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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of EP22158610.0, filed on 02/24/2022, and PCT/EP2023/054536, filed on 02/23/2023.
Preliminary Amendment
Applicant submitted a preliminary amendment on 07/25/2024. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Status of Claims
Claims 1-20 are pending.
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: “evaluation unit” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 20 is objected to because of the following informalities:
In Claim 20, line 1, “the system according claim 11” should read, “the system according to claim 11”.
Appropriate correction is required.
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 14 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because:
Claim 14 recites “a computer program comprising instructions which, when the program is executed by a control unit of a system, cause the system to perform the method according to claim 1.” Where the computer program comprising program code instructions can be interpreted as software per se.
Therefore, the elements recited in Claim 14 are non-statutory. Similarly, computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs are not physical “things.” They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural or functional interrelationships between the computer program and other claimed elements of a computer which permit the computer’s functionality to be realized. In contrast, a claimed non-statutory computer-readable medium encoded with computer program is a computer element which defines structural and functional relationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. Accordingly, it is important to distinguish claims that define descriptive material per se from claims that define statutory inventions.
Claim 15 is directed to “a computer-readable storage medium.” Applicant’s specification states, “As used herein, the term "computer-readable storage medium" specifically may refer to a non-transitory data storage means, such as a hardware storage medium having stored thereon computer-executable instructions. The computer-readable data carrier or storage medium specifically may be or may comprise a storage medium such as a random-access memory (RAM) and/or a read-only memory (ROM).” The broadest reasonable interpretation of a claim drawn to a computer-readable storage medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. See Subject Matter Eligibility of Computer Readable Media, 1351 OG 212 (26 Jan 2010). See MPEP 2111.01. Signals are nothing but the physical characteristics of a form of energy, and such is nonstatutory natural phenomena. See, e.g., In re Nuitjen, 500 F. 3d 1346, 1357 (Fed. Cir. 2007)(slip. op. at 18)(“A transitory, propagating signal like Nuitjen’s is not a process, machine, manufacture, or composition of matter.’… Thus, such a signal cannot be patentable subject matter.”). Thus, claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In order to overcome this rejection, Applicant may amend the claim by inserting the term “non-statutory” before “computer readable storage medium.”
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7-11, 13-16, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kaehler et al. (U.S. Patent Pub. No. 2021/0080321 A1, hereafter referred to as Kaehler).
Regarding Claim 1, Kaehler teaches a method of obtaining at least one item of object information on at least one object by spectroscopic measurement (Abstract, Paragraph [0241], Kaehler teaches a method for identifying one or more characteristics of a target object using a wearable spectroscopy system. For example, the wearable spectroscopy system may be used to identify material or properties within a material.), the method comprising: i. acquiring spectroscopic data by using at least one spectrometer device (Paragraphs [0010], [0228-229], Figs. 5 & 6, Kaehler teaches a wearable spectroscopy system with a spectroscopy array (126) which detects ambient light of a first wavelength, emits the light of the first wavelength toward the target object, detects light of the first wavelength reflected by the target object, and subtracts an intensity of the detected ambient light of the first wavelength from an intensity of the detected light reflected by the target object to calculate a level of light absorption related to an emitted light and the reflected light of the target object.),
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within at least one spatial measurement range of the spectrometer device (Paragraphs [0087-90], Fig. 6, Kaehler teaches a wearable spectroscopy system comprising one or more light sources that emit light in an irradiated field of view. One or more electromagnetic radiation detectors are configured to receive reflected light from a target object irradiated by the one or more light sources within the irradiated field of view.);
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ii. acquiring, by using at least one imaging device (Paragraphs [0142-143], [0219], Fig. 5, Kaehler teaches a real-world capturing system (camera) to capture an object. The wearable spectroscopy system may have two forward-oriented cameras (124) for observing and detecting the world around the user.), image data of a scene within a field of view of the imaging device (Paragraphs [0219], [0248], Kaehler teaches each forward-oriented camera (124) for observing and detecting the world around the user has an associated field of view (18, 22). For example, a camera system may identify a series of pixels within a camera field of view (such as cameras 124 and field of view 18, 22 of Fig. 5) with an irregular, non-linear pattern.),
