DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 11-13, 15-25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gusyatin (2019/0011882).
Claim 1
Gusyatin (2019/0011882) discloses an automated system (para [0016]- systems can be automated) having an automated holographic optical apparatus situated to determine the phenotypical behavior of an object in a sample based on a detected variation over time of a hologram of the sample (para [0016]- an automated holographic optical apparatus situated to determine at least the antimicrobial susceptibility of a microorganism corresponding to an object in a sample volume based on a detected variation over time of a hologram of the sample volume...and a phenotypical behavior of the microorganism); wherein the holographic optical apparatus is an in-line holographic apparatus and the hologram is an in-line hologram (para [0016] - the holographic apparatus is an in-line holographic apparatus and the hologram is an in-line hologram); wherein the ln--line holographic optical apparatus includes one or a plurality of reference beam sources situated to direct the reference beam(s) to the sample volume (para [0016]- and the in-line holographic optical apparatus includes a reference beam source situated to direct a reference beam to the sample volume), a sample receptacle situated to hold the sample volume in view of the reference beam(s) (para [0016]- a sample receptacle situated to hold the sample volume in view of the reference beam) an optical sensor situated to detect the in-line hologram formed by the reference beam(s) and the sample volume (para [0016]- an optical sensor situated to detect in-line hologram formed by the reference beam and the sample volume) and a controller (622) coupled to the optical sensor (620) and that includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram (Fig. 6, para [0016]- a controller coupled to the optical sensor and that includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram; para [0087]- and a holography controller 622 coupled to the optical sensor 620; para [0093] a memory 636... at least one processor 638); and an output of at least one data calculation module (para [0017]- an output of at least one deeply supervised convolutional neural network associated with the measured hologram; para [0093]- program modules include routines, programs, objects, components, data structures, etc… that perform particular tasks or implement particular abstract data types; para [0123]-[0124]) and a phenotypical behavior of the cell unit (para [0017]- a phenotypical behav1or of Urn at least one microorganism). wherein the phenotypical behavior of the cell unit is classified based on the detected variation (para [0017]- wherein the phenotypical behavior is classified based on the detected variation).
Claim 2
Gusyatin (2019/0011882) discloses the output of the at !east one data calculation mode is determined by a raw hologram imaging processing data calculation module to calculate a variability metric between time-lapse images (Para [0009], [0095]- a display device 648 is situated to display images of the hologram 608 or holographic reconstructions of one or more planes of the sample volume 606, including time-lapse images or video recordings associated with microorganism growth or size variation; para [0106] the network layers can be supervised and the network activations can be trained to map raw hologram (interferometric) space into in-focus image plane at a specified focal distance; para [0109]. [0123]-[0124]).
Claim 3
Gusyatin (2019/0011882) discloses the variability metric is calculated not using holographic image reconstruction by Fourier transformation (Para [0009] [0106]- in deep learning approaches, such as convolutional neural networks, the network layers can be supervised and the network activations can be trained to map raw hologram (interferometric) space into in-focus image plane at a specified focal distance; Para [0095]- a display device 648 is situated to display images of the hologram 608 or holographic reconstructions of one or more planes of the sample volume 606, including time-lapse images or video recordings associated with microorganism growth or size variation; Para [0109]).
Claim 4
Gusyatin (2019/0011882) discloses that the at least one data calculation module contains a deeply supervised convolutional neural network (para [0123]-At 1612, the data is processed through the trained deeply supervised convolutional neural network; para [0093]- program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types).
Claim 5
Gusyatin (2019/0011882) discloses discloses an in-line holographic optical system (para [0016]- the holographic apparatus is an in-line holographic apparatus) comprising: a reference beam source (614) (Fig. 6, para [0087]- a reference beam source 614 situated to direct a reference beam 616 to the sample volume 606); a sample receptacle (618) below the reference beam source (614) (para [0087]- a reference beam source 614...a sample receptacle 618 situated to hold the sample volume 606 in view of the reference beam 616); an optical sensor (620) below the sample receptacle (618) (note: see the arrangement of elements 618 and 620 in Fig. 6, para [0087]- a sample receptacle 618...an optical sensor 620 situated to detect the in-line hologram 608 formed by the reference beam 616 and the sample volume 606); and a controller (622) coupled to the optical sensor (620) (para [0087]- a holography controller 622 coupled to the optical sensor 620).
Claim 6
Gusyatin (2019/0011882) discloses the controller (622) includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine a variation over time at an in-line hologram (Fig. 6, para [0016]- a controller coupled to the optical sensor and that includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram; para [0087]- and a holography controller 622 coupled to the optical sensor G20; para [0093]·- a memory 636...at least one processor 636).
Claim 7
Gusyatin (2019/0011882) discloses an in-line holographic system (para [0016]- the holographic apparatus is an in-line holographic apparatus) comprising: a reference beam source (614) (Fig. 6, para [0087]- a reference beam source 614 situated to direct a reference beam 616 to the sample volume 606); an illumination source (626) adjacent to the reference beam source (614) (Fig. 6, para {0088]- the reference beam source 614 includes a pinhole aperture 624 situated to receive an illumination 628 from an illumination source 626 and the reference beam 616); a sample receptacle (618) below the illumination source (626) (note: see the arrangement of elements 618 and 626 in Fig. 6 para [0087]- a sample receptacle 618 situated to hold the sample volume 606 in view of the reference beam 616; para [0088]- a pinhole aperture 624 situated to receive an illumination 628 from an illumination source 626 and the reference beam 616); an optical sensor (620) below the sample receptacle (618) (note: see the arrangement of elements 618 and 620 in Fig. 6, para [0087]- a sample receptacle 618… an optical sensor 620 situated to detect the in-line hologram 608 formed by the reference beam 616 and the
sample volume 606); and a hologram controller (622) coupled to the optical sensor (620) (para [0067]- a holography controller 622 coupled to the optical sensor 620).
Claim 8
Gusyatin (2019/0011882) discloses the illumination source is a single illumination source (para [0099]- a single illumination source can be used to illuminate the pinhole apertures 708a-708d).
Claim 9
Gusyatin (2019/0011882) discloses the illumination source comprises more than one illumination source (para [0099]- a single illumination source can be used to illuminate the pinhole apertures 708a-708d, and in other examples other quantities of illumination sources can be used).
Claim 11
Gusyatin (2019/0011882) discloses the controller (622) includes at least one processor (638) and one or' more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor (Fig. 6, para [0093]- the holography controller 622 is a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638), cause the controller to reconstruct the hologram image (para [0016]- the controller is configured to reconstruct the spatial characteristics of the sample volume based on the detected in-line hologram).
Claim 12
Gusyatin (2019/0011882) discloses the controller (622) includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor (Fig. 6, para [0093]- the holography controller 622 is a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638), cause the controller to remove uninformative noise or background from the hologram image (para [0096] a substantial set of the backwound objects and associated signal characteristics can be eliminated through image subtraction so as to improve a signal to noise ratio for the spatial difference comparison routine 646; para [0107]).
Claim 13
Gusyatin (2019/0011882) discloses the controller (622) includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor (Fig. 6, para [0093]- the holography
controller 622 is a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638), cause the controller to identify growth in independent subsections (para [0015]- tracking spatial differences to detect changes in growth of microorganisms over time; para [0016]- the sample reaction chambers include a plurality of growth channels having selective media; Fig. 6; para [0086]).
Claim 15
Gusyatin (2019/0011882) discloses an automated system (para [0016]- systems can be automated), comprising: an automated holographic optical apparatus situated to determine at least antimicrobial susceptibility of a microorganism corresponding to an object in a sample volume based on a detected variation over time of a hologram of the sample volume (para [0016]- an automated holographic optical apparatus situated to determine at least the antimicrobial susceptibility of a microorganism corresponding to an object in a sample volume based on a detected variation over time of a hologram of the sample volume), an output of at least one data calculation module (para [0017]- an output of at least one deeply supervised convolutional neural network associated with the measured hologram; para [0093]- program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types; para [0123]-[124], and a phenotypical behavior of the microorganism (para [0017]- a phenotypical behavior of the at least one microorganism).
Claim 16
Gusyatin (2019/0011882) discloses the phenotypical behavior of the microorganism is classified based on the detected variation and the output of the at least one data calculation module (para [0017]- wherein the phenotypical behavior is classified based on the detected variation and the output of the at least one deeply supervised convolutional neural network; para [0093] program modules include routines, programs, objects. components, data structures, etc., that perform particular tasks or implement particular abstract data types).
Claim 17
Gusyatin (2019/0011882) discloses a reference beam source (614) situated to direct a reference bean, (616) to the sample volume (606) (Fig. 6, para, [0016] the in-line holographic optical apparatus includes a reference beam source situated to direct a reference beam to the sample volume; para [0087]- a reference beam source 614 situated to direct a reference beam 616 to the sample volume 606), a sample receptacle (618) situated to hold the sample volume in view of the reference beam (para [0016]- a sample receptacle situated to hold the sample volume in view of the reference beam; para [0087]- a sample receptacle 618), an optical sensor (620) situated to detect the in-line hologram formed by the reference beam and the sample volume (para [0016]- an optical sensor situated to detect the in-line hologram formed by the reference beam and the sample volume; para [0087]- an optical sensor 620), and a controller (622) coupled to the optical sensor (620) and that includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram (para [0016]- a controller coupled to the optical sensor and that includes at least one processor and one or more computer readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram; para [0093]- the holography controller 622...a memory 636... processor 638).
Claim 18
Gusyatin (2019/0011882) discloses an output of at least one data calculation module (para [0017]- an output of a! least one deeply supervised convolutional neural network associated with the measured hologram; para [0093]- program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types; para [0123]-[0124]), and a phenotypical behavior of the cell unit (para [0017]- a phenotypical behavior of the at least one microorganism), wherein the phenotypical behavior of the cell unit is classified based on the detected variation (para [0017]- wherein the phenotypical behavior is classified based on the detected variation).
Claim 19
Gusyatin (2019/0011882) discloses a system for tracking a detected variation over time (para [0016} systems can be automated...an object in a sample volume based 011 a detected variation over time of a hologram of the sample volume), comprising a light source (626) (Fig. 6, para [0088]- an illumination source 626); an optical sensor (620) below the light source (626) (note: see the arrangement of elements 620 and 626 in Fig. 6, para [0087]- an optical sensor 620 situated to detect the in-line hologram 608 formed by the reference beam 616 and the sample volume 606; para [0088]- an illumination 628 from an illumination source 626 and the reference beam 616 is directed lens-free from the pinhole aperture 624 to the sample volume 606); and a hologram controller (622) coupled to the optical sensor the wherein the controller includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that responsive to execution by the al least one processor, cause the controller to determine a variation over time of an in-line hologram (para [0016]- a controller coupled to the optical sensor and that includes at least one processor and one or more computer-readable storage media including stored instructions that, responsive to execution by the at least one processor, cause the controller to determine the variation over time of the in-line hologram; para [0087]- and a holography controller 622 coupled to the optical sensor 620; para [0093]- a memory 636...at least one processor 638).
Claim 20
Gusyatin (2019/0011882) discloses an automated system (para [0016]- systems can be automated), comprising: an automated in-line holographic optical apparatus situated to detect variation over time of an in-line hologram of a sample volume (para [0016]- an object in a sample volume based on a detected variation over time of a hologram of the sample volume... the holographic apparatus is an in-line holographic apparatus; para [0095], [0123]-[0124]).
Claim 21
Gusyatin (2019/0011882) discloses the variation over time of the in-line hologram is calculated using a data calculation module (para [0016]- an object in a sample volume based on a detected variation over time of a hologram of the sample volume, an output of at least one deeply supervised convolutional neural network: para [0093]- program modules include routines. programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types; para [0123]-[124]).
Claim 22
Gusyatin (2019/0011882) discloses a computer-implemented machine for characterizing a plurality of particles (para [0113] the disclosed technology may be implemented with other computer system configurations; para [0109]- the network is trained in a supervised fashion to recognize variational spatial patterns due to, by way of example, multiple species of bacteria and fungus versus other biological or non-biological particles), comprising: a processor (638) (Fig. 6, para [0093]- at least one processor 638): and a tangible computer-readable medium (636) operatively connected to the processor (638) and including computer code configured (para [0093]- a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638; para [0094] The memory 636 can includes read only memory (ROM) and random access memory (RAM)) to: generate an in-line hologram of a first particle of the plurality of particles at a first time (para [0102]- FIG. 8 depicts an example method 800 for detecting the presence of a microorganism. At 804, a first in-line hologram of a sample volume is detected at a first time); and generate an in-line hologram of a second particle of the plurality of particles at a second time (para [0102]- and at 812, a second in-line hologram of the sample volume is detected at a second time): and determine a variation over time of the in-line hologram (para [0·102]- At 818, a variation over time associated with the in-line holograms is determined (e.g., between the first and second in-line holograms) that is associated with an indication that one or more objects immobilized in the sample volume is a microorganism; para [0123]-[0124]).
Claim 23
Gusyatin (2019/0011882) discloses the variation over time of the in-line hologram is calculated using a data calculation module (para [0102]- At 818, a variation over time associated with the in-line holograms is determined (e.g., between the first and second in-line holograms) that is associated with an indication that one or more objects immobilized in the sample volume is a microorganism; para [0017]- an output of at least one deeply supervised convolutional neural network associated with the measured hologram; para [0093]- program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types; para [0123]-[0124]).
Claim 24
Gusyatin (2019/0011882) discloses a computer-implemented machine for differentiating a plurality of particles from bacteria (para [0113]- the disclosed technology may be implemented with other computer system configurations: para [0109]- the network strained in a supervised fashion to recognize variational spatial patterns due to, by way of example, multiple species of bacteria and fungus versus other biological or non-biological particles), comprising: a processor (638) (Fig. 6, para [0093]- at least one processor 638); and a tangible computer-readable medium (636) operatively connected to the processor (638) and including computer code (para [0093]- a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638; para [0094]- The memory 636 can includes read only memory (ROM) and random access memory (RAM)) configured to: generate an in-line hologram of a first particle of the plurality of particles at a first time (para [0102]- FIG. 8 depicts an example method 800 for detecting the presence of a microorganism. At 804, a first in-line hologram of a sample volume is detected at a first time); generate an in-line hologram of a first bacteria of the plurality of bacteria at a first time (para [0102]- FlG. 8 depicts an example method 800 for detecting the presence of a microorganism. At 804, a first in-line hologram of a sample volume is detected a! a first time; para [0109}- the network is trained in a supervised fashion to recognize variational spatial patterns clue to, by way of example, multiple species of bacteria and fungus versus other biological or non-biological particles); generate an in-line hologram of a second particle of the plurality of particles at a second time (para [0102] and at 812, a second in-line hologram of the sample volume is detected at a second time); generate an in-line hologram of a second bacteria of the plurality of bacteria at a second time (para [0102]- and at 812, a second in-line hologram of the sample volume is detected at a second time; para [0109]- the network is trained in a supervised fashion to recognize variational spatial patterns due to, by way of example, multiple species of bacteria and fungus versus otr1er biological or non-biological particles); differentiating a plurality of particles from bacteria based on a variation over time oi the in-line hologram (para [0009], [0102]- At 818, a variation over time associated with the in-line holograms is determined (e.g., between the first and second in-line holograms) that is associated with an indication that one or more objects immobilized in the sample volume is a microorganism; para [0104], [0109]- the network is trained in a supervised fashion to recognize variational spatial patterns due to, by way of example, multiple species of bacteria and fungus versus other biological or non-biological particles; para [0123]-[0124]).
Claim 25
Gusyatin (2019/0011882) discloses the variation over time of the in-line hologram is calculated using a data calculation module (para [0102}- At 818 a variation over time associated with the in-line holograms is determined (e.g., between the first and second in-line holograms) that is associated with an indicated on that one or more objects immobilized in the sample volume is a microorganism; para [0104], [0017]- an output of at least one deeply supervised convolutional neural network associated with the measured hologram; para [0093]- program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types; para [0123]-[0124]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gusyatin (2019/0011882) and in further view of HAMALAINEN et al (2019/0250559).
Claim 10
Gusyatin (2019/0011882) discloses the apparatus of claim 7, Gusyatin further discloses wherein the controller (622) includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that, responsive to execution by !he at least one processor (Fig. 6, para [0093]- the holography controller 622 is a computing device that includes a memory 636 that can include one or more computer readable instructions, such as program modules, that can be executed by at least one processor 638), Gusyatin does not disclose cause the controller to filter the hologram directly. However, HAMALAINEN et al (2019/0250559) drawn to in-line holography imaging (abstract, para [0010]), discloses, filter the hologram directly (para [0063]- a filtered hologram pattern 481; para [0066], [0068]). It would have been obvious to a person of ordinary skill in the art to filter the hologram of HAMALAINEN et al (2019/0250559) with the system of Gusyatin, to improve image reliability (see HAMALAINEN et al (2019/0250559), para [0006]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gusyatin (2019/0011882) and in further view of Ozcan et al. (20120218379).
Claim 14
Regarding claim 14, Gusyatin (2019/0011882) discloses the apparatus of claim 7, Gusyatin (2019/0011882) further discloses wherein the controller (622) includes at least one processor (638) and one or more computer-readable storage media (636) including stored instructions that responsive to execution by the at least one processor (Fig. 6, para [0093]- the holography controller 622 is a computing device that includes a memory; 636 that can include one or more computer readable instructions, such as program modules that can be executed by at least one processor 638), Gusyatin (2019/0011882) does not disclose cause the controller to globalize the local signal. However, Ozcan et al. (20120218379) drawn to holography (para [0011]),
discloses, cause the controller (26) to globalize the local signal (Fig. 2, para [0039]- The computer 26 includes one or more processors (not shown),..runs software that acquires an image of the sample 12 that includes the holographic amplitude or intensity; para [0091]- For digital reconstruction of the object images from their holograms there are two approaches that were taken: (1) Back-propagate the Fourier components of the intensity of each object hologram… These two techniques independently enabled twin-image free reconstruction of the micro-objects from their raw holograms. These digital reconstruction approaches can actually be considered to be part of a broader umbrella of interferometric and non-interferometric phase-retrieval techniques: para [0096] computation involves multiplying the Fourier transform). It would have been obvious to a person of ordinary skill in the art to cause the controller to globalize the local signal of Ozcan et al. (20120218379) with the system of Gusyatin (2019/0011882), to improve performance (see Ozcan et al. (20120218379), para [0005]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL PATRICK STAFIRA whose telephone number is (571)272-2430. The examiner can normally be reached M-F 6:30am-3pm.
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/MICHAEL P STAFIRA/Primary Examiner, Art Unit 2877 September 25, 2025