DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12 March 2025 has been entered. Claims 1-20 have been examined and are pending.
Examiner of Record
The Examiner of record has changed as of this Office action; Applicant is advised to direct all inquiries concerning this communication to Syed Hasan at 571-270-5008.
Pertinent Prior Art
Prior art that is considered pertinent to applicant's disclosure but not currently relied upon:
US20140129545
Abstract
A search query and sorting criteria are received from a web browser, the sorting criteria including a coarse level of granularity and a fine level of granularity. Results are received from a search engine based on the search query and the search results are organized based on the multiple sorting criteria
US20200202168
Pars. 31-47
Processing image data representing objects and producing coarse classifications and fine classifications, and training data includes samples labeled as coarse or fine levels.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 in claim 13: statistical analysis code configured to … and determining code configured to …
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claims 1-10 and 12-20, there are 36 amendments of the term “based on” to “when.” The amendments introduce significant ambiguity and do not provide a sufficient basis to determine the intended meaning. It appears that substantial portions of the intended amendments may be missing. The remarks do not provide any insight about the apparent omission of claim limitations. Dependent claim 11 is likewise rejected.
Claim limitation 13 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Claim 13 includes “statistical analysis code configured to…” and “determining code configured to…” The specification is devoid of adequate structure to perform the claimed functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. All respective dependent claims are likewise rejected.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al., Pub. No.: US 20210383231, hereinafter Liu, in view of Wang et al., Pub. No.: WO2019200782A1, hereinafter Wang (see provided English translation), and further in view of Chen et al., Pub. No.: US 20200210761, hereinafter Chen.
Regarding claim 1, Liu teaches, an information pushing method, performed by at
least one processor (Liu [0037]: processor) and comprising:
[…]
extracting, […], an information feature of candidate information from an image, the information feature comprising a coarse-grained feature and a fine-grained feature, a number of tail value samples of the coarse-grained feature being greater than a number of tail value samples of the fine-grained feature (Liu [0040]: The present invention discloses the
target cross-domain detection and understanding method .... The method of detecting and
understanding a notable target according to a specific intention is efficient, objective and
comprehensive, and can effectively enhance the environmental visual perception ability and
active safety. Meanwhile, in conjunction with a coupling relationship between a coding position
probability and image features, diagonal vertexes of a target candidate frame are efficiently
located, network complexity is simplified, the difficulty and redundancy of fusion are avoided,
the expenses of system calculation are saved, and the application needs for actual detection can
be met.);
Liu does not clearly teach, obtaining, via inputting the coarse-grained feature into a
first neural network, a first feature of the candidate information when an intermediate
feature, the intermediate feature being obtained in a process of extracting the coarsegrained
feature; However, Chen [0013] teaches, "The method may include obtaining, by the
computing device, image data representing a structure of a subject, determining, by the
computing device, a plurality of candidate classifications of the structure and their respective
probabilities by inputting the image data into a classification model, wherein the classification
model includes a backbone network for determining a backbone feature of the structure, a
segmentation network for determining a segmentation feature of the structure, and a density
classification network for determining a density feature of the structure, and determining, by the
computing device, a target classification of the structure based on the probabilities of the
plurality of candidate classifications."