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the scene comprising at least a part of the object and at least a part of the spatial measurement range of the spectrometer device (Paragraph [0248], Fig. 5, Kaehler teaches locating initial targets (e.g., blood vessels, muscle tissue, bone tissue, or other tissue) is conducted using image processing. Cancerous cells or otherwise irregular cells commonly have irregular borders. The camera system may identify a series of pixels within a camera field of view with an irregular, non-linear pattern and prompt attention to identify such as a border to a potentially unhealthy cell. These measures may be used to identify areas of interest for spectroscopic scan. Additionally, as shown in Fig. 5, the field of view (18, 22) of each forward-facing camera (124) at least partially overlaps with field of view (126) of spectroscopy array (126).);
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and iii. evaluating the spectroscopic data of step i. (Paragraph [0010], Kaehler teaches calculating a level of light absorption related to the emitted light and the reflected light from the target object.) and at least one item of image information derived from the image data of step ii. (Paragraphs [0246], [0248], Kaehler teaches using real world cameras, identifying subpixels within a field indicative of irregularities. For example, color contrast between pixels is detected during real world capture and the pixels are further altered to highlight such contrast as potential unhealthy cells. Real world capture (cameras) can detect irregular lines among pixel clusters and the pixels bounded by the irregular lines are marked, such as by a virtual color overlay, on user display. Locating desired targets (e.g., blood vessels, muscle tissue, bone tissue, or other tissue) may be conducted using image processing techniques, such as by color, grayscale, intensity or thresholding.), for obtaining the at least one item of object information on the at least one object (Paragraph [0238], Kaehler teaches providing an output indicative of a particular characteristic of the target object, such as a material property or tissue property of the target object.), wherein the item of image information comprises at least one item of re-semblance information on the object (Paragraphs [0146], [0217], [0247], Kaehler teaches applying a label to the captured target object indicative of the identified property. The label may be a virtual image of a similar tissue, such as referenced in a medical book, superimposed near the target object for ready comparative analysis by the user. The Examiner interprets displaying a similar tissue to be “resemblance information” since it provides a shared property (visual similarity) between the object being measured and a similar object (tissue).), wherein the method comprises predicting spectroscopic data and/or (Paragraph [0145], Kaehler teaches matching the wavelength of reflected light received by a detector from the target object to a particular characteristic such as a particular material, tissue type, or property (e.g., a change in one or more chemical properties or compositions of a tissue) of an underlying tissue. For example, an inward electromagnetic radiation emitter emits light in the IR spectrum to the retina of the user, receives reflected light, and matches the wavelength of reflected light to determine a physical property such as the tissue type or oxygen concentration of the tissue. The Examiner interprets “and/or” to mean only one of the limitations are required (“A and/or B” means one of: “A”, “B”, or “A and B” meet the limitation).) at least spectroscopically derivable property for regions of the object (Paragraph [0234], Fig. 6, Kaehler teaches emitted light (613) may impact upon a tissue source (624) and reflected light (615) may indicate the presence of irregular cells (626) among regular cells (625). As the light source (612) irradiates the tissue source (624), irregular cells (626) will return a different light property to photodetectors than regular cells (625). Irregular cells (626) may be cancerous, be part of scar tissue, or even healthy cells amongst the tissue simply indicating or having a difference with surrounding cell. The Examiner interprets different regions of the tissue source to be regions of “regular cells” and “irregular cells.”), which resemble each other in at least one property of the image data (Paragraph [0234], Kaehler teaches the spectroscopy array identifies the tissue source, e.g., arm. The tissue source may contain both “irregular cells” (e.g., cancerous, scar tissues, healthy cells indicating a difference) and “healthy cells.” However, the irregular cells will return a different light property than the healthy/regular cells. The Examiner interprets the healthy/regular cells and the irregular cells resemble each other in at least one property, which is being present in the tissue source (e.g., arm). Further, the cell types “resemble” each other in the fact that they are both contained within the tissue, within the arm.)
