DETAILED ACTION
Notice of AIA Status
The present application, filed on 5/20/2024, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-16 are pending.
Claims 1-16 are rejected.
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 1-16 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without adding significantly more.
Step 1: Claims 1-15 are directed to a process which is a statutory category under 35 U.S.C. §101 (see MPEP §2106 “the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter”).
Step 2A, Prong 1: an abstract idea is identified. Claim 1 recites the abstract idea: “determining whether - the reception place is free for receiving the sample container and - the reception place is configured to receive the sample container, by applying a machine learning algorithm for image analysis of the image of the reception place in the data processing device” which can be performed in the human mind because a person can determine whether - the reception place is free for receiving the sample container and - the reception place is configured to receive the sample container. In other words, this abstract idea constitutes an observation, evaluation or judgment that can practically be performed in the human mind because a person can visually inspect the reception place or an image thereof and effectively determine whether the reception place is free to receive the sample container and whether the reception place is configured to receive the sample container. Recitation of a generic processor and a machine learning algorithm as tools for performing the determination doesn’t remove the underlying abstract idea from the mental-process grouping (see the USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: • a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.). Therefore, the claimed invention is directed to a mental process, which US courts have consistently deemed abstract ideas (see MPEP 2106 “the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’” 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).
Step 2A, Prong 2: has the abstract idea been integrated into a particular practical application? Once the abstract idea is performed, if it is not the case the reception place is determined as free and configured to receive the sample container, the method completes without “placing the sample container in the reception place comprises verifying an expected container height of the sample container, said expected container height determined from scanning the sample container and/or from determining the container type from an image of the container, wherein verifying the expected container height comprises determining a measured container height of the sample container, wherein the verification result is assigned as positive if the measured container height is equal to or within a permissible limit of the expected container height, and wherein the at least one trained pattern is further determined via the machine learning algorithm using the verification result of the expected container height” (see MPEP 2111.04).
Claim 1 recites “wherein the at least one trained pattern is further determined via the machine learning algorithm using the verification result of the expected container height” which specifies how the trained pattern is determined using a verification result, but does not require the verification result be generated during every performance of the claimed method. If the reception place is determined not to be free and suitably configured to receive a sample container, claim 1 does not require the subsequent placement, height-verification or verification-result-based training operations.
Claim 1 also recites an imaging device that acquires images used in the determination steps. This image acquisition constitutes data gathering and does not integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Even when the contingent operations are performed, claim 1 recites the physical placement and height-verification operations at a high level of generality without specifying an improved imaging, robotic-placement or height-measurement mechanism which amounts to just generally applying the abstract idea per MPEP 2106.05(f), and also is just generally linking the abstract idea to a field of use per MPEP 2106.05(h), which are not particular practical applications.
Step 2B: does the claim recite any elements which are significantly more than the abstract idea? Other than the abstract idea, claim 1 recites “a laboratory automation system, the laboratory automation system comprising: - a carrier comprising a reception place for receiving a sample container configured to contain a sample to be analyzed by a laboratory device; - a placement device configured to pick and place the sample container; - an imaging device; and - a data processing device comprising at least one processor and a memory” which is well-understood, routine and conventional (see MPEP 2106.05 d).
Sinz (US20180217176) teaches a laboratory automation system (laboratory automation system 10 in [0055]), the laboratory automation system comprising: - a carrier (sample container carrier 140 in [0058]) comprising a reception place for receiving a sample container (sample container 145 in [0058]) configured to contain a sample to be analyzed by a laboratory device (laboratory stations in [0046]) (see also [0004], which recites “sample containers containing samples to be analyzed) (see also [0058], which recites “a sample container carrier 140 can carry a respective sample container 145, embodied as laboratory tube”); - a placement device (pick and place device in [0055], which recites “a gripping device 25, e.g. in the form of a pick-and-place device”)) configured to pick and place the sample container (see [0063], which recites “the sample container 145 can be gripped by the gripping device 25”).
In addition, Goemann-Thoss (US20150127270) teaches a laboratory automation system (laboratory machine 1 in [0215]) comprising an imaging device (image acquisition unit in [0127]); and - a data processing device (digital data processing system in [0111]) comprising at least one processor (processor in [0111]) and a memory (main memory in [0111]).