obtaining, via inputting the coarse-grained feature and the fine-grained feature into
a second neural network (Chen [0006]: In some embodiments, the determining a plurality of
candidate classifications of the structure by inputting the image data into a classification model
may include obtaining the backbone feature, the segmentation feature, and the density feature by
inputting the image data into the backbone network, the segmentation network, and the density
classification network, respectively, and determining a probability of each of the plurality of
candidate classifications of the structure based on the backbone feature, the
segmentation feature, and the density feature.) a second feature of the candidate information when the information feature and the intermediate feature (Chen [0091]: In 640, a plurality of candidate classifications and their respective probabilities may be determined based
on the backbone feature, the segmentation feature, and the density feature.);
obtaining, when the first feature and the second feature, target information from
a plurality of pieces of candidate information (Chen [007 4]: In 530, a target classification of
the structure may be determined based on at least a part of the probabilities of the plurality of
candidate classifications. In some embodiments, the target classification of the structure may be
determined by the classification detennination module 430.); and
pushing the target information to one or more devices (Chen [0056]: The
classification determination module 430 may determine a target classification of the structure of
the subject. In some embodiments, the target classification of the structure may be determined
based on probabilities of a plurality of candidate classifications. The plurality of candidate
classifications of the structure and their respective probabilities may be determined by inputting
the image data into a classification model. The classification model may trained according to a
focal loss function, at least one weight of the focal loss function each of which corresponds to
one of the plurality of candidate classifications being different from weights of the focal loss
function corresponding to the remainder of the plurality of candidate classifications. In some
embodiments, the target classification of the pulmonary nodule may be determined by
identifying a candidate classification of the structure corresponding to a largest
probability among the probabilities of the plurality of candidate classifications. The identified
candidate classification may be designated as the target classification of the structure.).
It would have been obvious before the effective filing date of the claimed invention to a
person having ordinary skill in the art to incorporate the teaching of Liu et al. to the Chen's
system by adding the feature of candidate information data. The references (Liu and Chen) teach
features that are analogous art and they are directed to the same field of endeavor, such as
databases. Ordinary skilled artisan would have been motivated to do so to provide Liu' s system with enhanced data. (See Chen [Abstract], [0227], [0240], [0276]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the
ever-increasing amounts of data that are processed.
The combination does not expressly disclose performing statistical analysis on an image such that each feature in the image is organized in a queue according to one or more categories, the queue comprising a plurality of samples; determining a tail of the queue that is a subset of the plurality of samples; extracting, from the tail of the queue, however, Wang discloses these limitations in that each of Wang’s categories is divided into multiple subsets, the number of samples in the subset being used as its relevance statistic. See step S23 on page 6 where subset populations 40, 100 and 10 are sorted as 100, 40, 10 (i.e. a queue) wherein the last one (i.e. tail of the queue) of the sorted subset populations, 10, is the sparsest with the most diverse pictures, and is read, understood and processed accordingly (i.e. extracting, from the tail of the queue). Wang, second paragraph on page 6 makes it clear that “for each category, the number of samples included in each subset is taken as the correlation between each subset and the category in which each subset is located.”
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Wang’s teaching would have allowed Liu to improve feature characterization by using statistically identified tail samples to distinguish and process features at different granularities.
Regarding claim 13, Liu teaches, an information pushing apparatus, comprising:
at least one memory configured to store program code; and at least one processor
configured to read the program code and operate as instructed by the program code, the
program code comprising (Liu [0037]: Computer equipment includes a memory, a processor,
and a computer program stored in the memory and capable of running on the processor, wherein
the processor implements the steps of the target cross-domain detection and understanding
method based on attention estimation when executing the computer program.):
[…];
information feature extraction code, configured to cause the at least one processor
to extract […] information feature of candidate information from an image, the information feature comprising a coarse-grained feature and a fine-grained feature, a number of tail value samples of the coarse- grained feature being greater than a number of tail value samples of the fine-grained feature (Liu [0040]: The present invention discloses the
target cross-domain detection and understanding method .... The method of detecting and
understanding a notable target according to a specific intention is efficient, objective and
comprehensive, and can effectively enhance the environmental visual perception ability and
active safety. Meanwhile, in conjunction with a coupling relationship between a coding position
probability and image features, diagonal vertexes of a target candidate frame are efficiently
located, network complexity is simplified, the difficulty and redundancy of fusion are avoided,
the expenses of system calculation are saved, and the application needs for actual detection can
be met.);
Liu does not clearly teach, first feature obtaining code, configured to cause the at
least one processor to obtain, via inputting the coarse-grained feature into a first neural
network, a first feature of the candidate information when an intermediate feature, the
intermediate feature being obtained in a process of extracting the coarse-grained feature;
However, Chen [0013] teaches, "The method may include obtaining, by the computing device,
image data representing a structure of a subject, determining, by the computing device, a
plurality of candidate classifications of the structure and their respective probabilities by
inputting the image data into a classification model, wherein the classification model includes a
backbone network for determining a backbone feature of the structure, a segmentation network
for determining a segmentation feature of the structure, and a density classification network for
determining a density feature of the structure, and determining, by the computing device, a
target classification of the structure based on the probabilities of the plurality of
candidate classifications."
second feature obtaining code, configured to cause the at least one processor to
obtain, via inputting the coarse-grained feature and the fine-grained feature into a second
neural network (Chen [0006]: In some embodiments, the determining a plurality of
candidate classifications of the structure by inputting the image data into a classification model
may include obtaining the backbone feature, the segmentation feature, and the density feature by
inputting the image data into the backbone network, the segmentation network, and the density
classification network, respectively, and determining a probability of each of the plurality of
candidate classifications of the structure based on the backbone feature, the
segmentation feature, and the density feature.)l a second feature of the candidate information
when the information feature and the intermediate feature (Chen [0091]: In 640, a
plurality of candidate classifications and their respective probabilities may be determined based
on the backbone feature, the segmentation feature, and the density feature.);
information obtaining code, configured to cause the at least one processor to obtain
when the first feature and the second feature, target information from a plurality of
pieces of the candidate information when the first feature and the second feature (Chen
[0074]: In 530, a target classification of the structure may be determined based on at least a part
of the probabilities of the plurality of candidate classifications. In some embodiments, the
target classification of the structure may be determined by the classification determination
module 430.); and
information pushing code, configured to cause the at least one processor to push
the target information to one or more devices (Chen [0056]: The classification determination
module 430 may determine a target classification of the structure of the subject. In some
embodiments, the target classification of the structure may be determined based
on probabilities of a plurality of candidate classifications. The plurality of candidate
classifications of the structure and their respective probabilities may be determined by inputting
the image data into a classification model. The classification model may trained according to a
focal loss function, at least one weight of the focal loss function each of which corresponds to
one of the plurality of candidate classifications being different from weights of the focal loss
function corresponding to the remainder of the plurality of candidate classifications. In some
embodiments, the target classification of the pulmonary nodule may be determined by
identifying a candidate classification of the structure corresponding to a largest
probability among the probabilities of the plurality of candidate classifications. The identified
candidate classification may be designated as the target classification of the structure.).
It would have been obvious before the effective filing date of the claimed invention to a
person having ordinary skill in the art to incorporate the teaching of Liu et al. to the Chen's
system by adding the feature of candidate information data. The references (Liu and Chen) teach
features that are analogous art and they are directed to the same field of endeavor, such as
databases. Ordinary skilled artisan would have been motivated to do so to provide Liu' s system
with enhanced data. (See Chen [Abstract], [0227], [0240], [0276]). One of the biggest
advantages of network machine learning database algorithms is their ability to improve over
time. Machine learning technology typically improves efficiency and accuracy thanks to the
ever-increasing amounts of data that are processed.
The combination does not expressly disclose statistical analysis code configured to cause the at least one processor to perform statistical analysis on an image such that each feature in the image is organized in a queue according to one or more categories, the queue comprising a plurality of samples; determining code configured to cause the at least one processor to determine a tail of the queue that is a subset of the plurality of samples; extract, from the tail of the queue, however, Wang discloses these limitations in that each of Wang’s categories is divided into multiple subsets, the number of samples in the subset being used as its relevance statistic. See step S23 on page 6 where subset populations 40, 100 and 10 are sorted as 100, 40, 10 (i.e. a queue) wherein the last one (i.e. tail of the queue) of the sorted subset populations, 10, is the sparsest with the most diverse pictures, and is read, understood and processed accordingly (i.e. extracting, from the tail of the queue). Wang, second paragraph on page 6 makes it clear that “for each category, the number of samples included in each subset is taken as the correlation between each subset and the category in which each subset is located.”
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Wang’s teaching would have allowed Liu to improve feature characterization by using statistically identified tail samples to distinguish and process features at different granularities.
Regarding claim 20, it is analogous to claim 1 and therefore likewise rejected.
Regarding claim 2, the method according to claim 1, wherein the obtaining of the
first feature comprises: performing feature extraction on the coarse-grained feature to obtain at least one first intermediate feature of the candidate information; obtaining a first weight of each of the at least one first intermediate feature when the coarse-grained feature; and
obtaining the first feature of the candidate information when the at least one
first intermediate feature and the first weight of each of the at least one first intermediate
feature (Liu [0040]: The present invention discloses the target cross-domain detection and
understanding method. The regional weight can be partially reduced through spatial
probability control and salient point pooling and by taking a spatial probability control layer as
an input image channel, and an edge salient cross point pooling layer can help a network to better
locate a target point; through cross-domain guiding semantic extraction and knowledge transfer,
an inclusion relation between target depth visual features and guiding semantics for different
domains is explored, network training is guided, and cross-domain invariant features are
extracted to enhance the cross-domain perception of a model; and by analyzing
a target notability degree, a semantic hierarchy cross-domain perception mapping effect and a
back propagation mechanism are explored, and a problem of accuracy in
notable target prediction and guiding semantic understanding under a specific intention is
solved.).
Regarding claim 3, the method according to claim 2, wherein the obtaining of the
second feature comprises: performing feature extraction on the information feature to obtain at least one second intermediate feature of the candidate information; obtaining second weights when the information feature, the second weights comprising a second weight of each of the at
least one second intermediate feature and a second weight of each of the at least one first
intermediate feature; and obtaining the second feature of the candidate information when the second weight of each of the at least one second intermediate feature, the second
weight of each of the at least one first intermediate feature, the at least one second
intermediate feature, and the at least one first intermediate feature (Chen [0056]: The
classification determination module 430 may determine a target classification of the structure of
the subject. In some embodiments, the target classification of the structure may be determined
based on probabilities of a plurality of candidate classifications. The plurality of candidate
classifications of the structure and their respective probabilities may be determined by inputting
the image data into a classification model. The classification model may trained according to a
focal loss function, at least one weight of the focal loss function each of which corresponds to
one of the plurality of candidate classifications being different from weights of the focal loss
function corresponding to the remainder of the plurality of candidate classifications. In some
embodiments, the target classification of the pulmonary nodule may be determined by
identifying a candidate classification of the structure corresponding to a largest
probability among the probabilities of the plurality of candidate classifications. The identified
candidate classification may be designated as the target classification of the structure.).
Regarding claim 4, the method according to claim 3, wherein the obtaining of the
second weights is further when on a popularity vector of the candidate information, the
popularity vector indicating historical conversion times of the candidate information (Chen
[0032]: At least one weight of the focal loss function each of which corresponds to one of the
plurality of candidate classifications being different from weights of the focal loss function
corresponding to the remainder of the plurality of candidate classifications. By adjusting the
weights corresponding to the plurality of candidate classifications, negative effects induced by
imbalance of samples corresponding to different candidate classifications on the classification
model may be reduced, and a more robust classification model may be obtained.).
Regarding claim 5, the method according to claim 4, wherein the obtaining of the
second weights comprises: splicing the information feature and the popularity vector to obtain a first spliced feature of the candidate information; and obtaining the second weights when the first spliced feature (Chen [0094]: For example, the combination of the backbone feature, the
segmentation feature, and the density feature may be realized by converting the backbone
feature, the segmentation feature, and the density feature into a one-dimensional backbone
feature vector, a one-dimensional segmentation feature vector, and a one-dimensional density
feature vector, respectively, and splicing the one-dimensional backbone feature vector, the onedimensional segmentation feature vector, and the one-dimensional density feature vector to
obtain a one-dimensional classification feature vector. The one-dimensional classification
feature vector may be used to determine the plurality of candidate classifications and the
probabilities corresponding to each candidate classification.).