In regards to Claim 2, Kaehler teaches the method according to claim 1, wherein the at least one item of image information comprises at least one of the following - at least one image derived from the image data of step ii (Paragraphs [0273], [0248], Kaehler teaches the spectroscopy system’s forward-facing camera (124) may be configured to image an object and perform image analysis on the images to determine the presence of features on the objects. Under BRI, the Examiner interprets “at least one of the following” to mean only one of the following limitations is required.); - at least one item of spatial information on the spatial measurement range within the scene (Paragraph [0143], Kaehler teaches a camera system of a given field of view defines an area available for highlighting and labelling. For example, a camera may encompass a 5-degree field of view, 10-degree field of view, etc.); - at least one item of identification information on the at least one object (Paragraphs [0273], [0248], Kaehler teaches the spectroscopy system’s forward-facing camera may be configured to image an object and the system may be configured to determine the presence of features on the objects. The initial task of locating desired targets is conducted using digital image processing.); - at least one item of orientation information on the at least one object (Paragraph [0273], Kaehler teaches performing image analysis on captured images for object pose (position and orientation) estimation.); - at least one item of direction information (Paragraph [0273], Kaehler teaches performing image analysis on captured images for object pose (position and orientation) estimation.), and - at least one item of resemblance information on the object (Paragraphs [0246-247], Kaehler teaches cameras may identify subpixels within a field of view indicative of irregularities and displaying the tissue or material property of the tissue to the user. The display provided to the user may comprise a virtual image of similar tissue or object identified by the absorption database juxtaposed proximate to the target object.).
In regards to Claim 7, Kaehler teaches the method according to claim 1, wherein the at least one item of image information comprises at least one item of identification information on the at least one object (Paragraphs [0273], [0248], [0234], [0146], Kaehler teaches detecting objects in or features (e.g., properties) of objects. Objects or properties of objects may be detected using computer vision techniques. Locating desired targets may be conducted using pattern, shape, or texture recognition techniques. A label may be applied to the target object indicative of the identified property. The label may be a textual label or prompt within a display or a virtual image.), wherein the method comprises applying at least one identification algorithm to the at least one item of image information for deriving the at least one item of identification information from the at least one item of image information (Paragraphs [0273-276], [0248], Kaehler teaches a variety of machine learning algorithms may be used to learn to identify the presence of objects or features of objects. Examples of machine learning algorithms include object recognition algorithms, instance-based algorithms, decision tree algorithms, clustering algorithms, association rule learning algorithms, etc. The objects or features may be detected based on one or more criteria (e.g., absorbance, light reflection, and/or light scattering at one or more wavelengths.) Locating desired targets may be performed using digital image processing (such as by color, grayscale, and/or intensity thresholding analysis using various filters).)
In regards to Claim 8, Kaehler teaches the method according to claim 7, wherein step iii. comprises applying at least one spectroscopic evaluation algorithm to the spectroscopic data of step i. (Paragraphs [0273-276], Kaehler teaches analyzing absorption determinations, and/or reflected and/or scattered light measurements using one or more computer vision algorithms.), wherein the spectroscopic evaluation algorithm is selected in accordance with the item of identification information (Paragraph [0273-276], Kaehler teaches selecting and using one or more computer vision algorithms as appropriate to analyze images, make absorption determinations, and/or reflected scattered light measurements acquired by the outward facing imaging system to object recognition, object pose estimation, learning, indexing, motion estimation, or image restoration. A variety of machine learning algorithms may be used to identify the presence of objects or features of objects. Once trained, the machine learning algorithms are stored by the spectroscopy system. Individual models may be customized for individual data sets. Additional models specific to data type, data set, conditional situations, or other variations may be generated.).
In regards to Claim 9, Kaehler teaches the method according to claim 8, wherein the method comprises providing a plurality of spectroscopic evaluation algorithms for different items of identification information (Paragraph [0273-276], Kaehler teaches selecting and using one or more computer vision algorithms as appropriate to perform various tasks. Tasks may include analyzing images, absorption determinations, and/or reflected scattered light measurements acquired by the outward facing imaging system to object recognition, object pose (position and orientation) estimation, learning, indexing, motion estimation, or image restoration. In addition, individual machine learning models/algorithms may be customized for individual data sets. A base model may be stored as a starting point to generate additional models specific to a data type, a data set, conditional situations, or other variations.).
In regards to Claim 10, Kaehler teaches the method according to claim 1, wherein the method further comprises providing the at least one item of object information on the at least one object (Paragraph [0151], Kaehler teaches overlaying a virtual image on subsets of pixels having reflected light luminance differences to “flag” them for further analysis and provide visual contrast to the isolated pixels to serve as a notice to a user of the different properties of the subpixels identified.).
In regards to Claim 11, Kaehler teaches a system for obtaining at least one item of object information on at least one object by spectroscopic measurement (Abstract, Paragraph [0008], Kaehler teaches a wearable spectroscopy system for identifying one or more characteristics of a target object. For example, for identifying material type by the object’s reflective light properties.), the system comprising I. at least one spectrometer device configured for acquiring spectroscopic data (Paragraphs [0010], [0228-229], Figs. 5 & 6, Kaehler teaches a wearable spectroscopy system with a spectroscopy array (126) which detects ambient light of a first wavelength, emits the light of the first wavelength toward the target object, detects light of the first wavelength reflected by the target object, and subtracts an intensity of the detected ambient light of the first wavelength from an intensity of the detected light reflected by the target object to calculate a level of light absorption related to an emitted light and the reflected light of the target object.) within at least one spatial measurement range of the spectrometer device (Paragraphs [0087-90], Fig. 6, Kaehler teaches a wearable spectroscopy system comprising one or more light sources that emit light in an irradiated field of view. One or more electromagnetic radiation detectors are configured to receive reflected light from a target object irradiated by the one or more light sources within the irradiated field of view.); II. at least one imaging device (Paragraphs [0142-143], [0219], Fig. 5, Kaehler teaches a real-world capturing system (camera) to capture an object. The wearable spectroscopy system may have two forward-oriented cameras (124) for observing and detecting the world around the user.), configured for acquiring image data of a scene within a field of view of the imaging device (Paragraph [0219], Fig. 5, Kaehler teaches each forward-oriented camera (124) for observing and detecting the world around the user and has an associated field of view (18, 22).), the scene comprising at least a part of the object and at least a part of the spatial measurement range of the spectrometer device (Paragraph [0248], Figs. 5 & 6, Kaehler teaches locating initial targets (e.g., blood vessels, muscle tissue, bone tissue, or other tissue) is conducted using image processing. Cancerous cells or otherwise irregular cells commonly have irregular borders. The camera system may identify a series of pixels within a camera field of view with an irregular, non-linear pattern and prompt attention to identify such as a border to a potentially unhealthy cell. These measures may be used to identify areas of interest for spectroscopic scan.); and III. at least one evaluation unit (Paragraphs [0209], [0238], Kaehler teaches a local processing and data module which may comprise a processor/controller to assist in processing, caching, and storing data captured from sensors such as electromagnetic emitters and detectors, image capture devices (such as cameras).) configured for evaluating the spectroscopic data acquired by the spectrometer device (Paragraph [0149], Kaehler teaches the controller may be configured to calculate a ratio of first wavelength light measurement to second wavelength light measurement. The ratio may be further converted to an oxygen saturation reading or material identifier.) and at least one item of image information derived from the image data acquired by the imaging device (Paragraph [0248], Kaehler teaches the controller operates the software for performing digital image processing (color, grayscale, and/or intensity thresholding analysis using various filters, pattern, shape recognition). The controller may use the intensity of the center of the targeted object and the intensity of the surrounding objects/tissue to determine contrast/optical density with the targeted object to determine abnormalities.), for obtaining the at least one item of object information on the at least one object (Paragraph [0238], Kaehler teaches providing an output indicative of a particular characteristic of the target object, such as a material property or tissue property of the target object.), wherein the item of image information comprises at least one item of resemblance information on the object (Paragraphs [0146], [0217], [0247], Kaehler teaches applying a label to the captured target object indicative of the identified property. The label may be a virtual image of a similar tissue, such as referenced in a medical book, superimposed near the target object for ready comparative analysis by the user.), wherein the evaluation unit is configured for predicting spectroscopic data (Paragraph [0130], Fig. 4D, Kaehler teaches using calibration curves depicting a ratio of light of interest relative to another light to predict properties of underlying tissue as a function of the light incident to it as shown in Fig. 4D. Under BRI, the Examiner interprets “and/or” to mean only one of the limitations are required.) and/or at least spectroscopically derivable property for regions of the object (Paragraph [0234], Fig. 6, Kaehler teaches emitted light (613) may impact upon a tissue source (624) and reflected light (615) may indicate the presence of irregular cells (626) among regular cells (625). As the light source (612) irradiates the tissue source (624), irregular cells (626) will return a different light property to photodetectors than regular cells (625). Irregular cells (626) may be cancerous, be part of scar tissue, or even healthy cells amongst the tissue simply indicating or having a difference with surrounding cell. The Examiner interprets different regions of the tissue source to be regions of “regular cells” and “irregular cells.”), which resemble each other in at least one property of the image data (Paragraph [0234], Kaehler teaches the spectroscopy array identifies the tissue source, e.g., arm. The tissue source may contain both “irregular cells” (e.g., cancerous, scar tissues, healthy cells indicating a difference) and “healthy cells.” However, the irregular cells will return a different light property than the healthy/regular cells. The Examiner interprets the healthy/regular cells and the irregular cells resemble each other in at least property, which is being present in the tissue source (e.g., arm). Further, the cell types “resemble” each other in the fact that they are both contained within the tissue, within the arm.).
In regards to Claim 13, Kaehler teaches the system according to claim 11, further comprising at least one display device configured for providing the at least one item of object information on the at least one object (Paragraphs [0008-9], Figs. 2A-2D, Kaehler teaches identifying, based on the levels of light absorption, a characteristic of the target object, such as a material characteristic, and displaying the identified characteristic to the user on the head-mounted display system (62).).
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In regards to Claim 14, Kaehler teaches a computer program comprising instructions which, when the program is executed by a control unit of a system, cause the system to perform the method according to claim 1 (Paragraph [0278], Kaehler teaches that each of the processes, methods, and algorithms described and/or depicted may be embodied in, and fully or partially automated by, code modules executed by one or more physical computing systems, hardware computer processors, application-specific circuitry, and/or electronic hardware configured to execute specific and particular computer instructions. A code module may be compiled and linked into an executable program or written in an interpreted programming language.).
In regards to Claim 15, Kaehler teaches a computer-readable storage medium comprising instructions which, when the program is executed by a control unit of a system, cause the system to perform the method according to claim 1 (Paragraph [0280], Kaehler teaches code modules or any type of data may be stored on any type of non-transitory computer-readable medium. The results of the disclosed processes or process steps may be stored, persistently or otherwise, in any type of non-transitory, tangible computer storage or may be communicated via a computer-readable transmission medium.).
In regards to Claim 16, Kaehler teaches the method according to claim 1, wherein the at least one imaging device is a camera (Paragraph [0219], Fig. 5, Kaehler teaches the spectroscopy system has two forward-facing cameras (124) for observing and detecting the world around the user.).
In regards to Claim 19, Kaehler teaches the method according to claim 7, wherein step iii. comprises applying at least one spectroscopic evaluation algorithm to the spectroscopic data of step i. (Paragraphs [0273-276], Kaehler teaches selecting one or more computer vision algorithms to perform the tasks of image analysis, absorption determinations, and/or reflected and/or scattered light measurements acquired by the outward facing imaging system to object recognition, object pose estimation, learning, indexing, motion estimation, or image restoration, etc. Examples of computer vision algorithms include Scale-invariant feature transform (SIFT), speeded up robust features (SURF), oriented FAST and rotated BRIEF (ORB), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK), Viola-Jones algorithm, Eigenfaces approach, Lucas-Kanade algorithm, Horn-Schunk algorithm, Mean-shift algorithm, visual simultaneous location and mapping (vSLAM) techniques, and others.), wherein the spectroscopic evaluation algorithm is selected in accordance with the type of the at least one object (Paragraph [0276], Kaehler teaches using individual customized models/algorithms for individual data sets. For example, a base model may be used as a starting point to generate additional models specific to the type of data, data set (e.g., a set of absorbance, light reflection, and/or light scattering values obtained at one or more wavelengths), conditional situations, or other variations.).
Regarding Claim 20, Kaehler teaches the system according claim 11, wherein the at least one imaging device is a camera (Paragraph [0219], Fig. 5, Kaehler teaches the spectroscopy system has two forward-facing cameras (124) for observing and detecting the world around the user.).
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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Claims 3-6, 12, and 17-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kaehler et al. (U.S. Patent Pub. No. 2021/0080321 A1, hereafter referred to as Kaehler) in view of Carmi et al. (U.S. Patent Pub No. 2018/0184972 A1, hereafter referred to as Carmi).
Regarding Claim 3, Kaehler teaches the method according to claim 1 wherein the at least one item of image information comprises at least one image derived from the image data of step ii. (Paragraph [0273], Kaehler teaches the spectroscopy system’s forward-facing camera may be configured to image an object and perform image analysis on the images to determine the presence of features on the objects.).
Kaehler does not explicitly disclose wherein steps i. and ii. are performed repeatedly, wherein the at least one item of object information in step iii. comprises a combination of spectroscopic object information derived from the repetitions of step i. and at least one item of spatial information on the spatial measurement range (118) within the scene derived from the repetitions of step ii., wherein the method comprises indicating at least one of the spatial information and the spectroscopic object information in the image.
