DETAILED 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
Claims 1–19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–18 of IDS cited U.S. Patent No. 11,373,394 B2.
Claim 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of IDS cited U.S. Patent No. 11,373,394 B2 in view of Lakemond (US 2016/0292837 A1).
Claims 1–19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 12,131,564 B2.
Claim 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of U.S. Patent No. 12,131,564 B2 in view of Lakemond (US 2016/0292837 A1).
Claim(s) 19 and 1,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated1 by IDS cited Hamsici et al. (US 2011/0255781 A1):
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Zhao et al. (US 2010/0166323 A1):
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Barlaud et al. (US 2010/0254573 A1):
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Lakemond (US 2016/0292837 A1):
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 limitation(s) is/are:
“the descriptor generation system2 being configured to3:
obtain scale-space data…
the descriptor generation system4 further…configured to5:
determine a set of samples…
generate the feature descriptor”
in claim 1;
“the feature descriptor generator6 is further configured such that7 if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale-space data associated with length scales above and below the determined length scale”
in claim 2.
“the descriptor generation system is configured to identify the location in the image …and
… the descriptor generation system is configured to identify at least one of a location and a length scale in the scale-space data”
in claim 8.
“the feature descriptor generator is configured to sample” in claim 9
“the descriptor generation system is configured to one or more of:
store the determined set …and the feature descriptor generator is configured to generate the feature descriptor…; and
determine a measure of rotation”
in claim 12.
“the descriptor generation system8 being for9 generating the feature descriptor”
in claim 19;
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:
Regarding claim 1’s:
“the descriptor generation system being configured to:
obtain scale-space data…” see filtering acts in applicant’s disclosure page 34: full boxed-in sentence of lines 20-24:
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Regarding claim 1’s:
“the descriptor generation system further…configured to:
determine a set of samples…
generate the feature descriptor” see acts in applicant’s figure 5:
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Regarding claim 2:
“the feature descriptor generator10 is further configured such that11 if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale-space data associated with length scales above and below the determined length scale”
in claim 2, see applicant’s page 40 about probabilistically locating a scale space (fig. 8A: pyramid floors 0-5) with respect to a threshold and interpolative sampling thereof (fig. 8A: in-between pyramid floors 0-5, such as fig. 8A:806 is between pyramid floors) and then generation (fig. 8C:820: spiraling solid-line circles extending to an outer dashed-line ring resulting in the circles of figures 1A,1B: spiraling circles) of a feature descriptor based thereon:
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Regarding claim 8:
“the descriptor generation system is configured to identify the location in the image …and
… the descriptor generation system is configured to identify at least one of a location and a length scale in the scale-space data”
see applicant’s disclosure.
Regarding clam 12:
“the feature descriptor generator is configured to sample” in claim 9
“the descriptor generation system is configured to one or more of:
store the determined set …and the feature descriptor generator is configured to generate the feature descriptor…; and
determine a measure of rotation”
see applicant’s disclosure.
Regarding the claimed “the descriptor generation system being for generating the feature descriptor” in claim 19 see acts of said fig. 5:
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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.
35 USC § 101 – Positive Statement
Due to 35 USC 112(f) being invoked in claims 1 and 19, claims 1 and 19 reflect the disclosed technological (computer) field improvement (page 17, 1st para, line 12: “significant reduction in the processing”) in applicant’s disclosure: fig. 5:
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Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1–19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–18 of IDS cited U.S. Patent No. 11,373,394 B2. Regarding Claims 1–19:
The following table illustrates the correspondence between the claimed limitation of 1–19 of the current application and the claimed limitation of 1–18 of 11,373,394 Patent.
18,926,104 (instant app)
11,373,394 (U.S. Patent)
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system further having a feature descriptor generator implemented in hardware configured to:
determine a set of samples characterising a location in an image by
sampling the
scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples.
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system having a feature descriptor generator
configured to:
determine a set of samples characterising a location in an image by sampling, using a sampling unit, the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples;
wherein the descriptor generation system is configured to place a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale, and the relatively smaller scale-space representation comprises a descriptor pyramid and the relatively larger scale-space representation comprises an image pyramid.
2. The descriptor generation system according to claim 1, in which the feature descriptor generator is further configured such that if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale- space data associated with length scales above and below the determined length scale.
2. A descriptor generation system according to claim 1, in which the feature descriptor generator is further configured such that if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale-space data associated with length scales above and below the determined length scale.
3. The descriptor generation system according to claim 1, in which the pre-filtered representation of the image has been filtered using one or more of a low-pass filter, a Gaussian filter and a box filter.
3. A descriptor generation system according to claim 1, in which the pre-filtered representation of the image has been filtered using one or more of a low-pass filter, a Gaussian filter and a box filter.
4. The descriptor generation system according to claim 1, in which the location in the image is one or more of:
a point in the image;
a pixel location in the image; and
a keypoint in the image.
4. A descriptor generation system according to claim 1, in which the location in the image is one or more of:
a point in the image;
a pixel location in the image; and
a keypoint in the image.
5. The descriptor generation system according to claim 1, in which the feature descriptor generator is configured to sample the scale-space data by:
analysing portions of the scale-space data representing the location at different length scales to determine a measure of likelihood for each analysed portion, the measure of likelihood representing the likelihood of a feature being at the respective length scale;
determining the portion of the scale-space data resulting in a turning point in the measure of likelihood; and
determining a length scale at which to sample the scale-space data to determine the set of samples in dependence on the determined portion.
5. A descriptor generation system according to claim 1, in which the feature descriptor generator is configured to sample the scale-space data by:
analysing portions of the scale-space data representing the location at different length scales to determine a measure of likelihood for each analysed portion, the measure of likelihood representing the likelihood of a feature being at the respective length scale;
determining the portion of the scale-space data resulting in a turning point in the measure of likelihood; and
determining a length scale at which to sample the scale-space data to determine the set of samples in dependence on the determined portion.
Claim 6:
6. The descriptor generation system according to claim 1,
in which the descriptor generation system is further configured to place a relatively smaller scale-space representation within a relatively
larger scale-space representation in dependence on an identified length
scale.
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system having a feature descriptor generator
configured to:
determine a set of samples characterising a location in an image by sampling, using a sampling unit, the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples;
wherein the descriptor generation system is configured to place a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale, and the relatively smaller scale-space representation comprises a descriptor pyramid and the relatively larger scale-space representation comprises an image pyramid.