Regarding claim 6, the method according to claim 1, wherein the obtaining of the
target information comprises: fusing the first feature and the second feature to obtain a fused feature of the candidate information; obtaining an estimated event probability of the candidate
information when the fused feature, the estimated event probability marking an
estimated probability of a specified event after corresponding information is displayed; and
obtaining the target information when the estimated event probability (Liu [Abstract]:
Through spatial probability control and salient point pooling and in conjunction with a coupling
relationship between a coding position probability and image features, diagonal vertexes of
a target candidate frame are efficiently located, and network complexity is simplified so as to
meet application needs for actual detection; through cross-domain guiding semantic
extraction and knowledge transfer, an inclusion relation between target depth visual features and
guiding semantics for different domains is explored, network training is guided, and crossdomain
invariant features are extracted to enhance the cross-domain perception ofa model; and
by analyzing a target notability degree, a semantic hierarchy cross-domain perception mapping
effect and a back propagation mechanism are explored, and a problem of accuracy in notable
target prediction and guiding semantic understanding under a specific intention is solved.).
Regarding claim 7, the method according to claim 6, wherein the fusing of the first
feature and the second feature comprises: obtaining a third weight of the second feature when the information feature; and fusing the first feature and the second feature when the third weight of the second feature to obtain the fused feature (Liu [0099]: "performing feature fusion on the intention feature and a target visual feature: f.sub.fusion=f(x)ffif.sub.int, where f(x) represents a visual feature extracted by a target through the NEGSS-Net backbone network; represents a channel splicing operator; f.sub.fusion represents the fused feature;").
Regarding claim 8, the method according to claim 7, wherein the obtaining of the
third weight is further when a popularity vector of the candidate information, the
popularity vector indicating historical conversion times of the candidate information (Chen
[0102]: In some embodiments, the training sample set may be generated based on the historical
image data, the structures labeled in the image data, and the target classifications corresponding
to the structures in the historical image data . ... Alternatively, the training sample set may be
generated by the processing device 120. For example, the processing device 120 may preprocess
historical image data representing structures of a plurality of subject with center points
of the structures labeled. Merely by way of example, the historical image data may be resample
to generate resampled image data having a target image resolution. Optionally, the processing
device 120B may further normalize the resampled image data. As another example, the
processing device 120 may extract one or more image crops from the resampled image data, and
normalize each of the image crop( s). ).
Regarding claim 9, the method according to claim 8, wherein the obtaining of the
third weight comprises: splicing the information feature and the popularity vector to obtain a second spliced feature of the candidate information; and obtaining the third weight of the second feature when the second spliced feature (Chen [0094]: For example, the combination of the
backbone feature, the segmentation feature, and the density feature may be realized by
converting the backbone feature, the segmentation feature, and the density feature into a onedimensional backbone feature vector, a one-dimensional segmentation feature vector, and a oneApplication/ dimensional density feature vector, respectively, and splicing the one-dimensional backbone feature vector, the one-dimensional segmentation feature vector, and the one-dimensional
density feature vector to obtain a one-dimensional classification feature vector. The onedimensional
classification feature vector may be used to determine the plurality of candidate
classifications and the probabilities corresponding to each candidate classification.).
Regarding claim 10, the method according to claim 7, wherein the fusing of the first
feature and the second feature comprises: weighting the second feature when the second feature to obtain a weighted feature of the candidate information; and adding the weighted feature and the first feature to obtain the fused feature (Chen [0073]: In some embodiments, each of the plurality of candidate classifications may correspond to a weight in the focal loss function. At least one weight of the focal loss function each of which corresponds to one of the plurality of
candidate classifications being different from weights of the focal loss function corresponding to
the remainder of the plurality of candidate classifications. For example, a smaller weight may be
assigned to a benign pulmonary nodule, and a larger weight may be assigned to a malignant
pulmonary nodule. By ad;usting the weights corresponding to the plurality of candidate
classifications, negative effects induced by imbalance of samples corresponding to different
candidate classifications on the classification model may be reduced, and a more robust
classification model may be obtained.).
Regarding claim 11, the method according to claim 6, wherein:
the obtaining of the first feature comprises processing the coarse-grained feature
through a first extraction branch in a probability estimation model; the obtaining of the
second feature comprises processing the information feature and the intermediate feature
through a second extraction branch in the probability estimation model (Liu [0040]: The
present invention discloses the target cross-domain detection and understanding method .... The
method of detecting and understanding a notable target according to a specific intention is
efficient, objective and comprehensive, and can effectively enhance the environmental visual
perception ability and active safety. Meanwhile, in conjunction with a coupling relationship
between a coding position probability and image features, diagonal vertexes ofa target
candidate frame are efficiently located, network complexity is simplified, the difficulty and
redundancy of fusion are avoided, the expenses of system calculation are saved, and the
application needs for actual detection can be met.);
the obtaining of the fused feature comprises processing the first feature and the
second feature through a fusion branch in the probability estimation model; and the
obtaining of the estimated event probability comprises processing the fused
feature through an estimation branch in the probability estimation model (Liu [0099]:
"performing feature fusion on the intention feature and a target visual feature:
f.sub.fusion=f(x)ffif.sub.int, where f(x) represents a visual feature extracted by a target through
the NEGSS-Net backbone network; represents a channel splicing operator; f.sub.fusion
represents the fused feature;").
Regarding claim 12, the method according to claim 11, further comprising, before
extracting the information feature of the candidate information:
extracting an information feature of sample information; processing the coarsegrained
feature of the sample information through the first extraction branch to obtain a
first feature of the sample information (Chen [0094]: For example, the combination of the
backbone feature, the segmentation feature, and the density feature may be realized by
converting the backbone feature, the segmentation feature, and the density feature into a onedimensional backbone feature vector, a one-dimensional segmentation feature vector, and a onedimensional density feature vector, respectively, and splicing the one-dimensional backbone
feature vector, the one-dimensional segmentation feature vector, and the one-dimensional
density feature vector to obtain a one-dimensional classification feature vector. The onedimensional
classification feature vector may be used to determine the plurality of candidate
classifications and the probabilities corresponding to each candidate classification.);
processing the information feature of the sample information and the intermediate
feature of the sample information through the second extraction branch to obtain a second
feature of the sample information; processing the first feature of the sample information
and the second feature of the sample information through the fusion branch to obtain a
fused feature of the sample information; processing the fused feature of the candidate
information through the estimation branch in the probability estimation model to obtain
an estimated event probability of the sample information; (Chen [0073]: In some
embodiments, each of the plurality of candidate classifications may correspond to a weight in
the focal loss function. At least one weight of the focal loss function each of which corresponds
to one of the plurality of candidate classifications being different from weights of the focal loss
function corresponding to the remainder of the plurality of candidate classifications. For
example, a smaller weight may be assigned to a benign pulmonary nodule, and a larger
weight may be assigned to a malignant pulmonary nodule. By ad;usting
the weights corresponding to the plurality of candidate classifications, negative effects induced
by imbalance of samples corresponding to different candidate classifications on the
classification model may be reduced, and a more robust classification model may be obtained.);
obtaining a loss function value when the estimated event probability of the
sample information, an event probability label of the sample information, and a training
weight of the sample information, the training weight being inversely related to a
popularity of the sample information, the event probability label indicating a labeling
probability of the specified event after the sample information is displayed; and updating a
parameter of the probability estimation model when the loss function value (Chen
[0056]: The classification detennination module 430 may detennine a target classification of the
structure of the subject. In some embodiments, the target classification of the structure may be
determined based on probabilities ofa plurality of candidate classifications. The plurality of
candidate classifications of the structure and their respective probabilities may be determined by
inputting the image data into a classification model. The classification model may trained
according to a focal loss function, at least one weight of the focal loss function each of which
corresponds to one of the plurality of candidate classifications being different from weights_gf
the focal loss function corresponding to the remainder of the plurality of candidate
classifications. In some embodiments, the target classification of the pulmonary nodule may be
determined by identifying a candidate classification of the structure corresponding to a
largest probability among the probabilities of the plurality of candidate classifications. The
identified candidate classification may be designated as the target classification of the
structure.).
