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 .
Notice to Applicants
This communication is in response to the action filed on 07/01/2024.
Claims 1-17 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 07/01/2024 has been considered.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-12, and 16-20 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 2022/0237890 A1 to CHOI et al. (hereinafter “CHOI”).
As per claim 1, CHOI discloses a training apparatus (a computing system and method of operation in order to train a neural network using data in an unsupervised manner; abstract; figs 2, 6-8; paragraphs [0006-0011], [0052-0053], [0060-0061], [0070]), comprising processing circuitry configured to: acquire a plurality of items of subject data and a target cluster number (the system includes a processor circuit 1210 in fig 12, which provides input data acquirer 210 which generates input data from original data 201 and using clustering unit 240 to assign cluster values to each group of clustered data; figs 2, 6-8, 12; paragraphs [0052-0053], [0060-0061], [0070-0071], [0076]); iteratively train a learning model on the plurality of items of subject data by unsupervised learning based on learning conditions (the computing system is adapted to iteratively update the neural network models parameters based on training results generated using the input subject data and original data 201 based on preset learning conditions/rules which are set and taught to the models using a teacher model trained to generate consistent clustering between different views of the same image and further, performs training on a student model to mimic a prediction of an on-the-fly self-supervised teacher; figs 2, 6-8, 12; paragraphs [0052-0053], [0058-0061]); estimate a feature cluster number based on a plurality of feature vectors corresponding to the plurality of items of subject data (the computing system is adapted to calculate/compute clusters using the clustering unit 240 in order to estimate a code value for each respective cluster (acting substantially as a feature cluster number) and is based on (corresponding to) extracted backbone feature data by inputting data to the neural network model and propagating the input data, said backbone feature data is data abstracted from the input data and is input in a form of a feature vector; figs 1-2, 6-8, 12; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076]); and update the learning conditions based on the feature cluster number and the target cluster number (the neural network models are updated using the electronic apparatus in order to apply updated parameters of the neural network model such that a sum of partial losses calculated based on the first embedded data, the first view data, the second embedded data, the second view data, and the clustering result is minimized; paragraphs [0053-0056], [0058-0061]).
As per claim 2, CHOI discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to output the plurality of feature vectors by inputting the plurality of items of subject data to the learning model (the processor of the computing system performs operation in order to train the neural network models with the feature vectors output from the input data after the data has been preprocessed by the computing system; fig 10A-10D; paragraphs [0132-0138]).
As per claim 3, CHOI discloses the training apparatus according to claim 2, wherein the processing circuitry is further configured to: calculate a first loss using a first technique which yields a smaller loss as an error between a first feature vector and a second feature vector obtained from different items of subject data included in the plurality of items of subject data increases (the computing system is adapted to calculate loss values using the feature vectors of the clustered data and is further adapted to calculate a first loss at module 251 of fig 2, and computes first partial loss as the resulting loss value of the clustered data; abstract; fig 2; paragraphs [0070], [0078]), the first technique including a first temperature parameter for controlling a sensitivity of the error (the computing system and the loss calculation methods includes a temperature parameter which relates to the cross entropy value between the code value and embedded first data and is found by taking a dot product of all of the clusters of the prototype cluster vector C and the first embedded data; abstract; fig 2; paragraphs [0070], [0078], [0094]).
As per claim 4, CHOI discloses the training apparatus according to claim 3, wherein the processing circuitry is further configured to: update the learning conditions in such a manner that the first temperature parameter is increased if the feature cluster number is smaller than the target cluster number (Equation 8 is used but not limited to represent a SoftMax probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to increase to an integer greater than or equal to 1 and less than or equal to variable “m”, which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8); and update the learning conditions in such a manner that the first temperature parameter is decreased if the feature cluster number is larger than the target cluster number (Equation 8 is used but not limited to represent a SoftMax/Min probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to decrease to an integer greater than or equal to 1 and less than or equal to variable “m” which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8).
As per claim 5, CHOI discloses wherein the processing circuitry is further configured to: calculate a second loss using a second technique which yields a smaller loss as a correlation between feature vector elements decreases (the computing system is adapted to further use the clustering unit 240 to calculate both a second loss using the second loss calculator and a corresponding second temperature parameter for the respective cluster using equation 8; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]), the second technique including a second temperature parameter for controlling a sensitivity of the correlation (the computing system and the loss calculation methods includes a temperature parameter which relates to the cross entropy value between the code value and embedded first data and is found by taking a dot product of all of the clusters of the prototype cluster vector C and the first embedded data; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]); and update the learning conditions by changing the second temperature parameter (and by changing the value of the temperature parameter the cluster conditions change wherein cluster conditions are represented by variables “C”, “m”, and “I” and the parameters are automatically updated in order to meet the predetermined loss threshold for both loss calculations respectively; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]).
