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 .
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.
Regarding Claim 1 – The claim recites a biological information acquiring unit that acquires time-series information... See MPEP 2181. The claim limitation uses the term biological information acquiring unit. The “biological information acquiring unit” is modified by functional language “that acquires time-series information….” The biological information acquiring unit is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0087]-[0088] describes hardware such as a sensor (electrode) that can be used to obtain biological signal data of the user. For examination purposes the biological information acquiring unit is construed to be hardware such as a processor.
Regarding Claim 1 – The claim recites a prediction information acquiring unit that acquires prediction information... See MPEP 2181. The claim limitation uses the term a prediction information acquiring unit. The “a prediction information acquiring unit” is modified by functional language “that acquires prediction information….” The prediction information acquiring unit is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0071]-[0072] describes the prediction information acquiring unit to be carried out by a processing unit. For examination purposes the prediction information acquiring unit is construed to be hardware such as a processor.
Regarding Claim 1 – The claim recites a future information output unit that outputs future information... See MPEP 2181. The claim limitation uses the term a future information output unit. The “a future information output unit” is modified by functional language “that acquires prediction information….” The future information output unit is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0078] describes the future information output unit to be a display. For examination purposes the future information output unit is construed to be hardware such as a display.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is 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 limitation 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 § 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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-11 are drawn to a processing apparatus, which is within the four statutory categories (i.e. apparatus). Claim 12 is drawn to a processing method, which is within the four statutory categories (i.e. process). Claim 13 is drawn to a non-transitory computer readable medium, which is within the four statutory categories (i.e. manufacture).
Step 2A of the Alice/Mayo Test - Prong One
The independent claim 1 (and substantially similar with independent claims 12, 13) recites:
An information processing apparatus comprising:
a biological information acquiring unit that acquires time-series information on a status of a living body based on measurement results of a biological signal of a user;
a prediction information acquiring unit that acquires prediction information on future time- series information on the user, using learning information configured using time-series information acquired from measurement results of a biological signal in the past, and the time-series information; and
a future information output unit that outputs future information on a future status of the user based on the prediction information.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the information processing apparatus, biological information acquiring unit, prediction information acquiring unit, future information output unit, non-transitory computer readable medium enabling a processor, the limitations in the context of this claim encompass following rules or instructions to organize user biological information in order to provide the user with predictions on the future status of the user. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-11 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the information processing apparatus, biological information acquiring unit, prediction information acquiring unit, future information output unit, non-transitory computer readable medium enabling a processor, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0071]-[0072], [0078], [0087]-[0088], [0144], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-11 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-11 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the information processing apparatus, biological information acquiring unit, prediction information acquiring unit, future information output unit, non-transitory computer readable medium enabling a processor, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0071]-[0072], [0078], [0087]-[0088], [0144]); using the information processing apparatus, biological information acquiring unit, prediction information acquiring unit, future information output unit, non-transitory computer readable medium enabling a processor, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-2, 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Westerink et al. (US 2020/0100718).
Regarding claim 1, Westerink discloses an information processing apparatus comprising:
a biological information acquiring unit that acquires time-series information on a status of a living body based on measurement results of a biological signal of a user; ([0004] a skin conductance sensor for sensing the skin conductance of the user, the skin conductance over time forming a skin conductance trace [0033] In an embodiment the psychophysiological signal trace is a skin conductance signal trace indicative of a skin conductance of the user over time. An advantage of this embodiment is that the skin conductance can be conveniently measured, for example, with a wearable device and has been found to provide a reliable indication of the neural stress response of the user.
a prediction information acquiring unit that acquires prediction information on future time- series information on the user, using learning information configured using time-series information acquired from measurement results of a biological signal in the past, and the time-series information; and ([0034] Optionally, the processing unit can be configured to determine said first contribution to the estimated future cortisol level trace in an upcoming second predetermined window based on the one or more determined stimulus responses in the psychophysiological signal trace in a past first predetermined time window; and to determine said second contribution to the estimated future cortisol level trace in the upcoming second predetermined time window based on one or more anticipated future stimulus responses, which are expected to occur in said second predetermined time window [0040] Optionally, the processing unit can be configured to determine said second contribution to the estimated future cortisol level trace based on historic data indicative of one or more anticipated future stimulus responses)
a future information output unit that outputs future information on a future status of the user based on the prediction information. ([0049] Based on the estimated future emotional state of the user an output can be rendered to the user, for example in the form of an advice not to drive a car for such time period into the future [0085] an output unit for outputting the emotional state of the user 1… for example a smartphone running a corresponding app, can be used and coupled to the device 10 or wearable device 30).
