A DETAILED CORRESPONDENCE
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
As to the amended claims and remarks filed on 3/2/26, the previous 112(b) rejections are withdrawn.
Based on the claim amendments and remarks, the previous 101 rejection has been modified to address the claim amendments.
As to the amended claims and remarks, the previous prior art rejection has been modified to address the claim amendments.
Claim Status
Claims 1-22 are pending with claims 1-11 and 16-22 being examined and claims 12-15 deemed withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected based on the following analysis:
Step 2A, Prong One: Identify the law of nature/natural phenomenon/abstract ideas.
Claim 1 recites the abstract ideas of “identifying…by using a… algorithm…” in lines 11-17, and “identifying… in lines 20-21, and “identifying…by using a… algorithm…” in lines 26-28, and “identifying…” in lines 29-30 which are all mental processes and/or math. The examiner believes that “generating a virtual representation” is also an abstract idea as it can be performed with pencil and paper or by a general-purpose computer. MPEP 2106.04(a)(2)III is clear that using pencil and paper or a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea.
Step 2A Prong Two: Has the abstract idea been integrated into a particular practical application?
No. After the abstract ideas are performed then no action is taken.
If the “generating a virtual representation” is deemed not to be an abstract idea, then once all of the identifying steps in lines 11-21 and lines 26-30 have taken place, then a virtual representation of the work deck is generated using the first information or the second information. However, generating a virtual representation of the identified information is not a particular practical application. Displaying or generating a virtual representation does not integrate the exception into a practical application because it is insignificant post-solution activity, similar to the alarm in Parker v. Flook. See MPEP 2106.04(d) and 2106.05(g).
The claim also recites acquiring a first image with a first optical recording device and acquiring a second image with a second optical recording device. However, this is just using the optical devices to gather data (identification data) to be used in the abstract idea. However, data gathering to be used in the abstract idea does not integrate the judicial exception into a practical application because data gathering is insignificant extra-solution activity, and not a particular practical application. See MPEP 2106.05(g). Additionally, this is recited at such a high level of generality that it amounts to just generally linking the abstract idea to a field of use per MPEP 2106.05(h), which is not a particular practical application.
The claims recite a processing system to perform the method, which is just a general-purpose computer. A general-purpose computer is not a particular machine, and performing the abstract idea on a general purpose computer is not enough to integrate the exception into a practical application (MPEP 2106.05(b)I.).
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
The claim recites the additional elements of acquiring a first image with a first optical recording device and acquiring a second image with a second optical recording device. These additional elements do not amount to significantly more as they are well-understood, routine, and conventional (WURC) in the art as evidenced by Huang et al (US 20240127015; hereinafter “Huang”) and Eckard et al (US 20140036070; hereinafter “Eckard”). Huang discloses acquiring a first image with a first optical recording device and acquiring a second image with a second optical recording device (Huang teaches a first imager 01 and a second imager 02; Fig. 2. Huang teaches that the first imager/scanning component identifies a first feature/barcode and then decodes the information to determine the identification; [82-84]. Huang teaches that the imager information is captured by the image information obtaining component; [86]. Huang teaches that when scanning via first scanning component that there is processing that can determine when there is an issue or scanning fails such that further identification is needed; [86]. When further identification is needed, then the second imager as the image obtaining component is used to help with identification; [86,126]. Huang teaches that the identification can be based off of both the scanning information and/or the identifier information; [82-84, 86, 87, 89, 115, 126, 136, 140-142, 144-148]. The software processing/decoding that takes place would be via an algorithm/instructions in the processing to help take the barcode scan information and convert that information into the identification). Eckard discloses acquiring a first image with a first optical recording device and acquiring a second image with a second optical recording device (Eckard teaches using two cameras 5/5’ to acquire images 6/6’ of a lab workdeck; Fig. 1-10, [12, 26, 27]. Eckard teaches that each camera can detect and identify consumables; [35, 36]. Eckard teaches that the identification is based on position 10’, and also based on two different markings on the consumables 10’’/10’’’; [38, 39, 43], Fig. 2, 8. Eckard teaches that the first feature ID/position is determined [66], and that this is then done for each feature including the second feature/ID [71], and can be done as a third feature/image as well [76]. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], Fig. 9-10. Therefore, Eckard teaches using the information from both cameras in order to provide a proper reading on the consumables. The software processing/decoding that takes place would be via an algorithm/instructions in the processing to help take the barcode scan information and convert that information into the identification. Eckard also teaches that algorithms are used to associate images for assessment; [9]). Also, see prior art references in 35 USC 102 and/or 103 rejections and “other references cited” section below.
