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 .
Information Disclosure Statement
The information disclosure statement filed 11/23/2025 fails to comply with 37 CFR 1.98(a)(1), which requires the following: (1) a list of all patents, publications, applications, or other information submitted for consideration by the Office (The Examiner notes there is no Chinese patent with the number 08288070, although an attached copy of a foreign patent lists a number of 108288070); (2) U.S. patents and U.S. patent application publications listed in a section separately from citations of other documents; (3) the application number of the application in which the information disclosure statement is being submitted on each page of the list; (4) a column that provides a blank space next to each document to be considered, for the examiner’s initials; and (5) a heading that clearly indicates that the list is an information disclosure statement. The information disclosure statement has been placed in the application file, but the information referred to therein (specifically, the second citation in the “Foreign Patent Documents” section) has not been considered.
Response to Arguments
Applicant’s arguments, see pages 9-12, filed 11/06/2025, with respect to the rejection(s) of the claims under 35 U.S.C. § 102 and 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed 11/06/2025 have been fully considered but they are not persuasive.
Regarding the rejection of the claims under 35 U.S.C. § 101, the applicant has argued "measuring neurophysiological responses of the human subjects to the data items in the reference set," requires specialized equipment that cannot be obtained through mental processes, and that this measurement is not integral to the claimed process as opposed to mere data gathering (pages 6-7). However, the applicant is arguing features which are not claimed. Claim 1 does not recite any sort of specialized equipment—although claims are read in light of the specification, reading limitations from the specification into the claims is improper. A preferred embodiment informs the scope of a claim, but the scope is not limited to expressly that embodiment. Furthermore, measuring neurophysiological responses of human subjects is well known, does not impose meaningful limits on the claim such that it is not nominally or tangentially related to the invention, and amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output)—all of which are characteristics of insignificant extra-solution activity data gathering. See MPEP 2106.05(g).
The Applicant has also argued the claims recite a novel combination of features that bridges neurophysiological measurement with content delivery. The applicant asserts the claims recite improvement in technology by a specific combination of neurophysiological measurements, brain type classification, clustering, mapping creation, and brain type prediction, which solve the technical problem of determining content personalization when organizations have limited information about new users by predicting brain types for new users without requiring neurophysiological measurements from those new users. The applicant asserts this combination is not well-understood, routine, or conventional in the field (pages 7-9). However, this asserted improvement amounts to a novel way of combining, analyzing, calculating, and receiving information, which is itself an abstract idea. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. In other words, the improvement in technology cannot come from improvement in the abstract idea. See MPEP 2106.05(a).
For these reasons, the rejection of the claims under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding Claim 1, the claim recites a method for content delivery. Thus, the claim is directed to a process, which is one of the statutory categories of invention (Step 1).
The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong One). The following limitations set forth a judicial exception:
dividing a reference group of human subjects into multiple segments according to one or more segmentation criteria
collecting subjective responses of the human subjects to a reference set of data items
classifying the human subjects into multiple brain types by clustering the human subjects according to the measured neurophysiological responses to the reference set of data items
based on the collected subjective responses, defining a mapping between the segmentation criteria and the brain types
applying the mapping in predicting a brain type of a human subject outside the reference group based solely on non-neurophysiological information from the human subject outside the reference group
selecting a content offering for presentation to the human subject responsively to the predicted brain type
These limitations describe a mathematical calculation and/or a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. These limitations also describe certain methods of organizing human activity relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner also notes that nothing from the claim(s) suggests an undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps.
For example:
Dividing a reference group of human subjects into multiple segments according to one or more segmentation criteria is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Collecting subjective responses of the human subjects to a reference set of data items amounts to managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
classifying the human subjects into multiple brain types by clustering the human subjects according to the measured neurophysiological responses to the reference set of data items is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
“Based on the collected subjective responses, defining a mapping between the segmentation criteria and the brain types” is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
applying the mapping in predicting a brain type of a human subject outside the reference group based solely on non-neurophysiological information from the human subject outside the reference group is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Selecting a content offering for presentation to the human subject responsively to the predicted brain type is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application (Step 2A, Prong Two).
