalography (EEG) device);
(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking (See Paragraphs [0086], [0090]-[0092]: The step of determining comprises the use of at least one algorithm, such as an algorithm for pattern recognition, which the Examiner is interpreting the determining step to encompass inputting the defined exposure pattern and the measured physiological and/or psychometric responses as [0090] describes the step of automatically determining may furthermore comprise a step of automatically interpreting the identified at least one induced response pattern, or at least one feature thereof, to provide the at least one response indicator, and the Examiner is interpreting the algorithm to encompass an artificial neural network trained to predict long-term liking as the central server unit which returns interpreted data, such as Attention, Liking, Memory and Action (ALMA) and other values determined by the central server unit.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Parra to include (I) predicting an individual's long-term liking to a predetermined olfactive stimulus, wherein predicting the individual's long-term liking comprises: (b) for each exposure, measuring the individual's physiological and/or psychometric response using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device; (c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking as taught by Olsen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of collecting data in such a way that synchronised data from several test persons can be analysed, interpreted, combined, averaged and displayed at the same time (See Summary of the Invention of Olsen in Paragraph [0031]).
As per claim 63, Parra/Olsen discloses the method of claim 62 as described above. Parra further teaches wherein EEG signals measured in step (b) are processed by applying a low-pass filter to remove signal components with frequencies above 50 Hz, a high-pass filter to remove signal components with frequencies below 0.5 Hz (See Paragraphs [0150]: After extracting the EEG/EOG segments corresponding to the duration of each movie, the signals were high-pass filtered (0.5 Hz) and notch filtered (60 Hz), which the Examiner is interpreting to encompass the claimed portion as the notch filtered is for filtering out specific frequency interference, which could be reduced to 50 Hz), and by performing an Independent Component Analysis (ICA) to identify and remove independent components corresponding to non-brain signals (See Paragraph [0182]: Both the Correlated Components Analysis (CCA) described herein and the classical canonical correlation analysis explicitly correlate two data sets; instead, one may also apply conventional source separation algorithms such as Independent Components Analysis (ICA) to a concatenated data matrix of the form [X.sub.1X.sub.2], which the Examiner is interpreting to encompass the claimed portion as the ICA are also powerful in extracting artifactual components which may then be straightforwardly subtracted from the data.)
Response to Arguments
In the Remarks filed on May 8, 2026, the Applicant argues that the newly amended and/or added claims overcome the Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the previous Claim Objection(s). However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the newly added Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) the Office Action's characterization of the claims is overgeneralized and does not account for the claims as a whole. Stratofiex, Inc. v. Aeroquip Corp., 713 F.2d 1530, 1537-38 (Fed. Cir. 1983). The eligibility inquiry must consider the claim elements individually and as an ordered combination, without oversimplifying the claims or describing them at an unduly high level of abstraction. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18 (2014); and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1313-14 (Fed. Cir. 2016). The claims are not directed merely to evaluating or organizing consumer preferences. Rather, claim 46 recites a specific ordered combination involving: (i) repeated exposure to a predetermined olfactive or taste stimulus according to a defined exposure pattern, (ii) measuring physiological and/or psychometric responses for each exposure using specified measurement devices, (iii) inputting the defined exposure pattern and measured responses into an artificial neural network trained to predict long-term liking, (iv) using the artificial neural network to predict long-term liking at a predetermined future time point, (v) identifying a stimulus predicted to have the highest long-term liking, and (vi) incorporating the identified stimulus into a consumer product;
(2) the rejection mischaracterizes the claimed method as a human or mental activity despite the recited physiological signal acquisition and artificial neural network modeling. The claimed prediction is generated from measured physiological and/or psychometric responses using an artificial neural network trained to predict long-term liking. As explained in the Second Jacobs Declaration, physiological signals such as EEG and GSR are noisy, high-dimensional, and variable Predicting long-term liking from such signals requires specialized signal processing and modeling techniques. Thus, the claims are directed to a technical process for extracting predictive information from physiological and/or psychometric response data, not to a person merely following instructions or organizing human activity;