The dependent claims 2-11 and 16-20 undergo a similar analysis and do not appear to resolve any of the above issues. Claims 2, 3, 6, 7, 8, 9, 11, 16, 19, 21, 22 just further recite details of the abstract ideas (determining, identifying, selecting, algorithm, etc…) under step 2A prong one. Claims 4-5, 17, 18 recite further details of the abstract idea under step 2A prong one, and also add structural details which are WURC under step 2B (see reference(s) used in prior art rejection below). Claims 10, 20 just recite further structural details which are WURC under step 2B (see reference(s) used in prior art rejection below).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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 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 1-11, 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al (US 20240127015; hereinafter “Huang”; already of record) in view of Eckard et al (US 20140036070; hereinafter “Eckard”; already of record) in view of Sievert et al (US 20210025791; hereinafter “Sievert”) or alternatively in view of Davis et al (US 20220373569; hereinafter “Davis”).
As to claim 1, Huang teaches a computer implemented method for identifying, by a processor, a labware item, the labware item comprising a first optical feature and a second optical feature, wherein the method comprises the steps of: acquiring of a first image of the labware item with at least a first optical recording device, the first image displaying at least a portion of the first optical feature; acquiring a second image of the labware item with at least a second optical recording device, the second image displaying at least a portion of the second optical feature; and identifying the first optical feature in the first image by using at least a first identification algorithm thereby obtaining first identification data, the first identification data encoding first information on the first optical feature and information indicative of whether at least a further identification is needed, where the information indicative of whether at least a further identification is needed specifies whether the identification of the first optical feature by using the first identification algorithm has been inconclusive and/or insufficient for identifying the labware item, wherein if, according to the information encoded in the first identification data, the at least further identification is not needed, the method further comprises the step of: identifying the labware item by using at least the first information on the first optical feature, and generating a virtual representation of the labware item, and wherein if, according to the information encoded in the first identification data, the at least further identification is needed, the method further comprises the steps of: identifying the second optical feature in the second image by using at least a second identification algorithm thereby obtaining second identification data, the second identification data encoding information on the second optical feature; and identifying the labware item by using at least the information on the second optical feature, and generating a virtual representation of the labware item, and where the virtual representation is used to automatically validate, and if needed correct a laboratory procedure (Huang teaches a first imager 01 and a second imager 02; Fig. 2. Huang teaches that the first imager/scanning component identifies a first feature/barcode and then decodes the information to determine the identification; [82-84]. Huang teaches that the imager information is captured by the image information obtaining component; [86]. Huang teaches that when scanning via first scanning component that there is processing that can determine when there is an issue or scanning fails such that further identification is needed; [86, 87]. When further identification is needed, then the second imager as the image obtaining component is used to help with identification; [86,126]. Huang teaches that the identification can be based off of both the scanning information and/or the identifier information; [82-84, 86, 87, 89, 115, 126, 136, 140-142, 144-148]. The software processing/decoding that takes place would be via an algorithm/instructions in the processing to help take the barcode scan information and convert that information into the identification. Huang teaches displaying the image as a virtual representation, where the image is used for the user to check/validate; [87, 124, 133, 135, 137]. The examiner notes that how the virtual representation is used does not further define the method as it is not an active method step. Further, the examiner notes the extensive use of conditional/contingent limitations. “If” identification is needed, and “if” correction is needed do not necessarily have to occur since they are conditional statements (MPEP 2111.04)).
