DETAILED ACTION
Claims 1,2,3,4,5,6,8,9,10,12,13,14,15,16,17,18 and 19 and 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1):
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of MURATA et al. (JP 2002-41513 A) with SEARCH machine translation:
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of ZENG et al. (CN 115115984 A) with SEARCH machine translation:
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of SIA et al. (US 2023/0274538 A1):
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of Pham et al. (US 11,055,566 B1):
Response to Amendment
The amendment was received 7/1/2026. Claims 1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18 and
19 and
20 pending:
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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.
Claims 1,2,3,4,5,6,8,9,10,12,13,14,15,16,17,18 and 19 and 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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Step zero: establish broadest reasonable interpretation (in the footnotes);
Step 1: clam 1 is a machine; claim 19 is a process; claim 20 a manufacture;
Step 2A, prong 1:
The claim(s) recite(s) (claim 1 representative) math and a mental process:
model1…
obtain…data…
determine a propagation field…
identifies latent features…
determine…output data…
compare portions…
perform…processes:
1. A device comprising:
one or more memories configured to receive and store input data for use by a first machine learning model;
one or more processors, coupled to the memories, configured to:
obtain the input data from the one or more memories;
determine a propagation field for the input data, wherein the field identifies latent features within the input data; and
determine, with the first machine learning model, output data based on the field and the input data, wherein, while determining the output data, the one or more processors are configured to:
compare portions of the propagation field to at least one corresponding
threshold; and
perform, with the first machine learning model, one or more feature propagation processes for the portions of the input data associated with the portions of the propagation field that satisfy the at least one corresponding threshold, the one or more feature propagation processes being based on the latent features identified by the propagation field,
wherein the output data is determined based on the one or more feature
propagation processes performed for the portions of the input data associated with the portions of the propagation field that satisfy the at least one corresponding threshold.
Step 2A, prong 2:
This judicial exception is not integrated into a practical application because the additional elements such as:
“memories”
“machine learning”
“processors”
“input”
“propagation2 field”
“latent features”
“output”
“threshold”
“feature propagation”:
is not improving the function of a computer in view of applicant’s disclosure:
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Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements (such as “feature propagation” and “machine learning” plus computer stuff plus “threshold”) individually and in combination with the abstract adheres to the conventional in view of applicant’s disclosure of problems with propagating [0003][0030][0034]:
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In contrast, claim 11:
11. (Original) The device of claim 10, wherein the one or more processors are further configured, while performing the one or more feature propagation processes, to:
restrict feature propagation to only occur within portions of the data having the same
semantic category in the propagation field.
“reflects”3 the disclosed improvement of computer function at [0034]:
[0034] One solution to these problems is to initially determine a propagation field based on the data and to use that field to guide4 the feature propagation5. Different techniques for guiding the feature propagation are discussed in greater detail below. In one example, to reduce the problems associated random initialization, an epistemic field may be determined based on previous values for features, and may be used as initialization values for propagation. Such fields may serve as an initial guess informed by previous observations, which can be more accurate than the random selections of traditional PatchMatch processes. For instance, if sequential frames of a moving car are being processed, knowledge of the car's consistent trajectory can provide a less random and more probable initialization. Consequently, this reduces the number of iterations necessary to reach convergence, which reduces processing time and latency, allowing for faster analysis and improved model performance. This may also mitigate error propagation by decreasing the opportunities for errors to accumulate and propagate through each iteration. In another example, fields may be used to restrict6 feature propagation within particular regions of data (such as within the foreground/background of an image). Such fields may be particularly beneficial in reducing feature propagation errors caused by regions where features are indistinct, as it maintains the propagation of features within corresponding portions of the data.
Response to Arguments
III. Claim Rejections
A. 35 U.S.C. § 101 Rejection
Applicant's arguments filed 7/1/2026 have been fully considered but they are not persuasive:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 8, 3rd para, 1st S: “guiding”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Step 2A, Prong One
Applicants state in page 9,2nd para:
Applicant respectfully submits that the amended claims do not recite an abstract idea under any category. In particular, the amended claims recite a specific method for guiding feature propagation in a machine learning model by comparing portions of a propagation field to at least one corresponding threshold and selectively performing feature propagation for portions of the input data associated with portions of the propagation field that satisfy the threshold. This is not a mathematical relationship or formula, nor is it a method of organizing human activity, nor is it a process that can be practically performed in the human mind. Accordingly, the amended claims are not directed to an abstract idea under Prong One.
The examiner respectfully disagrees since claim 1’s “obtain7…data” is
(1) the identified “specific limitation”8 and
(2) the identified specific limitation falls “within at least one of the groupings of abstract ideas”: “Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion”.