(3) the Office Action does not account for evidence that the claimed process provides a technical improvement in physiological-signal processing and predictive modeling. The Second Jacobs Declaration explains that the claimed method achieved a normalized RMSE of approximately 0.6 standard deviations, compared with a non-informative baseline normalized RMSE of 1. This improvement is attributed to the claimed combination of structured exposure, physiological/psychometric signal acquisition and processing, and artificial-neural-network-based modeling. Accordingly, the claims do not merely apply a generic computer to an abstract idea; they recite a specific technical approach that improves the ability to process and model physiological data for predicting long-term liking;
(4) the pending claims do not fall within the ''certain methods of organizing human activity'' grouping identified in the USPTO eligibility guidance. The claims do not recite a business practice, a commercial transaction, a sales method, a legal obligation, an interpersonal interaction, or a method of managing behavior between persons. Instead, the claims recite a technical process in which physiological and/or psychometric responses are measured using specified devices and then provided, together with a defined exposure pattern, to an artificial neural network trained to predict long-term liking of an olfactive or taste stimulus. The pending claims also do not fall within the ''mental processes'' grouping because they cannot practically be performed in the human mind or with pen and paper. The claimed prediction is generated from measured physiological and/or psychometric responses using an artificial neural network trained to predict long-term liking. The claim requires measurement using an EEG device, GSR device, eye-tracker device, and/or video recording device. The claim further requires inputting the defined exposure pattern and measured responses into an artificial neural network trained to predict long-term liking and using that artificial neural network to predict the individual's long-term liking at a predetermined future time point. These operations are not observations, evaluations, judgments, or opinions that can practically be performed in the human mind. The Second Jacobs Declaration supports this distinction. Dr. Jacobs explains that predicting long-term liking from physiological signals presents a technical challenge due to the noisy, high-dimensional, and variable nature of such signals, and requires specialized signal processing and modeling techniques. Such processing and modeling are not mental steps and cannot be performed by a person merely following instructions. Second Jacobs Declaration, para. 3. Accordingly, when the claims are considered as a whole, they do not recite a judicial exception. Therefore, the claims are eligible at Step 2A, Prong One;
(5) even assuming, solely for purposes of argument, that the claims recite an abstract idea, the claims integrate any alleged abstract idea into a practical application. Under Step 2A, Prong Two, a claim is not ''directed to'' a judicial exception if the claim as a whole integrates the exception into a practical application. The MPEP explains that one way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field. Such an application meaningfully limits the claim and goes beyond merely linking a judicial exception to a technological environment. Here, the claims integrate any alleged abstract idea into a practical application in at least three ways. First, the claims apply the prediction in a concrete product-making process. For example, claim 46 does not end with merely presenting information or reporting a preference score. It requires identifying a predetermined sensory stimulus predicted to have the highest long-term liking from among a plurality of predetermined sensory stimuli and incorporating the identified stimulus into a consumer product. Thus, the prediction is used in a practical product-making context to make a consumer product. As explained in the First Jacobs Declaration, the claimed invention is useful for designing consumer products that are consistently well received by consumers while avoiding trial-and-error approaches associated with new fragrances and flavorings. First Jacobs Declaration, paras. 2 and 3. Second, the claim recites a specific technical process for obtaining and processing physiological and/or psychometric response information. The claimed method requires repeated exposure to a predetermined stimulus according to a defined exposure pattern and measuring physiological and/or psychometric responses for each exposure using specified measurement devices. The measured responses and defined exposure pattern are then used as inputs to an artificial neural network trained to predict long-term liking. This ordered combination is more than a generic instruction to apply an abstract idea on a computer. Third, the Second Jacobs Declaration provides evidence that the claimed process improves physiological-signal processing and predictive modeling. Dr. Jacobs explains that predicting long-term liking from physiological signals presents a technical challenge because such signals are noisy, high-dimensional, and variable, and require specialized signal processing and modeling techniques. Second Jacobs Declaration, para. 3. Dr. Jacobs further explains that the claimed approach uses structured exposure protocols, physiological signal acquisition and processing, and machine-learning-based modeling to extract predictive information from physiological signals. Second Jacobs Declaration, paras. 