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 § 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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the computer program is not eligible patented subject matter. Claim 9 is directed to, a detection program. However, one of ordinary skilled in the art would broadly interpret, the claimed program to encompass ineligible transitory signal embodiments as "signal" per se and software per se, which does not fall within one of the statutory categories of the invention (i.e. process, machine, manufacture or a composition of matter) and broadly interpret the computer readable medium to typically cover forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. Therefore, a transitory computer readable medium would reasonably be interpreted by one of ordinary skill in the art as signal, per se. Thus, subject matter "a program" is not limited to that which falls within a statutory category of invention. Therefore, the claim contains ineligible non-statutory subject matter. “ Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations see MPEP 2106.03.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an image generation unit configured”, “a type input reception unit configured”, “a training data generation unit configured to”, “a trained model generation unit configured to”, “an image input unit configured to”, “a result acquisition unit configured to”, “a type output unit configured to” in claim 8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim 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.
Claims 1-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by Goldberg (US 2022/0261990) in view of Olszowy (US 2022/0318668).
As per claims 1 and 9, Goldberg teaches, a viral particle detection method and detection program for detecting a type of a viral particle that is a test subject (Goldberg, ¶[0045] “ It measures the number of infectious viral particles in a sample by observing the effects of infection on a culture of susceptible cells.” This represents detecting a type of a viral particle that is a test subject ), comprising: generating a plurality of pieces of training image data in which the viral particle appears (Goldberg, ¶[0072] “The model builder 300 illustrates a sample 302 (sample two of one through five) of a training dataset that is shown in the right side of the frame.” generating a plurality of pieces of training image data in which the viral particle appears, and ¶[0075] “The machine learning service allows a user to build machine learning models on large-scale training data collected from images and metadata with a variety of features and learning frameworks” ); receiving, for each of the generated plurality of pieces of training image data, input of a viral particle appearing in the training image data and position information of the viral particle in the training image data (Goldberg, ¶[0070] “The objects tagged or labeled in the image can be recognized by machine learning with annotations (type of object, location) added as part of the metadata 206 of an image.” This represents the training image data and position information of the viral particle in the training image data, by having the image being able to be recognized by machine learning with annotations (type of object, location); generating a plurality of pieces of training data by associating the combinations input for each of the plurality of pieces of training image data with the respective pieces of training image data (Goldberg, ¶ [0102] “The ensemble model is a machine learning technique that combines several base AI models together in order to produce one optimal predictive AI model.” Combining the models represents combining the pieces); generating a trained model by performing machine learning on the generated plurality of pieces of training data (Goldberg, ¶[0063] “The machine learning (AI) model 104 can be trained for use with one or more machine learning algorithms (including image processing algorithms) and then used with the one or more machine learning algorithms for classification/analysis of objects within images of biological samples (infected, uninfected, treated or untreated), including biological cells and their sub-cellular structure.” ); inputting a determination image data in which the viral particle appears into the trained model (Goldberg, ¶[0101] “After images of the biological cell samples are captured, the training workflow 551 generates a background artificial intelligence model 562” this represents inputting a determination image data after being captured in which the viral particle appears into the trained ); acquiring an estimation result output from the trained model accompanying input of the determination image data; outputting the acquired estimation result as the combination corresponding to the viral particle appearing in the determination image data (Goldberg, ¶[0101] “After images of the biological cell samples are captured, the training workflow 551 generates a background artificial intelligence model 562 and a cell ensemble artificial intelligence model 568 that can be used by the classification workflow 552 to estimate or predict viral infectivity in a biological cell sample under test.” This represents outputting the acquired estimation result as the combination corresponding to the viral particle appearing in the determination image data by having classification workflow 552 to estimate or predict viral infectivity in a biological cell sample under test ).
Goldberg doesn’t clearly teach, a type of the viral particle appearing in the training image data and position information of the viral particle in the training image data.
