DETAILED ACTION
Claims 1–2, 4–14, 16–17 and 19–23 are pending in the instant application.
This Office Action is in response to Applicant’s argument filed on 01/08/2026.
THIS OFFICE ACTION IS MADE FINAL.
Response to Arguments
1. Examiner thanks Applicant clearly proposed the claimed language to overcome the claim objection.
2. Examiner also thanks Applicant’s amendment to overcome the 103 rejection. However, while updating extensive searches based on the amendment, Examiner found that the claimed invention is commonly known in ordinary skill of art. Therefore, Applicant’s arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1–2, 4–14, 16–17 and 19–23 are rejected under pre-AIA 35 U.S.C. § 103(a) as being unpatentable over Li (U.S. 12,412,389 B2) in view of Shamsheyeva et al. (U.S. 11,603,550 B2)
Li discloses a system for microorganism detection, comprising:
a database configured to electronically store data, the data including a detection model trained based on historical microorganism identification and cell count data; (Per Fig. 1 at step S5, Li discloses a RCNN target detection model. Li col. 6 lines 22–25. Step S5: Perform transfer learning by using a Faster RCNN based on the source domain dataset and the target domain dataset, to obtain a multi-source algae image target detection model.) and
a processing device in communication with the database, the processing device configured to: (Li discloses a system for algae image target detection. Id. col. 7 line 62 – col. 8 line 20. [p]rovides a system for multi-source algae image target detection, including: a building module configured to build automated algae crawling tool;)
receive as input an electronic image of a water sample. (Per Fig. 2, Li collects algae images and processes them in his system. Id. col. 5 lines 58–62. Based on features of collected algae images, such as clarity, color, style, the size of algae, and whether a foreign substance exists, each image is input into a pre-trained binary classifier to determine whether the image is algae, and then filtering is performed.)
Li fails to specifically disclose electronically detect at least one microorganism in the electronic image;
electronically identify a colony shape and a colony size of the detected at least one microorganism;
execute the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and
estimate a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria.
However, in related art, Shamsheyeva discloses electronically detect at least one microorganism in the electronic image; (Per Fig. 1, Shamsheyeva’s analysis module 140 receives microorganism image data. Shamsheyeva col. 17 line 66 – col. 18 line 20. [a]nalysis module 140 may be capable of receiving image data associated with a microorganism.)
electronically identify a colony shape and a colony size1 of the detected at least one microorganism; (Per Fig. 1, Shamsheyeva’s analysis module 140 deciphers microorganism’s signals in terms of a shape and a size while shifting a shape. Id. col. 20 line 65 – col. 21 line 14. Analysis module 140 may detect signals having various properties including, for example, a range of intensities, a range of sizes, and a range of shapes.)
execute the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and (Per Fig. 1, Shamsheyeva’s analysis module 140 discloses object detection in images identifying individuated microorganisms. Id. col. 21 lines 46–65. [a]nalysis module 140 may employ wavelet transforms because the wavelet transforms are suitable for signal characterization (e.g., object detection in images).)
estimate a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria. (Through Figs. 18–18B, Shamsheyeva discloses cell count in terms of sum of integrated pixel intensity. Id. col. 47 lines 29–37. Sum of integrated pixel intensities of individual clones closely parallels clone mass and/or cell count from standard methods.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Shamsheyeva into the teachings of Li to rapidly count microorganism particles. Id. col. 30 lines 40–51.
Regarding claim 16, Li discloses a method for microorganism detection, comprising:
receiving as input to a system for microorganism detection an electronic image of a water sample, (Per Fig. 1 at step S5, Li discloses a RCNN target detection model. Li col. 6 lines 22–25. Step S5: Perform transfer learning by using a Faster RCNN based on the source domain dataset and the target domain dataset, to obtain a multi-source algae image target detection model.) the system for microorganism detection including (i) a database configured to electronically store data, the data including a detection model trained based on historical microorganism identification and cell count data, (Li discloses a system for algae image target detection. Id. col. 7 line 62 – col. 8 line 20. [p]rovides a system for multi-source algae image target detection, including: a building module configured to build automated algae crawling tool;) and (ii) a processing device in communication with the database. (Per Fig. 2, Li collects algae images and processes them in his system. Id. col. 5 lines 58–62. Based on features of collected algae images, such as clarity, color, style, the size of algae, and whether a foreign substance exists, each image is input into a pre-trained binary classifier to determine whether the image is algae, and then filtering is performed.)
Li fails to specifically disclose electronically detecting at least one microorganism in the electronic image;
electronically identifying a colony shape and a colony size of the detected at least one microorganism;
executing the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and
estimating a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria.
