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.
Response to Amendment
In the amendment filed on October 3, 2025, the following has occurred: claim(s) 46-63 have been added and claim(s) 16, 21, 26-28, 31-45 have been cancelled. Now, claim(s) 46-63 are pending.
Affidavit
The Examiner has reviewed the Affidavit, “Declaration of Jonathan Jacobs Pursuant to 37 C.F.R. § 1.132” from October 3, 2025. The Examiner has reviewed the Affidavit in light of the newly added claims. The Examiner does not acknowledge that the Affidavit overcomes the 35 U.S.C. 103 rejection(s) as described below in the 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 46 objected to because of the following informalities: “…the exposure pattern…” in p. 2, ll. 17, “…the identified sensory stimulus…” in p. 3, ll. 1. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “…the defined exposure pattern…”, “…the identified predetermined sensory stimulus…”.
Claim 62 objected to because of the following informalities: “…the exposure pattern…” in p. 5, ll. 14, “…the sensory stimulus…” in p. 5, ll. 17, “…the identified sensory stimulus…” in p. 5, ll. 24. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “…the defined exposure pattern…”, “…a sensory stimulus…”, “…the identified predetermined sensory stimulus…”.
Claim 63 objected to because of the following informalities: “…the EEG signals…” in p. 5, ll. 25. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “…EEG signals…”.
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 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 sensory stimuli;
(III) identifying a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of 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 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 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 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 manual steps encompass Certain Methods of Organizing Human Activity, as the manual steps could be accomplished by a physician, a nurse, an engineer, or a person in a similar position.
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 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 a sensory 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 sensory stimuli;
(III) identifying a predetermined sensory stimulus predicted to have the highest long-term liking from amongst the plurality of 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 olfactive stimulus and predict the individual's long- term liking of a 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 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 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 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 manual steps encompass Certain Methods of Organizing Human Activity, as the manual steps could be accomplished by a physician, a nurse, an engineer, or a person in a similar position.
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 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 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 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 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 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 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 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 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 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 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 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 a 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 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 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 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 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 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 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 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 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 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 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 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 October 3, 2025, 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 claims have been redrafted. The rejection should not apply to the new claims for the reasons outlined below. the claims do not recite an abstract idea. The claims require specific technological elements and a structured procedure. The individual must be exposed to the sensory stimulus at least three times during an initial exposure period according to a defined exposure pattern. For each exposure, the claims entail objective measurements of physiological and/or psychometric responses using concrete devices such as an electroencephalography (EEG) device, a galvanic skin response (GSR) device, an eye-tracker, or a video recording device. These data, together with the exposure pattern, are input into an artificial neural network trained to predict long-term liking. Importantly, the claims specify that the predicted liking must correspond to a future time point at least ten times later than the initial exposure period. Finally, the method culminates in a tangible application-incorporating the identified sensory stimulus into the manufacture of a consumer product. These recitations go well beyond mere mental steps or abstract mathematical concepts. They require concrete machinery and defined exposure protocols that cannot be carried out by the human mind;
(2) even if the claim were considered to involve a judicial exception, it is integrated into a practical application under Step 2A, Prong 2. The Office Action suggests that the claims merely recite a computer configured to perform a function, but this characterization overlooks the express requirements of the claims. The claims do not merely call for generic computer processing; they provide for a specific exposure protocol, defined as at least three exposures following a pattern. It recites the use of concrete devices to obtain physiological data. It imposes a temporal relationship between the exposure period and the predicted time point, requiring prediction of long term liking over a period at least ten times longer than the initial exposures. And critically, the claim concludes with a practical, real-world step: incorporating the identified sensory stimulus into the consumer product. The claims therefore apply machine learning to solve a specific, technological problem in consumer product design, i.e., how to predict and incorporate long-term consumer preference into consumer products and avoid issues such as stimulus fatigue or sensory-specific aversion;