Regarding claim 2, Westerink discloses the information processing apparatus according to claim 1, wherein the prediction information acquiring unit includes a pre-processing unit that performs predetermined filter processing on the time-series information, and the prediction information acquiring unit acquires the prediction information, using the time- series information after the processing by the pre-processing unit. (Westerink [0098] Advantageously, as also shown in FIG. 10, the psychophysiological trace or the stimulus responses determined based thereon can be used in reversed form using the most recent stimulus responses in the near future and the less recent measurement values or stimulus responses in the further future. By mirroring the input this way, the near future estimates will become more reliable than further future estimates, which can be advantageous for coaching a user. In addition or in the alternative, since the most recent time is usually most reliable, it is also possible to consider the most recent psychophysiological signal trace and/or stimulus responses determined based thereon several times. Optionally, an average future stimulus response can be anticipated per time segment, for example an average expected stimulus response contribution per minute, for example by taking an average of one or more determined stimulus responses in a predetermined past time window.
Regarding claim 12, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 13, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
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 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.
Claim 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Westerink in view of Hassani, ("A Brief Introduction to Singular Spectrum Analysis," 2010).
Regarding claim 3, Westerink discloses the information processing apparatus according to claim 2, but does not appear to explicitly disclose the following, however, Hassani teaches it is old and well known in the art of healthcare data processing wherein the filter processing is singular spectrum analysis. (Hassani pg. 1 a powerful technique known as Singular Spectrum Analysis (SSA) has been developed in the field of time series analysis. SSA is novel and powerful technique applicable to many practical problems such as the study of classical time series, multivariate statistics, multivariate geometry, dynamical systems and signal processing).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Westerink, to incorporate wherein the filter processing is singular spectrum analysis. (Hassani pg. 1 a powerful technique known as Singular Spectrum Analysis (SSA) has been developed in the field of time series analysis, as taught by Hassani, in order to process time-series signal data and to forecast new data points. See Hassani pg. 1.
Regarding claim 4, Westerink-Hassani teaches the information processing apparatus according to claim 3, and Hassani further teaches wherein the pre-processing unit is configured to acquire the time-series information after the processing, using results having degrees up to a predetermined highest degree obtained through the singular spectrum analysis, and the predetermined degree is 1 or higher and 10 or lower. (Hassani pg. 1 a powerful technique known as Singular Spectrum Analysis (SSA) has been developed in the field of time series analysis. SSA is novel and powerful technique applicable to many practical problems such as the study of classical time series, multi variate statistics, multivariate geometry, dynamical systems and signal processing; pg. 3 select a group of l (1 ≤ l ≤ L) eigen–vectors… (Reconstruction of the one-dimensional series): compute the matrix ˜ X = ||˜ k=1 Pik PT ik X as an approximation to X. Transition to the one–dimensional series can now be achieved by averaging over the diagonals of the matrix ˜ X {one dimensional matrix construed as degree between 1 and 10}).
Claim 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Westerink in view of Kim et al. (US 2023/0368879).
Regarding claim 5, Westerink discloses the information processing apparatus according to claim 1, but does not appear to explicitly disclose the following, however, Kim teaches it is old and well known in the art of healthcare data processing wherein the prediction information acquiring unit acquires the prediction information using a machine learning method, using the learning information and the time-series information. (Kim [0052] Returning to FIG. 2, the learning system 225 obtains information from the records datastore 280 on one or more subjects and applies machine-learning and/or artificial intelligence models to generate predictions. [0056] The learning system 225 may train the machine-learning models using training data including one or more training instances. A training instance includes training input data and output data… for training a model on ADR prediction, the training input data may include known longitudinal health and medical history obtained for a previous patient from the records datastore 280, and the training output data may include a known ADR that occurred for the patient during a relevant period of time).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Westerink, to incorporate wherein the prediction information acquiring unit acquires the prediction information using a machine learning method, using the learning information and the time-series information, as taught by Kim, in order to generate predictions using specific relevant periods of time applicable to the predicted likelihoods. See Kim [0052].
Regarding claim 6, Westerink-Kim discloses the information processing apparatus according to claim 5, and Kim further teaches wherein the learning information constitutes a neural network model with an encoder and a decoder using Transformer. (Kim [0052] Returning to FIG. 2, the learning system 225 obtains information from the records datastore 280 on one or more subjects and applies machine-learning and/or artificial intelligence models to generate predictions. [0055] The models are configured as one or a combination of artificial neural networks (ANNs), recurrent neural networks (RNNs), deep learning neural networks (DNNs), bi-directional neural networks, transformers, classifiers such as support vector machines (SVMs), decision trees, regression models such as logistic regression, linear regression, stepwise regression, generative models such as transformer-based architectures including GPT, BERT, encoder-decoders, variational autoencoders (VAE's), diffusion models, cross-modal transformer architectures, and the like. The trained models are stored in the model datastore 285).