As per claim 6, CHOI discloses the training apparatus according to claim 5, wherein the processing circuitry is further configured to: update the learning conditions in such a manner that the second temperature parameter is increased if the feature cluster number is smaller than the target cluster number (the second cluster will also have equation 8 applied as but not limited to represent a SoftMax probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to increase to an integer greater than or equal to 1 and less than or equal to variable “m”, which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8); and update the learning conditions in such a manner that the second temperature parameter is decreased if the feature cluster number is larger than the target cluster number (Equation 8 is used but not limited to represent a SoftMax/Min probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to decrease to an integer greater than or equal to 1 and less than or equal to variable “m” which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8).
As per claim 7, CHOI discloses the training apparatus according to claim 2, wherein the processing circuitry is further configured to: calculate a first loss using a first technique which yields a smaller loss as an error between a first feature vector and a second feature vector obtained from different items of subject data included in the plurality of items of subject data increases (the computing system is adapted to calculate loss values using the feature vectors of the clustered data and is further adapted to calculate a first loss at module 251 of fig 2, and computes first partial loss as the resulting loss value of the clustered data; abstract; fig 2; paragraphs [0070], [0078]), the first technique including a first temperature parameter for controlling a sensitivity of the error (the computing system and the loss calculation methods includes a temperature parameter which relates to the cross entropy value between the code value and embedded first data and is found by taking a dot product of all of the clusters of the prototype cluster vector C and the first embedded data; abstract; fig 2; paragraphs [0070], [0078], [0094]); calculate a second loss using a second technique which yields a smaller loss as a correlation between feature vector elements decreases (the computing system is adapted to further use the clustering unit 240 to calculate both a second loss using the second loss calculator and a corresponding second temperature parameter for the respective cluster using equation 8; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]), the second technique including a second temperature parameter for controlling a sensitivity of the correlation (the computing system and the loss calculation methods includes a temperature parameter which relates to the cross entropy value between the code value and embedded first data and is found by taking a dot product of all of the clusters of the prototype cluster vector C and the first embedded data; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]); and update the learning conditions by changing at least one of the first temperature parameter, the second temperature parameter (and by changing the value of the temperature parameter the cluster conditions change wherein cluster conditions are represented by variables “C”, “m”, and “I” and the parameters are automatically updated in order to meet the predetermined loss threshold for both loss calculations respectively; figs 1-2; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076-0078]), and a balancing parameter for adjusting degrees of influence of the first loss and the second loss (the system performs a balancing process by performing normalization procedures at step 437 to the clusters and the corresponding data; figs 4-5; paragraphs [0090], [0111]).
As per claim 8, CHOI discloses the training apparatus according to claim 7, wherein the processing circuitry is further configured to: update the learning conditions in such a manner that at least one of the first temperature parameter, the second temperature parameter, and the balancing parameter is increased if the feature cluster number is smaller than the target cluster number (Equation 8 is used but not limited to represent a SoftMax probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to increase to an integer greater than or equal to 1 and less than or equal to variable “m”, which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8); and update the learning conditions in such a manner that at least one of the first temperature parameter, the second temperature parameter, and the balancing parameter is decreased if the feature cluster number is larger than the target cluster number (Equation 8 is used but not limited to represent a SoftMax/Min probability having a temperature parameter of a dot product between all clusters of the prototype cluster vector C and the first embedded data Zni and the system applies the updated parameters as learning conditions taught by the teaching model in the case that C is smaller than Zni and causes the temperature parameter to decrease to an integer greater than or equal to 1 and less than or equal to variable “m” which are applied as rules/learning conditions; paragraphs [0058-0061], [0094]; equation 8).
As per claim 9, CHOI discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to: determine whether or not the feature cluster number satisfies predetermined conditions (for example for the feature cluster number would need to satisfy the listed condition of the temperature parameter must be an integer greater than or equal to 1 and less than or equal to variable “m” which are applied as rules/learning conditions to the teacher model of the computing system; paragraphs [0056-0061], [0092-0097]); terminate the iterative training of the learning model if it is determined that the predetermined conditions are satisfied (electronic apparatus may iteratively train using the input data the neural network model until the calculated loss becomes less than the threshold loss (predetermined condition to satisfy); fig 1; paragraphs [0056-0061]); and change the learning conditions if it is determined that the predetermined conditions are not satisfied (further the system in operation step 140 is able to update parameters during the iterative training in order to meet the threshold or converge to a minimum whichever occurs first; fig 1; paragraphs [0056-0061]).