4, 7. The declaration also reports that the model achieved a normalized RMSE of approximately o.6 standard deviations, compared with a non-informative baseline normalized RMSE of 1. Second Jacobs Declaration, paras. 4 and 6. This evidence supports that the claimed method provides an improved technique for processing and modeling physiological data to enable reliable prediction of long-term hedonic response. The Office Action's analysis does not adequately address this practical application. Instead, the Office Action treats the artificial neural network and measurement devices as generic components. But the claim does not recite these elements merely as a technological environment. The devices generate the physiological and/or psychometric response data used by the artificial neural network, and the artificial neural network produces the claimed prediction of long-term liking from those data and the defined exposure pattern. The recited components therefore are integral to the claimed practical application. Accordingly, even if the claims were found to recite an abstract idea, the claims integrate any such abstract idea into a practical application and are therefore patent eligible under Step 2A, Prong Two;
(6) even if the claims were directed to an abstract idea under Step 2A (which they are not), the claims recite significantly more than any alleged abstract idea under Step 2B. At Step 2B, the analysis asks whether the claim includes additional elements, individually or as an ordered combination, that amount to significantly more than the judicial exception. The claims satisfy that standard. The claimed ordered combination is not routine preference testing and is not generic computer implementation. Rather, the claims recite a specific process involving repeated exposure according to a defined exposure pattern, device-based measurement of physiological and/or psychometric responses, artificial-neural-network-based prediction of long-term liking, selection of a stimulus based on that prediction, and incorporation of the selected stimulus into a consumer product. The ordered combination is not conventional preference testing and is not a generic computer implementation of a mental process. Dr. Jacobs explains that conventional approaches, such as self-report or initial liking measurements, rely on subjective inputs and do not involve processing physiological signal data. Dr. Jacobs further explains that such conventional approaches are unable to model nonlinear and high-dimensional relationships present in physiological responses and do not provide a technical solution to the problem of predicting long-term liking. Second Jacobs Declaration, para. 6. The Second Jacobs Declaration also provides objective evidence that the claimed approach produces improved predictive performance. The declaration explains that a normalized RMSE of approximately o.6 was achieved, which is an improvement over the non-informative baseline normalized RMSE of 1. Second Jacobs Declaration, paras. 4 and 6. Dr. Jacobs further explains that this predictive performance results from the specific combination of structured exposure protocols, synchronization and processing of physiological signals, and use of a trained artificial neural network. Second Jacobs Declaration, para. 7. This evidence supports that the ordered combination is not merely routine or conventional data gathering and analysis, but provides a technical improvement in processing and modeling physiological data to predict long-term liking. The Office Action's Step 2B analysis is incomplete because it does not evaluate the ordered combination as a whole and does not account for the technical-improvement evidence of record. Labeling individual components as generic does not resolve whether the claimed ordered combination amounts to significantly more. Here, the ordered combination provides a specific technical solution to the problem of predicting long-term liking from noisy, high-dimensional physiological and/or psychometric response data and applies that prediction in making a consumer product. Accordingly, the claims recite significantly more than any alleged abstract idea and are patent eligible under Step 2B;