However, in related art, Shamsheyeva discloses electronically detecting at least one microorganism in the electronic image; (Per Fig. 1, Shamsheyeva’s analysis module 140 receives microorganism image data. Shamsheyeva col. 17 line 66 – col. 18 line 20. [a]nalysis module 140 may be capable of receiving image data associated with a microorganism.)
electronically identifying a colony shape and a colony size of the detected at least one microorganism; (Per Fig. 1, Shamsheyeva’s analysis module 140 deciphers microorganism’s signals in terms of a shape and a size while shifting a shape. Id. col. 20 line 65 – col. 21 line 14. Analysis module 140 may detect signals having various properties including, for example, a range of intensities, a range of sizes, and a range of shapes.)
executing the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and (Per Fig. 1, Shamsheyeva’s analysis module 140 discloses object detection in images identifying individuated microorganisms. Id. col. 21 lines 46–65. [a]nalysis module 140 may employ wavelet transforms because the wavelet transforms are suitable for signal characterization (e.g., object detection in images).)
estimating a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria. (Through Figs. 18–18B, Shamsheyeva discloses cell count in terms of sum of integrated pixel intensity. Id. col. 47 lines 29–37. Sum of integrated pixel intensities of individual clones closely parallels clone mass and/or cell count from standard methods.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Shamsheyeva into the teachings of Li to rapidly count microorganism particles. Id. col. 30 lines 40–51.
Regarding claim 20, Li discloses a non-transitory computer-readable medium storing instructions for microorganism detection that are executable for microorganism detection that are executable by a processing device, wherein execution of the instructions by the processing device causes the processing device to:
receive as input to a system for microorganism detection an electronic image of a water sample, (Per Fig. 1 at step S5, Li discloses a RCNN target detection model. Li col. 6 lines 22–25. Step S5: Perform transfer learning by using a Faster RCNN based on the source domain dataset and the target domain dataset, to obtain a multi-source algae image target detection model.) the system for microorganism detection including (i) a database configured to electronically store data, the data including a detection model trained based on historical microorganism identification and cell count data, (Li discloses a system for algae image target detection. Id. col. 7 line 62 – col. 8 line 20. [p]rovides a system for multi-source algae image target detection, including: a building module configured to build automated algae crawling tool;) and (ii) a processing device in communication with the database. (Per Fig. 2, Li collects algae images and processes them in his system. Id. col. 5 lines 58–62. Based on features of collected algae images, such as clarity, color, style, the size of algae, and whether a foreign substance exists, each image is input into a pre-trained binary classifier to determine whether the image is algae, and then filtering is performed.)
Li fails to specifically disclose electronically detect at least one microorganism in the electronic image;
electronically identify a colony shape and a colony size of the detected at least one microorganism;
execute the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and
estimate a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria.
However, in related art, Shamsheyeva discloses electronically detect at least one microorganism in the electronic image; (Per Fig. 1, Shamsheyeva’s analysis module 140 receives microorganism image data. Shamsheyeva col. 17 line 66 – col. 18 line 20. [a]nalysis module 140 may be capable of receiving image data associated with a microorganism.)
electronically identify a colony shape and a colony size of the detected at least one microorganism; (Per Fig. 1, Shamsheyeva’s analysis module 140 deciphers microorganism’s signals in terms of a shape and a size while shifting a shape. Id. col. 20 line 65 – col. 21 line 14. Analysis module 140 may detect signals having various properties including, for example, a range of intensities, a range of sizes, and a range of shapes.)
execute the detection model to identify an organism responsible for the detected at least one microorganism based on the colony shape and the colony size; and (Per Fig. 1, Shamsheyeva’s analysis module 140 discloses object detection in images identifying individuated microorganisms. Id. col. 21 lines 46–65. [a]nalysis module 140 may employ wavelet transforms because the wavelet transforms are suitable for signal characterization (e.g., object detection in images).)
estimate a cell count of the responsible organism based on the identified organism, the colony shape, and the colony size determined from the electronic image; wherein the at least one microorganism includes at least one of bacteria, algae, or cyanobacteria. (Through Figs. 18A–18B, Shamsheyeva discloses cell count in terms of sum of integrated pixel intensity. Id. col. 47 lines 29–37. Sum of integrated pixel intensities of individual clones closely parallels clone mass and/or cell count from standard methods.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Shamsheyeva into the teachings of Li to rapidly count microorganism particles. Id. col. 30 lines 40–51.
Regarding claim 2, Li as modified by Shamsheyeva, discloses the system, wherein the cyanobacteria is a cyanobacterial bloom, (Per Fig. 2, Li collects algae images and processes them in his system. Li col. 5 lines 58–62. Based on features of collected algae images, such as clarity, color, style, the size of algae, and whether a foreign substance exists, each image is input into a pre-trained binary classifier to determine whether the image is algae, and then filtering is performed.) and wherein the processing device is configured to determine a genus of the cyanobacteria communities making up the cyanobacterial bloom based on the electronic image. (Per Fig. 2, Li discloses crawled algae images which are trained in the detection model to apply data enhancement operations. Id. col. 6 lines 6–9. And the crawled algae images are as input for the target detection algorithm.)
Regarding claim 4, it has been rejected in the same manner as claim 2.
