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. Such claim limitation(s) is/are: vibration creators, vibration mapping unit, artificial intelligence processing unit, artificial intelligence mapping module, noise correction module, gain correction module, environment assessment module, medium assessment module, conditioning unit, converter, a training module, a Fast Fourier Transform mapping module, in claims 1-18.
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. Paragraph 0053 of the Specification sates that different modules may comprise one or more hardware, software and firmware components.
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 § 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(s) 1-6, 11-15, 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lal et al. (US 2021/0003534 A1), hereinafter Lal.
Regarding Claim 1, Lal teaches: A system for creating and mapping vibrations for micro-vibration assessment, the system comprising:
one or more vibration creators configured to generate one or more vibration signals comprising at least one of predetermined patterns and intensities in a medium (paragraph 0006);
one or more sensors configured to detect and capture the one or more vibration signals propagated in the medium (paragraph 0006);
a vibration mapping unit configured to analyse the captured vibration signals to generate and communicate a vibration map (paragraph 0196); and
an artificial intelligence processing unit configured to isolate and identify one or more of individual signals, signal patterns, and signatures of the captured vibrations signals by using the vibration map communicated by the vibration mapping unit, for providing an analysis of micro-vibrations to a user device (paragraph 0197).
Regarding Claim 2, Lal teaches: The system of claim 1, wherein the one or more vibration creators comprise at least one of piezoelectric transducers, electromagnetic actuators, acoustic transducers, vibration motors, pneumatic actuators, mechanical shakers, and ultrasonic transducers (paragraph 0006-0009).
Regarding Claim 3, Lal teaches: The system of claim 1, wherein the medium comprises at least one of a human body, a mattress, furniture, a civil infrastructure, and a material, wherein the medium is an environment or substance used to assess and analyze micro-vibrations (paragraph 0006-0009).
Regarding Claim 4, Lal teaches: The system of claim 1, wherein the one or more sensors comprise at least one of piezoelectric sensors and force sensors for enabling detection of at least one of pressure, weight distribution, and one or more physiological parameters within the medium (paragraph 0006-0009).
Regarding Claim 5, Lal teaches: The system of claim 4, wherein the force sensors are configured to sense one or more forces applied on the medium from external sources and communicate the detected vibrations or forces to the vibration mapping unit, wherein the vibration mapping unit is configured to identify one or more material properties to optimize signal conditioning and measuring resulting abnormalities in the medium (paragraph 0006-0009; 0067).
Regarding Claim 6, Lal teaches: The system of claim 1, wherein the vibration map generated by the vibration mapping unit comprises a visual representation of the vibration signals captured by the one or more sensors (figures 2C, 2D, figure 24).
Regarding Claim 11, Lal teaches: The system of claim 1, wherein the user device is configured to monitor the vibrations in real time and analyze the one or more individual signals provided by the artificial intelligence processing unit and facilitating real time feedback to a user (paragraph 0149).
Regarding Claim 12, Lal teaches: A method for creating and mapping vibrations for micro-vibration assessment, the method comprising:
generating, by one or more vibration creators, one or more vibration signals comprising at least one of predetermined patterns and intensities in a medium (paragraph 0006);
detecting and capturing, by one or more sensors, one or more vibration signals propagated in the medium (paragraph 0006);
analyzing, by a vibration mapping unit, the captured vibration signals to generate and communicate a vibration map (paragraph 0196); and
isolating and identifying, by an artificial intelligence processing unit, one or more of individual signals, signal patterns, and signatures of the captured vibrations signals by using the vibration map communicated by the vibration mapping unit, for providing an analysis of the micro-vibrations to a user device (paragraph 0197).
Regarding Claim 13, Lal teaches: The method of claim 12, comprising the one or more sensors comprise at least one of piezoelectric sensors and force sensors for enabling detection of at least one of pressure, weight distribution, and one or more physiological parameters within the medium (paragraph 0006-0009).
Regarding Claim 14, Lal teaches: The method of claim 13, comprising configuring the force sensors for sensing one or more forces applied on the medium from external sources, and communicate the detected vibrations or forces to the vibration mapping unit, wherein the vibration mapping unit is configured to identify one or more material properties to optimize signal conditioning and measuring resulting abnormalities in the medium (paragraph 0006-0009; 0067).
Regarding Claim 15, Lal teaches: The method of claim 12, comprising generating the vibration map comprising a visual representation of the vibration signals captured by the one or more sensors (figures 2C, 2D, figure 24).
Regarding Claim 18, Lal teaches: The method of claim 12, comprising monitoring, by the user device, the vibrations and analyzing the one or more individual signals, in real-time, provided by the artificial intelligence processing unit and facilitating real-time feedback to a user (paragraph 0149).
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.
Claim(s) 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lal in view of Saatchi et al. (WO 2018146479 A1), hereinafter Saatchi.
Regarding Claim 9, Lal teaches: The system of claim 1, but does not mention explicitly wherein the artificial intelligence processing unit comprises:
a training module configured to generate at least one training set incorporating clean and standardized signals, and apply one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals; and
a Fast Fourier Transform (FFT) mapping module configured to compute at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals.
Saatchi teaches a training module configured to generate at least one training set incorporating clean and standardized signals, and apply one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals (page 30 lines 4-9); and
a Fast Fourier Transform (FFT) mapping module configured to compute at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals (page 29 lines 10-14). It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the system to include wherein the artificial intelligence processing unit comprises:
a training module configured to generate at least one training set incorporating clean and standardized signals, and apply one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals; and
a Fast Fourier Transform (FFT) mapping module configured to compute at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals in order to get cleaner and more accurate data.
Regarding Claim 10, Lal teaches: The system of claim 1. While Lal mentions signal processing circuitry, Lal does not mention explicitly wherein the one or more individual signals comprise one or more of a clean vibration signal, a medium noise, an environmental noise, an object noise, a stationary noise and a non-stationary noise.
Saatchi teaches determining noise and using CMMR (page 24). It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the system to include wherein the one or more individual signals comprise one or more of a clean vibration signal, a medium noise, an environmental noise, an object noise, a stationary noise and a non-stationary noise in order to obtain a clean signal.
Regarding Claim 17, Lal teaches: The method of claim 12, but does not explicitly mention comprising configuring the artificial intelligence processing unit by:
generating, by a training module, at least one training set incorporating clean and standardized signals, and applying one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals; and
computing, by Fast Fourier Transform (FFT) mapping module, at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals.
Saatchi teaches:
configuring the artificial intelligence processing unit by:
generating, by a training module, at least one training set incorporating clean and standardized signals, and applying one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals (page 30 lines 4-9); and
computing, by Fast Fourier Transform (FFT) mapping module, at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals (page 29 lines 10-14).
It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the method comprising configuring the artificial intelligence processing unit by:
generating, by a training module, at least one training set incorporating clean and standardized signals, and applying one or more of sources, levels, and interactions of the one or more individual signals to produce the clean vibration signals; and
computing, by Fast Fourier Transform (FFT) mapping module, at least one Fast Fourier Transform (FFT) of the one or more captured vibration signals in order to get cleaner and more accurate data.
Claim(s) 7, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lal in view of Saatchi further in view of Hirano et al. (US 20200113513 A1), hereinafter Hirano.
Regarding Claim 7 and 17, Lal teaches: The system of claim 1, and method of claim 12, wherein the vibration mapping unit comprises:
an artificial intelligence mapping module configured to analyze the vibration map and identify patterns to enhance the accuracy and reliability of the system (paragraph 0197, 0185, 0200);
a medium assessment module configured to assess and analyse the at least one of pressure, weight distribution, and one or more physiological parameters of the medium (paragraph 0009);
a controller configured to manage the activation, timing, and intensity of the one or more vibration creators, ensuring control over the vibration generation process (paragraph 0272);
a power unit configured to supply power to one or more components of the system, ensuring continuous and stable operation (paragraph 0236);
a conditioning unit configured to prepare the detected and captured one or more sensors data for analysis, wherein the analysis comprises amplification, filtering, and other pre-processing of captured one or more sensors data (paragraph 0136; 0217-0220);
a transmitter configured to transmit the one or more sensors data to a remote receiver, enabling remote data analysis and real-time monitoring of the medium (paragraph 0197);
a converter configured to transform input signals into a vectorized format in a windowed fashion, facilitating the capture of time, frequency, and domain-specific signal features, thereby enabling advanced signal processing and analysis (paragraph 0157); and
the receiver configured to collect and receive the one or more sensors data transmitted by the transmitter, which is further processed or displayed for user monitoring or analysis (paragraph 0205).
Lai does not explicitly mention a noise correction module configured to identify and filter unwanted environmental vibrations or noise that interfere with accurate detection of micro-vibrations;
a gain correction module configured to adjust the sensitivity of the one or more sensors to ensure accurate detection of clean vibration signals, optimizing the system performance by accounting for variations in signal strength;
an environment assessment module configured to assess and analyse the environmental conditions of the medium, and factors comprising temperature, humidity, and external vibrations, to provide context for vibration data.
Saatchi teaches a noise correction module configured to identify and filter unwanted environmental vibrations or noise that interfere with accurate detection of micro-vibrations (page 24);
a gain correction module configured to adjust the sensitivity of the one or more sensors to ensure accurate detection of clean vibration signals, optimizing the system performance by accounting for variations in signal strength (page 24);
It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the system to include a noise correction module configured to identify and filter unwanted environmental vibrations or noise that interfere with accurate detection of micro-vibrations;
a gain correction module configured to adjust the sensitivity of the one or more sensors to ensure accurate detection of clean vibration signals, optimizing the system performance by accounting for variations in signal strength, for better signal processing.
Hirano teaches an environment assessment module configured to assess and analyse the environmental conditions of the medium, and factors comprising temperature, humidity, and external vibrations, to provide context for vibration data (paragraph 0190, 0324, 0336, 0344).
It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the system and method to include an environment assessment module configured to assess and analyse the environmental conditions of the medium, and factors comprising temperature, humidity, and external vibrations, to provide context for vibration data.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lal in view of Singh (US 20230360388 A1).
Regarding Claim 8, Lal teaches: The system of claim 1, but does not mention explicitly wherein the artificial intelligence processing unit comprises at least one generative model comprising at least one of Generative Adversarial Networks (GANs) and Large Language Models (LLMs), to derive the one or more individual signals of the one or more captured vibration signals.
Singh teaches that generative model comprising at least one of Generative Adversarial Networks (GANs) and Large Language Models (LLMs) are known in the art as part of AI systems (paragraph 0062; 0150). It would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the system to include wherein the artificial intelligence processing unit comprises at least one generative model comprising at least one of Generative Adversarial Networks (GANs) and Large Language Models (LLMs), to derive the one or more individual signals of the one or more captured vibration signals as the substitution of one AI technique for another would have yielded predictable results to one of ordinary skill.
Conclusion
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JAY SHAH
Primary Examiner
Art Unit 3791
/JAY B SHAH/Primary Examiner, Art Unit 3791
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