Carmi is in the same field of art of performing spectrometry to detect radiation from a sample and process the resulting signal to obtain and display information about the object/sample, such as spectral, physical, and chemical information. Further, Carmi teaches wherein steps i. and ii. are performed repeatedly (Paragraphs [0391], [0345], Fig. 39, Carmi teaches using the spectrometer to measure a plate of food containing a plurality of different food items. The user interface (UI) of the spectrometer directs the user to take a picture of the entire plate using the camera. The UI may subsequently guide the user to take measurements of different areas of the plate, containing different food items, with the spectrometer. The Examiner interprets taking multiple measurements (different areas of the plate) to be acquiring spectroscopic data and image data repeatedly.), wherein the at least one item of object information in step iii. comprises a combination of spectroscopic object information derived from the repetitions of step i. (Paragraphs [0391], [0306], [0285], [0396], Carmi teaches determining one or more properties of each measured food item via the item’s spectral signature such as the item’s chemical composition/identity, calories, fat content, sodium content, etc. One or more algorithms are used for performing spectrum analysis. Once all the items are sampled, the spectrometer system can track and provide the full nutritional properties being consumed over the meal.), and at least one item of spatial information on the spatial measurement range (118) within the scene derived from the repetitions of step ii (Paragraph [0389], Carmi teaches a camera display similar to that shown in Fig. 36A which indicates to the user that the spectrometer is at the correct distance away from the sample surface for measurement.) wherein the method comprises indicating at least one of the spatial information (Paragraphs [0389-390], Carmi teaches an indication layer on the camera display which comprises a computer projected target to align with the optical beam of the spectrometer, when the spectrometer is positioned at the correct distance from the sample surface. The spectrometer system may apply the estimated distance between the sample surface and the spectrometer to analysis of the spectrometer measurements. For example, the spectrometer system may reduce or eliminate the components of the measurement signal that can be attributed to the specific distance as estimated by the camera analysis.). and the spectroscopic object information in the image (Paragraph [0391], [0396], Carmi teaches an information layer may be displayed wherein different food items on the plate are marked according to one or more of the items’ properties as determined from the spectral data. The information layer may show one or more properties of each sample component as determined from the spectrometer results.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kaehler by performing the steps of repeatedly acquiring information about each object in the field of view by taking spectroscopic measurements and taking images within a field of view and providing an indication to the user regarding the collected spectroscopic information and the position information that is taught by Carmi, to make the invention that measures a sample (such as a plate) comprising a plurality of different components (food items); thus, one of ordinary skilled in the art would be motivated to combine the references since once all the items in the field of view are sampled, the spectrometer system can provide and track the full nutritional properties being consumed over the meal (Carmi, Paragraph [0391]). Further, the information derived from the spectral measurements of the foods can provide a more detailed and accurate account of the user’s dietary intake (Carmi, Paragraph [0399]).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 4, Kaehler in view of Carmi discloses the method according to claim 3, wherein, between the repetitions of steps i. and ii., at least one of the scene, the field of view and the object is modified (Paragraph [0391], Fig. 37, Carmi teaches using the spectrometer to measure a plate of food containing a plurality of different food items. The UI of the spectrometer first directs the user to take a picture of the entire plate using the camera. The UI may subsequently guide the user to take measurements of different areas of the plate, containing different food items, with the spectrometer. The Examiner interprets the different food items to be different objects. Additionally, the Examiner interprets by taking measurements of different areas of the plate, the scene and the field of view are also changed between measurements.).
In regards to Claim 5, Kaehler in view of Carmi discloses the method according to claim 3, wherein the method generates the at least one image of the scene with at least two items of spectroscopic object information (Paragraph [0391], Carmi teaches using the spectrometer to measure a plate of food containing a plurality of different food items. The UI of the spectrometer directs the user to take a picture of the entire plate using the camera. The UI may subsequently guide the user to take measurements of different areas of the plate, containing different food items, with the spectrometer. One or more properties of each measured food item may be determined via the item’s spectral signature (e.g., item’s chemical composition/identity, calories, fat content, sodium, etc.). An information layer may be displayed to the user wherein different food items on the plate are marked according to one or more of the items’ properties as determined from the spectral data. The Examiner interprets calories and fat content for the food item(s) to be at least two items of spectroscopic object information, for example.) and corresponding spatial information on the spatial measurement range within the image for each item of spectroscopic object information (Paragraph [0387], Carmi teaches the spectrometer may be configured to access the smartphone camera to provide the user field of view of the sample surface. A crosshair or other type of indication layer, indicating the measurement area of the sample surface, may be added to the field of view to aid the user’s aiming of the spectrometer. A camera display similar to that shown in Fig. 36A can indicate to the user that the smartphone-integrated spectrometer is at the correct distance away from the sample surface for measurement.).