(7) the eligibility analysis here is analogous to the USPTO's 2024 AI Subject Matter Eligibility Example 48. In that example, the USPTO explains that claims reciting artificial-intelligence-based processing of speech signals can integrate an abstract idea into a practical application when the claim reflects an improvement to a technical field rather than merely invoking a neural network as a generic tool. In particular, the eligible claims in Example 48 used DNN-based signal processing as part of an ordered process that improved speech-separation or speech-to-text technology. Similarly, the present claims do not merely invoke an artificial neural network to report a preference score. Rather, the claims use device-acquired physiological and/or psychometric response data and a defined exposure pattern as inputs to an artificial neural network trained to predict long-term liking, and applies the resulting prediction to select and incorporate a sensory stimulus into a consumer product. As in Example 48, the claimed ordered combination reflects a practical application of AI-based signal processing to solve a technical problem, here predicting long-term liking from noisy, high-dimensional physiological and/or psychometric response data;
(8) Parra does not teach or suggest predicting an individual's long-term liking of an olfactive or taste stimulus. Following KSR Int'l Co. v. Teleflex, Inc., 550 U.S. 398 (2007), the Federal Circuit has held that all claim limitations must be found in the prior art before an obviousness analysis proceeds. Abbott Laboratories v. Sandoz, Inc., 544 F.3d 1341, 1351 (Fed. Cir. 2008). At most, Parra predicts aggregate audience behavioral responses, such as viewership or audience behavior, based on neural responses to media stimuli. The Office Action reaches the claimed limitation only by interpreting Parra's audience-level behavioral prediction as individual long-term liking, but those are materially different prediction targets. The Office Action itself repeatedly maps Parra's ''viewership or audience behavior response'' to the claimed ''individual's long-term liking." For example, the Examiner states that Parra's predictor generates estimates of ''viewership or audience behavior response," and then interprets that as predicting an individual's long-term liking. Viewership, audience retention, tweets, ratings, or consumption behavior are not the same as an individual's long-term hedonic liking of a fragrance/flavor. Therefore, the rejection should be withdrawn;
(9) Olsen does not cure the deficiencies in Parra. Olsen discloses immediate liking and memory as separate response indicators. It does not teach predicting long-term liking. The Office Action's position improperly converts ''memory of liking'' into ''long-term liking," but those are different psychological constructs. See First Declaration of Dr. Jacobs. The claimed method predicts a future hedonic response. Olsen merely classifies whether a stimulus is liked/not liked and whether it is stored in memory. The Final Office Action makes clear that the rejection depends on equating memory-related disclosure in Olsen with long-term liking. It states that Olsen's Figure 2 describes ''Memory'' as ''Long-term memory'' and reasons that a test person would store memory of liking a stimulus, which would identify that the person ''might like the stimuli in the future." The Examiner argues that identifying liking plus storage of that liked stimulus in long-term memory indicates the person likely developed long-term liking. Applicant respectfully disagrees, as that reasoning conflates distinct concepts. ''Long-term memory of a liked stimulus'' is cognitive retention. ''Long-term liking'' is a sustained or evolving hedonic response. A person can remember liking something without continuing to like it. Conversely, a person may develop liking over repeated exposures without a memory classification being the same thing. First Declaration of Dr. Jacobs, paras. 5 and 6. Accordingly, the rejection should be withdrawn;
(10) rejection identifies no teaching in Olsen of an artificial neural network trained to predict long-term liking. Following KSR Int'l Co. v. Teleflex, Inc., 550 U.S. 398 (2007), the Federal Circuit has held that all claim limitations must be found in the prior art before an obviousness analysis proceeds. Abbott Laboratories v. Sandoz, Inc., 544 F.3d 1341, 1351 (Fed. Cir. 2008). Olsen's generic disclosure of an algorithm or pattern recognition is not a disclosure of the specifically claimed trained ANN. The missing training target is critical. The claim requires an ANN trained to predict long-term liking, not merely an algorithm that detects or interprets immediate response indicators such as attention, liking, memory, or action. The rejection specifically relies on Olsen's general ''algorithm for pattern recognition'' disclosure and interprets it to encompass ''an artificial neural network trained to predict long-term liking." It states that Olsen's determining step uses ''at least one algorithm, such as an algorithm for pattern recognition," and then interprets that algorithm to encompass an ANN trained to predict long-term liking. This is too broad. A pattern-recognition algorithm that outputs ALMA-type values (attention, liking, memory, action) is not necessarily an ANN. More importantly, even if it were an ANN, it is not trained to predict long-term liking. Accordingly, the rejection should be withdrawn;