Regarding claim 14, the apparatus according to claim 13, wherein the first feature
obtaining code causes the at least one processor to obtain the first feature by (Liu [0037]: processor): performing feature extraction on the coarse-grained feature to obtain at least one
first intermediate feature of the candidate information, obtaining a first weight of each of
the at least one first intermediate feature when the coarse-grained feature, and
obtaining the first feature of the candidate information when the at least one
first intermediate feature and the first weight of each of the at least one first intermediate
feature (Liu [0040]: The present invention discloses the target cross-domain detection and
understanding method. The regional weight can be partially reduced through spatial
probability control and salient point pooling and by taking a spatial probability control layer as
an input image channel, and an edge salient cross point pooling layer can help a network to better
locate a target point; through cross-domain guiding semantic extraction and knowledge transfer,
an inclusion relation between target depth visual features and guiding semantics for different
domains is explored, network training is guided, and cross-domain invariant features are
extracted to enhance the cross-domain perception of a model; and by analyzing
a target notability degree, a semantic hierarchy cross-domain perception mapping effect and a
back propagation mechanism are explored, and a problem of accuracy in
notable target prediction and guiding semantic understanding under a specific intention is
solved.); and
wherein the second feature obtaining code causes the at least one processor to obtain
the second feature by: performing feature extraction on the information feature to obtain
at least one second intermediate feature of the candidate information, obtaining second
weights when the information feature, the second weights comprising a second weight of
each of the at least one second intermediate feature and a second weight of each of the at
least one first intermediate feature (Chen [0032]: At least one weight of the focal loss function
each of which corresponds to one of the plurality of candidate classifications being different
from weights of the focal loss function corresponding to the remainder of the plurality of
candidate classifications. By adjusting the weights corresponding to the plurality of candidate
classifications, negative effects induced by imbalance of samples corresponding to different
candidate classifications on the classification model may be reduced, and a more robust
classification model may be obtained.), and
obtaining the second feature of the candidate information when the second
weight of each of the at least one second intermediate feature, the second weight of each of
the at least one first intermediate feature, the at least one second intermediate feature, and
the at least one first intermediate feature (Chen [0056]: The classification determination
module 430 may determine a target classification of the structure of the subject. In some
embodiments, the target classification of the structure may be determined based
on probabilities of a plurality of candidate classifications. The plurality of candidate
classifications of the structure and their respective probabilities may be determined by inputting
the image data into a classification model. The classification model may trained according to a
focal loss function, at least one weight of the focal loss function each of which corresponds to
one of the plurality of candidate classifications being different from weights of the focal loss
function corresponding to the remainder of the plurality of candidate classifications. In some
embodiments, the target classification of the pulmonary nodule may be determined by
identifying a candidate classification of the structure corresponding to a largest
probability among the probabilities of the plurality of candidate classifications. The identified
candidate classification may be designated as the target classification of the structure.).
Regarding claim 15, the apparatus according to claim 14, wherein the second
feature obtaining code causes the at least one processor to obtain the second weights by:
splicing the information feature and a popularity vector to obtain a first spliced
feature of the candidate information, the popularity vector indicating historical conversion
times of the candidate information; and obtaining the second weights when the first
spliced feature (Chen [0094]: For example, the combination of the backbone feature, the
segmentation feature, and the density feature may be realized by converting the backbone
feature, the segmentation feature, and the density feature into a one-dimensional backbone
feature vector, a one-dimensional segmentation feature vector, and a one-dimensional density
feature vector, respectively, and splicing the one-dimensional backbone feature vector, the onedimensional segmentation feature vector, and the one-dimensional density feature vector to
obtain a one-dimensional classification feature vector. The one-dimensional classification
feature vector may be used to determine the plurality of candidate classifications and the
probabilities corresponding to each candidate classification.).