Regarding claim 7, Westerink-Kim discloses the information processing apparatus according to claim 6, and Kim further teaches wherein the decoder of the neural network model is configured not to perform autoregression. (Kim [0055] The models are configured as one or a combination of artificial neural networks (ANNs), recurrent neural networks (RNNs)).
Regarding claim 8, Westerink-Kim discloses the information processing apparatus according to claim 5, and Kim further teaches wherein the learning information constitutes a neural network model using an Informer method, the neural network model being a model with an encoder and a decoder using Transformer without using either a ProbSparse Self Attention method or an Encoder-distilling method. (Kim [0055] The models are configured as one or a combination of artificial neural networks (ANNs), recurrent neural networks (RNNs), deep learning neural networks (DNNs), bi-directional neural networks, transformers, classifiers such as support vector machines (SVMs), decision trees, regression models such as logistic regression, linear regression, stepwise regression, generative models such as transformer-based architectures including GPT, BERT, encoder-decoders, variational autoencoders (VAE's), diffusion models, cross-modal transformer architectures, and the like. The trained models are stored in the model datastore 285).
Claim 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Westerink in view of Burton et al. (JP 2005/506115 A).
Regarding claim 9, Westerink discloses the information processing apparatus according to claim 1, but does not appear to disclose the following, however, Burton teaches it is old and well known in the art of healthcare data processing wherein the time-series information is information on transition of LF or HF related to heartbeat variability of the user, based on information showing an electrocardiogram of the user. (Burton [0009] State-of-the-art technologies provide a series of systems for monitoring patients while receiving anesthetic delivery, all of which are “blocked” or unconscious states (hypnosis, unconsciousness, sleep ), Auditory recall states (including auditory evoked potentials, complex frequencies, sensitive states), muscle paralysis, movement, and arousal states (including analysis of arousal and body movements), visual recall states (open and Monitor and validate a range of sensory parameters sufficient to monitor eye movements) and anxiety and stress conditions (including analysis of body temperature, blood pressure, oxygen saturation SAO2, heart rate variability, skin current measurement resistance [0030] uses spectral analysis to resolve it to various frequency components (ie LF-0.05-0.15 Hz, HF-0.15-0.5 Hz). HRV frequencies can be represented [0140] One such qualitative method is to qualitatively obtain a pulse transient time (PTT) by calculation using an electrocardiogram (ECG) waveform and a pulse waveform of a subject. The ECG waveform is usually obtained from an ECG surface electrode attachment placed on the chest).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Westerink, to incorporate wherein the time-series information is information on transition of LF or HF related to heartbeat variability of the user, based on information showing an electrocardiogram of the user, as taught by Burton, in order to monitor a person’s heart rate variability and predict how it relates to other conditions such as stress or the subject’s blood pressure, thus assisting medical personnel monitor patient’s vitals. See Burton [0025]-[0031].
Regarding claim 10, Westerink discloses the information processing apparatus according to claim 1, but does not appear to disclose the following, however, Burton teaches it is old and well known in the art of healthcare data processing wherein the time-series information is information on transition of LF or HF related to heartbeat variability, and the future information output unit acquires, as the future information, information on a time period during which the user is likely to feel stressed or a time period during which the user is likely to be relaxed in a predetermined period of time, based on the prediction information, and outputs the acquired future information. (Burton [0009] State-of-the-art technologies provide a series of systems for monitoring patients while receiving anesthetic delivery, all of which are “blocked” or unconscious states (hypnosis, unconsciousness, sleep ), Auditory recall states (including auditory evoked potentials, complex frequencies, sensitive states), muscle paralysis, movement, and arousal states (including analysis of arousal and body movements), visual recall states (open and Monitor and validate a range of sensory parameters sufficient to monitor eye movements) and anxiety and stress conditions (including analysis of body temperature, blood pressure, oxygen saturation SAO2, heart rate variability, skin current measurement resistance [0030] uses spectral analysis to resolve it to various frequency components (ie LF-0.05-0.15 Hz, HF-0.15-0.5 Hz). HRV frequencies can be represented [0140] One such qualitative method is to qualitatively obtain a pulse transient time (PTT) by calculation using an electrocardiogram (ECG) waveform and a pulse waveform of a subject. The ECG waveform is usually obtained from an ECG surface electrode attachment placed on the chest [0027] The study also presents the relationship between heart rate variability (HRV) and people reporting anxiety and perceived stress, and the relationship between the subject's blood pressure and heart rate and activities associated with increased stress (55, 56, 57)). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 11, Westerink discloses the information processing apparatus according to claim 1, but does not appear to disclose the following, however, Burton teaches it is old and well known in the art of healthcare data processing wherein the time-series information is information showing a PPG waveform of the user based on pulse waves of the user. (Burton [0140] The pulse waveform is obtained from a plethysmograph pulse waveform of a pulse oximeter probe attachment located on a patient's finger or the like). The motivation to combine the references is discussed above and incorporated herein.
Conclusion
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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