In addition, claim 1 further recites “the method comprises: - detecting an image of the reception place by the imaging device”. The “detecting an image of the reception place by the imaging device” amounts to insignificant extra- solution activity consisting of mere data gathering (see MPEP §2016.05 g). The USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline identifies examples that did not integrate a judicial exception into a practical application: merely including instructions to implement the abstract idea on a computer, or using the computer as a tool to perform an abstract idea, adding insignificant extra-solution activity to the judicial exception, generally linking the use of a judicial exception to a particular technological environment or field of use.
Technological improvement: the computer-implemented method of claim 1 doesn’t improve the functionality of a computer or of technology, rather the processor and the machine learning algorithm are a mere conventional tool that applies the abstract idea. The use of the computer doesn’t improve the functionality of the computer itself or provide technological advancement in field of endeavor (see MPEP 2106.05a).
Dependent claims 2-15 do not cure the eligibility deficiencies of claim 1 and are likewise unpatentable under 35 U.S.C. §101 (see MPEP §2016.07).
Claims 2-4 further image classification, comparison and suitability evaluation without any integration into a particular practical application under step 2A prong two.
Claims 5-7 specify geometrical criteria used in the abstract idea. These limitations do not integrate the abstract idea into a practical application.
Claim 8 provides container-type data which doesn’t integrate into a particular application under step 2A prong two.
Claim 9 provides scanning and database retrieval which doesn’t integrate into a particular application under step 2A prong two.
Claim 10 provides second-image acquisition as mere data gathering.
Claim 11 provides determining a position from the image which doesn’t integrate into a particular application under step 2A prong two.
Claim 12 limits the image to a single reception place and doesn’t integrate the abstract idea into a practical application.
Claim 13 specifies that the imaging device is attached to the placement device which is well-understood, routine, conventional (Silbert (US20160178654) teaches the imaging device (one or more cameras 508 in [0101]) attached to the placement device (robot arm 112 in [0101], which recites “as shown in FIGS. 16 and 17, one or more cameras (508) are mounted on a robotic arm (112, 506) to provide visual feedback to multiple areas of the instrument”). This physical arrangement merely positions the imaging device for acquiring the image used in the abstract determination and does not meaningfully apply the abstract idea beyond the data gathering operation.
Claim 14 specifies determining a trained pattern via the machine learning algorithm using training images indicative of a plurality of reception places, but fails to integrate into a practical application.
Claim 15 specifies determining a further trained pattern using the height verification result but also fails to integrate into a practical application.
In summary, claims 1-15 don’t amount to significantly more than the judicial exception and as a result the claims are unpatentable under 35 U.S.C. §101.
Claim 16 is also ineligible under 35 U.S.C. 101.
Step 1: Claim 16 is directed to a system which is a statutory category under 35 U.S.C. §101 (see MPEP §2106 “the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter”).
Step 2A, Prong 1: an abstract idea is identified. Claim 16 recites the abstract idea: “- determine whether - the reception place is free for receiving the sample container and - a reception place type of the reception place is suitable for receiving the sample container, by applying a machine learning algorithm for image analysis of the image of the reception place in the data processing device, wherein determining whether the reception place is configured to receive the sample container comprises processing at least one trained pattern to determine the reception place type” which can be performed in the human mind because a person can determine whether - the reception place is free for receiving the sample container and - the reception place is suitable for receiving the sample container. In other words, this abstract idea constitutes an observation, evaluation or judgment that can practically be performed in the human mind because a person can visually inspect the reception place or an image thereof and effectively determine whether the reception place is free to receive the sample container and whether the reception place is configured to receive the sample container. Recitation of a generic processor and a machine learning algorithm as tools for performing the determination doesn’t remove the underlying abstract idea from the mental-process grouping (see the USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: • a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.). Therefore, the claimed invention is directed to a mental process, which US courts have consistently deemed abstract ideas (see MPEP 2106 “the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’” 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). Under step 2A prong one - MPEP 2106.04(a)(2)III is clear that using a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea.
Step 2A, Prong 2: has the abstract idea been integrated into a particular practical application? claim 16 recites the physical placement and height-verification operations at a high level of generality without specifying an improved imaging, robotic-placement or height-measurement mechanism which amounts to just generally applying the abstract idea per MPEP 2106.05(f), and also is just generally linking the abstract idea to a field of use per MPEP 2106.05(h), which are not particular practical applications.