Step 2A, Prong Two
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 9, 3rd para, 2nd S: “selectively performing feature propagation based on threshold driven comparisons of a propagation field”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 9, last para, 1st S: “guide feature propagation, including by comparing portions of the field to thresholds and selectively performing feature propagation”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 10, 2nd para, 1st S: “feature propagation is selectively performed”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Step 2B
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 10, last para, 1, 2nd S: “selectively performing feature propagation… selectively performed feature propagation processes”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 11, 2nd para, 1, 2nd S: “threshold-driven comparisons of a propagation field to selectively control”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
B. 35 U.S.C. § 102(a)(2) Rejection (Xiong)
1. Independent Claims 1, 19, and 20
Applicant's arguments filed 7/1/2026 have been fully considered but they are not persuasive:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. remarks, page 12, 3rd para, 1, last S: “comparing portions of a propagation field to a threshold value to determine whether feature propagation should be performed for associated portions of the input data.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments with respect to claim(s) 1 and Chen (US 2022/0121961 A1) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument:
Claim(s) 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8.165,386 B1), wherein Moon teaches a classification propagation threshold via figures 14,15:
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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.
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.
Claim(s) 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1):
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Re 1., Xiong teaches A device comprising910:11
one [[or more]] memories12 (fig. 10:286: “System Memory”) configured to receive and store input data for13 (special) use14 (or likewise: “the combination of two tasks realizes an application15 that can simultaneously generate 360-degree rendered video and recognize 3D objects” [0098], last S) by16 a (likewise) first (“generative” [0019]) machine learning model;
one [[or more]] processor17[[s]] (fig. 10:282: “Host Processor”), coupled to the memories, configured to:
obtain the input data from the one [[or more]] memories;
determine a18 propagation (Xiong teaches two propagation fields overlapping in meaning via color/radiance “propagation”:
(1) “ ‘coordinate-aligned [color] feature field’ ” [0111];
(2) “ ‘radiance19 field view synthesis operations’ ” [0115])
field (currently mapped20 to the first (1) color feature field having a “color”21-“feature”, [0032] 1st S, “coordinate-aligned” [0030] 2nd S: fig. 9:92: “Aggregate the multi-view visual data into a coordinate-aligned feature field”: fig. 2:13: “Latent Feature Space Z (z, θ, φ)”) for the input data, 22 (via “object recognition” [0036] 1st S) 23; and
determine2425 output26 data (fig. 2: data-arrows) based on the field and the input data, wherein27, while determining the output data, the one or more processors are configured to:
compare portions (or likewise “multi-view visual data…features compared” [0069] 1st S to [0071] 2nd S) of the propagation field to28 at least one corresponding threshold ; and
perform29 one or more feature propagation processes (or “learning process” [0026] 3rd S or “training process” [0034] 1st S or “synthesis process” [0054]) for the30 portions (or likewise “the multi-view visual data31 11” [0093]) of the input data32 associated with the portions of the propagation field that satisfy the at least one corresponding threshold , the one or more feature propagation processes being based on the latent features (“3D object” [0024] penult S) identified by the propagation (color/radiance) field,
wherein3334 the output data is35 determined (represented as arrows in fig. 2) based on the (datum) portions of the input data associated with the (feature) portions of the propagation field that satisfy the at least one corresponding threshold (fig. 2 with output data arrows:
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).
Xiong does not teach the difference of claim 1 of:
to at least one corresponding threshold…
that satisfy the at least one corresponding threshold…
that satisfy the at least one corresponding threshold.
Moon teach the difference of claim 1 of:
to at least one corresponding threshold …
that satisfy the at least one corresponding threshold…
that satisfy the at least one corresponding threshold (or likewise “to…the propagate threshold…greater-than” via c.15, ll.45-55 & figs. 14,15: PROPAGATE CRITERIA & PROPAGATE THRESHOLD:
A propagate criteria descriptor consists of propagate criterion, propagate threshold, and propagate target fields. The propagate criterion and propagate threshold specify the condition of the result field that must be met in order for the output result to be forwarded to the classification processing element specified by the propagate target field. The propagate criterion specifies the equality relationship that should be used to compare the result field and the propagate threshold. The propagate criterion may specify one of six equivalence relationships: "equal", "not-equal", "greater-than", "less-than", "greater-than-or-equal", or "less-than-or-equal".