Huang does not specifically teach first and second images of top view of a work deck of the data processing system, with the work deck being divided into a plurality of deck regions. However, Eckard teaches the analogous art of a computer implemented method for identifying labware, with two cameras that create first and second images of top view of a work deck of the data processing system, with the work deck being divided into a plurality of deck regions (Eckard teaches using two cameras 5/5’ to acquire images 6/6’ of a lab work deck; Fig. 1-10, [12, 26, 27]. Eckard teaches that each camera can detect and identify consumables; [35, 36]. Eckard teaches that a virtual display of the imaged work deck is created, where the work deck has a plurality of regions, allowing the user and computer to check that there are no errors with the consumables; [37, 38, 51, 57, 58, 72, 76, 77, 89], Fig. 2, 8-10. Therefore, Eckard teaches using the information from both cameras in order to provide a proper reading on the consumables). It would have been obvious to one of ordinary skill in the art to have modified the imaging devices which check laboratory consumables and present the consumable check information to a user of Huang to have used multiple cameras and provided a virtual image of the work deck for a user and the computer to check the layout as in Eckard because Eckard teaches that a virtual display allows the user and computer to check that there are no errors with the consumables; [37, 38, 51, 57, 58, 72, 76, 77, 89], Fig. 2, 8-10.
Modified Huang does not specifically teach that the first and second images are substantially identical views. However, Sievert teaches the analogous art of detecting laboratory objects, where the singular imaging detectors can be integrated (Sievert teaches multiple detectors at the same location and integrated into one overall detector, thereby having substantially the same field of view (FOV); [88]). It would have been obvious to one of ordinary skill in the art to have modified the detectors to detect the laboratory objects of modified Huang to have been integrated to have substantially the same FOV as in Sievert because Sievert teaches that multiple detectors being integrated or separate are obvious variants (Sievert; [88]). Further, it would have been obvious to a person having ordinary skill in the art before the effective filing date to choose two integrated detectors that provide substantially the same FOV “from a finite number of identified, predictable solutions, with a reasonable expectation of success” (see KSR rationale E and MPEP § 2144.07) and because Sievert teaches that multiple detectors being integrated or separate are obvious variants (Sievert; [88]).
Alternatively, Modified Huang does not specifically teach that the first and second images are substantially identical views. However, Davis teaches the analogous art of imaging components of a work deck and providing a virtual representation, where the imaging device can be fixed and provides a singular view of the entire work deck (Davis teaches that the imaging device can be fixed, and that the imaging device can provide an entire view of the work deck in order to track and evaluate all components at the various locations; [55, 57], Fig. 3). It would have been obvious to one of ordinary skill in the art to have modified the multiple detectors for detecting different features of the labware of modified Huang to be fixed and provide overviews of the entire work deck as in Davis, the resulting configuration would be the dual imaging devices used to check and validate identifiers of modified Huang both imaging the entire work deck as in Davis, because Davis teaches that the imaging device can provide information on all of the components at all of the locations (Davis; [55, 57]).
As to claim 2, Huang teaches the method according to claim 1, wherein the method comprises the step of: determining a first region of interest in the first image by using first position information about the position of the labware with respect to a work deck, and the step of identifying the first optical feature in the first image by using the first identification algorithm is carried out by using the first region of interest (Huang teaches the determination of the region/position as the area in which the imager images; see claim 1 above. Further Huang teaches a position index; [136, 137]).
As to claim 3, Huang teaches the method according to claim 1, further comprising the step of: acquiring third position information on the position of the labware item with respect to the work deck by using at least a position determining algorithm, wherein the first identification algorithm processes first input data, the first input data depending on the third position information (Huang teaches the determination of the region/position as the area in which the imager images; see claim 1 above. Further Huang teaches a position index; [136, 137]. Huang uses algorithms to process the data, and the third position could be any detected information from Huang that correlates with the first input data).