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In regards to Claim 6, Kaehler in view of Carmi teaches the method according to claim 3, wherein the image derived from the image data of step ii. is an image derived from the image data of the repetitions of step ii (Paragraph [0391], Carmi teaches an information layer may be displayed to the user via augmented reality, wherein the different food items on the plate are marked according to one or more of the items’ properties as determined from the spectral data. For example, high calorie items may be marked red.).
In regards to Claim 12, Kaehler teaches the system according to claim 11.
Kaehler does not explicitly disclose wherein the spectrometer device and the imaging device have a known orientation with respect to each other.
Carmi is in the same field of art of performing spectrometry to detect radiation from a sample and process the resulting signal to obtain and display information about the sample/object, such as spectral, physical, and chemical information. Further, Carmi teaches wherein the spectrometer device and the imaging device have a known orientation with respect to each other (Paragraphs [0384], [0386], Fig. 34A, Carmi teaches the components of the optical head may be oriented such that the field of view of the detector of the spectrometer is disposed on the same plane as the field of view of the camera. For example, the distance between the camera lens and the spectrometer module of the spectrometer may be in a range from about 1 mm to about 20 mm, or about 1 mm to about 10 mm.).
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Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kaehler by placing the spectrometer device and the imaging device with a known orientation with respect to each other that is taught by Carmi, to make the invention that uses the functionality of the smartphone’s camera in order to facilitate the user’s positioning and orientation of the spectrometer with respect to the sample surface during measurement; thus, one of ordinary skilled in the art would be motivated to combine the references since the housing may be configured to have the aperture disposed adjacent to the spectrometer and component modules, such that the smartphone camera may have a field of view that at least partially or completely overlaps with the field of view of the spectrometer such that the user may view the visible optical beam via the smartphone camera before and during the measurement with the spectrometer. (Carmi, Paragraph [0386]).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 17, Kaehler teaches the method according to claim 1, wherein the at least one item of image information comprises at least one of the following - at least one image derived from the image data of step ii (Paragraph [0273], Kaehler teaches the spectroscopy system’s forward-facing camera may be configured to image an object and perform image analysis on the images to determine the presence of features on the objects. Under BRI, the Examiner interprets “at least one item” and “at least one of the following” to mean only one of the limitations are required.); - at least one item of spatial information on an indication of the spatial measurement range at which the spectroscopic data was acquired within an image (Paragraphs [0215-216], Kaehler teaches capturing 3D points from the environment, and the pose (i.e., vector and/or origin position information relative to the world) of the cameras that capture those images or points may be determined, so that these points or images may be “tagged” or associated, with this pose information.); - at least one item of identification information on at least one of a type of the object (Paragraph [0273], Kaehler teaches performing image analysis on the images to determine presence of features on the objects. The system may perform object recognition using one or more computer vision algorithms.), a boundary of the object within the scene (Paragraph [0143], Kaehler teaches determining contours of an object with a real-world capturing system (camera).), (Paragraph [0273], Kaehler teaches using computer vision algorithms to perform object pose (position and orientation) estimation.), (Paragraph [0248], Kaehler teaches locating desired targets/objects using texture recognition.), a shape of the object (Paragraph [0248], Kaehler teaches locating desired targets/objects using shape recognition.), a contrast of the object (Paragraph [0248], Kaehler teaches using the intensity of the center of the targeted object and the intensity of the surrounding objects to determine contrast/optical density with the targeted object to determine abnormalities.), (Paragraph [0248], Kaehler teaches identifying areas of interest using the canera system for the spectroscopic scan.); - (Paragraphs [0146], [0217], [0247], Kaehler teaches applying a label to the captured target object indicative of the identified property. The label may be a virtual image of a similar tissue, such as referenced in a medical book, superimposed near the target object for ready comparative analysis by the user. The Examiner interprets providing a similar tissue to be “resemblance information” since it provides a shared property (visual similarity) between the object being measured and a similar object.), which is shared between different regions of the object (Paragraphs [0234], [0145], Kaehler teaches in a tissue sample, regular cells constitute the majority of cells in a sample under analysis and irregular cells constitute a minority of cells of the sample, the irregular cells exhibiting a different detectable property than the regular cells. The system matches the reflected light wavelength to determine the observed material, for example detecting the presence of cancerous cells among healthy cells. Cancerous cells reflect and absorb light different than healthy cells, a reflection of light at certain wavelengths can indicate the presence and amount of abnormality. The Examiner interprets the resemblance/shared property between the cancerous cells and healthy cells is that they are both “cells”, within the tissue (e.g., arm), however, they may have distinct spectral differences.).