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Since Xiong teaches classification with problems thereof via [0022]:
[0022] The technology described herein integrates two formerly separate research tasks: 3D object recognition and radiance-field-based novel view synthesis, which aims to classify and represent 3D objects based on visual information from multiple viewpoints, respectively. The existence of this problem is driven by two factors. First, the techniques described herein can generate a 360-degree view video and provide semantic labels for commercial purposes. Secondly, both tasks involve understanding and recognizing 3D object materials, shapes, and structures from multiple viewpoints. The challenges in these tasks include accounting for scene/environment properties, such as lighting conditions, and addressing deficiencies such as cross-scene/object generalization and learning efficiency in radiance field representation. The specific problem of interest is how to effectively integrate these two tasks and leverage their mutual benefits, where radiance-field-based (RF-based) novel view synthesis enhances 3D object recognition by capturing crucial object details, while 3D object recognition provides semantic knowledge to aid radiance-field-based view synthesis learning.
one of skill could or would have done is consult others as the solution and thus make Xiong’s be as Moon’s seeing in the change good via Moon, c.8,l.60 to c. 9,l.10:
Scene-based dynamic reconfiguration in the present invention may be of benefit in certain cases. For example, if many faces get detected, the system can ignore some neural network weights that are negligible or support vectors that have low weights, in order to ease the load. In another example, the system may need to switch to different face detector or demographics classifier parameters trained for a specific environment, such as in an environment where the lighting condition in the environment gets harsh and the input images from the environment does not provide optimal input data for the video analytic processing. In another example with regard to the emotion recognition, if there are too many faces in the environment and the emotion recognition requires heavy usage of the resource, the process of emotion recognition can become too expensive in terms of processing time. In this case, the hardware can be devoted to face detection for some period of time, and the emotion recognition can run on sampled faces rather than on all of the detected faces.
via creative, explicit, routing, inferential Supreme Court steps, A,B,C:
A) make a decoder program in memory (Xiong: fig. 10: 286) based on Xiong’s fig. 7:
A1) create code calling/returning from a classification/labeler (classifier) program:
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B) create the face demographics labeler program in computer memory 286 based on Moon’s fig. 2:
B1) input decoder decoded latent face-features to step 201: “NN FACE DETECTION”:
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C) obtain Moon’s propagation labeled thresholding circuitry of fig. 15 (high-level view at fig. 11:304: “INTELLIMINING PORT 0”)
C1) connect said Xiong’s computer-memory 286 to the circuitry of Moon’s fig. 5;
C2) create code in the computer-memory 286 (see Moon’s fig. 11:301:” MEMORY”, reproduced below, as a guide for creating code in the computer-memory 286) returning thresholded face-labels to the decoder program in said computer-memory 286 at step 76: “Decode the latent features into an object label”:
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D) Run decoder program calling face-labeler program in said memory 286;
E) see what happens (I foresee in said memory 286 goodness:
Using the combination of two tasks realizes an application that can simultaneously generate 360-degree rendered video and recognize 3D objects based on Scene-based dynamic reconfiguration:
Scene-based dynamic reconfiguration in the present invention may be of benefit in certain cases. For example, if many faces get detected, the system can ignore some neural network weights that are negligible or support vectors that have low weights, in order to ease the load. In another example, the system may need to switch to different face detector or demographics classifier parameters trained for a specific environment, such as in an environment where the lighting condition in the environment gets harsh and the input images from the environment does not provide optimal input data for the video analytic processing. In another example with regard to the emotion recognition, if there are too many faces in the environment and the emotion recognition requires heavy usage of the resource, the process of emotion recognition can become too expensive in terms of processing time. In this case, the hardware can be devoted to face detection for some period of time, and the emotion recognition can run on sampled faces rather than on all of the detected faces.)
Re 2. (Original), Xiong discloses The device of claim 1, further comprising a sensor36 (or “camera” [0045]) configured to capture the input data.
Re 3. (Original), Xiong discloses The device of claim 2, wherein the sensor is a camera, and the input data comprises pixel data (“at the position (m,n) in a specific color channel (c) in view I” [0030] penult S).
Re 4. (Original), Xiong discloses The device of claim 2, wherein the sensor is a position sensor, and the input data comprises position data for one or more objects (“based on visual information from multiple viewpoints” [0022] 1st S).
Re 5. (Original), Xiong discloses The device of claim 1, further comprising a modem (via “hardware”37 [0087] 2nd S) configured to receive at least a portion of the first machine learning model.
Re 6. (Original), Xiong discloses The device of claim 1, wherein performing the one or more feature propagation processes comprises determining initial propagation (“feature map”38 [0046]) values based on values (or “intensity”39 “value I(m,n,c)i” [0030] penult S: fig. 2: I0,I1,I2) of the propagation field.