As to claim 4, Huang teaches the
If it is deemed that the scanning identifier barcode reader of Huang is not a camera, then prior art Eckard teaches the analogous art of identifying identifiers using a camera (Eckard teaches identifying various images including barcodes; [43]. Eckard teaches that the identification takes place via cameras; [12, 26, 27]). It would have been obvious to one of ordinary skill in the art to have modified the first imager/scanning identifier of Huang to have used a camera as in Eckard because Eckard teaches that a camera can be used as an obvious variant to capture barcode identifiers (Eckard; [12, 26,27, 43]).
As to claim 5, Huang teaches the method according to claim 1, wherein the second optical recording device is a second camera, wherein the second identification algorithm depends at least on a second set of intrinsic calibration parameters associated to the second camera (Huang teaches that the image information obtaining component is a camera; [89]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis).
As to claim 6, Huang teaches the method according to claim 1, wherein if, according to the information encoded in the first identification data, at least a further identification is needed, the method further comprises the step of: selecting the second identification algorithm among a second pool of identification algorithms, wherein the step of selecting the second identification algorithm depends at least on the information encoded in the first identification data (Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 7, Huang teaches the method according to claim 1, wherein the second algorithm processes second input data, the second input data encoding the first information on the first optical feature (Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. Therefore, the use of the second algorithm includes the relationship to the first image data because the second algorithm is processed based on first data when first data is read and fails, such that the processing understands to then also use the second image data. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 8, Huang teaches the method according to claim 1,wherein the second identification data encodes information indicative of whether at least a further identification is needed, wherein if, according to the information encoded in the second identification data, at least a further identification is needed, the method further comprises the step of: identifying the first optical feature in the first image by using at least a third identification algorithm thereby obtaining third identification data, the third identification data encoding second information on the first optical feature, and wherein if, according to the information encoded in the second identification data, at least a further identification is needed, the step of identifying the labware item by using at least the information on the second optical feature is performed by using the second information on the first optical feature (In as much as claimed and as best understood, Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 9, Huang teaches the method according to claim 8, wherein the third algorithm processes third input data, the third input data encoding the first information on the first optical feature (In as much as claimed and as best understood, Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 10, Huang teaches the method according to claim 1, wherein the first optical feature and/or the second optical feature comprise an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and/or a color (Huang teaches identification of a barcode and also of characters; see claim 1 above).
As to claim 11, Huang teaches thefeature is performed by using the first information on the first optical feature (Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. Therefore, the use of the second algorithm includes the relationship to the first image data because the second algorithm is processed based on first data when first data is read and fails, such that the processing understands to then also use the second image data. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 16, Huang teaches the method according to claim 1, wherein the method comprises the step of: determining a second region of interest in the second image by using second position information on the position of the labware with respect to the work deck, and the step of identifying the second optical feature in the second image by using the second identification algorithm is carried out by using the second region of interest (Huang teaches the determination of the region/position as the area in which the imager images; see claim 1 above. Further Huang teaches a position index; [136, 137]).
As to claim 17, Huang teaches the method according to claim 1, wherein the first optical recording device is a first camera, wherein the first identification algorithm depends at least on a first set of extrinsic calibration parameters associated to the first camera (Huang teaches the first imager as a scanning component, which can be a barcode scanner such that the light is captured and converted and therefore the examiner believes that the barcode scanner is a camera with an imaging lens to capture the light; [82, 83]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis).
If it is deemed that the scanning identifier barcode reader of Huang is not a camera, then prior art Eckard teaches the analogous art of identifying identifiers using a camera (Eckard teaches identifying various images including barcodes; [43]. Eckard teaches that the identification takes place via cameras; [12, 26, 27]). It would have been obvious to one of ordinary skill in the art to have modified the first imager/scanning identifier of Huang to have used a camera as in Eckard because Eckard teaches that a camera can be used as an obvious variant to capture barcode identifiers (Eckard; [12, 26,27, 43]).