Kaehler does not explicitly disclose -
Carmi is in the same field of art of performing spectrometry to detect radiation from a sample and process the resulting signal to obtain and display information about the sample/object, such as spectral, physical, and chemical information. Further, Carmi teaches (Paragraph [0393], Carmi teaches applying computer vision algorithms to the images to extract certain visual properties of the sample such as size.), a color of the object (Paragraph [0393], Carmi teaches applying computer vision algorithms to the images to extract certain visual properties of the sample such as color.), a volume of the object (Paragraph [0391], Carmi teaches applying computer vision algorithms to estimate the volume of each food item on a plate.)- at least an indication of an orientation of the spectrometer device relative to the at least one object (Paragraph [0386], Carmi teaches the smartphone-integrated spectrometer can use the functionality of the smartphone’s camera to facilitate the user’s positioning and orientation of the spectrometer with respect to the sample surface during measurement.); - at least an indication of a direction between the spectrometer device and the at least one object (Paragraph [0396], Carmi teaches an indication layer may be provided in camera view to guide the user in determining the correct position of the spectrometer.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kaehler by obtaining identification information (size, color, volume) for the object and indicating an orientation and a direction between the spectrometer device and the object that is taught by Carmi, to make the invention that extracts visual properties of the sample and indicates the pose of the spectrometer relative to the object of interest to guide the user; thus, one of ordinary skilled in the art would be motivated to combine the references since extracting visual properties from images can improve the efficiency and accuracy of sample identification (Carmi, Paragraph [0393]) and providing an indication to the user can help the user visualize the area of the sample material being measured, and thereby provide guidance to the user in adjusting the position or angle of the spectrometer to position the measurement area over the desired area of the sample material/object (Carmi, Paragraph [0167]).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 18, Kaehler in view of Carmi teaches the method according to claim 3, wherein at least one of a combined image (Paragraph [0391], Carmi teaches directing the user to take a picture of a whole plate containing a plurality of different food items. One or more properties of each measured food item may be determined via the item’s spectral signature. The Examiner interprets a picture of a plate with a plurality of different food items to be a “combined” image since there are several different types of food “combined” (in) the image.) and a selected image of images derived from the image data of the repetitions of step ii (Paragraph [0391], Carmi teaches the user taking measurements of different areas of the plate, containing different food items, with the spectrometer. One or more properties may be determined via the food item’s spectral signature. An information layer may be displayed to the user, wherein different food items on the plate are marked according to one or more of the items’ properties as determined from the spectral data. The Examiner interprets “a selected image” broadly, since the claim is silent to the meaning of “selected image.” The Examiner is interpreting the selected image as the image displayed to the user marked with the one or more items’ properties.).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schwartzer et al. (U.S. Patent Pub. No. 2019/0340749 A1) teaches a system and method for evaluating characteristics of a vegetable/ fruit by processing an image to produce image analysis results and analyzing near-infrared (NIR) illumination reflected from the vegetable/fruit to produce reflection analysis results and calculating values that reflect characteristics of the vegetable or fruit based on image analysis results and based on reflection analysis results.
Pruneri et al. (U.S. Patent Pub. No. 2023/0393078 A1) teaches a method for identification of objects by illuminating at least part of the object with electromagnetic radiation and spectroscopically obtaining spectral data for one or more regions of the object and generating an identification result for the object using a trained machine learning model. The trained model processes the spectral data for the regions and generates model outputs from which the identification result is derived.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYDNEY L BLACKSTEN whose telephone number is (571)272-7651. The examiner can normally be reached 8:30am-4:30pm.
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/SYDNEY L BLACKSTEN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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