Regarding claim 16, the apparatus according to claim 13, wherein the information
obtaining code causes the at least one processor to obtain the target information by:
fusing the first feature and the second feature to obtain a fused feature of the
candidate information; obtaining an estimated event probability of the candidate
information when the fused feature, the estimated event probability marking an
estimated probability of a specified event after corresponding information is displayed; and
obtaining the target information when the estimated event probability (Liu [Abstract]:
Through spatial probability control and salient point pooling and in conjunction with a coupling
relationship between a coding position probability and image features, diagonal vertexes of
a target candidate frame are efficiently located, and network complexity is simplified so as to
meet application needs for actual detection; through cross-domain guiding semantic
extraction and knowledge transfer, an inclusion relation between target depth visual features and
guiding semantics for different domains is explored, network training is guided, and crossdomain
invariant features are extracted to enhance the cross-domain perception ofa model; and
by analyzing a target notability degree, a semantic hierarchy cross-domain perception mapping
effect and a back propagation mechanism are explored, and a problem of accuracy in notable
target prediction and guiding semantic understanding under a specific intention is solved.).
Regarding claim 17, the apparatus according to claim 16, wherein the information
obtaining code causes the at least one processor to fuse the first feature and the second
feature by: obtaining a third weight of the second feature when the information feature; and
fusing the first feature and the second feature when the third weight of the second
feature to obtain the fused feature (Liu [0099]: "performing feature fusion on the intention
feature and a target visual feature: f.sub.fusion=f(x)ffif.sub.int, where f(x) represents a visual
feature extracted by a target through the NEGSS-Net backbone network; represents a channel
splicing operator; f.sub.fusion represents the fused feature;").
Regarding claim 18, the apparatus according to claim 17, wherein the information
obtaining code causes the at least one processor to obtain the third weight by:
splicing the information feature and a popularity vector to obtain a second spliced
feature of the candidate information, the popularity vector indicating historical conversion
times of the candidate information; and obtaining the third weight of the second feature
when the second spliced feature (Chen [0094]: For example, the combination of the
backbone feature, the segmentation feature, and the density feature may be realized by
converting the backbone feature, the segmentation feature, and the density feature into a onedimensional backbone feature vector, a one-dimensional segmentation feature vector, and a onedimensional density feature vector, respectively, and splicing the one-dimensional backbone
feature vector, the one-dimensional segmentation feature vector, and the one-dimensional
density feature vector to obtain a one-dimensional classification feature vector. The onedimensional
classification feature vector may be used to determine the plurality of candidate
classifications and the probabilities corresponding to each candidate classification.).
Regarding claim 19, the apparatus according to claim 17, wherein the information
obtaining code causes the at least one processor to fuse the first feature and the second
feature by: weighting the second feature when the second feature to obtain a weighted
feature of the candidate information; and adding the weighted feature and the first feature
to obtain the fused feature (Chen [0073]: In some embodiments, each of the plurality of
candidate classifications may correspond to a weight in the focal loss function. At least
one weight of the focal loss function each of which corresponds to one of the plurality of
candidate classifications being different from weights of the focal loss function corresponding to
the remainder of the plurality of candidate classifications. For example, a smaller weight may be
assigned to a benign pulmonary nodule, and a larger weight may be assigned to a malignant
pulmonary nodule. By ad;usting the weights corresponding to the plurality of candidate
classifications, negative effects induced by imbalance of samples corresponding to different
candidate classifications on the classification model may be reduced, and a more robust
classification model may be obtained.).
Response to Arguments
Applicant's arguments filed 27 January 2025 have been fully considered. Wang et al., Pub. No.: WO2019200782A1 has been applied to address claim amendments (see provided English translation).
Conclusion
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/SYED H HASAN/Primary Examiner, Art Unit 2154
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