Claim 7:
7. The descriptor generation system according to claim 6, in which
the relatively larger scale-space representation comprises an image pyramid.
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system having a feature descriptor generator
configured to:
determine a set of samples characterising a location in an image by sampling, using a sampling unit, the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples;
wherein the descriptor generation system is configured to place a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale, and the relatively smaller scale-space representation comprises a descriptor pyramid and the relatively larger scale-space representation comprises an image pyramid.
8. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to identify the location in the image in accordance with one or more location identification or detection algorithms, and in which the descriptor generation system is configured to identify at least one of a location and a length scale in the scale- space data associated with the identified location in the image.
6. A descriptor generation system according to claim 1, in which the descriptor generation system is configured to identify the location in the image in accordance with one or more location identification or detection algorithms, and in which the descriptor generation system is configured to identify at least one of a location and a length scale in the scale-space data associated with the identified location in the image.
9. The descriptor generation system according to claim 8, in which the feature descriptor generator is configured to sample the scale-space data one or more of:
in dependence on the identified length scale;
at a level in the scale-space data associated with the identified length scale; and
by interpolating between data at levels in the scale-space data associated with length scales above and below the identified length scale.
7. A descriptor generation system according to claim 6, in which the feature descriptor generator is configured to sample the scale-space data, wherein the sampling is performed by one or more of:
in dependence on the identified length scale;
at a level in the scale-space data associated with the identified length scale; and
by interpolating between data at levels in the scale-space data associated with length scales above and below the identified length scale.
10. The descriptor generation system according to claim 1, in which the data in the scale- space data having been filtered at different length scales corresponds to filtered samples to be extracted in respect of different radial distances in the descriptor pattern from the centre of the descriptor pattern.
8. A descriptor generation system according to claim 1, in which the
data in the scale-space data having been filtered at different length scales corresponds to filtered samples to be extracted in respect of different radial distances in the descriptor pattern from the centre of the descriptor pattern.
11. The descriptor generation system according to claim 1, in which the descriptor pattern comprises at least one ring surrounding the location in the image, in which the at least one ring is one of a circle, a wavy circle and a polygon.
9. A descriptor generation system according to claim 1, in which the descriptor pattern comprises at least one ring surrounding the location in the image, in which the at least one ring is one of a circle, a wavy circle and a polygon.
12. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to one or more of:
store the determined set of samples in an array, and the feature descriptor generator is configured to generate the feature descriptor in dependence on the determined set of samples by forming a modified array; and
determine a measure of rotation for the location in the image, the measure of rotation describing an angle between an orientation of the image and a characteristic direction of the image at the location, and generate the feature descriptor in dependence on the determined measure of rotation.
10. A descriptor generation system according to claim 1, in which the descriptor generation system is configured to:
store the determined set of samples in an array, and the feature descriptor generator is configured to generate the feature descriptor in dependence on the determined set of samples by forming a modified array; and/or
determine a measure of rotation for the location in the image, the measure of rotation describing an angle between an orientation of the image and a characteristic direction of the image at the location, and generate the feature descriptor in dependence on the determined measure of rotation.
13. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to form the modified array by one or more of:
shifting elements of at least one portion of the array along a number of positions in the respective portion of the array, the number of positions being determined in dependence on the determined measure of rotation; and
interpolating between two or more samples of the determined set of samples.
12. A descriptor generation system according to claim 10, in which the descriptor generation system is configured to form the modified array by one or more of:
shifting elements of at least one portion of the array along a number of positions in the respective portion of the array, the number of positions being determined in dependence on the determined measure of rotation; and
interpolating between two or more samples of the determined set of samples.
14. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples have been obtained from one or more ring of the descriptor pattern.
13. A descriptor generation system according to claim 12, in which
the two or more samples of the determined set of samples have been obtained from one or more ring of the descriptor pattern.
15. The descriptor generation system according to claim 14, in which the descriptor generation system is configured to form the modified array by interpolating between the two or more samples of the determined set of samples along a portion of the shape of the ring to which the two or more samples correspond.
16. A descriptor generation system according to claim 13, in which the descriptor generation system is configured to form the modified array by interpolating between the two or more samples of the determined set of samples along a portion of the shape of the ring to which the two or more samples correspond.
16. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples between which interpolation is performed have been obtained from adjacent rings in the descriptor pattern.
14. A descriptor generation
system according to claim 12, in which the two or more samples of the determined set of samples between which interpolation is performed have been obtained from adjacent rings in the descriptor pattern.
17. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples comprise N1 samples from a first ring and N2samples from a second ring, where N1s N2, in which the first ring is radially inside the second ring.
15. A descriptor generation system according to claim 12, in which the two or more samples of the determined set of samples comprise N.sub.1 samples from a first ring and N.sub.2 samples from a second ring, where N.sub.1≤N.sub.2, in which the first ring is radially inside the second ring.
18. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to discard the modified array once the feature descriptor has been generated.
11. A descriptor generation system according to claim 10, in which the descriptor generation system is configured to discard the modified array once the feature descriptor has been generated.
19. A method of generating a feature descriptor in a descriptor generation system, the descriptor generation system having a feature descriptor generator implemented in hardware, the descriptor generation system being for generating the feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising:
obtaining scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
at the feature descriptor generator:
determining a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generating the feature descriptor in dependence on the determined set of samples.
17. A computer-implemented method for generating a feature descriptor
for a location in an image for use in performing descriptor matching in analysing the image, the method comprising:
obtaining scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
determining a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image;
generating the feature descriptor in dependence on the determined set of samples; and
placing a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale, and the relatively smaller scale-space representation comprises a descriptor pyramid and the relatively larger scale-space representation comprises an image pyramid.
20. A non-transitory computer readable storage medium having stored thereon a computer readable dataset description of a descriptor generation system as set forth in claim 1 that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the descriptor generation system.
No corresponding claim:
see below double patenting rejection of claim 20 in view of Lakemond (US 2016/0292837 A1).
Table 1
The table (Table 1) above shows that independent claims 1,9,19 of this Application is not identical to the claims of U.S. Patent No. 11,373,394 B2. However, the claims are not patentably distinct. The U.S. Patent No. 11,373,394 B2 is narrower than independent claims 1, 9,19 since it includes several additional limitations not found in claim 1, 9,19 of the instant Application.
Claim 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of IDS cited U.S. Patent No. 11,373,394 B2 in view of Lakemond (US 2016/0292837 A1).
Regarding claim 20, U.S. Patent No. 11,373,394 B2 does not teach claim 20.
Lakemond teaches claim 20 as shown in the below 35 USC 103 rejection of claim 20. Thus one of skill in the art can make U.S. Patent No. 11,373,394 B2’s be as Lakemond’s for the same reasons as in the 35 USC 103 rejection via said similar Supreme court steps in the 35 USC 103 rejection of claim 20.
Claims 1–19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 12,131,564 B2. Regarding Claims 1–19:
The following table illustrates the correspondence between the claimed limitation of 1–20 of the current application and the claimed limitation of 1–20 of 12,131,564 Patent.
18,926,104 (instant app)
12,131,564 (U.S. Patent)
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system further having a feature descriptor generator implemented in hardware configured to:
determine a set of samples characterising a location in an image by
sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples.
1. A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system having a feature descriptor generator executed on a processor configured to:
determine a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples.
2. The descriptor generation system according to claim 1, in which the feature descriptor generator is further configured such that if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale- space data associated with length scales above and below the determined length scale.
2. The descriptor generation system according to claim 1, in which the feature descriptor generator is further configured such that if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale-space data associated with length scales above and below the determined length scale.
3. The descriptor generation system according to claim 1, in which the pre-filtered representation of the image has been filtered using one or more of a low-pass filter, a Gaussian filter and a box filter.
3. The descriptor generation system according to claim 1, in which the pre-filtered representation of the image has been filtered using one or more of a low-pass filter, a Gaussian filter and a box filter.
4. The descriptor generation system according to claim 1, in which the location in the image is one or more of:
a point in the image;
a pixel location in the image; and
a keypoint in the image.
4. The descriptor generation system according to claim 1, in which the location in the image is one or more of:
a point in the image;
a pixel location in the image; and
a keypoint in the image.
5. The descriptor generation system according to claim 1, in which the feature descriptor generator is configured to sample the scale-space data by:
analysing portions of the scale-space data representing the location at different length scales to determine a measure of likelihood for each analysed portion, the measure of likelihood representing the likelihood of a feature being at the respective length scale;
determining the portion of the scale-space data resulting in a turning point in the measure of likelihood; and
determining a length scale at which to sample the scale-space data to determine the set of samples in dependence on the determined portion.
5. The descriptor generation system according to claim 1, in which the feature descriptor generator is configured to sample the scale-space data by:
analysing portions of the scale-space data representing the location at different length scales to determine a measure of likelihood for each analysed portion, the measure of likelihood representing the likelihood of a feature being at the respective length scale;
determining the portion of the scale-space data resulting in a turning point in the measure of likelihood;
determining a length scale at which to sample the scale-space data to determine the set of samples in dependence on the determined portion.
6. The descriptor generation system according to claim 1, in which the descriptor generation system is further
configured to place a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale.
6. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to place a relatively smaller scale-space representation within a relatively larger scale-space representation in dependence on an identified length scale.
7. The descriptor generation system according to claim 6, in which the relatively larger scale-space representation comprises an image pyramid.
7. The descriptor generation system according to claim 6, in which the relatively larger scale-space representation comprises an image pyramid.
8. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to identify the location in the image in accordance with one or more location identification or detection algorithms, and in which the descriptor generation system is configured to identify at least one of a location and a length scale in the scale- space data associated with the identified location in the image.
8. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to identify the location in the image in accordance with one or more location identification or detection algorithms, and in which the descriptor generation system is configured to identify at least one of a location and a length scale in the scale-space data associated with the identified location in the image.
9. The descriptor generation system according to claim 8, in which the feature descriptor generator is configured to sample the scale-space data one or more of:
in dependence on the identified length scale;
at a level in the scale-space data associated with the identified length scale; and
by interpolating between data at levels in the scale-space data associated with length scales above and below the identified length scale.
9. The descriptor generation system according to claim 8, in which the feature descriptor generator is configured to sample the scale-space data one or more of:
in dependence on the identified length scale;
at a level in the scale-space data associated with the identified length scale; and
by interpolating between data at levels in the scale-space data associated with length scales above and below the identified length scale.
10. The descriptor generation system according to claim 1, in which the data in the scale- space data having been filtered at different length scales corresponds to filtered samples to be extracted in respect of different radial distances in the descriptor pattern from the centre of the descriptor pattern.
10. The descriptor generation system according to claim 1, in which the data in the scale-space data having been filtered at different length scales corresponds to filtered samples to be extracted in respect of different radial distances in the descriptor pattern from the centre of the descriptor pattern.
11. The descriptor generation system according to claim 1, in which the descriptor pattern comprises at least one ring surrounding the location in the image, in which the at least one ring is one of a circle, a wavy circle and a polygon.
11. The descriptor generation system according to claim 1, in which the descriptor pattern comprises at least one ring surrounding the location in the image, in which the at least one ring is one of a circle, a wavy circle and a polygon.
12. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to one or more of:
store the determined set of samples in an array, and the feature descriptor generator is configured to generate the feature descriptor in dependence on the determined set of samples by forming a modified array; and
determine a measure of rotation for the location in the image, the measure of rotation describing an angle between an orientation of the image and a characteristic direction of the image at the location, and generate the feature descriptor in dependence on the determined measure of rotation.
12. The descriptor generation system according to claim 1, in which the descriptor generation system is configured to one or more of:
store the determined set of samples in an array, and the feature descriptor generator is configured to generate the feature descriptor in dependence on the determined set of samples by forming a modified array; and
determine a measure of rotation for the location in the image, the measure of rotation describing an angle between an orientation of the image and a characteristic direction of the image at the location, and generate the feature descriptor in dependence on the determined measure of rotation.
13. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to form the modified array by one or more of:
shifting elements of at least one portion of the array along a number of positions in the respective portion of the array, the number of positions being determined in dependence on the determined measure of rotation; and
interpolating between two or more samples of the determined set of samples.
13. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to form the modified array by one or more of:
shifting elements of at least one portion of the array along a number of positions in the respective portion of the array, the number of positions being determined in dependence on the determined measure of rotation; and
interpolating between two or more samples of the determined set of samples.
14. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples have been obtained from one or more ring of the descriptor pattern.
14. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples have been obtained from one or more ring of the descriptor pattern.
15. The descriptor generation system according to claim 14, in which the descriptor generation system is configured to form the modified array by interpolating between the two or more samples of the determined set of samples along a portion of the shape of the ring to which the two or more samples correspond.
