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 .
Election/Restrictions
Applicant's election with traverse of Group I, claims 1-9, in the reply filed on 01 December 2025 is acknowledged. The traversal is on the ground(s) that no serious burden exists that would prevent the Examiner from examining all of the claims. This is not found persuasive because first, Applicants acknowledge that the claims claim more than one independent and distinct invention. Second, the Examiner in examining these additional independent and distinct inventions would need to set forth separate search queries for each invention, examine the references found, and potentially write a distinct rejection for each invention. These additional steps cause a serious burden for the Examiner, that is not required. Moreover, Applicants have not provided any evidence that a serious burden is lacking in this application.
The requirement is still deemed proper and is therefore made FINAL.
Claims 10-16 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 01 December 2025.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 24 August 2023, 26 November 2024, and 18 November 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
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 Rejections - 35 USC § 103
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.
Claims 1-9 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over TW 410274 B (Wu).
With respect to the limitation of claim 1, Wu discloses a conversion device for converting output information outputted from a gas detection-capable device for a specific odor molecule into response information indicating a response of an olfactory receptor (a conversion device that will self-adsorb specified odor molecules (adsorption film (3) provided with specificity or selectivity for odor molecules – Figures 1-2)), the conversion device comprising:
an output information acquisition unit configured to acquire output information outputted from a gas detection-capable device for a specific odor molecule (an output information acquisition part (15), obtains the output, i.e., electrical signal, from adsorption film (3), which is obtained from the output information output by a device having the above-mentioned gas detection function for a specified odor molecule);
a conversion unit configured to convert the acquired output information into response information based on output signals outputted from a plurality of gas detection-capable devices respectively corresponding to a plurality of odor molecules and response information indicating responses of a plurality of olfactory receptors respectively corresponding to the plurality of odor molecules (the conversion part (19~21) is based on a plurality of the above-mentioned gas detection functions for each of a plurality of odor molecules); and
an output unit configured to output the response information obtained through the conversion (output part (22) that outputs the converted response information). The output signal output by the functional device and the response information representing the response of the olfactory receptor to each of the above-mentioned plurality of odor molecules are converted into the above-mentioned response information (array communication output from 15). After changes, 19~21 are processed by conventional digital processing techniques, the specific encoding module of the odor can be obtained; the encoding module group representing the characteristics of each specific odor is then processed by a pre-planned neural network, and provide this odor sensing device with intelligent learning, memory, detection and identification functions).
With respect to the limitations of claim 2, Wu discloses that the conversion unit converts the acquired output information into response information indicating a response of an olfactory receptor using a prediction model trained through machine learning using output information outputted from the plurality of gas detection-capable devices respectively corresponding to the plurality of odor molecules as an explanatory variable and the response information indicating responses of the olfactory receptors respectively corresponding to the plurality of odor molecules as a target variable (processing of array telecommunication (19-21) changes includes encoding modules for each specific odor characteristic; the group is then subjected to pre-planned neural network-like processing (machine learning), where the neural network-like processing includes principal component analysis using multi-variable parameters). fails to expressly disclose a prediction model, but the prediction module is actually a conventional technology of machine learning. The neural network endows the odor sensing device with "intelligent learning", memory, The functions of detecting and identifying odors are within the capabilities of those with ordinary knowledge in the technical field).
With respect to the limitations of claim 3, Wu discloses that the conversion unit uses a prediction model trained through machine learning using, as an explanatory variable, a numerical value, a function, or a spatial or temporal indicator that is calculated using a mathematical, statistical, or machine learning technique from the output information outputted from each of the gas detection-capable devices, or a variable newly created through feature engineering (prediction model, which is a numerical value, function, spatial or time series indicator, calculated using mathematical, statistical, or machine learning methods of the output information output from the device with the above gas detection function, or using characteristic values, the variables newly created in the project are obtained by mechanical learning as explanatory variables; and using mechanical/machine learning to generate variables is well-known in the art).