Re 8. (Currently Amended), Xiong of the combination of Xiong-Moon teaches The device of claim 1 [[7]], wherein the one or more processors are further configured, while comparing (feature) portions of the propagation field to the at least one corresponding (symbol-token) threshold, to:
compare (via the ratio/quotient of equation (14)), to the at least one corresponding threshold,40
(A) an average (via “mean pooling” [0073] 2nd S) of values within the portions of the propagation field,
(B) maximum values within the portions of the propagation field,
(C) minimum values within portions of the propagation field, or
(D) a combination thereof.
Re 9. (Currently Amended), Xiong of the combination of Xiong-Monn teaches The device of claim 1 [[7]], wherein the data is (“3D” [0019]) multidimensional data, and wherein the one or more processors are configured to perform the one or more feature propagation processes on a per-dimension basis41 (via “each source ray are first integrated by weighted pooling W.sub.j latent features Z at all points in each ray direction” [0061]).
Re 10. (Original), Xiong discloses The device of claim 1, wherein the propagation field includes a semantic field (or “semantic information”- “radiance-field-based novel view synthesis (2R-TRM)” [0021] 2nd to last S, wherein “the term ‘radiance field view synthesis operations’ refers to an intricate technique that captures and reconstructs the visual appearance of objects or scenes by employing a process that involves capturing images from varying view points and subsequently approximating the radiance field (e.g., a mathematical representation that describes the visual appearance of an object as a function of viewing direction).” [0115] 1st S) that identifies42 one or more semantic features (“to latent features” [0034]) that indicate contextual43 information (or “scene44”-“information” [0026] 2nd S) regarding associated (“2D image”-“set” [0115] last S) portions of the data.
Re 13. (Original), Xiong teaches The device of claim 1, wherein the field is determined at least in part based on extrinsic data (via “The ‘Extra Data’ column” [0082] last S: Fig. 5: “Extra Data”) that is received separately from the data.
Re 14. (Original), Xiong teaches The device of claim 1, further comprising a display device (fig.10:290), configured to display a user interface (or likewise “programming45 interfaces” [0129]), and
wherein the one or more processors are further configured to receive user (action) input (via “host processor46”-“input”: fig. 10:288: “IO”) via the user interface, and
wherein the one or more processors are further configured to:
determine, based on the user input, a first type (or “category A and category B” [0072] 5th S) of propagation field from among a plurality of types of propagation field; and
determine the propagation field (to be aligned) based on47 the first type of propagation field.
Re 15. (Original), Xiong discloses The device of claim 1, wherein the propagation field is determined (or aligned) by the first machine learning model (figs. 4,6: “Transformer” listed twice).
Claim 17 is rejected like claim 15:
Re 17. (Original), Xiong discloses The device of claim 1, wherein the one or more processors are further configured to determine the propagation field using a second machine learning model separate from the first machine learning model.
Claim 19 is rejected like claim 1:
Re 19. (Currently Amended), Xiong-Moon teaches A method comprising:
receiving data for use by a first machine learning model;
determining a propagation field for the data, wherein the field identifies latent features within the data; [[and]]
determining, with the first machine learning model, output data based on the field and the data, wherein,.
while determining the output data, the first machine learning model is configured to:
compare portions of the propagation field to at least one corresponding threshold:
and
perform, with the first machine learning model, one or more feature propagation
processes for the portions of the data associated with the portions of the
propagation field that satisfy the at least one corresponding threshold, the one
or more feature propagation processes being based on the latent features
identified by the propagation field,
wherein the output data is determined based on the one or more feature propagation processes performed for the portions of the data associated with the portions of the propagation field that satisfy the at least one corresponding threshold.
Claim 20 is rejected like claims 1 and 19:
Re 20. (Currently Amended), Xiong-Moon teaches A non-transitory, computer-readable medium storing instructions which, when executed by a processor, cause the processor to:
receive data for use by a first machine learning model;
determine[[ing]] a propagation field for the data, wherein the field identifies latent features within the data; [[and]]
determine[[ing]], with the first machine learning model, output data based on the field and the data, w,
while determining the output data, the processor is configured to:
304182169.1
compare portions of the propagation field to at least one corresponding threshold;
and
perform, with the first machine learning model, one or more feature propagation
processes for the portions of the data associated with the portions of the propagation field that satisfy the at least one corresponding threshold, the one or more feature propagation processes being based on the latent features identified by the propagation field,
wherein the output data is determined based on the one or more feature propagation processes performed for the portions of the data associated with the portions of the propagation field that satisfy the at least one corresponding threshold.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of MURATA et al. (JP 2002-41513 A) with SEARCH machine translation:
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Re 11., Xiong of the combination of Xiong-Moon teaches The device of claim 10, wherein the one or more processors are further configured, while performing the one or more feature propagation processes, to:
restrict feature propagation (i.e. training with lighting features) to only occur within (feature) portions (fig. 1: “Semantic-Guided Latent Features”) of the data having the same semantic category (fig. 1:15: “sofa”) in the propagation field.