However, Olszowy teaches, a type of the viral particle appearing in the training image data and position information of the viral particle in the training image data (Olszowy, ¶[0091] “Due to the nature of the cell carpet, cell subpopulations may be approximated by dividing the image(s) into a regular grid, with each grid element described based on shape and texture parameters and predict viral activity in the same way as described.” This represents a type of the viral particle appearing in the training image data and ¶[0092] “Furthermore, because the methods are based in image-based trained models, the methods permit determination of a degree of cytopathic effects by predicting information such as how large, how many, and specific location of the plaques.” Represents position information of the viral particle in the training image data by having the location of the plaques which is the type of the viral particle since the point or area occupied by a physical object: location is the definition of position).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Goldberg with those of Olszowy to be able to identify a type of viral particle in the training image data along with the position, which is also the location, since position is a type of location.
The motivation would have been to improve and understand both the functional virus titer, via a plaque assay which can be the location see Olszowy ¶[005] “understand both the functional virus titer, via a plaque assay,”.
As per claim 2, Goldberg in view of Olszowy teaches, the viral particle detection method according to claim 1, wherein in the step of receiving input, input of any one of a plurality of types including a type indicating a whole particle, a type indicating a hollow particle, a type indicating a damaged particle, and a type indicating an intermediate is received as the type of the viral particle appearing in each of the plurality of pieces of training image data (Goldberg, This represents the whole particle ¶ [0045] “The plaque assay is one of the most important assays in virology. It measures the number of infectious viral particles in a sample by observing the effects of infection on a culture of susceptible cells. This assay counts the number of infectious viral particles in a sample (plaque-forming units or PFUs) which is in contrast to assays based on amplifying genetic material (e.g., rtPCR), which only count the number of viral genomes. The reason this assay is crucial in vaccine and antiviral development is that often these treatment strategies interfere with the viral infection pathway. In fact, an increase in the particle (or genome) to PFU ratio is often used as an indicator of the presence of neutralizing antibodies in a candidate vaccine, or effectiveness of an antiviral drug.” And ¶ [0092] With the traditional assay workflow shown in panel 501, results are provided in a readout in the form of “holes” in the culture where plaques have formed after two to fifteen (2-15) days.” Represents a hollow particle).
As per claim 3, Goldberg in view of Olszowy teaches, the viral particle detection method according to claim 2, wherein the type indicating the damaged particle includes a plurality of damage types corresponding to a damage status of the viral particle, and in the step of receiving input, input of any one of the plurality of types including the plurality of damage types is received as the type of the viral particle appearing in each of the plurality of pieces of training image data (Goldberg, ¶ [0152] Along with traditional deep-learning, a large number of pixel-based features are used to correlate early detection of viral morphology. Specialized visualization is added to demonstrate how certain features better correlate with early detection of viral damage to cells.” The damage cells represent a damage status of the viral particle ).
As per claim 8, Goldberg teaches, an information processing device for detecting a type of a viral particle that is a test subject, comprising: an image generation unit configured to generate a plurality of pieces of training image data in which the viral particle appears (Goldberg, ¶[0072] “The model builder 300 illustrates a sample 302 (sample two of one through five) of a training dataset that is shown in the right side of the frame.” generating a plurality of pieces of training image data in which the viral particle appears represents image geniting unit, and ¶[0075] “The machine learning service allows a user to build machine learning models on large-scale training data collected from images and metadata with a variety of features and learning frameworks” ); a type input reception unit configured to, for each of the generated plurality of pieces of training image data, receive input of a combination of the type of the viral particle appearing in the training image data and position information of the viral particle in the training image data (Goldberg, ¶[0070] “The objects tagged or labeled in the image can be recognized by machine learning with annotations (type of object, location) added as part of the metadata 206 of an image.” This represents the training image data and position information of the viral particle in the training image data, by having the image being able to be recognized by machine learning with annotations (type of object, location); a training data generation unit configured to generate a plurality of pieces of training data by associating the combinations input for each of the plurality of pieces of training image data with the respective plurality of pieces of training image data (Goldberg, ¶ [0102] “The ensemble model is a machine learning technique that combines several base AI models together in order to produce one optimal predictive AI model.” Combining the models represents combining the pieces); a trained model generation unit configured to generate a trained model by performing machine learning on the generated plurality of pieces of training data (Goldberg, ¶[0063] “The machine learning (AI) model 104 can be trained for use with one or more machine learning algorithms (including image processing algorithms) and then used with the one or more machine learning algorithms for classification/analysis of objects within images of biological samples (infected, uninfected, treated or untreated), including biological cells and their sub-cellular structure.” ); an image input unit configured to input determination image data in which the viral particle appears into the trained model (Goldberg, ¶[0101] “After images of the biological cell samples are captured, the training workflow 551 generates a background artificial intelligence model 562” this represents inputting a determination image data after being captured in which the viral particle appears into the trained ); a result acquisition unit configured to acquire an estimation result output from the trained model accompanying input of the determination image data; and a type output unit configured to output the acquired estimation result as the combination corresponding to the viral particle appearing in the determination image data (Goldberg, ¶[0101] “After images of the biological cell samples are captured, the training workflow 551 generates a background artificial intelligence model 562 and a cell ensemble artificial intelligence model 568 that can be used by the classification workflow 552 to estimate or predict viral infectivity in a biological cell sample under test.” This represents outputting the acquired estimation result as the combination corresponding to the viral particle appearing in the determination image data by having classification workflow 552 to estimate or predict viral infectivity in a biological cell sample under test ).