15. The descriptor generation system according to claim 14, in which the descriptor generation system is configured to form the modified array by interpolating between the two or more samples of the determined set of samples along a portion of the shape of the ring to which the two or more samples correspond.
16. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples between which interpolation is performed have been obtained from adjacent rings in the descriptor pattern.
16. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples between which interpolation is performed have been obtained from adjacent rings in the descriptor pattern.
17. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples comprise N1 samples from a first ring and N2samples from a second ring, where N1s N2, in which the first ring is radially inside the second ring.
17. The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples comprise N.sub.1 samples from a first ring and N.sub.2 samples from a second ring, where N.sub.1≤N.sub.2, in which the first ring is radially inside the second ring.
18. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to discard the modified array once the feature descriptor has been generated.
18. The descriptor generation system according to claim 12, in which the descriptor generation system is configured to discard the modified array once the feature descriptor has been generated.
19. A method of generating a feature descriptor in a descriptor generation system, the descriptor generation system having a feature descriptor generator implemented in hardware, the descriptor generation system being for generating the feature descriptor for a location in an image for use in performing descriptor matching in analysing the image,
the method comprising:
obtaining scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
at the feature descriptor generator:
determining a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generating the feature descriptor in dependence on the determined set of samples.
19. A computer-implemented method for generating a feature descriptor
for a location in an image for use in performing descriptor matching in analysing the image, the method performed by one or more processors executing code that causes the one or more processors to perform the method comprising:
obtaining scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
determining a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generating the feature descriptor in dependence on the determined set of samples.
20. A non-transitory computer readable storage medium having stored thereon a computer readable dataset description of a descriptor generation system as set forth in claim 1 that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the descriptor generation system.
20. A non-transitory computer readable storage medium having stored thereon computer readable instructions that when executed at a computer system cause the computer system to:
obtain scale-space data representative of the image, said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales;
determine a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of a feature descriptor, wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples.
Table 1
The table (Table 1) above shows that independent claims 1, 9,19 of this Application is not identical to the claims of U.S. Patent No. 12,131,564 B2. However, the claims are not patentably distinct. The U.S. Patent No. 12,131,564 B2 is narrower than independent claims 1, 9,19 since it includes several additional limitations not found in claim 1, 9,19 of the instant Application.
Claim 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of U.S. Patent No. 12,131,564 B2 in view of Lakemond (US 2016/0292837 A1).
Regarding claim 20, U.S. Patent No. 12,131,564 B2 does not teach claim 20.
Lakemond teaches claim 20 as shown in the below 35 USC 103 rejection of claim 20. Thus one of skill in the art can make U.S. Patent No. 12,131,564 B2’s be as Lakemond’s for the same reasons as in the 35 USC 103 rejection via said similar Supreme court steps in the 35 USC 103 rejection of claim 20.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 19 and 1,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated12 by IDS cited Hamsici et al. (US 2011/0255781 A1):
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Re 19., Hamsici discloses A method of generating a feature descriptor in a descriptor generation system, the descriptor generation system having a feature descriptor generator implemented in hardware, the descriptor generation system being for generating the feature descriptor13 (see Hamsici’s fig. 12, reproduced below, regarding 35 USC 112(f)) for a location in an image 14, the method (likewise) comprising:
obtaining scale-space data representative of the image (or likewise “[0038] In an image processing stage 104, the captured image 108 is then processed by generating a corresponding scale space 120 (e.g., Gaussian scale space)”), said scale-space data comprising a pre-filtered representation of the image at a plurality of length scales (or likewise “ [0049] FIG. 4 illustrates the generation of an orientation map along the x-orientation. An x-filter [-1 0 1] is applied along the x-orientation to pixel gradient values .beta..sub.ij of a scale space level 204 to generate a corresponding image derivative 221 having a plurality of values .delta..sup.x.sub.ij.” via figs. 1 & 4:
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at the feature descriptor generator (or likewise “a local feature descriptor generator over scale space 1328” [0103] 2nd S via fig. 13:1328
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determining a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern {for generating15 the feature descriptor}16 of the feature descriptor17 (or likewise, via fig. 12:1204: [0093] 1st S & [0094] 2nd & last Ss: “for generating a local feature descriptor…The point may be a sample point from … points sampled from a contour of a shape18” of a face driving a race car:
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wherein the descriptor pattern (of fig. 12:1204) is used to determine length scales (or likewise a “derivative”19-“scale”-“scale-space” [0010] 8th S: fig. 12:1208: “image derivative”) at which the samples are to be sampled from the pre-filtered representation of the image (via fig. 12:
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generating20 the feature descriptor in dependence on the determined set of samples (this last limitation is been understood in the previous limitations of claim 19).
Claim 1 rejected like claim 19:
Re 1., Hamsici discloses A descriptor generation system for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the descriptor generation system being configured to:
obtain scale-space data (see Hamsici’s fig. 4: “X-Filter” & fig. 5: “Y-Filter” & fig. 6:602: “Steerable Filter” regarding 35 USC 112(f)) representative of the image, said scale-space data comprising21 (i.e., to be equal to) a pre-filtered representation of the image at a plurality of length scales;
the descriptor generation system further having a feature descriptor generator implemented in hardware configured to:
determine a set of samples characterising a location in an image by sampling the scale-space data representative of the image in accordance with a descriptor pattern of the feature descriptor,
wherein the descriptor pattern is used to determine length scales at which the samples are to be sampled from the pre-filtered representation of the image; and
generate the feature descriptor in dependence on the determined set of samples.
Re 3., Hamsici discloses The descriptor generation system according to claim 1, in which22 the pre-filtered23 representation (equal to said “said scale-space data” in claim 1) of the image has been filtered using one or more of a low-pass filter, a Gaussian filter (or likewise “a filter (e.g., a Gaussian filter) to obtain a smoothed (convolved) orientation map I.sub.o.sup..SIGMA..” [0055]) and a box filter (via fig. 1:121: “Orientation Map Generation”:
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Re 4., Hamsici discloses The descriptor generation system according to claim 1, in which the location in the image is
a point24 in the image (or likewise ”an identified point 702 (e.g., keypoint, sample point, pixel, etc.) in the scale space 204” [0058] 1st S via fig. 7:702:
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26.
Re 6., Hamsici discloses The descriptor generation system according to claim 1, in which the descriptor generation system is further configured 27.
Re 7., Hamsici discloses The descriptor generation system according to claim 6, 28.
Re 8., Hamsici discloses The descriptor generation system according to claim 1,
in which the descriptor generation system is configured to identify the location in the image in accordance with one or more location identification or detection algorithms (or likewise “algorithms…identifying…points of interest in an image…For instance, local image computations may be performed…to locate the points of interest” via [0005][0006]:
[0005] Various applications may benefit from having a machine or processor that is capable of identifying objects in a visual representation (e.g., an image or picture). The field of computer vision attempts to provide techniques and/or algorithms that permit identifying objects or features in an image, where an object or feature may be characterized by descriptors identifying one or more points (e.g., all pixel points, keypoints of interest, etc.). These techniques and/or algorithms are often also applied to face recognition, object detection, image matching, 3-dimensional structure construction, stereo correspondence, and/or motion tracking, among other applications. Generally, object or feature recognition may involve identifying points of interest in an image for the purpose of feature identification, image retrieval, and/or object recognition. Preferably, the points may be selected and/or processed such that they are invariant to image scale changes and/or rotation and provide robust matching across a substantial range of distortions, changes in point of view, and/or noise and changes in illumination. Further, in order to be well suited for tasks such as image retrieval and object recognition, the feature descriptors may preferably be distinctive in the sense that a single feature can be correctly matched with high probability against a large database of features from a plurality of target images.
[0006] For instance, local image computations may be performed using a Gaussian Pyramid to locate the points of interest. A number of computer vision algorithms, such as SIFT (scale invariant feature transform), are used to compute such points and then proceed to extract localized features around them as an initial step towards detection of particular objects in a scene or classifying a queried object based on it features.)
, and
in which the descriptor generation system is configured to identify
Re 9., Hamsici discloses The descriptor generation system according to claim 8,
in which the feature descriptor generator is configured to sample the scale-space data
by interpolating2930 between data at levels in the scale-space data associated with length scales above and below the identified length scale (or likewise “Daisy descriptors31 overcome this problem by replacing the linear interpolations with smoothing (e.g., Gaussian smoothing) of the oriented derivatives.” [0041] 4th S via fig. 2: right-side: “Smoothed Orientation Maps” replacing interpolation:
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Re 10., Hamsici discloses The descriptor generation system according to claim 1,
in which the data in the scale-space data having been filtered at different length scales corresponds to filtered samples to be extracted (or likewise “extract the Daisy descriptors from multiple scale space levels of a scale space pyramid”, [0063] last S, via the Daisy descriptor of fig. 7) in respect of different radial distances in the descriptor pattern from the centre of the descriptor pattern (via the Daisy descriptor of fig. 7:
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Re 11., Hamsici discloses The descriptor generation system according to claim 1,
in which the (face-shape) descriptor pattern comprises at least one ring (or likewise the circles in fig. 7) surrounding the location in the image,
in which the at least one ring is one of a circle, a wavy circle and a polygon.
Re 12., Hamsici discloses The descriptor generation system according to claim 1, in which the descriptor generation system is configured to
3233
determine a measure of rotation (or likewise “ for an angle .phi., the corresponding image derivative value may be obtained as:
.delta..sub.ij.sup.z=.delta..sub.ij.sup.x Cos(.phi.)+.delta..sub.ij.sup.y Sin(.phi.)” [0053] penult S) for the location in the image, the measure of rotation describing an angle between an orientation of the image 3435
Re 13., Hamsici discloses The descriptor generation system according to claim 12, in which the descriptor generation system is configured
36
interpolating between two or more sample37
(or likewise “Daisy descriptors38 overcome this problem by replacing the linear interpolations with smoothing (e.g., Gaussian smoothing) of the oriented derivatives.” [0041] 4th S via fig. 2: right-side: “Smoothed Orientation Maps” replacing interpolation:
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Re 14., Hamsici discloses The descriptor generation system according to claim 13, in which
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Re 15., Hamsici discloses The descriptor generation system according to claim 14, in which the descriptor generation system is configured
Re 16., Hamsici discloses The descriptor generation system according to claim 13, in which the two or more samples of the determined set of samples between which interpolation is performed have been obtained from adjacent rings (or likewise fig. 10: “Smoothed Orientation Maps” replacing interpolation and touching circles) in the descriptor pattern (via fig. 10:
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Re 17., Hamsici discloses The descriptor generation system according to claim 13,
in which 39 the determined set of samples comprise N1 samples from a first ring and N2 samples from a second ring,
where N1 < N2, in which the first ring is radially inside the second ring (via fig. 9:
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Re 18., Hamsici discloses The descriptor generation system according to claim 12, in which the descriptor generation system is configured 40 once the feature descriptor has been generated (via said fig. 12:
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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) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Zhao et al. (US 2010/0166323 A1):
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Re 2., Hamsici teaches The descriptor generation system according to claim 1, in which the feature descriptor generator is further configured such that if a determined length scale does not match one of the length scales of the pre-filtered representation of the image then said sampling the scale-space data comprises interpolating between data at levels in the scale-space data associated with length scales above and below the determined length scale.
Hamsici does not teach the difference of claim 2 of:
applicant’s disclosure, page 40, 1st para, of “comparison…likelihood” under 35 USC 112(f);
(a determined length scale)41 does not match (one of the length scales of the pre-filtered representation of the image) then (said sampling the scale-space data comprises) interpolating between data at levels.
Zhao teach the difference of claim 2 of:
applicant’s disclosure, page 40, 1st para, of “comparison…likelihood” under 35 USC 112(f) (or likewise diamond-decision “Y” or “N” confidence “S” boxes: 262,264 of fig. 2F:
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(a determined length scale)42 does not match (one of the length scales of the pre-filtered representation of the image) (or said likewise diamond-decision “Y” or “N” confidence “S” boxes: 262,264 of fig. 2F: “Matching Integration”) then (said sampling the scale-space data comprises) interpolating (fig. 2F:266: “Geometry Interpolation”) between data at levels (via fig. 2B: “Level”:
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Since Hamsici teaches “match” image with “interpolation” with “demanding” “problem” thereof:
[0041] A Daisy descriptor is defined to find the correspondence between two viewpoints of an object. Since every pixel correspondence in two images is desired for a match, an efficient way to achieve this is to define one or more descriptors for the images which can then be compared. Traditional descriptors such as Scale-Invariant Feature Transform (SIFT) and Gradient Location and Orientation Histogram (GLOH) build their descriptors by first taking an oriented derivative of the image and then representing the oriented derivative in a specified spatial region with an orientation histogram. This procedure is computationally demanding because it requires calculating tri-linear interpolations (i.e., two for spatial and one for orientation) for every pixel gradient of the corresponding histogram bins. Daisy descriptors overcome this problem by replacing the linear interpolations with smoothing (e.g., Gaussian smoothing) of the oriented derivatives. Furthermore, the spatial binning layout used to generate a Daisy descriptor (i.e., larger bins moving out from the point over multiple levels of smoothed orientation maps) allows more robustness to scale, location, and orientation changes.
one of skill in the art could or would have done is refer to others as the solution and thus make Hamsici’s be as Zhao’s seeing in the change that ”the use of feature matching in response to region matching can improve reliability of the matched points of interest”, Zhao [0013] 7th S, via explicit, creative, routine, inferential Supreme court steps A,B,C.D:
A) obtain a computer;
A1) create a computer program based on Hamsici’s fig. 1:
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B) create a computer program based on Zhao’s fig. 2A:
B1) the Laplacian Pyramid 220 and Feature Matching 250 is redundant and is not needed in the program:
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C) create code connecting both computer programs:
C1) see below figures 1 and 2A as a guide for the code connections/callings:
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D) run programs;
D) see what happens (I foresee: the use of feature matching in response to region matching can improve reliability of the matched points of interest).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Barlaud et al. (US 2010/0254573 A1):
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Re 5., Hamisci teaches The descriptor generation system according to claim 1, in which the feature descriptor generator is configured to sample the scale-space data by:
analysing43 portions of the scale-space data representing the location at different length scales (or likewise “identify some or all points44 or45 features at each scale space for the image” [0092] 3rd S) to determine a measure (or likewise “the non-negative values of a corresponding oriented image derivative” [0034] 3rd S) of likelihood for each analysed portion, the measure of likelihood representing the likelihood of a feature being at the respective length scale (via fig 1:122: “Feature/Point Detection”:
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determining the portion of the scale-space data resulting in (or said likewise “the positive components of the oriented image derivatives” [0047] penult S) a turning point in the measure of likelihood; and
determining a length scale (or said likewise a “derivative”46-“scale”-“scale-space” [0010] 8th S: fig. 12:1208: “image derivative”) at which to sample the scale-space data to determine the set of samples in dependence on the determined portion.
Hamisci does not teach the difference of claim 5 of:
likelihood (for each analysed portion, the measure of) likelihood representing the likelihood of (a feature) being at (the respective length scale)…
a turning (point in the measure of) likelihood.
Barlaud teach the difference of claim 5 of:
likelihood (for each analysed portion, the measure of)47 likelihood representing the likelihood of (a feature) being at (the respective length scale) (or likewise “a single probability density function which describes the local features of the object through all the scales” [0103] last S, via fig. 3:12: “Computation of the cross-entropy”:
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a turning48 (point4950 in the measure of) likelihood (or likewise a probability- similarity surpassing degree via “the similarities computed at each stage 9 are compared and ranked from the lowest51 to the highest52” [0109]).
Since Hamsici suggests alternatives for acquiring a query image via an example of using a digital sensor:
[0037] FIG. 1 is a block diagram illustrating the functional stages for performing object recognition on a queried image by using efficiently generated Daisy descriptors. At an image capture stage 102, a query image 108 may be captured or otherwise obtained. For example, the query image 108 may be captured by an image capturing device, which may include one or more image sensors and/or an analog-to-digital converter, to obtain a digital captured image. The image sensors (e.g., charge coupled devices (CCD), complementary metal semiconductors (CMOS)) may convert light into electrons. The electrons may form an analog signal that is then converted into digital values by the analog-to-digital converter. In this manner, the image 108 may be captured in a digital format that may define the image I(x, y), for example, as a plurality of pixels with corresponding color, illumination, and/or other characteristics.
one of skill in the art of image taking could or would have referred to others as an alternative for the query images and thus make Hansici’s be as Barlund’s seeing in the change good via Barlund’s [0122][0123]:
[0122] Several practical applications rely on good image categorization and can be developed using this technology. A first example is the automatic recognition of objects at the cash register in a supermarket or shop. The issue is to build an automatic cash register (without bar code) that simply recognizes the item shown and that will replace the human cashier. The task will be made possible via an image categorization algorithm that learns to recognize each item in the shop by analyzing a set of images of this item taken from different angles.
[0123] Another practical application of image categorization is the design of systems that help disabled people by analyzing the indoor and outdoor scenes and describing their main components.
via explicit, creative, routine, inferential Supreme court steps, A,B,C:
A) obtain computer;
A1) create a computer program based on Hamsici’s fig. 1:
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A2) create a computer program based on Barlund’s fig. 8:
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A3) create code connecting programs;
A3.1) use below illustration for the connections/module calling:
A3.11) boxes with an “X” are redundant and not needed in the programs:
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B) run program;
C) see what happens (I foresee:
[0122] Several practical applications rely on good image categorization and can be developed using this technology. A first example is the automatic recognition of objects at the cash register in a supermarket or shop. The issue is to build an automatic cash register (without bar code) that simply recognizes the item shown and that will replace the human cashier. The task will be made possible via an image categorization algorithm that learns to recognize each item in the shop by analyzing a set of images of this item taken from different angles.
[0123] Another practical application of image categorization is the design of systems that help disabled people by analyzing the indoor and outdoor scenes and describing their main components.).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Hamsici et al. (US 2011/0255781 A1) in view of Lakemond (US 2016/0292837 A1):
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Re 20., Hamsici teaches A non-transitory computer readable storage medium (or likewise “ non-transitory mediums” [0107] 2nd S) having stored thereon a computer readable dataset description of a descriptor generation system as set forth in claim 1 that, when processed in an integrated circuit (or likewise “an application specific integrated circuit (ASIC)” [0109] 1st S) system (or likewise “the overall system” [0111] last S or “different systems” [0112[ 1st S), causes the integrated circuit system to manufacture an integrated circuit (or said likewise “an application specific integrated circuit (ASIC)” [0109] 1st S) embodying the descriptor generation system.
Hamsici does not teach the difference of claim 20 of:
a…system, causes the…system to manufacture.
Lakemond teach the difference of claim 20 of:
a…system, causes the…system to manufacture (or likewise “cause a…system to…manufacture” via Lakemond’s published claim 20:
20. A non-transitory computer readable storage medium having stored thereon a computer readable dataset description of an integrated circuit that when processed, causes a layout processing system to generate a circuit layout description used to manufacture an image processing system, comprising: gradient determining logic configured to determine image gradient indications for at least one image; filter cost determining logic configured to determine filter costs for image regions based on the determined image gradient indications for the at least one image; and processing logic configured to process data relating to the at least one image, the processing logic comprising filtering logic configured to apply a filtering operation using the determined filter costs for the image regions, so that the processing logic is configured to perform processing for image regions in dependence upon image gradients of the at least one image.
Since Hamsici suggests changing a circuit for image processing in a mobile device:
[0091] The processing circuit 1102 may be adapted53 to process an image and generate one or more descriptors identifying the image and/or features within the image. For this purpose, the processing circuit 1102 may also include or implement a scale space generation circuit 1110, a feature/point detection circuit 1114, an image derivative generation circuit 1121, an orientation map generation circuit 1112, an orientation map smoothing circuit 1113, and/or a descriptor generation over scale space circuit 1116. The processing circuit 1102 may implement one or more features and/or methods described in FIGS. 8-10 and 12. In one example, the processing circuit 1102 may simply implement the operations in stored in the various modules in the storage device. In another example, each of the circuits within the processing circuit 1102 may be adapted to carry out the operations in a corresponding modules stored within the storage device 1108.
[0101] FIG. 13 is a block diagram illustrating an exemplary mobile device adapted to perform image processing for purposes of image or object recognition using a local descriptor generated over multiple levels of a scale space. The mobile device 1300 may include a processing circuit 1302 coupled to an image capture device 1304, a wireless communication interface 1310 and a storage device 1308. The image capture device 1304 (e.g., digital camera) may be adapted to capture a query image 1306 of interest and provide it to the processing circuit 1302.
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one of skill in the art of circuits could or would have done is refer to others for adjusting the circuit 1302 to process an image and thus make Hamsici’s be as Lakemonds seeing in the change a manipulation of an image “to suit one’s purpose or advantage” via Lakemond [0053]:
[0053] In step S314, the filter cost determining logic 210 determines filter costs for image regions based on the determined image gradient indications for the short and long exposure images I.sub.S and I.sub.L. The “filter costs” of the image regions are weightings54 for use in applying filtering to the image regions. That is, the filter cost for an image region is a measure of the effect that the image region has in terms of an attribute that is filtered. For example, the attribute may be “closeness” in terms of whether two image regions are connectable. In this case, the filter costs of image regions can be used by a connectivity filter to define the closeness of two image regions based on a sum of the filter costs along a path between the two image regions. Since the filter cost of an image region depends on image gradients at the image region, the closeness of two image regions depends upon the image gradients along the path. In another example, the attribute may be “smoothness”, such that the filter costs, defining the smoothness of image regions, can be used by a blending filter to determine the rate at which blending masks vary across the image regions.
via explicit, creative, routine, inferential Supreme court steps A,B,C,D:
A) Create a circuit:
A1) obtain layout information (i.e., “definitions of circuit elements and data defining rules for combining those circuit elements” Lakemond [0100] last S) of Hamsici’s figure 13: 1318: “Image Processing Circuit”;
A2) obtain “software which ‘describes’ or defines the configuration of hardware that implements a module, functionality, component, block, unit or logic described above, such as HDL (hardware description language) software, as is used for designing integrated circuits, or for configuring programmable chips, to carry out desired functions” (Lakemond [0100] 1st S)
A2.1) include the manipulative image filtering function of Lakemond’s fig. 2:
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A3) run the designing integrated circuits software
A4) combine both circuit diagrams:
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A4) make the circuit itself based on combined circuit diagrams:
A4.1) goto store buy electronic circuits stuff:
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B) put the bult circuit inside the Mobile device
B1) route a new query image input line:
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C) see what happens (I foresee a manipulation of a race-car query image “to suit one’s purpose or advantage”)
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
IDS cited Krig (Computer Vision Metrics: Survey, Taxonomy, and Analysis)
Krig teaches “feature descriptor” & “descriptor pattern” via page 154, 3rd para:
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--A feature descriptor may be designed by using one or more shapes and patterns together. For example, the hypothetical descriptor pattern in Figure 4-8 (left image) uses one pattern for pixels close to the interest point, another pattern uses pixels farther away from the center to capture circular pattern information, and another pattern covers a few extrema points. An excellent example of tuned sampling patterns is the FREAK descriptor, discussed next—
as the closest to the claimed “a descriptor pattern of the feature descriptor” of claim 1.
IDS cited Roy et al. (FWLBP: A Scale Invariant Descriptor for Texture Classification)
Roy teaches “a new descriptor…Pattern” via page ii, lcol, 2nd para shown in fig. 4:
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In this paper we approach the quantization problem by proposing a new descriptor called Fractal Weighted Local Binary Pattern (FWLBP) based on a commonly used method of combining fractal dimension proposed by Chaudhuri and Sarkar [27]. This method is based on differential box counting (DBC) algorithm [28]. Where instead of directly measuring a texture surface, the measures at different scales are obtained by means of counting the number of boxes of different size, which can cover the whole surface.
as the closest to the claimed “a descriptor pattern” of claim 1.
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
1MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
2 GENERIC WORD (thing): system: an assemblage or combination of things or parts forming a complex or unitary whole. (Dictionary.com)
3 “configured to” is a transition word
4 GENERIC WORD (thing: system: any assemblage or set of correlated members, wherein assemblage is defined: a group of persons or things gathered or collected; an assembly; collection; aggregate. (Dictionary.com)
5 “configured to” is a transition word
6 GENERIC WORD (thing): generator: a person or thing that generates. (Dictionary.com)
7 “configured such that” is a transition word
8 GENERIC WORD (thing): system: an ordered and comprehensive assemblage of facts, principles, doctrines, or the like in a particular field of knowledge or thought, wherein assemblage is defined: a group of persons or things gathered or collected; an assembly; collection; aggregate. (Dictionary.com)
9 “being for” is a transition word
10 GENERIC WORD (thing): generator: a person or thing that generates. (Dictionary.com)
11 “configured such that” is a transition word
12MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
13 MPEP 2111.02 Effect of Preamble [R-07.2022]
II. PREAMBLE STATEMENTS RECITING PURPOSE OR INTENDED USE, 2nd para:
During examination, statements in the preamble reciting the purpose or intended use (“for generating the feature descriptor”) of the claimed invention must be evaluated to determine whether or not the recited purpose or intended use results in a structural difference (or, in the case of process claims, manipulative difference) between the claimed invention and the prior art. If so, the recitation serves to limit the claim. See, e.g., In re Otto, 312 F.2d 937, 938, 136 USPQ 458, 459 (CCPA 1963) (The claims were directed to a core member for hair curlers and a process of making a core member for hair curlers. The court held that the intended use of hair curling was of no significance to the structure and process of making.); In re Sinex, 309 F.2d 488, 492, 135 USPQ 302, 305 (CCPA 1962) (statement of intended use in an apparatus claim did not distinguish over the prior art apparatus). To satisfy an intended use limitation which is limiting, a prior art structure which is capable of performing the intended use as recited in the preamble meets the claim. See, e.g., In re Schreiber, 128 F.3d 1473, 1477, 44 USPQ2d 1429, 1431 (Fed. Cir. 1997) (anticipation rejection affirmed based on Board’s factual finding that the reference dispenser (a spout disclosed as useful for purposes such as dispensing oil from an oil can) would be capable of dispensing popcorn in the manner set forth in appellant’s claim 1 (a dispensing top for dispensing popcorn in a specified manner)) and cases cited therein. See also MPEP § 2112 - MPEP § 2112.02.
14 The crossed text (“for use in performing descriptor matching in analysing the image”) has no preamble effect or preamble result and thus does not limit the scope of claim 19.
15 “generating” expresses the product or goal of the preamble’s generating a feature descriptor
16 This in curly brackets {for generating the feature descriptor } is the manipulative effect or manipulative result of the preamble: without this manipulative effect/result, claim 19 does not make logical sense when generating the feature descriptor as indicated in claim 19’s preamble.
17 “of the feature descriptor” is the ultimate result or effect (said goal) of the preamble’s generation of a feature descriptor.
18 shape: something used to give form, as a mold or a pattern. wherein form is defined: a particular condition, character, or mode in which something appears. (Dictionary.com)
19 derivative: In calculus, the slope of the tangent line to a curve at a particular point on the curve. Since a curve represents a function, its derivative can also be thought of as the rate of change of the corresponding function at the given point. Derivatives are computed using differentiation, wherein line is defined: Mathematics. a continuous extent of length, straight or curved, without breadth or thickness; the trace of a moving point. (Dictionary.com)
20 “generating” at the last limitation of claim 19 expresses the product or said goal of the preamble’s generating a feature descriptor
21 comprising: to include or contain, wherein include is defined: to contain, as a whole does parts or any part or element, wherein contain is defined: to be equal to. (Dictionary.com)
22 Claim 3’s “in which” is introducing a manipulative “wherein” clause giving meaning and purpose to the steps of claim 1 and thus limits claim 3. In general: if a “wherein” clause does not give meaning and purpose to a claim, then that “wherein” clause does not limit, wherein “wherein” is defined: in what or in which.(Dictionary.com)
23 filter: Computers. an algorithm that categorizes, sorts, prioritizes, or blocks data through rule-based protocols. (Dictionary.com)
24 point: the important or essential thing. (Dictionary.com)
25 and: (used to connect alternatives). (Dictionary.com)
26The crossed text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
27 The crossed text of the “wherein” (i.e., “in which”) clause is not “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").
28 The crossed text is crossed for the same reasons as in claim 6.
29 interpolating: to make an interpolation. (Dictionary.com)
30 BROAD CLAIM LANGUAGE: -ing (of “interpolating”): a suffix of nouns formed from verbs, expressing the action of the verb (interpolate) or its result, product, material, etc. [or likewise “replacing the linear interpolations with smoothing”] (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. (Dictionary.com)
31 descriptor: Computers. a data item that stores the attributes of some other datum. (Dictionary.com)
32 and: (used to connect alternatives). (Dictionary.com)
33 The crossed text “does not limit the scope of a claim” (claims 12 and 13 and 14,15) via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives (“store the determined set of samples in an array, and the feature descriptor generator is configured to generate the feature descriptor in dependence on the determined set of samples by forming a modified array; and determine a measure of rotation for the location in the image, the measure of rotation describing an angle between an orientation of the image and a characteristic direction of the image at the location, and generate the feature descriptor in dependence on the determined measure of rotation”), the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
34 and: (used to connect alternatives). (Dictionary.com)
35 The crossed text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
36 and: (used to connect alternatives). (Dictionary.com)
37 The crossed text “does not limit the scope of a claim” (claims 12 and 13 and 14) via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
38 descriptor: Computers. a data item that stores the attributes of some other datum. (Dictionary.com)
39 The crossed text “does not limit the scope of a claim” (claims 12 and 13 and 14 and 15 and 16 and 17) via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
40 The crossed text “does not limit the scope of a claim” (claims 12 and 13 and 14 and 15 and 16 and 17) via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
41 (italics) represent claim limitations already taught
42 (italics) represent claim limitations already taught
43 analyze: to separate (a material or abstract entity) into constituent parts or elements; determine the elements or essential features of (opposed to synthesize).
44 point: an individual part or element of something. (Dictionary.com)
45 or: (used to connect alternative terms for the same thing). (Dictionary.com)
46 derivative: In calculus, the slope of the tangent line to a curve at a particular point on the curve. Since a curve represents a function, its derivative can also be thought of as the rate of change of the corresponding function at the given point. Derivatives are computed using differentiation, wherein line is defined: Mathematics. a continuous extent of length, straight or curved, without breadth or thickness; the trace of a moving point. (Dictionary.com)
47 (italics) represent claim limitations already taught
48 BROAD CLAIM LANGUAGE: turn: to get beyond or pass (a certain age, time, amount, etc.). (Dictioary.com)
49 point: a degree or stage. (Dictionary.com)
50 turning point=passing degree
51 -est: a suffix forming the superlative degree of adjectives (low) and adverbs, wherein superlative is defined: of the highest kind, quality, or order; surpassing all else or others; supreme; extreme (Dictionary.com)
52 -est: a suffix forming the superlative degree of adjectives (high) and adverbs wherein superlative is defined: of the highest kind, quality, or order; surpassing all else or others; supreme; extreme. Dictionary.com)
53 adapt: (often foll by to) to adjust (someone or something, esp oneself) to different conditions, a new environment, etc (Dictionary.com)
54 weighting: to bias or slant toward a particular goal or direction; manipulate, wherein manipulate is defined: to adapt or change (accounts, figures, etc.) to suit one's purpose or advantage. (Dictionary.com)
Prosecution Insights