With respect to the limitations of claim 4, Wu discloses that the conversion unit uses a prediction model trained through machine learning using, as a target variable, a numerical value, a function, or a spatial or temporal indicator that is calculated using a mathematical, statistical, or machine learning technique from the response information of each of the olfactory receptors, or a variable newly created through feature engineering (prediction model, which is a numerical value, function, spatial or time series indicator calculated using mathematical, statistical, or machine learning methods of the output information output from the device with the above gas detection function, or using characteristic values. The variables newly created in the project are obtained by mechanical learning as explanatory variables; and using mechanical/machine learning to generate variables is well-known in the art).
With respect to the limitations of claim 5, Wu discloses that the conversion unit converts the acquired output information into response information using a prediction model trained through machine learning using time-series data and the number of feature patterns from the output information outputted from each of the plurality of gas detection-capable devices as an explanatory variable, and time-series data and the number of feature patterns from the response information of the plurality of olfactory receptors as a target variable (conversion part uses a prediction model to convert the obtained output information into response information. The prediction model is to the time series data and the number of eigenvalue patterns of the output information output from a plurality of devices with the above gas detection functions are used as explanatory variables, and the time series data and the number of eigenvalue patterns of the response information of a plurality of the above olfactory receptors are used as target variables. It is obtained through mechanical learning (device analyzes odor molecules by sensing changes in telecommunications, and the resonance frequency counter already contains the actual technical content of time series).
With respect to the limitation of claim 6, Wu disclose that the conversion unit uses a prediction model trained through machine learning using, as an explanatory variable, a presence or absence of output information from each of the plurality of gas detection-capable devices (conversion part uses the following prediction model, which predicts the output information of the device with a plurality of the above gas detection functions. Whether or not it is used as an explanatory variable is obtained through mechanical learning).
With respect to the limitations of claim 7, Wu discloses that conversion unit uses a prediction model trained through machine learning using, as a target variable, a presence or absence of a response of each of a plurality of receptors included in the olfactory receptors (within the conversion device, the conversion part uses the following prediction model, which predicts the plurality of receptors included in the above-mentioned olfactory receptor, such that the presence or absence of a reaction is obtained through mechanical learning as the target variable).
With respect to the limitations of claim 8, Wu discloses that the conversion unit uses a prediction model trained through machine learning using, as an explanatory variable, mass-to-charge ratio and intensity based on the output information outputted from each of the gas detection-capable devices (conversion unit uses a prediction model obtained by mechanical learning using the mass charge and intensity of the output information output from the device having the gas detection function as explanatory variables; and the variables of the principal component analysis is an electrical signal obtained from a self-sensing device).
With respect to the limitations of claim 9, Wu discloses an estimation unit configured to estimate a descriptor representing a characteristic of the specific odor from the response information obtained through the conversion, wherein the output unit outputs the thus estimated descriptor (although reference does not explicitly disclose that the device has a presumption part, reference discloses that the conversion device includes a computer for processing and outputting the final odor. Under the molecular sensing information, the computer can be equivalent to what is requested in this item: "The inference unit infers a term representing the characteristics of the specified odor based on the converted response information, and the above-mentioned output unit outputs the inferred term).
Claims 1-9 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 2005/0208673 (Labreche et al.).