Goldberg doesn’t clearly teach, a type of the viral particle appearing in the training image data and position information of the viral particle in the training image data.
However, Olszowy teaches, a type of the viral particle appearing in the training image data and position information of the viral particle in the training image data (Olszowy, ¶[0091] “Due to the nature of the cell carpet, cell subpopulations may be approximated by dividing the image(s) into a regular grid, with each grid element described based on shape and texture parameters and predict viral activity in the same way as described.” This represents a type of the viral particle appearing in the training image data and ¶[0092] “Furthermore, because the methods are based in image-based trained models, the methods permit determination of a degree of cytopathic effects by predicting information such as how large, how many, and specific location of the plaques.” Represents position information of the viral particle in the training image data by having the location of the plaques which is the type of the viral particle since the point or area occupied by a physical object: location is the definition of position).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Goldberg with those of Olszowy to be able to identify a type of viral particle in the training image data along with the position, which is also the location, since position is a type of location.
The motivation would have been to improve and understand both the functional virus titer, via a plaque assay which can be the location see Olszowy ¶[005] “understand both the functional virus titer, via a plaque assay,”.
Allowable Subject Matter
Claims 4-7 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
As per claim 4, the limitations “damage types include a type indicating the viral particle whose shape is deformed, a type indicating the viral particle whose outer edge is at least partially damaged, and a type indicating the viral particle that is broken into a plurality of pieces.” Were not found in the prior art as in damage sub-types (deformed, edge-damaged, broken into pieces) were not considered in the prior art in this undertaking of diagnostic workflow.
And as far as claim 6, “subjecting the ultrafiltered viral particle to affinity purification; washing the viral particle subjected to affinity purification; staining the washed viral particle with a predetermined staining agent” were also not found specifically in the prior art with regards to particle subjected to affinity purification in this undertaking of diagnostic workflow.
References of particular interest when considering amendments to the current claims:
Homman (US 2012/0243768) Abstract “A cell is provided that contains a plurality of virus particles. A first image of a first virus particle and a second image of a second virus particle are taken by electron microscopy technology. The first virus particle is characterized as being in a first maturity stage and the second virus particle as being in a second maturity stage. The first image and the second image are transformed to first and second gray scale profiles, respectively, based on pixel data. The first and second gray scale profiles are then saved as first and second templates, respectively. A third virus particle in a third image is identified. The third image is transformed into a third gray scale profile. The third gray scale is compared to the first and second template to determine a maturity stage of the third virus particle.”
Cochrane (US patent 12,586,183) Abstract “The disclosure concerns a method of obtaining information about the disease status of an individual by processing input data using a trained machine learning algorithm model to generate an output representing the information. The disclosure also concerns a related computer program, data processing apparatus, and system, as well as a method for training a machine learning algorithm model to generate information about the disease status of an individual. The information may, for example, relate to the presence, absence or type of M. tuberculosis complex infection in the individual.”
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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