As to claim 18, Huang teaches the method according to claim 1, wherein the second optical recording device is a second camera, wherein the second identification algorithm depends at least on a second set of extrinsic calibration parameters associated to the second camera (Huang teaches that the image information obtaining component is a camera; [89]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis).
As to claim 19, Huang teaches the method according to claim 8, wherein the third algorithm processes third input data, the third input data encoding the information on the second optical feature (In as much as claimed and as best understood, Huang teaches that upon determining, via encoding in the first identification data, that the first identification failed, that further identification is needed and that the image information obtaining component is used in combination with the first identification data to determine the identification; see claim 1, [86, 115, 126, 140-142, 144-148]. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 20, Huang teaches the method according to claim 1, wherein the labware item comprises a plate, a tip, a tube, a reservoir, a tip box, a height adapter, a reservoir rack and/or a tube rack (Huang; [82, 136, 137]).
As to claim 21, Huang teaches the method according to claim 3, wherein the method further comprises the step of: selecting the first identification algorithm among a first pool of identification algorithms, wherein the step of selecting the first identification algorithm depends at least on the third position information (Huang teaches the determination of the region/position as the area in which the imager images; see claim 1 above. Further Huang teaches a position index; [136, 137]. Huang uses algorithms to process the data, and the third position could be any detected information from Huang that correlates with the first input data).
As to claim 22, Huang teaches the method according to claim 1, wherein the second optical feature is different from the first optical feature, and wherein the first optical feature is at least one of: an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and a color, wherein the second optical feature is at least one other, different from said at least one, of an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and a color (Huang teaches identification of a barcode and also of characters; see claim 1 above).
Claims 1-11, 16-22 are rejected under 35 U.S.C. 102a1/a2 as being anticipated by Eckard et al (US 20140036070; hereinafter “Eckard”; already of record) in view of Sievert et al (US 20210025791; hereinafter “Sievert”) or alternatively in view of Davis et al (US 20220373569; hereinafter “Davis”).
As to claim 1, Eckard teaches a computer implemented method for identifying, by a processor, a labware item (Eckard; Figs. 1-10), the labware item comprising a first optical feature and a second optical feature, wherein the method comprises the steps of: acquiring of a first image of the labware item with at least a first optical recording device, the first image displaying a top view of a work deck of the data processing system, with the work deck being divided into a plurality of deck regions, at least a portion of the first optical feature; acquiring a second image of the labware item with at least a second optical recording device, the second image displaying at least a portion of the second optical feature; and identifying the first optical feature in the first image by using at least a first identification algorithm thereby obtaining first identification data, the first identification data encoding first information on the first optical feature and information indicative of whether at least a further identification is needed, where the information indicative of whether at least a further identification is needed specifies whether the identification of the first optical feature by using the first identification algorithm has been inconclusive and/or insufficient for identifying the labware item, wherein if, according to the information encoded in the first identification data, the at least further identification is not needed, the method further comprises the step of: identifying the labware item by using at least the first information on the first optical feature, and generating a virtual representation of the work deck by using at least the first information on the labware item, and wherein if, according to the information encoded in the first identification data, the at least further identification is needed, the method further comprises the steps of: identifying the second optical feature in the second image by using at least a second identification algorithm thereby obtaining second identification data, the second identification data encoding information on the second optical feature; and identifying the labware item by using at least the information on the second optical feature, and generating a virtual representation of the work deck by using at least the information on the second optical feature of the labware item, and where the virtual representation is used to automatically validate, and if needed correct a laboratory procedure (Eckard teaches using two cameras 5/5’ to acquire images 6/6’ of a lab workdeck; Fig. 1-10, [12, 26, 27]. Eckard teaches that each camera can detect and identify consumables; [35, 36]. Eckard teaches that a virtual display of the imaged work deck is created, where the work deck has a plurality of regions, allowing the user and computer to check that there are no errors with the consumables; [37, 38, 51, 57, 58, 72, 76, 77, 89], Fig. 2, 8-10. Eckard teaches that the identification is based on position 10’, and also based on two different markings on the consumables 10’’/10’’’; [38, 39, 43], Fig. 2, 8. Eckard teaches that the first feature ID/position is determined [66], and that this is then done for each feature including the second feature/ID [71], and can be done as a third feature/image as well [76]. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], Fig. 9-10. Therefore, Eckard teaches using the information from both cameras in order to provide a proper reading on the consumables. The software processing/decoding that takes place would be via an algorithm/instructions in the processing to help take the barcode scan information and convert that information into the identification. Eckard also teaches that algorithms are used to associate images for assessment; [9] The examiner notes that how the virtual representation is used does not further define the method as it is not an active method step. Further, the examiner notes the extensive use of conditional/contingent limitations. “If” identification is needed, and “if” correction is needed do not necessarily have to occur since they are conditional statements (MPEP 2111.04)).
