DETAILED ACTION
This office action is in response to communication filed on 28 May 2025.
Claims 1, 4, 5, 7 – 12, and 17 – 23 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an affect recognition unit” and “an evaluation value estimation unit” in claims 1 and 22. Claims 4 and 7 recite “an attractiveness value estimation unit.” Claims 4, 5, 7 – 12, 17 – 21, and 23 inherit the interpretation of claims 1 and 23.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 4, 5, 7 – 12, and 17 – 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The independent claims recite recognizing valence intensity and arousal of a user toward a subject thing, estimating the user evaluation value by using a learning model that receives the valence intensity and the arousal of the user as an input and outputs the user evaluation value, wherein the user evaluation value includes values concerning a plurality of evaluation items, and the learning model receives one set of values of the valence intensity and the arousal as an input and outputs the values concerning the plurality of evaluation items. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance section 2106 of the MPEP (hereinafter, MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the systems are directed to eligible categories of subject matter. Step 1 is satisfied.
With respect to Step 2A prong 1 of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of recognizing values, estimating values, and using those values as inputs in a model, which falls into the “mental processes” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106, as all claim functions can be performed in the human mind or pen to paper and require no technology. The limitations reciting the abstract idea in independent claims are recognizing valence intensity and arousal of a user toward a subject thing, estimating the user evaluation value by using a learning model that receives the valence intensity and the arousal of the user as an input and outputs the user evaluation value, wherein the user evaluation value includes values concerning a plurality of evaluation items, and the learning model receives one set of values of the valence intensity and the arousal as an input and outputs the values concerning the plurality of evaluation items.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to various named units, to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: various named units. These elements have been considered, but merely serve to tie the invention to a particular operating environment, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. This does not amount to significantly more than the abstract idea, and it is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of defining how the model is used and the data is graphed on axes, by way of example, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 5, 7 – 12, and 17 – 23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. P.G. Pub. 2023/0047787 (hereinafter, Chappell).
Regarding claim 1, Chappell teaches an evaluation value estimation system that estimates a user evaluation value that is a value concerning aspiration of a user for a subject thing, the evaluation value estimation system comprising:
an affect recognition unit that recognizes valence intensity and arousal of the user toward the subject thing (¶ 8, “CNS expresses at least two orthogonal measures, for example, arousal and valence. As used herein, “arousal” means a state or condition of being physiologically alert, awake and attentive, in accordance with its meaning in psychology. High arousal indicates interest and attention, low arousal indicates boredom and lack of interest. “Valence” is also used here in its psychological sense of attractiveness or goodness. Positive valence indicates attraction, and negative valence indicates aversion.”); and
an evaluation value estimation unit that estimates the user evaluation value by using a learning model that receives the valence intensity and the arousal of the user as an input and outputs the user evaluation value (¶ 52, “The modules may include, for example, a correlation module 206 that correlates biometric feedback to one or more metrics such as arousal or valence. The correlation module 206 may include instructions that when executed by the processor 202 and/or 214 cause the server to correlate biometric sensor data to one or more neuro-physiological (e.g., emotional) states of the user, using machine learning (ML) or other processes. An event detection module 208 may include functions for detecting events based on a measure or indicator of one or more biometric sensor inputs exceeding a data threshold.”),
wherein the user evaluation value includes values concerning a plurality of evaluation items, and the learning model receives one set of values of the valence intensity and the arousal as an input and outputs the values concerning the plurality of evaluation items (¶ 52, “The modules may further include a calculation function 212 that when executed by the processor causes the server to calculate a Composite Neuro-physiological State (CNS) based on the sensor data and other output from upstream modules”) (¶ 110, “At 1016, the first player may perceive or sense the output of the calculated CNS in any one or more of suitable qualitative or quantitative forms, including, for example, digital representations (e.g., numerical values of arousal or valence or other biometric data such as temperature, perspiration, facial expressions, postures, gestures, etc.), percentages, colors, sounds (e.g., audio feedback, music, tactile feedbacks, etc.”).