The following limitations amount to insignificant extra-solution activity to the judicial exception, e.g. mere data gathering. See MPEP 2106.05(g).
measuring neurophysiological responses of the human subjects to the data items in the reference set
Therefore, these additional limitations do not integrate the judicial exception into a practical application.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, amounts to significantly more than the identified judicial exception (Step 2B):
The following limitations do not amount to significantly more than the abstract idea for substantially similar reasons applied in Step 2A, Prong Two.
measuring neurophysiological responses of the human subjects to the data items in the reference set
Dependent Claims 2-6, 9-10, and 16-25 also fail to add subject matter qualifying as significantly more to the abstract independent claims as they merely further limit the abstract idea.
Dependent Claims 7-8 and 11-15 also fail to add subject qualifying as significantly more to the abstract independent claims as they recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above.
Dependent Claims 7-8 and 11-15 also fail to add subject matter integrating the judicial exception or qualifying as significantly more to the abstract independent claims as they do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, Claims 1-25 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-8, 11-17, and 22-25 are rejected under 35 U.S.C. 103 as being unpatentable over Pradeep et al (US 20190156352 A1, cited in applicant’s IDS, hereinafter Pradeep) in view of Kocher et al (US 20220054072 A1, hereinafter Kocher) and Barnett et al (US 20150206174 A1, hereinafter Barnett).
Regarding Claim 1, Pradeep discloses a method for content delivery (See Figs. 6-7 and entire document), comprising:
dividing a reference group of human subjects into multiple segments (“A personalization repository system 133 provides information about particular users or groups of users”, [0044]; “FIG. 6 illustrates one example of building a priming repository system for personalized content delivery”, [0070]) according to one or more segmentation criteria (“According to various embodiments, the personalization repository system 133 identifies sets of personal preferences…The information may be obtained using historical purchase behavior, demographic based purchasing profiles, user survey inputs, or even neuro-response data etc…”, [0044]);
collecting subjective responses of the human subjects to a reference set of data items (“At 601, stimulus material is provided to multiple subjects. According to various embodiments, stimulus includes streaming video and audio. In particular embodiments, subjects view stimulus in their own homes in group or individual settings. In some examples, verbal and written responses are collected…”, [0070]; also see [0031]-[0032]);
measuring neurophysiological responses of the human subjects to the data items in the reference set (“In other examples, verbal and written responses are correlated with neuro-response measurements…”, [0070]);
classifying the human subjects into multiple brain types (“According to various embodiments, the data analyzer customizes and extracts the independent neurological and neuro-physiological parameters for each individual in each modality”, [0047]; “Determining various profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. The synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness”, [0076]; “ At 613, priming levels and resonance for various products, services, and offerings are determined at different locations in the stimulus material”, [0084]) according to the measured neurophysiological responses to the reference set of data items (“According to various embodiments, post-stimulus versus pre-stimulus differential measurements of ERP time domain components in multiple regions of the brain (DERP) are measured at 607. The differential measures give a mechanism for eliciting responses attributable to the stimulus. For example the messaging response attributable to an advertisement or the brand response attributable to multiple brands is determined using pre-resonance and post-resonance estimates”, [0083]);
based on the collected subjective responses, defining a mapping between the segmentation criteria and the brain types (“According to various embodiments, a set of weights and functions use a combination of rule based and fuzzy logic based decision making to determine the areas of maximal overlap between the priming repository system and the personalization repository system. Clustering analysis may be performed to determine clustering of priming based preferences and personalization based preferences along a common normalized dimension, such as a subset or group of individuals”, [0046]);
applying the mapping in predicting a brain type of a human subject outside the reference group (“The information from a priming repository system 131 may be combined with information from a personalization repository system 133 using a priming and preference blender or integration system 181”, [0045]; “According to various embodiments, priming and preference attributes are weighted and blended to allow selection of neurologically effective stimulus material for individual users”, [0088]); and
selecting a content offering for presentation to the human subject responsively to the predicted brain type (“At 707, blended attributes are used to select stimulus material such as advertising, offers, informational content, marketing materials, etc. In some embodiments, stimulus material may be a coupon, a banner advertisement, a video stream, an audio clip, a message, etc. The source material may be a web page, a video stream, a sound file, a multimedia presentation, a billboard, etc.”, [0089]).
Modified Pradeep discloses the claimed invention except for expressly disclosing classifying the human subjects into multiple brain types by clustering the human subjects according to the measured neurophysiological responses to the reference set of data items; and
applying the mapping in predicting a brain type of a human subject outside the reference group based solely on non-neurophysiological information from the human subject outside the reference group.
However, Kocher teaches classifying the human subjects into multiple brain types (Step 36, Fig. 3) by clustering the human subjects according to the measured neurophysiological responses (Step 35, Fig. 3) to the reference set of data items (Element 10, Fig. 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pradeep by Kocher, by classifying the human subjects into multiple brain types by clustering the human subjects, because this is a way to differentiate between desired traits in the human subjects (See Element 45, Fig. 4 of Kocher).
Barnett teaches applying the mapping in predicting a measure of engagement (The examiner notes that different measures of engagement can come from different brain types) of a human subject outside the reference group based solely on non-neurophysiological information from the human subject outside the reference group (“The method 100 may additionally or alternatively be used to predict engagement based on previously acquired neural data; for example, the method 100 may predict the engagement of a twenty-three year old Asian female based on previously acquired measures of engagement for persons of similar demographics”, [0010]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the predicted brain types of Pradeep with the predicted measures of engagement from non non-neurophysiological information from the human subject outside the reference group of Barnett (i.e. predicting a brain type of a human subject outside the reference group based solely on non-neurophysiological information from the human subject outside the reference group), because subjects with similar non-neurophysiological information (e.g. similar demographics) can respond similarly to the same stimuli (e.g. have similar brain types), as taught by Barnett ([0036]).
Regarding Claim 2, modified Pradeep discloses the method according to claim 1, wherein applying the mapping comprises predicting the brain type based on a behavior of the human subject (“Effector and behavior responses are blended and combined with other measures”, [0025]; “In one particular example, neuro-response data may indicate that beverage advertisements would be suitable for a particular advertisement break. User preferences may indicate that a particular viewer prefers diet sodas”, [0046]; selecting user preferences is a behavior).
Regarding Claim 3, modified Pradeep discloses the method according to claim 1, wherein applying the mapping comprises predicting the brain type based on an interaction of the human subject with an item of content (Step 601, Fig. 6; “A variety of stimulus materials such as entertainment and marketing materials, media streams, billboards, print advertisements, text streams, music, performances, sensory experiences, etc. can be analyzed”, [0028]).
Regarding Claim 4, modified Pradeep discloses the method according to claim 1, wherein the segmentation criteria comprise demographic criteria (“According to various embodiments, the personalization repository system 133 identifies sets of personal preferences for products and services, audio characteristics, video characteristics, length, channel, delivery mode (television, radio, mobile, internet), emotional content, imagery, attention characteristics. The information may be obtained using historical purchase behavior, demographic based purchasing profiles…”, [0044]).
Regarding Claim 7, modified Pradeep discloses the method according to claim 1, wherein selecting the content offering comprises presenting a media item to the human subject (“At 709, existing content may be replaced with the personalized stimulus material. According to various embodiments, default content is included in source material. For example, a web page may have a default banner advertisement. In particular embodiments, the default banner advertisement is replaced with a personalized banner advertisement selected using a combination of priming characteristics and personal preferences.”, [0090]).
Regarding Claim 8, modified Pradeep discloses the method according to claim 1, wherein selecting the content offering comprises modifying a physical property of an output presented to the human subject (“The stimulus material having the strongest correlation is selected. In some embodiments, stimulus material may be a coupon, a banner advertisement, a video stream, an audio clip, a message, etc. The source material may be a web page, a video stream, a sound file, a multimedia presentation, a billboard, etc”, [0089]; multimedia presentations, video streams, and sound files comprise the steps of modifying the physical properties of the speaker vibration and light-emitting elements in order to play).
Regarding Claim 11, modified Pradeep discloses the method according to claim 1, wherein measuring the neurophysiological responses comprises collecting respective signals from one or more region of respective brains of the human subjects (“In particular embodiments, EEG response data is synthesized to provide an enhanced assessment of effectiveness. According to various embodiments, EEG measures electrical activity resulting from thousands of simultaneous neural processes associated with different portions of the brain”, [0072]).
Regarding Claim 12, modified Pradeep discloses the method according to claim 11, wherein collecting the respective signals comprises receiving magnetic resonance imaging (MRI) data (“According to various embodiments, the techniques and mechanisms of the present invention may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve personalized content delivery. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI)…”, [0023]).
Regarding Claim 13, modified Pradeep discloses the method according to claim 1, wherein measuring the neurophysiological responses comprises sensing vital signs of the human subjects (“The data collection device 105 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation)”, [0034]; “Autonomic nervous system measurement mechanisms include Galvanic Skin Response (GSR)”, [0024]).
Regarding Claim 14, modified Pradeep discloses the method according to claim 1, wherein measuring the neurophysiological responses comprises sensing gestures made by the human subjects (See the entirety of [0082], which discloses correlating facial gestures based on sensed emotions with EEG measurements).
Regarding Claim 15, modified Pradeep discloses the method according to claim 1, wherein measuring the neurophysiological responses comprises measuring a dilation of pupils of the eyes of the human subjects (“The data collection device 105 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation)…”, [0034]).
Regarding Claim 16, modified Pradeep discloses the method according to claim 1, wherein defining the mapping comprises:
extracting features from the data items (“In particular embodiments, a stimulus attributes repository is accessed to obtain attributes and characteristics of the stimulus materials”, [0072]);
defining a first classification of the neurophysiological responses of the human subjects to each of the extracted features according to the brain types of the human subjects (“another stimulus attributes data model 221 includes creation attributes 223, ownership attributes 225, broadcast attributes 227, and statistical, demographic and/or survey based identifiers 229 for automatically integrating the neuro-physiological and neuro-behavioral response with other attributes and meta-information associated with the stimulus”, [0056]);
defining a second classification of the subjective responses of the human subjects to each of the extracted features according to the segments to which the human subjects belong (“The survey and interview system 123 can also be used to obtain user responses about particular pieces of stimulus material”, [0040]); and
applying the first and second classifications in mapping between the segmentation criteria and the brain types (“According to various embodiments, a set of weights and functions use a combination of rule based and fuzzy logic based decision making to determine the areas of maximal overlap between the priming repository system and the personalization repository system. Clustering analysis may be performed to determine clustering of priming based preferences and personalization based preferences along a common normalized dimension, such as a subset or group of individuals”, [0046]).
Regarding Claim 17, modified Pradeep discloses the method according to claim 16, wherein the data items comprise images (“The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds”, [0031]), and the extracted features are selected from among spatial and spectral characteristics of the images (See Fig. 2; “stimulus purpose data model 213 also includes spatial and temporal information 219 about entities”, [0055]).
Regarding Claim 22, modified Pradeep discloses the method according to claim 16, wherein defining the first classification (“another stimulus attributes data model 221 includes creation attributes 223, ownership attributes 225, broadcast attributes 227, and statistical, demographic and/or survey based identifiers 229 for automatically integrating the neuro-physiological and neuro-behavioral response with other attributes and meta-information associated with the stimulus”, [0056]) comprises measuring a brain activity of the human subjects from one or more brain regions, and classifying each of the features according to the measured brain activity (“A variety of stimulus materials such as entertainment and marketing materials, media streams, billboards, print advertisements, text streams, music, performances, sensory experiences, etc. can be analyzed. According to various embodiments, enhanced neuro-response data is generated using a data analyzer that performs both intra-modality measurement enhancements and cross-modality measurement enhancements. According to various embodiments, brain activity is measured not just to determine the regions of activity, but to determine interactions and types of interactions between various regions”, [0028]).
Regarding Claim 23, modified Pradeep discloses the method according to claim 16, wherein defining the second classification comprises computing an arousal score with respect to each of the data items based on the subjective responses (“In other examples, verbal and written responses are correlated with neuro-response measurements”, [0070]), and classifying each of the features according to the arousal score (“According to various embodiments, a set of weights and functions use a combination of rule based and fuzzy logic based decision making to determine the areas of maximal overlap between the priming repository system and the personalization repository system. Clustering analysis may be performed to determine clustering of priming based preferences and personalization based preferences along a common normalized dimension, such as a subset or group of individuals”, [0046]).
Regarding Claim 24, modified Pradeep discloses the method according to claim 1, wherein predicting the brain type comprises presenting a data item to the human subject, receiving a response of the human subject to the presented data item (“At 703, preference characteristics are determined. User preferences including user profile information and attributes may be obtained from a personalization repository system. In particular embodiments, the user preferences may identify user interests, purchase patterns, location, income level, gender, preferred products and services, etc…”, [0087]), and predicting the brain type based on the received response (“At 707, blended attributes are used to select stimulus material such as advertising, offers, informational content, marketing materials, etc…”, [0089]).
Regarding Claim 25, modified Pradeep discloses the method according to claim 1, wherein predicting the brain type comprises receiving segmentation data with respect to the human subject (“At 703, preference characteristics are determined. User preferences including user profile information and attributes may be obtained from a personalization repository system. In particular embodiments, the user preferences may identify user interests, purchase patterns, location, income level, gender, preferred products and services, etc.”, [0087]), and predicting the brain type based on the segmentation data (“At 707, blended attributes are used to select stimulus material such as advertising, offers, informational content, marketing materials, etc…”, [0089]).
Claims 5-6 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Pradeep in view of Kocher and Barnett, and further in view of Crawford et al (US 20160103487 A1, cited in applicant’s IDS, hereinafter Crawford).
Regarding Claim 5, modified Pradeep discloses the method according to claim 1. Modified Pradeep discloses the claimed invention except for expressly disclosing wherein the segmentation criteria comprise psychographic criteria. However, Crawford teaches wherein the segmentation criteria comprise psychographic criteria. (“Information on regarding how brains respond to similar stimuli may be used to ascertain and score the “mental similarity” between different people or groups of people”, [0033]; “The collection of responses to the set of stimuli may be used to build characteristic mental profiles and serve to establish models of mental predilections that would be equivalent to concepts, such as the Meyers-Briggs® or Five Factor Model (FFM), for characterizing personality traits.”, [0034]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the psychographic criteria of Crawford to the segmentation criteria of modified Pradeep, because this creates more sophistication in matching subjects to like-minded marketing and messaging (See Crawford, [0033]).
Regarding Claim 6, modified Pradeep discloses the method according to claim 5, wherein the psychographic criteria comprise one or more measures of mental health of the human subjects (“According to various embodiments, the personalization repository system 133 identifies sets of personal preferences…The information may be obtained using historical purchase behavior, demographic based purchasing profiles, user survey inputs, or even neuro-response data etc…”, [0044]; neuro-response data can include EEG data in delta, theta, alpha, beta, and gamma frequency ranges, [0072]; “Alpha waves are prominent during states of relaxation”, [0073]).
Regarding Claim 9, modified Pradeep discloses the method according to claim 1. Modified Pradeep discloses the claimed invention except for expressly disclosing wherein selecting the content offering comprises presenting a proposal to the human subject to make an acquaintance with another person. However, Crawford teaches wherein selecting the content offering comprises presenting a proposal to the human subject to make an acquaintance with another person (“Specific examples where this idea may prove useful include a comparison of mental profiles …with relationship satisfaction to help identify and predict potential social compatibility (or incompatibility)”, [0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the proposal of Crawford to the content offering of modified Pradeep, because this is a way to help users find and maintain suitable relationships.
Regarding Claim 10, modified Pradeep discloses the method according to claim 1. Modified Pradeep discloses the claimed invention except for expressly disclosing wherein selecting the content offering comprises presenting a proposal to the human subject to join an organization. However, Crawford teaches wherein selecting the content offering comprises presenting a proposal to the human subject to join an organization (“Specific examples where this idea may prove useful include a comparison of mental profiles with job satisfaction information to help identify and predict potential career matches (or mismatches)”, [0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the proposal of Crawford to the content offering of modified Pradeep, because this is a way to help users find and maintain suitable careers.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Pradeep in view of Kocher and Barnett, and further in view of Parra et al (US 10835147 B1, hereinafter Parra).