(11) stated motivation to combine is insufficient because it does not address the actual differences between the claims and the cited art. Obviousness requires a motivating reason to combine the art as claimed with a reasonable expectation of success. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007); Intelligent Bio-Sys., Inc. v. Illumina Cambridge Ltd., 821 F.3d 1359, 1367-68 (Fed. Cir. 2016). A general desire to collect and analyze synchronized response data does not provide a reason to modify Parra's audience-viewership prediction system to predict an individual's long-term liking of olfactive or taste stimuli using an ANN trained for that purpose. The rejection states that a person of ordinary skill would have modified Parra with Olsen so that data can be analyzed, interpreted, combined, averaged, and displayed. At most, that rationale might support using synchronized multi-person data generally. It does not explain why a person of ordinary skill would have modified Parra's audience-behavior prediction system to predict an individual's long-term liking of olfactive or taste stimuli using an ANN trained for that specific long-term hedonic prediction. Accordingly, the rejection should be withdrawn.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the newly amended claims are directed to an abstract idea without significantly more. The Examiner maintains that the limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Examiner maintains that the Applicant’s claims describe instructions for a person to follow. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the judicial exception is integrated into a practical application as the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and generally linking the abstract idea to a particular technological environment. This judicial exception is not integrated into a practical application because “using the artificial neural network to…” are recited at a high-level of generality. As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Additionally, the claims recite “…using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device”, “(c) inputting the defined exposure pattern and the measured physiological and/or psychometric responses into an artificial neural network trained to predict long-term liking;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The Examiner maintains that the claimed limitations, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Examiner does not acknowledge that the claims solve a specific, technological problem as the claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” (See MPEP 2106.05(a)(I)) which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention".) Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The claims are not patent eligible. The Examiner does not acknowledge that the claims solve a specific, technological problem as the claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” (See MPEP 2106.05(a)(I)) which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The Applicant claims that the claims do not recite a business practice, a commercial transaction, a sales method, a legal obligation, an interpersonal interaction, or a method of managing behavior between persons in argument (4), but does not argue that “managing relationships or interactions between people, (including social activities, teaching, and following rules or instructions)”. The Examiner maintains that the limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Applicant relies on the neural network at a level that amounts to "apply it" as applying a tool to perform an existing process, and an EEG device, GSR device, eye-tracker device, and/or video recording device at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (5), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the judicial exception is integrated into a practical application as the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and generally linking the abstract idea to a particular technological environment. This judicial exception is not integrated into a practical application because “using the artificial neural network to…” are recited at a high-level of generality. As set forth in the MPEP 2106.04(d) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Additionally, the claims recite “…using an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker device, and/or a video recording device”, “(c) inputting the defined exposure pattern and the measured responses into an artificial neural network trained to predict long-term liking;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The Examiner maintains that the claimed limitations, but for the recitation of generic computer component, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Examiner does not acknowledge that the claims solve a specific, technological problem as the claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” (See MPEP 2106.05(a)(I)) which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The step of “incorporating the identified predetermined sensory stimulus into the consumer product” does not describe that the “incorporating” cannot be executed by a person following instructions as the step is reciting “incorporating” without limitations to how the step is accomplished. The Examiner does not find the Affidavit persuasive as the Examiner maintains that the claims do not describe an improvement to a technology or technical field. The Applicant relies on the neural network at a level that amounts to "apply it" as applying a tool to perform an existing process. The Applicant’s claimed limitation of “repeated exposure to a predetermined stimulus according to a defined exposure pattern and measuring physiological and/or psychometric responses for each exposure using specified measurement devices” could be accomplished by a person following instructions. The Examiner does not acknowledge that the claims solve a specific, technological problem as the claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” (See MPEP 2106.05(a)(I)) which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The Examiner does not acknowledge that the claims clearly recite a clear improvement in a technology or technical field. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (6), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention".) Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The claims are not patent eligible. The Examiner does not acknowledge that the claims solve a specific, technological problem as the claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” (See MPEP 2106.05(a)(I)) which the courts have indicated may not be sufficient to show an improvement in computer-functionality. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. See Alice 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention".) Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The claims are not patent eligible. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (7), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge the similarity between the Applicant's claimed invention and USPTO's Al Subject Matter Eligibility Example 48 - Claim 2, as Example 48 - Claim 2 displays in the claim language the features that reflect the technical improvement. In contrast, the Applicant's claims recite "using the artificial neural network to predict..." which does not show a clear improvement to a technical field, but outputting a possible outcome. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (8), the Examiner does not find the Applicant’s argument(s) persuasive. The broadest reasonable interpretation of the claimed step of “(I) predicting an individual’s long-term liking to a predetermined sensory stimulus selected from olfactive and taste stimuli, wherein predicting the individual’s long-term liking comprises” steps (a)-(b) is extrapolating from known data points a future potential data point of long-term liking. This step does not require the individual’s long-term liking prediction to be accurate for the individual’s long-term liking, only that there is a potential long-term liking prediction derived from known conditions of exposing the individual to the predetermined sensory stimulus at least three times. The Examiner maintains that Parra's "A method of predicting response to a sensory stimulus, the method comprising using a processor to perform: receiving behavioral data representing a response of a first population of subjects to a reference sensory stimulus;" (see Claim 1) would be obvious to combine with Olsen's disclosure of "stimuli refers to any sensory activation or combination of activation ... be it audition, vision, gustation (taste), olfaction (smell) ... or other" in Paragraph [0047] of Olsen. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (9), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Olsen’s disclosure in Fig. 2, Paragraphs [0034] (“Yet another object of the invention is to provide a novel system, which ensures a higher impact and/or acceptance of a provided stimuli by engaging the nervous systems in the most efficient way, by providing knowledge about the test person's attention level, his preferences (positive, neutral or negative), memory activation (working or long-term) and intention of action to enable the developers of stimuli such as audio-visual and other stimuli to adjust the stimuli according to the response of the test persons.”), [0078] encompasses “long-term liking” as Fig. 2 describes the Memory as “Long-term memory”, “Memorizing: -repetition, -strength of input, -liking, -depth of processing”. The Examiner maintains that the “test person” as described in Paragraph [0082] “FIG. 2 illustrates the process of the invention from a user point of view, whereby attention leads to preference building in the form of liking (or disliking), an emotional coding of the stimuli received influences whether or not the test person assign memory capacity to the stimuli and a combination of these factors decides whether the test person is likely or unlikely to act on the stimuli received.” identifies that the test person would store memory of liking a stimuli as the test person assign memory capacity to the stimuli that would identify that the person might like the stimuli in the future. The Applicant’s claims do not require the confirmation that the prediction of the “long-term liking” is accurate, only that there is a prediction that a person would have “long-term liking” of a stimulus, which the Examiner asserts that the identification of a liking to a stimulus and storage of that liked stimulus in long-term memory displays that the person has been exposed to the stimulus over a long period of time, and it would be likely that the person has developed a long-term liking to the stimulus. The determination if a person has a “long-term liking” of a stimulus appears to require the person to be exposed to the stimulus at a later time to identify if the person still likes the stimulus, which does not require the person to remember