Xiong does not teach the difference of claim 11 of:
restrict (feature propagation) to only occur within (portions).
MURATA teach the difference of claim 11 of:
restrict (feature propagation) (“can also solve the problem that merging cannot be performed due to this unnecessary feature, and leads to a significant improvement in processing efficiency”, pg, 26, 9th txt blk) to only occur within (portions) (such that “only the necessary ones are propagated”, pg. 27, 3rd text blk).
Since Xiong teaches a feature, one of skill in the art of features can make Xiong’s be as MURATA’s seeing in the change “significant improvement in processing efficiency”, Chen pg, 26, 9th txt blk.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of ZENG et al. (CN 115115984 A) with SEARCH machine translation:
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Re 12., Xiong of the combination of Xiong-Moon teaches The device of claim 1, wherein the field includes an epistemic4849 field that identifies50 predicted features (via “object label prediction” [0023] last S) within the data based on previously-predicted features within corresponding (feature) portions of previous data.
Xiong does not teach the difference of claim 12 of:
epistemic5152 (field)…
previously-predicted features
ZENG teach the difference of claim 12 of:
epistemic5354 (i.e., “learning”55, pg. 11, last txt blk) (field)…
previously-predicted features (“which may be referred to as the description character56”, pg. 42, 1st txt blk).
Since Xiong teaches features and retrieval [0129] penult S:
“The storage and retrieval of pre-processing, intermediate, and final results may be stored in databases using SQL (Structured Query Language) or No-SQL programming interfaces, among others.”
, one of skill in the art can make Xiong’s retrieval be as ZENG’s retrieval, pg. 15, 2nd txt blk:
“The purpose of the terminal device 200 to perform accurate video retrieval through the video retrieval text is achieved.”
seeing the change “ obtain the high quality video characteristic “, ZENG pg. 41, 2nd txt blk, “so as to generate video data accurate video description information, through the video description information, it can realize accurate search (search) of the video data by the search text.”, ZENG, txt blk spanning pgs. 43,44.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of SIA et al. (US 2023/0274538 A1):
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Re 16., Xiong of the combination of Xiong-Moon teaches The device of claim 15, wherein the one or more processors are further configured to train the first machine learning model to determine the propagation field using gradient backpropagation of (“minimizing” [0065]) cross-entropy losses against training data (“used as pre-training data”) [0077] 3rd to last S) for the output of the first machine learning model.
Xiong does not teach the difference of claim 16 of:
gradient backpropagation.
SIA teach the difference of claim 16 of:
gradient backpropagation (“may be performed for parameter updating” [0126] 2nd to last S).
Since Xiong teaches classification, one of skill in the art of classification can make Xiong’s be as SIA’s seeing in the change “the feature extractor can generalize well…even if the image…is faint”, SIA [0126] last S.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong et al. (US 2024/0029455 A1) in view of Moon et al. (US 8,165,386 B1) as applied in claims 1,2,3,4,5,6,8,9,10,13,14,15,17 and 19 and 20 further in view of Pham et al. (US 11,055,566 B1):
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Re 18., Xiong of the combination of Xiong-Moon teaches The device of claim 1, wherein the one or more processors are further configured to:
determine a selection mask for the data,
wherein the propagation field is determined within (“5D” [0052] last S) regions identified by the selection mask, and
wherein the feature propagation process is performed within (“5D” [0052] last S) regions identified by the selection mask.
Xiong does not teach the difference of claim 18 of:
a selection mask…
the selection mask…
the selection mask.
Phan teach the difference of claim 18 of:
a selection mask (or “an object mask (e.g., selection mask) for the query object”, c.16,ll.45-50: fig. 1B:152: “Generate An Object Mask For The Detected Object”)…
the selection mask (“for each detected instance of the query object in the image”, c.3,ll.10-15: fig. 3A:308: “Generate An Object Mask For The Detected Query Object”)…
the selection mask (“for the detected query object”, c.4,ll.60-65: fig. 7B:704: object mask applied thereto).