Regarding Claim 18, modified Pradeep discloses the method according to claim 16, wherein the data items comprise audio items (“The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds”, [0031]). Modified Pradeep discloses the claimed invention except for expressly disclosing wherein the extracted features are selected from among spectrograms and spectral characteristics of the audio waves. However, Parra, which also discloses presenting stimuli to a subject (Abstract), teaches wherein the extracted features are selected from among spectrograms and spectral characteristics of the audio waves (“For an audio presentation or an audio component of an audio visual presentation, the features extracted may include pitch trajectory, sound envelope, speech envelope and spectral power or powers”, 5:19-22). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the extracted features of Parra to the features of modified Pradeep, because these extracted features can be correlated to the physiological responses of the subject (Parra, 5:13-15).
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pradeep in view of Kocher and Barnett, and further in view of McDaniel et al (US 20210256542 A1, hereinafter McDaniel).
Regarding Claim 19, modified Pradeep discloses the method according to claim 16, wherein the data items comprise odors (“The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds”, [0031]). Modified Pradeep discloses the claimed invention except for expressly disclosing wherein the extracted features are selected from among spectroscopic data and chemical characteristics of the odors. However, Daniel, which also discloses presenting stimuli to a subject (Abstract), teaches wherein the extracted features are selected from among spectroscopic data and chemical characteristics of the odors (“Sensory stimuli can include odors and tastes. Accordingly, sensory stimulus data includes identification of chemical compounds (and combinations thereof) that produce particular odors. Sensory stimulus data also includes identification of one or a plurality of olfactory receptors that are stimulated by a particular chemical compound or combination of compounds. Alternatively, or in addition, sensory stimulus data includes identification of one or a plurality of olfactory receptors that are not stimulated by a particular chemical compound or combination of compounds”, [0078]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the extracted features of McDaniel to the features of modified Pradeep, because these extracted features can be correlated to the physiological responses of the subject (McDaniel, [0011]).
Regarding Claim 20, modified Pradeep discloses the method according to claim 16, wherein the data items comprise flavors (“The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds”, [0031]). Modified Pradeep discloses the claimed invention except for expressly disclosing wherein the extracted features are selected from among spectroscopic data and chemical characteristics of the flavors. However, Daniel, which also discloses presenting stimuli to a subject (Abstract), teaches wherein the extracted features are selected from among spectroscopic data and chemical characteristics of the odors (“Sensory stimuli data can include specific information about the particular composition this can include, for example, the identity of chemicals in the composition as well as their chemical characteristics such as class of chemical compounds to which they belong in the relative amounts of each chemical in the composition constituting the sensory stimulus. A sensory stimulus such as an odor or taste can be simple or complex”, [0079]-[0080]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the extracted features of McDaniel to the features of modified Pradeep, because these extracted features can be correlated to the physiological responses of the subject (McDaniel, [0011]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Pradeep in view of Kocher and Barnett, and further in view of Kim et al (US 20220297309 A1, hereinafter Kim).
Regarding Claim 21, modified Pradeep discloses the method according to claim 16, wherein the data items comprise tactile stimuli (“The stimulus material may be a media clip, a commercial, pages of text, a brand image, a performance, a magazine advertisement, a movie, an audio presentation, and may even involve particular tastes, smells, textures and/or sounds”, [0031]). Modified Pradeep discloses the claimed invention except for expressly disclosing wherein the extracted features are selected from among vibrograms and spectral characteristics of the tactile stimuli. However, Kim teaches wherein the extracted features are selected from among vibrograms and spectral characteristics of the tactile stimuli (“tactile stimuli shown in Table 5 and FIG. 20 were applied to the tactile sensor prepared in Example 5, and the time information of signals having different patterns according to the type of tactile stimulus was visualized by calculating spectrogram, and the results are shown in FIG. 21”, [0201]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the extracted features of McDaniel to the features of modified Pradeep, because all of the claimed elements were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious to ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See Gadot et al US 20160213276 A1; [0144]).
See Coleman et al (US 20180285442 A1; [0132]).
See Zaltman et al (US 6099319 A).
See Hill et al (US 6422999 B1).
See Jung et al (US 20090163777 A1).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/JONATHAN E. COOPER/Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791