if they like the stimulus, but to identify at the time if they still like the stimulus. The Examiner maintains that the combination of Parra/Olsen encompasses the claims 46-63. The Examiner maintains that, under broadest reasonable interpretation, the Applicant's recitation of a neural network is interpreted as a pattern recognition model that outputs a prediction from the identified pattern which Olsen's "adaptive algorithm" teaches in Paragraph [0076]. The Examiner maintains that Olsen's disclosure in Fig. 2, Paragraphs [0034] ("Yet another object of the invention is to provide a novel system, which ensures a higher impact and/or acceptance of a provided stimuli by engaging the nervous systems in the most efficient way, by providing knowledge about the test person's attention level, his preferences (positive, neutral or negative), memory activation (working or long-term) and intention of action to enable the developers of stimuli such as audio-visual and other stimuli to adjust the stimuli according to the response of the test persons."), [0078] encompasses "long-term liking" as Fig. 2 describes the Memory as "Long-term memory", "Memorizing: -repetition, -strength of input, -liking, -depth of processing". The determination if a person has a "long-term liking" of a stimulus appears to require the person to be exposed to the stimulus at a later time to identify if the person still likes the stimulus, which does not require the person to remember if they like the stimulus, but to identify at the time if they still like the stimulus. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (10), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that, under broadest reasonable interpretation, the Applicant's recitation of a neural network is interpreted as a pattern recognition model that outputs a prediction from the identified pattern which Olsen's "adaptive algorithm" teaches in Paragraph [0076]. The Examiner maintains that Olsen's disclosure in Fig. 2, Paragraphs [0034] ("Yet another object of the invention is to provide a novel system, which ensures a higher impact and/or acceptance of a provided stimuli by engaging the nervous systems in the most efficient way, by providing knowledge about the test person's attention level, his preferences (positive, neutral or negative), memory activation (working or long-term) and intention of action to enable the developers of stimuli such as audio-visual and other stimuli to adjust the stimuli according to the response of the test persons."), [0078] encompasses "long-term liking" as Fig. 2 describes the Memory as "Long-term memory", "Memorizing: -repetition, -strength of input, -liking, -depth of processing". The Applicant recites “an artificial neural network trained to predict long-term liking”, but does not describe how the ANN is trained. The Applicant’s Specification describes the “training data” as “Training data is obtained by exposing an individual to a predetermined sensory stimulus during an initial time period of exposure (210). The individual's physiological and/or psychometric responses are measured during each exposure (220, 240).” (See Specification in p. 6, ll. 20-23). The training data that is described is similar to the prediction output of the ANN, and is recited at a level of generality that amounts to a pattern recognition algorithm that outputs a prediction from the identified pattern which is Olsen describes in Paragraph [0092] and also describes an “adaptive algorithm” in Paragraph [0076]. An algorithm that is trained to identify patterns implies a level of training to match inputs to outputs. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (11), the Examiner does not find the Applicant’s argument(s) persuasive. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Parra with Olsen with the motivation of collecting data in such a way that synchronised data from several test persons can be analysed, interpreted, combined, averaged and displayed at the same time (See Summary of the Invention of Olsen in Paragraph [0031]). The Examiner maintains that the combination of Parra/Olsen encompasses the claims 46-63. The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Palmer (U.S. Patent Pre-Grant Publication No. 2021/0022658), describes systems and methods for testing and assessing preference (e.g., taste preference).
Cohen et al. (U.S. Patent Pre-Grant Publication No. 2021/0361967), describes a brain state monitoring and stimulating component having at least one processor and memory, and a digital signal processing unit configured and operable to predict at least one brain state about to occur in a brain of a treated subject based on data or signals indicative of at least one brain state of the treated subject.
Dmochowski et al. (“Audience preferences are predicted by temporal reliability of neural processing”), describes record neural activity from a group of naive individuals while viewing popular, previously-broadcast television content for which the broad audience response is characterized by social media activity and audience ratings.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Robert Morgan can be reached at (571) 272-6773. 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.
/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
Prosecution Insights