Since Xiong teaches recognition, one of skill in the art of recognition can make Xiong’s be as Phan’s seeing in the change “increased accuracy over conventional systems. For instance, the object selection system improves object detection accuracy by better identifying objects.”, Phan c.5,ll.35-40.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action:
Citation
Relevance
Durbhakula (US 2024/0061783 A1): same Applicant
Durbhakula teaches “propagations field…compared to the propagation threshold” via [0041], 10th S and fig. 7:709:
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“The total number of propagations stored by the prefetcher circuit 202 in the total number of propagations field 422 can be compared to the propagation threshold stored in the propagation threshold field 420 to determine the consistency (or lack thereof) of stride patterns in observed cache read requests.”
as the closest to the claimed “compare portions of the propagation field to at least one corresponding threshold” of claim 1.
Lee et al. (US 2019/0311202 A1)
Lee teaches “guided…propagation” via [0014] and fig. 5:
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“[0014] FIG. 5 illustrates an example method of video object segmentation using a reference-guided mask propagation technique according to certain embodiments.”
as the closest to applicant’s disclosure’s “guiding…propagation” in applicant’s specification, paragraph [0002]:
[0002] Aspects of the present disclosure relate generally to machine learning techniques, and more particularly, to methods and systems suitable for guiding feature propagation in machine learning applications using one or more fields.
CHEN et al. (US 2018/0204088 A1)
CHEN teaches “guiding…propagation” creating a mask via [0010] and fig. 4:
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“[0010] step (2), guiding manifold preserving foreground propagation by using a background prior condition in combination with a local linear embedding algorithm based on superpixel division, generating an image foreground probability map collaboratively;”
as the closest to applicant’s disclosure’s “guiding…propagation” in applicant’s specification, paragraph [0002]:
[0002] Aspects of the present disclosure relate generally to machine learning techniques, and more particularly, to methods and systems suitable for guiding feature propagation in machine learning applications using one or more fields.
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THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1model: a simplified representation of a system or phenomenon, as in the sciences or economics, with any hypotheses required to describe the system or explain the phenomenon, often mathematically, wherein representation is defined: the expression or designation by some term, character, symbol, or the like, wherein expression is defined: Mathematics. a symbol or a combination of symbols representing a value, relation, or the like, wherein relation is defined: Mathematics. a single- or multiple-valued function. (Dictionary.com)
2 propagation: the act of propagating, wherein propagating is defined: Computers. to cause (an update or other alteration) to take effect throughout a network of devices.
The active master database replicates updates to the standby master database, which propagates the updates to the subscribers. (Dictionary.com)
3 MPEP 2106.04(d)(1) Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two [R-10.2019], 2nd para:
The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification {at [0034]) sets forth an improvement in technology, the claim (claim 11) must be evaluated to ensure that the claim (claim 11) itself reflects the disclosed improvement (at [0034]). That is, the claim (claim 11) includes the components or steps of the invention that provide the improvement described in the specification (at [0034]). The claim (claim 11) itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").
4 guide: to force (a person, object, or animal) to move in a certain path. (Dictionary.com)
5 propagation: the act of propagating, wherein propagate is defined: Computers. to cause (an update or other alteration) to take effect throughout a network of devices. (Dictionary.com)
6 restrict: to confine or keep within limits, as of space, action, choice, intensity, or quantity, wherein action is defined: an exertion of power or force. (Dictionary.com)
7 Mental process: obtain: (tr) to gain possession of; acquire; get, wherein get is defined: to hear, notice, or understand (Dictionary.com)
8 MPEP 2106.04(a) Abstract Ideas [R-07.2022], 3rd & 4th paragraphs:
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. The groupings of abstract ideas, and their relationship to the body of judicial precedent, are further discussed in MPEP § 2106.04(a)(2).
