DETAILED ACTION
This action is in response to claims filed 12 December 2023 for application 18536589 filed 12 December 2023. Currently claims 1-11 are pending.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 limitation(s) 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 limitation(s) is/are: “first acquisition unit”, “second acquisition unit”, “training unit” and “prediction unit” in claim 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites AU intensities, however, does not define the acronym/abbreviation ‘AU’. Correction is required. AU has been interpreted as any quantifiable feature of the user.
Claim 1, 10 and 11 limitations “step of acquiring”, “step of training”, “step of predicting”, “first acquisition unit”, “second acquisition unit”, “training unit” and “prediction unit” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. No structure other than computer program product in [0054] has been identified. Therefore, the claims are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-9 are rejected based upon their dependence on claim 1.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 1, 10 and 11 are directed to the statutory category of a method, a system, and a method.
In step 2a prong 1, claims 1, 10 and 11 recite, in part, acquiring personality indicators, acquiring external features of a user, training a personality prediction model, and predicting personality indicators. The limitations of acquiring, training and predicting are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind with or without aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. The claims do not recite any additional elements and therefore cannot include a practical application.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the lack of additional elements precludes the claims from amounting to significantly more than the abstract idea itself. The claims are not patent eligible.
Claims 2-9 recite further limitations of using a survey, indicators are one of the big five personality traits, external features are AU intensities, wherein the features are facial expression or actions, features change with time, quantifying levels of contribution of each feature to indicators, normalizing external features, and prediction is performed in real time. These limitations amount to the same abstract idea identified above. None of these limitations introduce further additional elements and therefore do not amount to a practical application in step 2a prong 2 or significantly more than the abstract idea itself in step 2b.
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.
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.
Claim(s) 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Boyarshinov (US 20180075483 A1)(hereinafter “Boyar”) in view of Jaidka et al. (US 20170270544 A1) and further in view of Hayes et al. (US 20220192562).
Regarding claims 1, 10 and 11, Boyar discloses: A personality prediction method comprising:
a step of acquiring personality indicators (“ Personality scales may be generated for a particular person by collecting input data from a variety of source devices. The platform for computing the personality scales receives the input data and parses the data through computer perception channels, which can be any of audio, video, textual and other way of registering observable behavioral manifestations of an individual. Depending on the type of channel the input data goes through (i.e. audio, video, textual, etc.) an appropriate method of analysis is applied to generate observable behavioral manifestations. For example, an audio/visual data may be processed using computer vision, computer hearing or natural language processing algorithms. The observable behavioral manifestations may be used to extract diagnostic features or invariants, which are digitized measurable representations of behavioral aspects of the person.” [0018]);
a step of acquiring external features of the user ([0018-26] indicate external features);
a step of training a personality prediction model with correlations between the acquired external features and the personality indicators (“The machine learning models may be trained using data received from the users of the system. The models may be trained periodically or continuously. The models may be trained using social data that the system has collected. The machine learning model further correlate the personal profile obtained from one source with a second personality profile obtained from other sources.” [0061]); and
a step of predicting personality indicators of the user from the external features of the user by using the trained personality prediction model (“Personality scales, known also as psychometric scales, represent a model of a customer's personality and can be used by other modules of the system to predict customer's behavior in certain situations, to configure products and services targeted exactly at his needs and to issue recommendations to customer managers about the most effective communication strategies.” [0087]).
Boyar does not explicitly disclose: personality indicators representing personalities of a user; and
time-series data.
Jaidka discloses personality indicators representing personalities of a user (“In accordance with an embodiment, a character profile for a person is derived based on user-input data. In the examples provided herein, the user-input data includes, but is not limited to, textual input data, clickstream data, order information, and survey-response data.” [0023], “As used herein, a “character dimension” or “psychographic dimension” is intended to refer to a personality trait, a cognitive trait, or a situational trait of a person. A personality trait describes a characteristic or quality that forms an individual's distinctive persona and includes, but is not limited to, “openness”, “conscientiousness”, “extraversion”, “agreeableness”, and “neuroticism”.” [0021]).
Boyar and Jaidka are in the same field of endeavor of making predictions using personality data and are analogous. Boyar discloses the use of external features as indicators of personality. Jaidka teaches the use of the big five personality traits and their use in prediction. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the external features of personality as disclosed by Boyar by incorporating the known five personality traits as taught by Jaidka to yield predictable results as these traits are known to be useful in making predictions for individuals with those traits [Jaidka 0021].