Eckard does not specifically teach that the first and second images are substantially identical top views of the work deck. However, Sievert teaches the analogous art of detecting laboratory objects, where the singular imaging detectors can be integrated (Sievert teaches multiple detectors at the same location and integrated into one overall detector, thereby having substantially the same field of view (FOV); [88]). It would have been obvious to one of ordinary skill in the art to have modified the multiple camerass to detect the laboratory objects of Eckard to have been integrated to have substantially the same FOV as in Sievert because Sievert teaches that multiple detectors being integrated or separate are obvious variants (Sievert; [88]). Further, it would have been obvious to a person having ordinary skill in the art before the effective filing date to choose two integrated cameras that provide substantially the same FOV “from a finite number of identified, predictable solutions, with a reasonable expectation of success” (see KSR rationale E and MPEP § 2144.07) and because Sievert teaches that multiple detectors being integrated or separate are obvious variants (Sievert; [88]).
Alternatively, Eckard does not specifically teach that the first and second images are substantially identical top views of the work deck. However, Davis teaches the analogous art of imaging components of a work deck and providing a virtual representation, where the imaging device can be fixed and provides a singular view of the entire work deck (Davis teaches that the imaging device can be fixed, and that the imaging device can provide an entire view of the work deck in order to track and evaluate all components at the various locations; [55, 57], Fig. 3). It would have been obvious to one of ordinary skill in the art to have modified the multiple cameras for detecting different features of the labware of Eckard to be fixed and provide overviews of the entire work deck as in Davis, the resulting configuration would be the dual cameras used to check and validate identifiers of Eckard both imaging the entire work deck as in Davis, because Davis teaches that the imaging device can provide information on all of the components at all of the locations (Davis; [55, 57]).
As to claim 2, Eckard teaches the method according to claim 1, wherein the method comprises the step of: determining a first region of interest in the first image by using first position information about the position of the labware with respect to a work deck, and the step of identifying the first optical feature in the first image by using the first identification algorithm is carried out by using the first region of interest (Eckard teaches the determination of region/position as the area in which the imager images; see claim 1 above. Eckard teaches determination of position/arrangement [29-31, 35, 36, 51, 57, 58, 66, 71, 76, 89]).
As to claim 3, Eckard teaches the method according to claim 1, further comprising the step of: acquiring third position information on the position of the labware item with respect to the work deck by using at least a position determining algorithm, wherein the first identification algorithm processes first input data, the first input data depending on the third position information, and/or the method comprises the step of: selecting the first identification algorithm among a first pool of identification algorithms, wherein the step of selecting the first identification algorithm depends at least on the third position information (Eckard teaches the determination of region/position as the area in which the imager images, and correlates the ID with position; see claim 1 above. Eckard teaches determination of position/arrangement [29-31, 35, 36, 51, 57, 58, 66, 71, 76, 89]). Eckard teaches that the first feature ID/position is determined [66], and that this is then done for each feature including the second feature/ID [71], and can be done as a third feature/image as well [76]. Eckard uses algorithms to process the data, and the third position could be any detected information from Huang that correlates with the first input data).
As to claim 4, Eckard teaches the
As to claim 5, Eckard teaches the method according to claim 1, wherein the second optical recording device is a second camera, wherein the second identification algorithm depends at least on a second set of intrinsic calibration parameters associated to the second camera (Eckard teaches two cameras; Fig. 1-10, [12, 26, 27]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89]).
As to claim 6, Eckard teaches the method according to claim 1, wherein if, according to the information encoded in the first identification data, at least a further identification is needed, the method further comprises the step of: selecting the second identification algorithm among a second pool of identification algorithms, wherein the step of selecting the second identification algorithm depends at least on the information encoded in the first identification data (Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 7, Eckard teaches the method according to claim 1, wherein the second algorithm processes second input data, the second input data encoding the first information on the first optical feature (Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. Therefore, the use of the second algorithm includes the relationship to the first image data because the second algorithm is processed based on both image data combined from the first and second imagers, such that the processing understands to then also use the second image data. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 8, Eckard teaches the method according to claim 1,wherein the second identification data encodes information indicative of whether at least a further identification is needed, wherein if, according to the information encoded in the second identification data, at least a further identification is needed, the method further comprises the step of: identifying the first optical feature in the first image by using at least a third identification algorithm thereby obtaining third identification data, the third identification data encoding second information on the first optical feature, and wherein if, according to the information encoded in the second identification data, at least a further identification is needed, the step of identifying the labware item by using at least the information on the second optical feature is performed by using the second information on the first optical feature (In as much as claimed and as best understood, Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 9, Eckard teaches the method according to claim 8, wherein the third algorithm processes third input data, the third input data encoding the first information on the first optical feature (In as much as claimed and as best understood, Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 10, Eckard teaches the method according to claim 1, wherein the first optical feature and/or the second optical feature comprise an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and/or a color (Eckard teaches identification of barcodes and other characters; see claim 1 above, [39, 43]).
As to claim 11, Eckard teaches thefeature is performed by using the first information on the first optical feature (Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. Therefore, the use of the second algorithm includes the relationship to the first image data because the second algorithm is processed based on both image data combined from the first and second imagers, such that the processing understands to then also use the second image data. The examiner notes that the second algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 16, Eckard teaches the method according to claim 1, wherein the method comprises the step of: determining a second region of interest in the second image by using second position information on the position of the labware with respect to the work deck, and the step of identifying the second optical feature in the second image by using the second identification algorithm is carried out by using the second region of interest (Eckard teaches the determination of region/position as the area in which the imager images; see claim 1 above. Eckard teaches determination of position/arrangement [29-31, 35, 36, 51, 57, 58, 66, 71, 76, 89]).
As to claim 17, Eckard teaches the method according to claim 1, wherein the first optical recording device is a first camera, wherein the first identification algorithm depends at least on a first set of extrinsic calibration parameters associated to the first camera (Eckard teaches two cameras; Fig. 1-10, [12, 26, 27]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89]).
As to claim 18, Eckard teaches the method according to claim 1, wherein the second optical recording device is a second camera, wherein the second identification algorithm depends at least on a second set of extrinsic calibration parameters associated to the second camera (Eckard teaches two cameras; Fig. 1-10, [12, 26, 27]. The algorithm/instructions that helps to convert the information into identification for diagnostic purposes would be validated based on image calibration such that the device was known to work prior to performing automated analysis. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89]).
As to claim 19, Eckard teaches the method according to claim 8, wherein the third algorithm processes third input data, the third input data encoding the information on the second optical feature (In as much as claimed and as best understood, Eckard teaches determining first identification data and second identification data via each respective camera, and then using the determined identification information to make the identification. Eckard teaches that the device is calibrated by a user as a reference [40, 41], and that the reference images are stored [46, 47], and that the computer can then check and determine the identification and position automatically based on the imaged information [51, 57, 58, 89], where this is done for both cameras as references and then again to check the arrangement; Fig. 9-10. Therefore, the combination of the first image data (first algorithm) and second image data (second algorithm) together help to make up the third algorithm, such that the processing understands to use the combined information from the first image data and the second image data. The examiner notes that the third algorithm would be the information that is selected based on the acquired data and that in order to identify the consumable that multiple instructions and decisions (i.e. algorithms) would be present).