Under step 2A prong two - the mental steps are done by a general-purpose computer, which is not a particular machine. See MPEP 2106.05(b).
Step 2B: does the claim recite any elements which are significantly more than the abstract idea? Other than the abstract idea, claim 16 recites “a laboratory automation system, the laboratory automation system comprising: - a carrier comprising a reception place for receiving a sample container configured to contain a sample to be analyzed by a laboratory device; - a placement device configured to pick and place the sample container; - an imaging device; and - a data processing device comprising at least one processor and a memory” which is well-understood, routine and conventional (see MPEP 2106.05 d).
Sinz (US20180217176) teaches a laboratory automation system (laboratory automation system 10 in [0055]), the laboratory automation system comprising: - a carrier (sample container carrier 140 in [0058]) comprising a reception place for receiving a sample container (sample container 145 in [0058]) configured to contain a sample to be analyzed by a laboratory device (laboratory stations in [0046]) (see also [0004], which recites “sample containers containing samples to be analyzed) (see also [0058], which recites “a sample container carrier 140 can carry a respective sample container 145, embodied as laboratory tube”); - a placement device (pick and place device in [0055], which recites “a gripping device 25, e.g. in the form of a pick-and-place device”)) configured to pick and place the sample container (see [0063], which recites “the sample container 145 can be gripped by the gripping device 25”).
In addition, Goemann-Thoss (US20150127270) teaches a laboratory automation system (laboratory machine 1 in [0215]) comprising an imaging device (image acquisition unit in [0127]); and - a data processing device (digital data processing system in [0111]) comprising at least one processor (processor in [0111]) and a memory (main memory in [0111]).
In addition, claim 16 further recites “data processing device comprising at least one processor and a memory; and configured to: - detect an image of the reception place by the imaging device”. The “detect an image of the reception place by the imaging device” amounts to insignificant extra- solution activity consisting of mere data gathering (see MPEP §2016.05 g). The USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline identifies examples that did not integrate a judicial exception into a practical application: merely including instructions to implement the abstract idea on a computer, or using the computer as a tool to perform an abstract idea, adding insignificant extra-solution activity to the judicial exception, generally linking the use of a judicial exception to a particular technological environment or field of use.
Technological improvement: the laboratory automation system of claim 16 doesn’t improve the functionality of a computer or of technology, rather the processor and the machine learning algorithm is a mere conventional tool that applies the abstract idea. The use of the computer doesn’t improve the functionality of the computer itself or provide technological advancement in field of endeavor (see MPEP 2106.05a).
In summary, claims 1-16 don’t amount to significantly more than the judicial exception and as a result the claims are unpatentable under 35 U.S.C. §101.
Citation of Relevant Prior Ar
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kluckner (US20180365530) teaches “a specimen testing apparatus, comprising:
a track;
specimen carriers moveable on the track, the specimen carriers configured to carry specimen containers; and
a quality check module arranged on the track and adapted to determine characteristics of a specimen container, the quality check module comprising:
a plurality of cameras arranged at multiple viewpoints around an imaging location adapted to receive the specimen container, each of the plurality of cameras configured to capture multiple images of at least a portion of the specimen container at different exposures times and at different spectra having different nominal wavelengths from the multiple viewpoints; and
a computer coupled to the plurality of cameras, the computer configured and operable to:
select optimally-exposed pixels from the images at the different exposure times at each of the spectra to generate optimally-exposed image data for each spectra and viewpoint,
classify the optimally-exposed image data as at least being one of tube, cap, or label, and
identify a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra”, see claim 20 of Kluckner.
Conclusion
Claims 1-16 are not rejected over the prior art of record.