Hayes teaches time series data (“ At block 810, intention-behavior integration module 800 may execute a neural network, e.g. a recurrent neural network or another neural network suitable to operate over time series data, and may provide the neural network with, for example, intention 305 records, goal 365 records, task 325 records, behavior 310 records, outcome 340 records, peer 360 records, and personality type matrix 330 records; in so doing, intention-behavior integration module 800 may encode the records as vectors, tensors, or the like and may match the size of the encoded records to the input size of the neural network.” [0090]).
Boyar, Jaidka and Hayes are in the same field of endeavor of making predictions using personality data and are analogous. Boyar discloses the use of external features as indicators of personality. Jaidka teaches the use of the big five personality traits and their use in prediction. Hayes teaches the use of personality matrix data as time series data. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the external feature and personality trait prediction as taught by Boyar and Jaidka by incorporating the known time series personality data as taught by Hayes to yield predictable results of allowing certain types of models to process the personality time series data [Hayes 0090].
Regarding claim 2, Boyar does not explicitly disclose, however, Jaidka teaches: The personality prediction method of claim 1, wherein the personality indicators are identified through a survey for the user (“In accordance with an embodiment, a character profile for a person is derived based on user-input data. In the examples provided herein, the user-input data includes, but is not limited to, textual input data, clickstream data, order information, and survey-response data.” [0023]).
Regarding claim 3, Boyar does not explicitly disclose, however, Jaidka teaches: The personality prediction method of claim 2, wherein the personality indicators comprise an indicator representing openness to experience, an indicator representing conscientiousness, an indicator representing extraversion, an indicator representing agreeableness, and an indicator representing neuroticism (“As used herein, a “character dimension” or “psychographic dimension” is intended to refer to a personality trait, a cognitive trait, or a situational trait of a person. A personality trait describes a characteristic or quality that forms an individual's distinctive persona and includes, but is not limited to, “openness”, “conscientiousness”, “extraversion”, “agreeableness”, and “neuroticism”.” [0021]).
Regarding claim 4, Boyar discloses: The personality prediction method of claim 2, wherein the external features of the user are AU intensities ([0018-26] indicate external features that are interpreted as the AU intensities).
Regarding claim 5, Boyar discloses: The personality prediction method of claim 4, wherein the external features of the user comprise at least one of facial expressions and actions of the user ([0018-26] indicate both facial expressions and actions).
Regarding claim 6, Boyar discloses: The personality prediction method of claim 1, wherein, at the step of training, a plurality of external features change with time (“Static customer data is data that does not tend to change in real time. Examples of static data elements include a customer's name and address. Dynamic customer data is data that is changing in real-time. For example, dynamic customer data could include, without limitation, customer behavior and psychometric data extracted from photos, voice data from a call, a skype conversation, a video or the like.” [0091]).
Boyar does not explicitly disclose, however, Jaidka teaches: but personality indicators do not change with time (“The method of claim 1, wherein a personality trait describes a characteristic or quality that forms an individual's distinctive persona.” Claim 5).
Regarding claim 7, Boyar discloses: The personality prediction method of claim 6, wherein the step of training comprises quantifying levels of contribution of each external feature to respective personality indicators (“The machine learning models may be trained using data received from the users of the system. The models may be trained periodically or continuously. The models may be trained using social data that the system has collected. The machine learning model further correlate the personal profile obtained from one source with a second personality profile obtained from other sources.” [0061], see also [0064] and [0072]).
Regarding claim 8, Boyar discloses: The personality prediction method of claim 7, wherein the step of predicting comprises normalizing the external features by using averages of the levels of contribution of the external features to the respective personality indicators, and inputting the normalized external features to the personality prediction model (“Human personality is a complicated multilayer structure of measurable personality traits that may be represented by normalized personality scales which indicate the strength of manifestation of a personality trait of a person. The benefit of using personality scales when performing human personality diagnostics is that personality scales allow for the adoption new personality scales without affecting (or invalidating) results of previous human personality diagnostics. The structure of human personality can be stored and represented as an ontological graph, which describes the personality scales, methodologies, cognition channels, perception invariants (also referred to as diagnostic features) and all the relations between them.” [0017], see also [0064] and [0072]).
Regarding claim 9, Boyar discloses: The personality prediction method of claim 1, wherein the step of predicting is performed in real time (“Applying the set of data models 504 to the customer behavior data 518 may yield a personal profile for the customer. This personal profile may be generated in real-time. The profile is then used to generate a recommendation of a product/service offer based on the mood of a customer, determine intentions of a potential shoplifter, or the like by applying data models to the personal profile. In one embodiment, a report may be generated based on the results of applying the data model to the personal profile.” [0109]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Celiktutan et al. (Automatic Prediction of Impressions in Time and across Varying Context: Personality, Attractiveness and Likeability) discloses personality prediction using external features and big five personality traits and time-series data.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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