With respect to the limitations of claim 1, Labreche et al. disclose a conversion device for converting output information outputted from a gas detection-capable device for a specific odor molecule into response information indicating a response of an olfactory receptor, the conversion device comprising:
an output information acquisition unit configured to acquire output information outputted from a gas detection-capable device for a specific odor molecule (an odor measuring device of a first type, comprising a set of odor sensors adapted to generate a response (S) upon exposure to an odor substance);
a conversion unit configured to convert the acquired output information into response information based on output signals outputted from a plurality of gas detection-capable devices respectively corresponding to a plurality of odor molecules and response information indicating responses of a plurality of olfactory receptors respectively corresponding to the plurality of odor molecules (means for converting the sensor response data (S) of the odor measuring device into an odor intensity index (I); wherein the conversion means is provided in use with data defining a corresponding first function (Gj) for each of a set of reference compounds (rj), the first function (G) indicating the relationship between the concentration (Cm) of the reference compound and the response (Sm) of the set of odor sensors in the first type of odor measuring device to the reference compound (rj) at the concentration (Cm), and the conversion means is provided with data defining a corresponding second function (Fj) for each of the set of reference compounds (rj), which indicates the relationship between the concentration (Cn) of the reference compound and the odor intensity level (In) of the reference compound assigned by a sensory group at the concentration (Cn),
and is adapted to generate the odor intensity index (I) by applying a combination of the first function and the second function to the measured response data (Sx).
The present invention discloses a conversion device that converts output information from a device having a gas detection function for a specified odor molecule into response information showing the response of an olfactory receptor. The conversion
device comprises: an output information acquisition unit that acquires output information from a device having a gas detection function for a specified odor molecule; a conversion unit that converts the acquired output information into the response
information based on output signals from devices having a gas detection function for each of a plurality of odor molecules and response information showing the response of an olfactory receptor to each of the plurality of odor molecules); and
an output unit configured to output the response information obtained through the conversion (output part (22) that outputs the converted response information).
With respect to the limitations of claim 2, Labreche et al. further disclose wherein the conversion unit converts the acquired output information into response information indicating a response of an olfactory receptor using a prediction model trained through machine learning using output information outputted from the plurality of gas detection-capable devices respectively corresponding to the plurality of odor molecules as an explanatory variable and the response information indicating responses of the olfactory receptors respectively corresponding to the plurality of odor molecules as a target variable (method has two stages: a training stage and a measurement stage. In the training stage, samples of known reference compounds are prepared at different known concentrations. These samples are then provided to a sensory panel (a panel of human testers) and a gas sensing device. The method includes the following steps: providing data for each of a set of reference compounds (rj) defining a corresponding first function (Gj), the first function (Gj) indicating the relationship between the concentration (Cm) of the reference compound and the response (Sm) of a set of odor sensors in a first-type odor measuring device to the reference compound (rj) at the concentration (Cm); providing data for each of the set of reference compounds (rj) defining a corresponding second function (Fj), the second function (Fj) indicating the relationship between the concentration (Cn) of the reference compound and an odor intensity level (In) assigned to the reference compound by a sensory panel at the concentration (Cn); measuring the response (Sx) of a set of odor sensors in the first-type odor measuring device when exposed to a test odor sample (x); and applying the measured response data (Sx) by combining the first function and the second function).
With respect to the limitation of claim 3, Labreche et al. disclose that the conversion unit uses a prediction model trained through machine learning using, as an explanatory variable, a numerical value, a function, or a spatial or temporal indicator that is calculated using a mathematical, statistical, or machine learning technique from the output information outputted from each of the gas detection-capable devices, or a variable newly created through feature engineering (conversion unit uses a prediction model obtained by machine learning, using mathematical, statistical, or machine learning methods to calculate the resin and function as explanatory variables from the output information of the device with the gas detection function. This discloses some additional technical features. As for using spatial or temporal series indicators calculated from the output information, or variables newly generated by feature engineering as explanatory variables, machine learning is a standard practice in this field).
With respect to the limitations of claim 4, Labreche et al. disclose that the conversion unit uses a prediction model trained through machine learning using, as a target variable, a numerical value, a function, or a spatial or temporal indicator that is calculated using a mathematical, statistical, or machine learning technique from the response information of each of the olfactory receptors, or a variable newly created through feature engineering (conversion unit uses a prediction model obtained by machine learning, using mathematical, statistical, or machine learning methods to calculate the resin and function as explanatory variables from the output information of the device with the gas detection function. This discloses some additional technical features. As for using spatial or temporal series indicators calculated from the output information, or variables newly generated by feature engineering as explanatory variables, machine learning is a standard practice in this field).