Regarding claim 4, Chappell teaches the evaluation value estimation system according to claim 1, comprising an attractiveness value estimation unit that estimates, as an attractiveness value, a value obtained by weighting each of the values concerning the plurality of evaluation items outputted from the learning model as a result of the one set of values of the valence intensity and the arousal being inputted, and adding up the weighted values (¶ 90, “Referring again to the method 800 in which the foregoing expressions can be used (FIG. 8), a calibration process 802 for the sensor data is first performed to calibrate user reactions to known stimuli, for example a known resting stimulus 804, a known arousing stimulus 806, a known positive valence stimulus 808, and a known negative valence stimulus 810.”) (¶ 87, “the power vector {right arrow over (P)} can be defined variously. In any given computation of CNS the power vectors for the social interaction event and the expectation baseline should be defined consistently with one another, and the weighting vectors should be identical. A power vector may include arousal measures only, valence values only, a combination of arousal measures and valence measures, or a combination of any of the foregoing with other measures, for example a confidence measure. A processor may compute multiple different power vectors for the same user at the same time, based on different combinations of sensor data, expectation baselines, and weighting vectors.”).
Regarding claim 5, Chappell teaches the evaluation value estimation system according to claim 1, wherein the subject thing is food, and the plurality of evaluation items includes at least one of an evaluation item based on a sense of taste, an evaluation item based on a sense of smell, an evaluation item based on a sense of vision, and an evaluation item based on a sense of touch (¶ 69, “The one or more sensors may include, for example, electrodes or microphone to sense heart rate, a temperature sensor configured for sensing skin or body temperature of the user, an image sensor coupled to an analysis module to detect facial expression or pupil dilation, a microphone to detect verbal and nonverbal utterances, or other biometric sensors for collecting biofeedback data including nervous system responses capable of indicating emotion via algorithmic processing, including any sensor as already described in connection with FIG. 3 at 328.”) (Examiner note: the user’s view may include food items, and food as the subject thing does not functionally change the claim, only a label that is interchangeable).
Regarding claim 8, Chappell teaches the evaluation value estimation system according to claim 22, wherein the values concerning the plurality of evaluation metrics include at least one of a value concerning frequency of past consumption, a value concerning a degree of recommendation to others, a value concerning a degree of consuming intention, and a value concerning a degree of buying intention (¶ 189, “past responses of the player actor 2004 may indicate an association between a theme (such as unicorn) and positive arousal and valence values. Accordingly, for scenes intended to be happy, the analysis server 1230 may cause more objects to be displayed in accordance with the preferred theme in the virtual environment for the player actor 2004.”).
Regarding claim 17, Chappell teaches the evaluation value estimation system according to claim 1, wherein the affect recognition unit includes: an affect model presentation section that presents, to the user, an affect model that is a two-dimensional model or a three-dimensional model including at least a first axis representing valence intensity and a second axis representing arousal, in a form in which any coordinates in the affect model are selectable; and a selected-coordinate recognition section that recognizes selected coordinates that are coordinates selected by the user from the affect model when the subject thing is presented, and the evaluation value estimation unit receives the selected coordinates as an input (¶ 81, “FIG. 7A shows an arrangement 700 of neuro-physiological states relative to axes of a two-dimensional neuro-physiological space defined by a horizontal valence axis and a vertical axis arousal.”) (See at least Fig. 7A).
Regarding claim 18, Chappell teaches the evaluation value estimation system according to claim 17, wherein the learning model is correlation data indicating a correlation between reference coordinates and a reference evaluation value, the reference coordinates being coordinates selected from the affect model, the reference evaluation value being a value concerning aspiration for the subject thing or a similar thing having an identical or similar attribute to an attribute of the subject thing (¶ 77, “if most users exhibit similar biometric tells when engaged with similar social interactions (e.g., friendly, happy, angry, scary, seductive, etc.), each similar interaction can be classified with like interactions that provoke similar biometric data from users. As used herein, biometric data provides a “tell” on how a user thinks and feels about their experience of a video game or other application facilitating social interaction, i.e., the user's neuro-physiological response to the game or social interaction.”) (See at least Fig. 7A) (¶ 94, “the calculation function 820 may include comparing, at 824, an event power for each detected event, or for a lesser subset of detected events, to a reference for a social/game experience. A reference may be, for example, a baseline defined by a game designer or by the user's prior data. For example, in Poker or similar wagering games, bluffing is a significant part of game play. A game designer may compare a current event power (e.g., measured when a user is placing a bet) with a baseline reference (e.g., measured between hands or prior to the game).”).