Regarding claim 5, Li as modified by Shamsheyeva, discloses the system, wherein the processing device is configured to perform an enumeration of cells based on the colony shape and the colony size to estimate a cell concentration of the responsible organism. (Through Figs. 18A–18B, Shamsheyeva discloses cell count in terms of sum of integrated pixel intensity. Id. col. 47 lines 29–37. Sum of integrated pixel intensities of individual clones closely parallels clone mass and/or cell count from standard methods.)
Regarding claim 6, Li as modified by Shamsheyeva, discloses the system, wherein the processing device is configured to receive as input environmental data and generate a likelihood of a bloom event based on historical data and the environmental data. (Li discloses domain dataset to process a multi-source algae image target detection model. Li col. 6 lines 22–25. Step S5: Perform transfer learning by using a Faster RCNN based on the source domain dataset and the target domain dataset, to obtain a multi-source algae image target detection model.)
Regarding claim 7, it has been rejected in the same manner as claim 6.
Regarding claim 8, Li as modified by Shamsheyeva, discloses the system, wherein the processing device is configured to receive as input the electronic image of the water sample from a digital and/or traditional microscope. (Li discloses that the algae images are captured by an optical microscope. Li col. 6 lines 18–21. Target domain data are algae images that are obtained by on-site sampling and that are not identified by biological taxonomists (usually obtained by a single device, the optical microscope).)
Regarding claim 9, it has been rejected in the same manner as claim 8.
Regarding claim 10, Li as modified by Shamsheyeva, discloses the system, wherein the processing device is configured to receive as input the electronic image of the water sample from a camera configured to capture and transmit a new electronic image of additional water samples at a predetermined time interval. (Per Fig. 1, Shamsheyeva’s analysis module 140 discloses a growth rate of a microorganism over time. Shamsheyeva col. 18 lines 21–38. [a]nalysis module 140 may be configured to evaluate a growth rate of an object and/or object site over time.)
Regarding claim 11, it has been rejected in the same manner as claim 10.
Regarding claim 12, Li as modified by Shamsheyeva, discloses the system, wherein the data include bloom guidelines regarding toxin levels. (Per Fig. 1, Shamsheyeva’s analysis module 140 filters foreign debris whereabout microorganism. Shamsheyeva col. 21 lines 24–45. These points of amplitude may correspond to particles (e.g., microorganisms, noise, foreign objects, debris, and/or the like) that can be filtered, identified or otherwise analyzed.)
Regarding claim 13, it has been rejected in the same manner as claim 6.
Regarding claim 14, Li as modified by Shamesheyeva, discloses the system, wherein the processing device is configured to generate a confidence level of the identified type of the at least one microorganism, and the estimated cell count of the detected at least one microorganism, and wherein if the confidence level is below a threshold value, the processing device is configured to generate a review request of the at least one of the identified type of the at least one microorganism or the estimated cell count. (Shamesheyeva’s analysis detects cell growth in organism constitutes. Then, his system determines whether another analysis is required if a threshold exceeds the microorganism clones. Shamesheyeva col. 32 lines 19–28. The system required 40 or more clones that exceeded a threshold score in order to proceed with analysis.)
Regarding claim 17, it has been rejected in the same manner as claim 6.
Regarding claim 19, it has been rejected in the same manner as claim 8.
Regarding claim 21, Li as modified by Shamsheyeva, discloses the system, wherein estimating the cell count comprises generating a first mask or outline around the colony shape, and generating a second mask or outline around negative space within the first mask where no at least one microorganism is detected, to identify the colony size. (Shamsheyeva’s analysis module 140 restricts non-microorganism objects calculating a threshold range. Shamsheyeva col. 24 line 50 – col. 25 line 2. The error may be used as a threshold range or limit to identify microorganism object sites (e.g., organisms for evaluation) and eliminate debris and/or foreign objects that are not microorganism objects.)
Regarding claim 22, Li as modified by Shamsheyeva, discloses the system, wherein the processing device is configured to estimate a three-dimensional volume for the colony size based on the identified organism and the colony shape. (Per Fig. 1, Shamsheyeva’s analysis module 140 recognizes a microorganism object in a rectangular box. Shamsheyeva col. 24 line 50 – col. 25 line 2. [a]nalysis module 140 may create a microorganism object as a rectangular box with length and width equal to the corresponding microorganism object's length and width and the height of 1.)
Regarding claim 23, Li as modified by Shamsheyeva, discloses the system, wherein estimating the cell count comprises executing a cell counting model to correlate a number of pixels within the colony size with a known cell count-to-pixel ratio for the identified organism, and identifying the cell count for the three-dimensional volume. (Through Figs. 18A–18B, Shamsheyeva discloses cell count in terms of sum of integrated pixel intensity. Shamsheyeva col. 47 lines 29–37. Sum of integrated pixel intensities of individual clones closely parallels clone mass and/or cell count from standard methods.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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 BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-16:00 (EST).
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 See also his col. 23 lines 49–59. The analysis module 140 detects a location of microorganism in terms of size and morphology—i.e., a shape. [a]nalysis module 140 is able to determine potential microorganism locations, of variable size and morphology,