9 MPEP 2111.03 I. COMPRISING, 2nd para 2nd to last S: “the claim transition "comprising" allowed for additional component(s) that were functionally similar to the members”
10BROAD CLAIM LANGUAGE -ing (of “comprising” or any other “-ing” word in claims 1-20): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein likewise is defined: in like manner; in the same way; similarly, wherein like is defined: corresponding or agreeing in general or in some noticeable respect; similar; analogous, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
11 colon: the sign (:) used to mark a major division in a sentence, to indicate that what follows is an elaboration, summation, implication, etc., of what precedes (“comprising”) (Dictionary.com)
12 MEANING & PURPOSE I: memory: Also called storage. Also called computer memory,. Computers. the components of the computer in which such information is stored, wherein information is defined: Computers. important or useful facts obtained as output from a computer by means of processing input data with a program, wherein useful is defined: being of use or service; serving some purpose; advantageous, helpful, or of good effect. (Dictionary.com)
13 “for” is a preposition, wherein preposition is defined: any member of a class of words found in many languages that are used before nouns (“use”), pronouns, or other substantives to form phrases (“for use by a first machine learning model”) functioning as modifiers of verbs (“configured”: for-use-configured or “receive” for-use-receive), nouns (“memories”: --one or more for-use-memories--), or adjectives (“input”: for-use-input data), and that typically express a spatial, temporal, or other relationship, as in, on, by, to, since. (Dictionary.com)
14 MEANING & PURPOSE II (i.e., “for use”) of claim 1 regarding “wherein” clauses (there are no relevant “wherein” clauses in claim 1 for MEANING & PURPOSE II: “for use by a first machine learning model”, wherein use is defined: a way of being employed or used; a purpose for which something is used. (Dictionary.com)
15 application: the act of putting to a special use or purpose. (Dictionary.com)
16 by: in consequence, as a result, or on the basis of. (Dictionary.com)
17 MEANING & PURPOSE III: processor: Computers. a controller, the key component of a computing device that contains the circuitry necessary to interpret and execute electrical signals fed into the device. (Dictionary.com): there are no “wherein” clauses in claim 1 directed to MEANING & PURPOSE III: i.e., “to interpret and execute electrical signals” wherein signal is defined: A fluctuating quantity or impulse whose variations represent information. (Dictionary.com)
18 a: not any particular or certain one of a class or group. (Dictionary.com)
19 radiance: radiant brightness or light, wherein light is defined: Physics. Also called radiant energy. Also called luminous energy. electromagnetic radiation to which the organs of sight react, ranging in wavelength from about 400 to 700 nanometers and propagated at a speed of 186,282 miles per second (299,972 kilometers per second), considered variously as a wave, a stream of particles, or a quantum phenomenon, wherein electromagnetic radiation is defined: radiation consisting of self-sustaining oscillating electric and magnetic fields at right angles to each other and to the direction of propagation. It does not require a supporting medium and travels through empty space at the speed of light See also photon, wherein wavelength is already defined in the above color-footnote. (Dictionary.com)
20 The claim mapping of the first (1) color field is equally applicable the second (2) radiance field
21 color: The sensation produced by the effect of light waves striking the retina of the eye. The color of something depends mainly on which wavelengths of light it emits, reflects, or transmits, wherein wave is defined: Physics. a progressive disturbance propagated from point to point in a medium or space without progress or advance by the points themselves, as in the transmission of sound or light, wherein wavelength is defined: Physics. the distance, measured in the direction of propagation of a wave, between two successive points in the wave that are characterized by the same phase of oscillation. . (Dictioanary.com)
22 identifies: to serve as a means of identification for, wherein for is defined: intended to belong to, or be used in connection with (Dictionary.com)
23 This “wherein” clause is not giving meaning and purpose to either MEANING & PURPOSE I or MEANING & PURPOSE II and thus this wherein clause is not a limitation in claim 1
24 comma: the punctuation mark(,) indicating a slight pause in the spoken sentence and used where there is a listing of items or to separate a nonrestrictive clause or phrase from a main clause (Dictionary.com)
25 This comma phrase does not limit (nonrestrictive) claim 1 under the broadest reasonable interpretation
26 output: Computers. the process of transferring data from internal storage to an external medium, as paper or microfilm, wherein storage is defined: Computers. memory. (Dictionary.com)
27 This “wherein” clause is giving meaning and purpose to said MEANING & PURPOSE I (i.e., “one or more memories”) of claim 1 and thus is a limitation in claim 1
28 to: (used for expressing addition or accompaniment) with. (Dictionary.com)
29 This comma phrase does not limit (nonrestrictive) claim 1 under the broadest reasonable interpretation
30 the: (used, especially before a noun, with a specifying or particularizing effect, as opposed to the indefinite or generalizing force of the indefinite article a oran ) (Dictionary.com)
31 data: a plural of datum, wherein datum is defined: a single piece of information, as a fact, statistic, or code; an item of data. (Dictionary.com)
32 data: a plural of datum, wherein datum is defined: a single piece of information, as a fact, statistic, or code; an item of data. (Dictionary.com)
33 This “wherein” clause “is a limitation in a claim… where the clause gave ‘meaning and purpose to the manipulative steps’ ”via MPEP 2111.04 "Adapted to," "Adapted for," "Wherein," "Whereby," and Contingent Clauses [R-10.2019]
I. "ADAPTED TO," "ADAPTED FOR," "WHEREIN," and "WHEREBY"
Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are:
(A) "adapted to" or "adapted for" clauses;
(B) "wherein" clauses; and
(C) "whereby" clauses.