With respect to claim 1, Wu (US20170124704, cited in the 5/20/24 IDS) teaches a method for operating a laboratory automation system (lab automation system in [0037]) (see the abstract, which recites “methods and systems for detecting properties of sample tubes in a laboratory environment”), the laboratory automation system (lab automation system) comprising:
- a carrier (carrier in [0034]) comprising a reception place (tube holder in [0034]) for receiving a sample container (sample tube in [0035]) configured to contain a sample (fluid sample in [0034]) to be analyzed by a laboratory device (analyzer in [0037]);
- a placement device (sample handling robot arm in [0005]) configured to pick and place the sample container (sample tube) (see [0005], which recites “a sample handling robot arm may pick up a tube, remove it from the tray and place it into a carrier”);
- an imaging device (image capture system 140 in [0046]); and
- a data processing device (controller 520 in [0058]) comprising at least one processor (image processor 524 in [0059]) and a memory (memory devices 540 in [0058]); wherein the method comprises:
- detecting an image of the reception place (tube holder) by the imaging device (image capture system 140) (see [0046], which recites “the series of FIGS. 2A-2F illustrate the depth of information that is obtained from the images, enabling the determination of the following characteristics: a center point of each tube in set 130 (e.g., the x-y location determined by correlating image features corresponding to a tube holder”);
- determining whether
- the reception place (tube holder) is free for receiving the sample container (sample tube) see [0011], which recites “automatically determining whether each of a plurality of slots contains a sample tube”) (see also [0034], which recites “carriers may be specialized to a given payload in an IVD environment, such as having a tube holder to engage and carry a sample tube… carriers can be configured to include one or more slots (e.g., a carrier may hold one or a plurality of sample vessels)”) by applying a machine learning algorithm (see [0087]) for image analysis of the image of the reception place (tube holder) in the data processing device (controller 520).
Wu neither teaches nor reasonably suggests determining whether the reception place is configured to receive the sample container by applying a machine learning algorithm for image analysis of the image of the reception place in the data processing device wherein determining the reception place is configured to receive the sample container comprises processing at least one trained pattern to determine the reception place type.
With respect to claim 16, Wu (US20170124704, cited in the 5/20/24 IDS) teaches a laboratory automation system (lab automation system in [0037]), comprising:
- a carrier (carrier in [0034]) comprising a reception place (tube holder in [0034]) for receiving a sample container (sample tube in [0035]) configured to contain a sample (fluid sample in [0034]) to be analyzed by a laboratory device (analyzer in [0037]);
- a placement device (sample handling robot arm in [0005]) configured to pick and place the sample container (sample tube) (see [0005], which recites “a sample handling robot arm may pick up a tube, remove it from the tray and place it into a carrier”);
- an imaging device (image capture system 140 in [0046]); and
- a data processing device (controller 520 in [0058]) comprising at least one processor (image processor 524 in [0059]) and a memory (memory devices 540 in [0058]); and configured to:
- detect an image of the reception place (tube holder) by the imaging device (image capture system 140) (see [0046], which recites “the series of FIGS. 2A-2F illustrate the depth of information that is obtained from the images, enabling the determination of the following characteristics: a center point of each tube in set 130 (e.g., the x-y location determined by correlating image features corresponding to a tube holder”);
- determine whether - the reception place (tube holder) is free for receiving the sample container (sample tube) (see [0011], which recites “automatically determining whether each of a plurality of slots contains a sample tube”) (see also [0034], which recites “carriers may be specialized to a given payload in an IVD environment, such as having a tube holder to engage and carry a sample tube…Carriers can be configured to include one or more slots (e.g., a carrier may hold one or a plurality of sample vessels)”) by applying a machine learning algorithm (see [0087]) for image analysis of the image of the reception place (tube holder) in the data processing device (controller 520).
Wu neither teaches nor reasonably suggests that the data processing device is configured to determine whether a reception place type of the reception place is suitable for receiving the sample container by applying a machine learning algorithm for image analysis of the image of the reception place in the data processing device, wherein determining whether the reception place is configured to receive the sample container comprises processing at least one trained pattern to determine the reception place type; and place the sample container in the reception place by the placement device if the reception place is determined as free and suitable for receiving the sample container; wherein placing the sample container in the reception place comprises verifying an expected container height of the sample container, said expected container height determined from scanning the sample container and/or from determining the container type from an image of the container, wherein verifying the expected container height comprises determining a measured container height of the sample container, wherein the verification result is assigned as positive if the measured container height is equal to or within a permissible limit of the expected container height, and wherein the at least one trained pattern is further determined via the machine learning algorithm using the verification result of the expected container height.
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/JONATHAN BORTOLI/Examiner, Art Unit 1797
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