With respect to the limitations of claim 5, Labreche et al. disclose that the conversion unit converts the acquired output information into response information using a prediction model trained through machine learning using time-series data and the number of feature patterns from the output information outputted from each of the plurality of gas detection-capable devices as an explanatory variable, and time-series data and the number of feature patterns from the response information of the plurality of olfactory receptors as a target variable (the specific method for converting output information into response information using a predictive model in the additional technical features is standard practice in the art).
With respect to the limitations of claim 6, Labreche et al. disclose
that the conversion unit uses a prediction model trained through machine learning using, as an explanatory variable, a presence or absence of output information from each of the plurality of gas detection-capable devices (a gas detector has three regions in response to gas samples of different concentrations: a non-response region MZ1, in
which the sensor does not detect anything at all and has a minimum output Min (zero output or the minimum output it is capable of), which runs from the minimum gas concentration up to the gas concentration at a first threshold level MTh1; a detection region MZ2, which starts from the first threshold level and runs to the gas concentration at a second threshold level MTh2, in which the sensor detects the odor and responds to it with a changing output; and a saturation region MZ3 starting at the second threshold level MTh2, in which the sensor output is saturated (i.e., it takes the maximum possible value Max regardless of the sample concentration. Setting up a "predictive model by using the presence or absence of output information from a device with multiple gas detection functions as an explanatory variable for machine learning" is a common practice in the field).
With respect to the limitations of claim 7, Labreche et al. disclose that the conversion unit uses a prediction model trained through machine learning using, as a target variable, a presence or absence of a response of each of a plurality of receptors included in the olfactory receptors (as shown in Figure 1, at very low concentrations, the human panel is insensitive to changes in odor concentration. The human panel only begins to detect the odor when the odor concentration exceeds a threshold Thl. However, from this threshold concentration Th1 up to the second threshold level Th2, there is a relationship between the "odor intensity value" assigned to the sample by the human panel and the concentration of the sample. When the odor concentration exceeds the second threshold Th2, the human panel cannot detect any further changes. The graph in Figure 1 can be considered to have three regions: Region Z1: No response region. In this region, the human tester will not smell any odor. Region Z2: Detection region. In this region, the human tester smells the gas and can assign a value to it that varies with the gas concentration. Region Z3: Saturation region. In this region, the human tester can smell the odor of the sample, but assigns the highest possible value independent of the sample concentration. Setting up a "predictive model obtained by machine learning using the presence or absence of responses from multiple receptors included in the olfactory receptor as the target variable" is a standard practice in the art).
With respect to the limitation of claim 8, Labreche et al. further disclose that the conversion unit uses a prediction model trained through machine learning using. The examiner argues that using a prediction mode obtained by machine learning using mass-to-charge ratio and intensity based on the output information outputted from each of the gas detection-capable devices is well known in the art and the specific variables are obvious choices of design.
With respect to the limitations of claim 9, Labreche et al. further disclose an estimation unit configured to estimate a descriptor representing a characteristic of the specific odor from the response information obtained through the conversion, wherein the output unit outputs the thus estimated descriptor (Step 1 of Figure 4, the first step in this process is to select a set of reference odors from which sensory data should be obtained. If the training phase is carried out when the final application is known, the selection of reference odors preferably depends on the final application. For example, if the final application consists of a quantification of the odor intensity emitted from a waste disposal site, the set of reference odors may include the following odors shown in Table 2: rotten eggs, pungent fish, decay, etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL SEAN LARKIN whose telephone number is 571-272-2198. The examiner can normally be reached M-F 9:00 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Laura Martin can be reached at 571-272-2160. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL S LARKIN/Primary Examiner, Art Unit 2855