Regarding claim 19, Chappell teaches the evaluation value estimation system according to claim 18, wherein the reference coordinates are coordinates selected with regard to the subject thing or the similar thing from the affect model by a test user who evaluates the subject thing or the similar thing beforehand, and the correlation data is data indicating a correlation between the reference coordinates and the reference evaluation value given by the test user (¶ 52, “Each processor 202, 214 of the server 200 may be operatively coupled to at least one memory 204 holding functional modules 206, 208, 210, 212 of an application or applications for performing a method as described herein. The modules may include, for example, a correlation module 206 that correlates biometric feedback to one or more metrics such as arousal or valence. The correlation module 206 may include instructions that when executed by the processor 202 and/or 214 cause the server to correlate biometric sensor data to one or more neuro-physiological (e.g., emotional) states of the user, using machine learning (ML) or other processes. An event detection module 208 may include functions for detecting events based on a measure or indicator of one or more biometric sensor inputs exceeding a data threshold. The modules may further include, for example, a normalization module 210. The normalization module 210 may include instructions that when executed by the processor 202 and/or 214 cause the server to normalize measures of valence, arousal, or other values using a baseline input.”).
Regarding claim 20, Chappell teaches the evaluation value estimation system according to claim 17, wherein the learning model is a prediction model that is generated through machine learning using reference coordinates and a reference evaluation value for training data, and that outputs the user evaluation value from the selected coordinates inputted, the reference coordinates being coordinates selected from the affect model, the reference evaluation value being a value concerning aspiration for the subject thing or a similar thing having an identical or similar attribute to the subject thing (¶ 85, “Target story arcs based on a video game or other application facilitating social interaction can be stored in a computer database as a sequence of targeted values in any useful neuro-physiological model for representing user neuro-physiological state in a social interaction, for example a valence/arousal model. Using the example of a valence/arousal model, a server may perform a difference calculation to determine the error between the planned/predicted and measured arousal and valence.”).
Regarding claim 21, Chappell teaches the evaluation value estimation system according to claim 20, wherein the reference coordinates are coordinates selected with regard to the subject thing or the similar thing from the affect model by a test user who evaluates the subject thing or the similar thing beforehand, and the prediction model is generated through machine learning using the reference coordinates and the reference evaluation value given by the test user for training data (¶ 90, “Referring again to the method 800 in which the foregoing expressions can be used (FIG. 8), a calibration process 802 for the sensor data is first performed to calibrate user reactions to known stimuli, for example a known resting stimulus 804, a known arousing stimulus 806, a known positive valence stimulus 808, and a known negative valence stimulus 810. The known stimuli 806-810 can be tested using a focus group that is culturally and demographically like the target group of users and maintained in a database for use in calibration. For example, the International Affective Picture System (ZAPS) is a database of pictures for studying emotion and attention in psychological research. For consistency with the content platform, images of these found in the IAPS or similar knowledge bases may be produced in a format consistent with the targeted platform for use in calibration.”).
Regarding claim 22, the claim recites substantially similar limitations to claim 1. Therefore, claim 22 is similarly rejected for the reasons set forth above with respect to claim 1.
Regarding claim 7, the claim recites substantially similar limitations to claim 4. Therefore, claim 7 is similarly rejected for the reasons set forth above with respect to claim 4.
Regarding claim 9, the claim recites substantially similar limitations to claim 18. Therefore, claim 9 is similarly rejected for the reasons set forth above with respect to claim 18.
Regarding claim 10, the claim recites substantially similar limitations to claim 19. Therefore, claim 10 is similarly rejected for the reasons set forth above with respect to claim 19.
Regarding claim 11, the claim recites substantially similar limitations to claim 20. Therefore, claim 11 is similarly rejected for the reasons set forth above with respect to claim 20.
Regarding claim 12, the claim recites substantially similar limitations to claim 21. Therefore, claim 12 is similarly rejected for the reasons set forth above with respect to claim 21.
Regarding claim 23, the claim recites substantially similar limitations to claim 17. Therefore, claim 23 is similarly rejected for the reasons set forth above with respect to claim 17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA GURSKI whose telephone number is (571)270-5961. The examiner can normally be reached Monday to Thursday 7am to 5pm EST.
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/AMANDA GURSKI/Primary Examiner, Art Unit 3625
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