As per claim 10, CHOI discloses the training apparatus according to claim 9, wherein the predetermined conditions are that a difference between the feature cluster number and the target cluster number is equal to or smaller than a predetermined value, or that the feature cluster number is equal to or greater than a lower-limit value of the target cluster number and equal to or smaller than an upper-limit value of the target cluster number (the electronic apparatus may train either one or both of the first neural network model and the second neural network model based on a loss that is calculated based on a combination of any two or more of the first embedded data, the first view data, the second embedded data, the second view data, and an embedded data clustering result, providing an example, the electronic apparatus is used to update parameters of the neural network model such that a sum of partial losses calculated based on the first embedded data, the first view data, the second embedded data, the second view data, and result is the clustering result is minimized, as another example, the electronic apparatus may update parameters of the second neural network model until the calculated loss becomes less than a threshold loss (the target cluster number is equal to or smaller than a predetermined value) or converges to be minimized (that the feature cluster number is equal to or greater than a lower-limit value), where the electronic apparatus iteratively updates the parameters of the neural network model until the calculated loss becomes less than the threshold loss; fig 1; paragraphs [0056-0061]).
As per claim 11, CHOI discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to update the learning conditions so as to exclude one or more items of subject data from the plurality of items of subject data, based on the number of items of subject data belonging to each of a plurality of clusters corresponding to the feature cluster number (the computing system comprising the processor 1210 is configured to update parameters of the teacher model and includes the ability to operate the first projection model by excluding the dropout layers from a propagation path when propagating data to the first view generation model 231, or operate the first drop model by including the dropout layers in the propagation path, and acts as a way to drop out unneeded features; figs 1-2 and 12; paragraphs [0056-0061], [0073]).
As per claim 12, CHOI discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to: generate one or more feature cluster labels corresponding to the feature cluster number (during the unsupervised visual representation learning rich feature information is used to solve pretext tasks and assign labels to the clustering results of the images; figs 1-2 and 12; paragraphs [0056-0061], [0073] [0080-0082]); and cumulatively hold the one or more feature cluster labels every time the learning conditions are updated (a self-supervised model, the training may be performed in a way to train a student model using a result of clustering on a teacher model to mimic a relative similarity between data points in an embedding space of the teacher model which acts as the cumulative model which holds all of the cluster labels and is updated to reflect the updated learning conditions/parameters; figs 1-2 and 12; paragraphs [0056-0061], [0073] [0080-0082]).
As per claim 16, CHOI discloses a training method (a computing system and method of operation in order to train a neural network using data in an unsupervised manner; abstract; figs 2, 6-8; paragraphs [0006-0011], [0052-0053], [0060-0061], [0070]), comprising: acquiring a plurality of items of subject data and a target cluster number (the system includes a processor circuit 1210 in fig 12, which provides input data acquirer 210 which generates input data from original data 201 and using clustering unit 240 to assign cluster values to each group of clustered data; figs 2, 6-8, 12; paragraphs [0052-0053], [0060-0061], [0070-0071], [0076]); iteratively training a learning model on the plurality of items of subject data by unsupervised learning based on learning conditions (the computing system is adapted to iteratively update the neural network models parameters based on training results generated using the input subject data and original data 201 based on preset learning conditions/rules which are set and taught to the models using a teacher model trained to generate consistent clustering between different views of the same image and further, performs training on a student model to mimic a prediction of an on-the-fly self-supervised teacher; figs 2, 6-8, 12; paragraphs [0052-0053], [0058-0061]); estimating a feature cluster number based on a plurality of feature vectors corresponding to the plurality of items of subject data (the computing system is adapted to calculate/compute clusters using the clustering unit 240 in order to estimate a code value for each respective cluster (acting substantially as a feature cluster number) and is based on (corresponding to) extracted backbone feature data by inputting data to the neural network model and propagating the input data, said backbone feature data is data abstracted from the input data and is input in a form of a feature vector; figs 1-2, 6-8, 12; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076]); and updating the learning conditions based on the feature cluster number and the target cluster number (the neural network models are updated using the electronic apparatus in order to apply updated parameters of the neural network model such that a sum of partial losses calculated based on the first embedded data, the first view data, the second embedded data, the second view data, and the clustering result is minimized; paragraphs [0053-0056], [0058-0061]).