As to claim 20, Eckard teaches the method according to claim 1, wherein the labware item comprises a plate, a tip, a tube, a reservoir, a tip box, a height adapter, a reservoir rack and/or a tube rack (Eckard; [27]).
As to claim 21, Eckard teaches the method according to claim 3, wherein the method further comprises the step of: selecting the first identification algorithm among a first pool of identification algorithms, wherein the step of selecting the first identification algorithm depends at least on the third position information (Eckard teaches the determination of region/position as the area in which the imager images, and correlates the ID with position; see claim 1 above. Eckard teaches determination of position/arrangement [29-31, 35, 36, 51, 57, 58, 66, 71, 76, 89]). Eckard teaches that the first feature ID/position is determined [66], and that this is then done for each feature including the second feature/ID [71], and can be done as a third feature/image as well [76]. Eckard uses algorithms to process the data, and the third position could be any detected information from Huang that correlates with the first input data).
As to claim 22, Eckard teaches the method according to claim 1, wherein the second optical feature is different from the first optical feature, and wherein the first optical feature is at least one of: an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and a color, wherein the second optical feature is at least one other, different from said at least one, of an indicium, an ideogram, a pictogram, a set of alphanumeric characters, a texture pattern, a hole pattern and a color (Eckard teaches identification of barcodes and other characters; see claim 1 above, [39, 43]).
Other References Cited
The prior art of made of record and not relied upon is considered pertinent to applicant's disclosure include;
Sherrill et al (20220120770; hereinafter “Sherrill”) teaches imager can view the whole deck or part of the deck as obvious variants; [26].
Response to Arguments
Applicant’s arguments filed on 3/2/26 have been considered.
With respect to Applicants arguments towards the prior art rejections, the arguments have been considered but are moot because the arguments are towards the amended claims and not the new ground of rejection.
With respect to Applicants arguments towards the 101 rejection, Applicant's arguments have been fully considered but they are not persuasive.
Applicants argue on pages 7-8 of their remarks that claim 1 does not recite an abstract idea. The examiner respectfully disagrees. Claim 1 recites the abstract ideas of “identifying…by using a… algorithm…” in lines 11-17, and “identifying… in lines 20-21, and “identifying…by using a… algorithm…” in lines 26-28, and “identifying…” in lines 29-30 which are all mental processes and/or math. The examiner believes that “generating a virtual representation” is also an abstract idea as it can be performed with pencil and paper or by a general-purpose computer. MPEP 2106.04(a)(2)III is clear that using pencil and paper or a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea.
Applicants argue on pages 8-9 of their remarks that there is an improvement to a particular technology in claim 1. The examiner respectfully disagrees. First, the applicants citations to page 2 of the specification state that typically the validating accuracy can be achieved using… high resolution cameras. MPEP 2106.05a provides guidance to analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. However, the assertion by applicants is simply that accuracy can be achieved, without typing the alleged improvement to the particular features in the specification or claims that achieve the alleged improvement. Further, applicants alleged improvement appears to be the “identifying” labware itself. However, the “identifying” is the abstract idea itself, and the alleged improvement cannot be the abstract idea, but must be in a particular technology. See MPEP 2106.05(a), paragraphs 4 - 7.
Applicants argue on page 9 of their remarks that the claim amounts to significantly more. The examiner respectfully disagrees, and directs applicants attention to step 2B of the 101 rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN R WHATLEY whose telephone number is (571)272-9892. The examiner can normally be reached Mon- Fri 8am-5pm.
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/BENJAMIN R WHATLEY/Primary Examiner, Art Unit 1798
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