(3) although analysis under Step 2B is unnecessary given the foregoing, the claims also recite an inventive concept. The combination of a structured exposure protocol, concrete physiological measuring devices, and an artificial neural network trained to predict long-term liking provides more than routine data gathering or conventional computer implementation. Unlike conventional approaches that measure only immediate hedonic response, the claims enable prediction of evolving, long-term preference and applies those predictions directly to consumer products. This constitutes an improvement to both the predictive modeling technology and the resulting consumer products. See MPEP § 2106.05. For these reasons, the claims are drawn to statutory subject matter, do not recite an abstract idea, and in any event integrates the recited features into a practical application with an inventive concept. Thus, the rejection should be withdrawn;
(4) the rejection should be withdrawn because it fails to properly account for long term liking. Since the Supreme Court's decision in KSR Int'l Co. v. Teleflex, Inc., 550 U.S. 398 (2007), the Federal Circuit has reiterated that all the claim limitations of the invention at issue must be found to exist in the prior art references before proceeding with an obviousness analysis. Abbott Laboratories v. Sandoz, Inc., 544 F.3d 1341, 1351 (Fed. Cir. 2009). Traditional methods for developing fragrances and flavorings rely heavily on consumer testing of immediate hedonic response ("liking") after initial exposure. However, such testing does not reliably predict whether consumers will continue to enjoy a particular fragrance or flavor over time. As a result, products that perform well in initial testing may lose consumer appeal quickly after launch, leading to failed products and wasted development resources. The Present Case addresses this problem by providing methods and systems that predict long-term hedonic response, thereby enabling the identification and selection of olfactory and taste stimuli with enduring consumer appeal. Declaration of Dr. Jacobs, para. 3. As explained in the specification, there is an important distinction between an individual's initial liking of a sensory stimulus and the individual's long-term liking of that same stimulus. Initial liking (or general liking) reflects the static hedonic or emotional response a person experiences upon first exposure to a stimulus. However, this initial response does not necessarily predict how the person will perceive the same stimulus upon subsequent exposures. This is well known.1 As stated in the specification, "an initial stimulus does not necessarily produce the same hedonic response on a subsequent exposure so that the initial hedonic response to a given sensory stimulus generally does not reliably ascertain the hedonic response to the same sensory stimulus at a later time." Specification, p. 1, lines 7-10. In other words, an individual's liking of a fragrance, flavor, or other sensory cue may change over time with repeated exposure, but the trajectory of that change-the evolution of the hedonic response-is generally unknown in advance. Declaration of Dr. Jacobs, para. 4. Paragraph [0078] of Olsen explains that whether a particular stimulus "is liked or not liked, is stored in long-term memory or discarded." The Examiner interprets this disclosure to mean that Olsen teaches long-term liking, reasoning that if a stimulus is both liked and stored in long-term memory, then Olsen must necessarily disclose long term liking. Respectfully, this interpretation is misguided. The Examiner's analysis conflates these two distinct processes by equating a long-term memory of a liked stimulus with long-term liking itself. Declaration of Dr. Jacobs, para. 7. As noted above, long-term liking of a stimulus (e.g., a pleasant scent) and a long term memory of a stimulus (e.g., a memory of a pleasant scent) are very different. The difference between long-term liking of a scent and having a long-term memory of a likable scent lies in the distinction between emotional preference and cognitive retention. Long-term liking refers to a sustained, evolving emotional response to a scent over time. It reflects how an individual's perception or enjoyment of a scent changes with repeated exposure across weeks, months, or even years. For instance, a person who finds the scent of lavender calming may continue to enjoy and seek it out as a source of comfort over time. Declaration of Dr. Jacobs, para. 8. In contrast, having a long-term memory of a likable scent involves the ability to recall and recognize a scent that was perceived as pleasant in the past. This is a static cognitive process that focuses on memory rather than an ongoing emotional response. An individual can retain a memory of a likable scent even if their current preferences have shifted. For example, someone might recall a pleasant smell from childhood but no longer enjoy the scent in adulthood. Declaration of Dr. Jacobs, para. 9. Olsen does not address long-term liking. Instead, Olsen merely discloses classifying an immediate emotional response (e.g., whether a stimulus is liked or not liked) and, separately, determining whether that stimulus is stored in long-term memory. Declaration of Dr. Jacobs, para. 10. Therefore, 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 added claims 46-61 and 62-63 are both 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 limitation of “(a) exposing the individual to a predetermined sensory stimulus at least three times during an initial exposure period, following a defined exposure pattern;” describes that a person must follow the instructions of exposing an individual to a predetermined sensory stimulus at least three times during an initial exposure period, following a defined exposure pattern. The limitation of “(b) for each exposure, measuring the individual's physiological and/or psychometric response” describes that a person must follow the instructions of measuring the individual's physiological and/or psychometric response. 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 limitation of “(d) predict the individual's long- term liking of the 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;” describes a minimum of the value that the predetermined future point can be, which could be an instructional constraint that a person could follow. The limitation of “(IV) incorporating the identified predetermined sensory stimulus into the consumer product” describes instructions for a person to follow to incorporate the identified predetermined sensory stimulus into the consumer product. 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 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 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 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 35 U.S.C. 103 rejection(s) stand.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683