The determination of whether each of these clauses is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps").
34 Likewise this “wherein” clause is also giving meaning and purpose to said MEANING & PURPOSE I (i.e., claim 1’s “one or more memories”) of claim 1 and thus this “wherein” clause is a limitation in claim 1
35 “is” essentially means look at a figure (Xiong’s figure 2) (Dictionary.com)
36 BROAD CLAIM LANGUAGE: sensor: a mechanical device sensitive to light, temperature, radiation level, or the like, that transmits a signal to a measuring or control instrument. (Dictionary.com)
37 hardware: A computer, its components, and its related equipment. Hardware includes disk drives, integrated circuits, display screens, cables, modems, speakers, and printers. (Dictionary.com)
38 map: Mathematics. function, wherein function is defined: In mathematics, a quantity whose value is determined by the value of some other quantity. For example, “The yield of this field is a function of the amount of fertilizer applied” means that a given amount of fertilizer will yield an amount of whatever crop is growing. (Dictionary.com)
39 physics a measure of field strength or of the energy transmitted by radiation See radiant intensity luminous intensity, wherein radiation is defined: Physics. the process in which energy is emitted as particles or waves, wherein wave is defined: Physics. a progressive disturbance propagated from point to point in a medium or space without progress or advance by the points themselves, as in the transmission of sound or light (Dictionary.com).
40 This comma phrase--, to the at least one corresponding threshold,-- does not limit claim 8 under the broadest reasonable interpretation of claim 8
41 BROAD CLAIM LANGUAGE: basis: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
42 identify: to serve as a means of identification for. (Dictionary.com)
43 BROAD CLAIM LANGUAGE: contextual: of, relating to, or depending on the context, wherein context is defined: the set of circumstances or facts that surround a particular event, situation, etc. Synonyms: climate, milieu, background (Dictionary.com)
44 scene: an area or sphere of activity, current interest, etc.., wherein sphere is defined: the place or environment within which a person or thing exists; a field of activity or operation, wherein environment is defined: the aggregate of surrounding things, conditions, or influences; surroundings; milieu. Synonyms: environs, locale (Dictionary.com)
45 programming: Digital Technology. to write code for (a computer program or application), wherein application is defined: A computer program with an interface, enabling people to use the computer as a tool to accomplish a specific task. Word processing, spreadsheet, and communications software are all examples of applications. (Dictionary.com)
46 computer: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. All computers contain a central processing unit that interprets and executes instructions; input devices, such as a keyboard and a mouse, through which data and commands enter the computer; memory that enables the computer to store programs and data; and output devices, such as printers and display screens, that show the results after the computer has processed data, wherein mouse is defined: Computers. a palm-sized, button-operated pointing device that can be used to move, select, activate, and change items on a computer screen, wherein pointing device is defined: Computers. an input device, as a mouse, stylus, or joystick, used to control movement of a cursor or pointer, wherein cursor is defined: Digital Technology. a movable, sometimes blinking, marker that indicates the position on a display screen where the next character entered from the keyboard will appear, or where user action is possible, wherein pointer is defined: Computers. A. an identifier giving the location in storage of something of interest, as a data item, table, or subroutine. B. a moveable icon in a graphical user interface, as an arrow, that marks the user’s location in the interface relative to areas of the screen where user input is possible. (Dictionary.com)
47 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com)
48 epistemic: of or relating to knowledge or the conditions for acquiring it. (Dictionary.com)
49 epistemic: denoting the branch of modal logic that deals with the formalization of certain epistemological concepts, such as knowledge, certainty, and ignorance. See also doxastic (Dictionary.com)
50 identify: to serve as a means of identification for. (Dictionary.com)
51 epistemic: of or relating to knowledge or the conditions for acquiring it. (Dictionary.com)
52 epistemic: denoting the branch of modal logic that deals with the formalization of certain epistemological concepts, such as knowledge, certainty, and ignorance. See also doxastic (Dictionary.com)
53 epistemic: of or relating to knowledge or the conditions for acquiring it. (Dictionary.com)
54 epistemic: denoting the branch of modal logic that deals with the formalization of certain epistemological concepts, such as knowledge, certainty, and ignorance. See also doxastic (Dictionary.com)
55 learning: the act or process of acquiring knowledge or skill, wherein knowledge is defined: acquaintance with facts, truths, or principles, as from study or investigation; general erudition, wherein study is defined: application of the mind to the acquisition of knowledge, such as by reading, investigation, or reflection. (Dictionary.com)
56 character: the aggregate of features and traits that form the individual nature of some person or thing. (Dictionary.com)
Prosecution Insights