As per claim 17, CHOI discloses a non-transitory computer-readable storage medium storing a program for causing a computer to execute processing comprising (a computing system comprising a memory and processor component to store and execute instructions, data, and programs to perform a method of operation in order to train a neural network using data in an unsupervised manner; abstract; figs 2, 6-8; paragraphs [0006-0011], [0052-0053], [0060-0061], [0070]): acquiring a plurality of items of subject data and a target cluster number (the system includes a processor circuit 1210 in fig 12, which provides input data acquirer 210 which generates input data from original data 201 and using clustering unit 240 to assign cluster values to each group of clustered data; figs 2, 6-8, 12; paragraphs [0052-0053], [0060-0061], [0070-0071], [0076]); iteratively training a learning model on the plurality of items of subject data by unsupervised learning based on learning conditions (the computing system is adapted to iteratively update the neural network models parameters based on training results generated using the input subject data and original data 201 based on preset learning conditions/rules which are set and taught to the models using a teacher model trained to generate consistent clustering between different views of the same image and further, performs training on a student model to mimic a prediction of an on-the-fly self-supervised teacher; figs 2, 6-8, 12; paragraphs [0052-0053], [0058-0061]); estimating a feature cluster number based on a plurality of feature vectors corresponding to the plurality of items of subject data (the computing system is adapted to calculate/compute clusters using the clustering unit 240 in order to estimate a code value for each respective cluster (acting substantially as a feature cluster number) and is based on (corresponding to) extracted backbone feature data by inputting data to the neural network model and propagating the input data, said backbone feature data is data abstracted from the input data and is input in a form of a feature vector; figs 1-2, 6-8, 12; paragraphs [0026-0028], [0053-0055], [0058-0061], [0076]); and updating the learning conditions based on the feature cluster number and the target cluster number (the neural network models are updated using the electronic apparatus in order to apply updated parameters of the neural network model such that a sum of partial losses calculated based on the first embedded data, the first view data, the second embedded data, the second view data, and the clustering result is minimized; paragraphs [0053-0056], [0058-0061]).
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 13-15 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0237890 A1 to CHOI et al. (hereinafter “CHOI”) in view of US 2024/0273134 A1 to YANG et al. (hereinafter “YANG”).
As per claim 13, CHOI discloses the training apparatus according to claim 1. CHOI fails to disclose wherein the processing circuitry is further configured to cause a correlation chart expressing the feature vectors by different components to be displayed.
YANG discloses wherein the processing circuitry is further configured to cause a correlation chart expressing the feature vectors by different components to be displayed (as seen in fig 10 a feature chart is generated using the computing system and further show correlation by clustering similar values of similar features of the expressed feature vectors which are expressed by adjusting adjustable weight values in formula 1; fig 10; paragraphs [0092-0099]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify CHOI to have a correlation chart expressing the feature vectors of YANG reference. The Suggestion/motivation for doing so would have been to provide the ability to prevent negative sample feature vectors in a negative sample container from being farther away from the inputted first sample tissue image as suggested by paragraph [0101] of YANG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine YANG with CHOI to obtain the invention as specified in claim 13.
As per claim 14, CHOI discloses the training apparatus according to claim 13. CHOI fails to disclose wherein the processing circuitry is further configured to cause the correlation chart and subject data corresponding to a coordinate point selected on the correlation chart to be displayed.
YANG discloses wherein the processing circuitry is further configured to cause the correlation chart and subject data corresponding to a coordinate point selected on the correlation chart to be displayed (the system is adapted to perform coordinate-based clustering by cropping the image into a smaller image corresponding to one of the clusters specific centers having a specific coordinate position in the image; fig 14-15; paragraphs [0248], [0280-0284]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify CHOI to have the correlation chart and subject data corresponding to a coordinate point of YANG reference. The Suggestion/motivation for doing so would have been to provide the ability to crop the input images based on the identified clusters based on desired feature vectors and to focus on a center point of said cluster in order to crop the desired data into a new smaller upscaled image as suggested by paragraphs [0280-0284] of YANG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine YANG with CHOI to obtain the invention as specified in claim 14.
As per claim 15, CHOI discloses the training apparatus according to claim 13. CHOI fails to disclose wherein the processing circuitry is further configured to cause the correlation chart and a plurality of items of training data included in a cluster corresponding to a region selected on the correlation chart to be displayed.
YANG discloses wherein the processing circuitry is further configured to cause the correlation chart and a plurality of items of training data included in a cluster corresponding to a region selected on the correlation chart to be displayed (the system is adapted to perform coordinate-based clustering by cropping the image into a smaller image (a region of the first input image) corresponding to one of the clusters specific centers having a specific coordinate position in the image; fig 14-15; paragraphs [0248], [0280-0284]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify CHOI to have the processing circuitry is further configured to cause the correlation chart and a plurality of items of training data included in a cluster corresponding to a region selected on the correlation chart to be displayed of YANG reference. The Suggestion/motivation for doing so would have been to provide the ability to crop the input images based on the identified clusters based on desired feature vectors and to focus on a center point of said cluster in order to crop the desired data into a new smaller upscaled image as suggested by paragraphs [0280-0284] of YANG. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine YANG with CHOI to obtain the invention as specified in claim 15.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 2023/0154159 A1
US 2021/0248514 A1
WO 2024/102423 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights