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 .
Response to Arguments
Applicant’s arguments filed 2/2/2026 with respect to the 35 U.S.C. 102 and 103 rejections of claim(s) 1-18 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 2/2/2026 have been fully considered but they are not persuasive. The Applicant argues that Claims 1-18 are 35 U.S.C. 101 eligible because the specification of the instant application states that PCA simplifies and dimensionally compresses the walking parameters while minimizing data loss. The Examiner disagrees and respectfully submits that while the specification describes these properties of PCA, they are no recited in the claims. The claims recite only that the computer applies PCA to obtain a “tendency of walking”. A technical improvement must be reflected in the claim itself, not merely described in the specification. The rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 13 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The subject matter of Claim 13 is already present in Claim 11, and is therefore non-limiting. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-20 recites at least one step or instruction for detecting, storing and processing walking data of a subject and estimate the emotion of the subject from the walking data using PCA and/or corrections, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claims 1-20 recites interfaces, storage and computers configured to input, store and obtain data and to manipulate walking data in various fashions to estimate emotion using PCA and/or corrections (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Further, Claims 1-20 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of Claims 1-20 are not integrated into a practical application under 2019 PEG because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: an interface, storage, computer/circuitry are generically recited computer elements in Claims 1-20 which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in Claims 1-20 are not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., an interface, storage, computer/circuitry as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Claims 1-20 are not integrated into a practical application under the 2019 PEG.
Accordingly, Claims 1-20 are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-20 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of an interface, storage, computer/circuitry. The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification (see under Configuration of emotion estimating device, Page 9 line 5 to Page 11 line 7) show that Applicant understands their computer components are well understood, routine and conventional. Accordingly, in light of Applicant’s specification, the claimed terms an interface, storage, computer/circuitry are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the interface, storage, computer/circuitry. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in 1-20 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, Claims 1-20 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-20 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in Claims 1-20 do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-20 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-20 amount to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-4, 7, 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over “See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data” to Zhao et al. (hereinafter, Zhao) in view of “Recognition of Affect Based on Gait Patterns” in view of Karg et al. (hereinafter, Karg).
Regarding Claims 1, 9 and 10, Zhao discloses an emotion estimating device and method that estimates an emotion of a subject (Abstract “…a novel method for monitoring individual’s anxiety and depression based on the Kinect-recorded gait pattern”), the emotion estimating device comprising inter alia:
an interface configured to receive input of walking data of the subject measured by a measurement device (Data collection procedures “…all the participants were asked to walk on the footpath, back and forth naturally as their daily performance, for two minutes with Kinect cameras continuously shooting…”) (The Kinect is the measurement device. The system receives its walking data output as input.) and emotion data obtained by quantifying the emotion of the subject (Data collection procedures “…each participant was firstly required to complete a series of questionnaires… 7-item Generalized Anxiety Disorder Scale (GAD-7), which asks about the states in past two weeks to calculate an anxiety score, and the 9-item Patient Health Questionnaire-Depression (PHQ-9), which asks about the depressive symptoms in past two weeks to calculate a depression score.”) (The GAD-7 and PHQ-9 produce numerical scores and quantify the emotional state of the subject);
a storage configured to store the walking data and the emotion data received by the interface (Model training “The model training and testing process was conducted through WEKA3.8, a tool as the collection of machine learning algorithms for data mining tasks.”) (Supporting information “The dataset of the study.”) (Building and training a model in WEKA on a stored dataset require both storing both the walking data and the questionnaire scores together. The published dataset confirms both were retained together); and
a computer configured to obtain corresponding data in which a plurality of walking parameters included in the walking data stored in the storage are associated with the emotion data (Model training “…we trained models using five frequently used regression algorithms, i.e. Simple Linear Regression (SLR), Linear Regression(LR), epsilon-SVR (e-SVR), nu-SVR (n-SVR) and Gaussian Processes (GP)…”) (The trained regression model is the “corresponding data” and is the data structure in which the plurality of walking parameters are associated with the emotion scores),
wherein the computer is configured to apply an analysis to the plurality of walking parameters to obtain a tendency exerted on the plurality of walking parameters for the emotion of the subject (Feature Extraction “For feature extraction, Fast Fourier Transforms (FFT) were conducted... We calculated the amplitude of FFT Xk which converts the sampled function from its original domain (time domain) to the frequency domain for each joint axis (X, Y and Z), and got 64 amplitude coefficients from each axis as features.” and Feature Selection “…we conducted the Pearson correlation… The correlation coefficients were calculated between anxiety/depression score and each feature (FFT amplitude) on each axis. Then, on each axis, we selected the 5 features with the largest absolute value of correlation coefficients…”),
the computer is further configured to obtain the corresponding data through the analysis of the plurality of walking parameters (Abstract “Fast Fourier Transforms (FFT) were conducted to extract features from the Kinect-captured gait data after preprocessing…”) (The FFT analysis is applied to the walking data to produce the features that feed into the regression model and the corresponding data is obtained through this analysis.) and a multiple regression analysis with a score of the emotion data as an objective variable and the plurality of walking parameters included in the walking data as explanatory variables (Model training “To predict the anxiety and depression scores, we trained models using five frequently used regression algorithms, i.e. Simple Linear Regression (SLR), Linear Regression(LR), epsilon-SVR (e-SVR), nu-SVR (n-SVR) and Gaussian Processes (GP)…”) (Model training “The Pearson correlation coefficient between the predicted scores of each model and the questionnaire scores was calculated as the predictive accuracy index of each model.”) (The questionnaire/emotion scores are the prediction targets, i.e., the objective variable. The gait features extracted from the walking data are the inputs, i.e., the explanatory variables. Linear Regression and Simple Linear Regression are both standard multiple regression models. The regression runs from walking parameters toward emotion score, confirming the directionality of objective and explanatory variables)., and
when the input of the walking data is newly received in the interface, the computer is configured to estimate the emotion of the subject from the plurality of walking parameters included in the newly received walking data based on the corresponding data (Discussion “…our gait-based predictive model may be more suitable than questionnaires for monitoring the continuous change of anxiety/depression severity of individuals… we trained and tested predictive models based on the continuous gaits data as short as 64 frames (about 2s). It means that we could possibly get enough data clips while participants naturally passing by the Kinect…”) (The trained model accepts newly received gait data and outputs an estimates an emotional state score – this is the “newly received walking data” estimation step.), and
output information indicating the estimated emotion of the subject from the interface (Discussion “…our classification models with high accuracy could be used to detect some certain symptoms relevant to depression, such as losing interest or pleasure, feeling down or depressed, and low energy…” ) (Abstract “This approach shows the potential to develop non-intrusive, low-cost methods for monitoring individuals’ mental health in real time.”);
(Claim 2) wherein the walking data includes at least one parameter of the plurality of walking parameters including a stride and a walking speed (Introduction “…depressed patients showed significantly lower gait velocity, reduced stride length, increased double limb support and larger swing time variability.”);
(Claim 3) wherein the walking data includes at least two parameters of a stride, a walking speed and a swing phase period (Introduction “…depressed patients showed significantly lower gait velocity, reduced stride length, increased double limb support and larger swing time variability.”);
(Claim 4) wherein the walking data includes at least three parameters of a stride, a walking speed, and a swing phase period (Introduction “…depressed patients showed significantly lower gait velocity, reduced stride length, increased double limb support and larger swing time variability.”); and
(Claim 7) An emotion estimating system comprising: a measurement device configured to measure the walking data of the subject (The Kinect is the measurement device. The system receives its walking data output as input.); and the emotion estimating device according to claim 1 (see rejection of Claim 1, above).
Zhao discloses the claimed invention except for expressly disclosing wherein the computer is configured to apply a main component analysis to the plurality of walking parameters to obtain a tendency exerted on the plurality of walking parameters for the emotion of the subject, where the computer is further configured to obtain the corresponding data through the main component analysis of the plurality of walking parameters.
However, Krag teaches extracting emotion for gait data using a mathematical analysis followed by a model – specifically, Krag teaches Principal component analysis (PCA), kernel PCA, linear discriminant analysis and general discriminant analysis are compared to either reduce temporal information in gait or extract relevant features for classification (Abstract “Principal component analysis (PCA), kernel PCA, linear discriminant analysis, and general discriminant analysis are compared to either reduce temporal information in gait or extract relevant features for classification.”) and concludes that “gait can be used as an additional modality for the recognition of affect…” (Abstract). One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify Zhao’s FFT and Pearson correlation feature extraction step, while retaining Zhao’s regression pipeline with emotion score as the objective variable and walking parameters as the explanatory variables, as Zhao expressly discloses that the FFT approach has a deficiency – “low-level features in our study (FFT amplitudes) may not provide any intuitive understanding of individual’s gait…” (Discussion) and Karg directly answers this deficiency by demonstrating the PCA on gait parameters extracts interpretable, high-level components revealing that emotion affects walking parameters and states in the Conclusion that PCA outperforms many other techniques.
Claim(s) 5, 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Karg as applied to claim 1 above, and further in view of “How we talk affects what we remember …” to Michalak et al. (hereinafter, Michalak).
Zhao in view of Karg disclose (Claim 8) an emotion estimating system comprising a measurement device configured to measure the walking data of the subject (The Kinect is the measurement device. The system receives its walking data output as input.) and the emotion estimating device according to claims 5 and 6 as follows:
Zhao in view of Karg does not expressly disclose (Claims 5 and 6) wherein when the emotion of the subject newly estimated from the walking data is discomfort, the computer is configured to obtain the walking data associated with comfort from the corresponding data, and output a walking advice from the interface in order to bring the walking data estimated as discomfort to the walking data associated with the comfort.
However, ‘916 teaches that gait features drive emotion classification, e.g., there exists gait signatures for emotions and retrieving a variety of emotional targets, including first and second emotional targets and collecting the first and second data related to such emotional targets (page 6, paragraph 1) and further once an emotion is classified making changes to adjust the user’s emotions (page 7, S4, sentiment analysis, paragraph 4). ‘916 further teaches that both happiness and sadness can be detected in gait (page 3, paragraph 3).
The combination of Zhao in view of Karg does not expressly disclose obtaining specific discomfort and comfort walking data and output walking advice to bring the walking data from discomfort to comfort, however, Michalak establishes a link between discomfort type walking and comfort type walking (page 121, col. 2 to page 122, col. 1 “In a comprehensive analysis of gait characteristics in patients suffering from a current episode of major depression, for example, it has been demonstrated that patients not only showed reduced walking speed (i.e., activity level) but also smaller arm-swing amplitudes, smaller amplitude of vertical movements of the upper body, larger amplitudes of lateral body sway, and a more slumped and forward-leaning posture than health controls.”) and further teaches at page 125, col. 1 that changing gait patterns from discomfort walking/slow walking to comfort walking/fast walking would have decrease the suffering of depressed people.
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify Zhao in view of Karg with ‘916, because ‘916 teaches that specific gait features can be used to classify emotions from the gait features (Abstract) and would have naturally selected these gait parameters as inputs to an emotion estimator because ‘916 expressly teaches that this specific gait metrics drive emotion classification.
Furthermore, one having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify Zhao in view of Karg with Michalak, because Michalak teaches at page 125, col. 1 that changing gait patterns from discomfort walking/slow walking to comfort walking/fast walking would have decrease the suffering of depressed people. In summary, as skilled artisan would have recognized that the combination of references would have suggested that walking characteristics can be adjusted to change the mood of the participant.
Conclusion
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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/SEAN P DOUGHERTY/Primary Examiner, Art Unit 3791