DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code, see paragraph [00331] on page 71 of the instant specification. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of the following informalities: Lines 10 - 11 of claim 1 recite, in part, “as active based on a context and result of the at least one APP node being satisfied, the context and result associated with” which appears to contain grammatical errors and/or minor informalities. The Examiner suggests amending the claim to --as active based on a context and a result of the at least one APP node being satisfied, the context and the result associated with-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: Line 5 of claim 4 recites, in part, “based on the image data and network of APP nodes,” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --based on the image data and the network of APP nodes,-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: Lines 7 - 8 of claim 4 recite, in part, “determine status of the activity based on the at least a portion of the at least one ordered sequence” which appears to contain grammatical errors and/or minor informalities. The Examiner suggests amending the claim to --determine a status of the activity based on the at least [[a]] portion of the at least one ordered sequence-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: Line 6 of claim 5 recites, in part, “in response to the action being taken as a function of the context” which appears to contain inconsistent claim terminology and/or minor informalities. The Examiner suggests amending the claim to --in response to the respective action being taken as a function of the respective context-- in order to maintain consistency with lines 4 - 5 of claim 5 and to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: Lines 1 - 2 of claim 8 recite, in part, “wherein digital computational learning system further comprises” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --wherein the digital computational learning system further comprises-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: Lines 4 - 5 of claim 8 recite, in part, “the image data and respective visual-image processing method of the library” which appears to contain a grammatical error, inconsistent claim terminology and/or minor informalities. The Examiner suggests amending the claim to --the image data and a respective visual. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: Line 6 of claim 9 recites, in part, “the at least a portion of the APP nodes” which appears to contain a grammatical error and/or minor informality. The Examiner suggests amending the claim to --the at least [[a]] portion of the APP nodes-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: Line 3 of claim 10 recites, in part, “the synthetic items representing perceived latent state” which appears to contain a grammatical error, inconsistent claim terminology and/or minor informalities. The Examiner suggests amending the claim to --the synthetic state items representing perceived latent states of-- in order to maintain consistency with lines 2 - 3 of claim 10 and to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Line 2 of claim 12 recites, in part, “information determined includes status of the activity,” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --information determined includes a status of the activity,-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: Lines 5 - 6 of claim 16 recite, in part, “the activity or sub-components thereof with the safety rules, compliance rules, or the combination thereof, (ii) matching the safety rules, compliance rules,” which appears to contain grammatical errors and/or minor informalities. The Examiner suggests amending the claim to --the activity or sub-components thereof with the safety rules, the compliance rules, or the combination thereof, (ii) matching the safety rules, the compliance rules,-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: Lines 7 - 8 of claim 16 recite, in part, “a manner in which the activity or sub-components therefore are performed” which appears to contain a minor informality. The Examiner suggests amending the claim to --a manner in which the activity or the sub-components therefore are performed-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: Line 3 of claim 17 recites, in part, “the plurality of sensors including the image sensor,” which appears to contain inconsistent claim terminology and/or a minor informality. The Examiner suggests amending the claim to --the plurality of sensors including the at least one image sensor,-- in order to maintain consistency with line 2 of claim 1 and to improve the clarity and precision of the claim. Appropriate correction is required.
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: “a digital computational learning system configured to determine” and “an attention control system configured to place” in claims 1, 2, 4, 6 - 11, 13, 15, 16, 18 and 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.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites the limitation "the library of image-processing methods" (emphasis added) in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the predication" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined 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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 - 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 5, 9 - 14, 16 - 22, 24 and 25 of U.S. Patent No. 12,165,073. Although the claims at issue are not identical, they are not patentably distinct from each other because instant claims 1 - 20 would have been obvious over and/or obvious variations of claims 1 - 5, 9 - 14, 16 - 22, 24 and 25 of U.S. Patent No. 12,165,073.
With regards to instant claim 1; Instant claim 1 differs from claim 1 of U.S. Patent No. 12,165,073 in that claim 1 of U.S. Patent No. 12,165,073 includes additional limitations that are not required by instant claim 1. However, the Examiner asserts that instant claim 1 which recites the opened ended transitional phrase "comprising", does not preclude the additional limitation(s) recited in claim 1 of U.S. Patent No. 12,165,073. Therefore, instant claim 1 is found to be anticipated by claim 1 of U.S. Patent No. 12,165,073; anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
The Examiner notes that the table provided herein below identifies the corresponding conflicting claim relationships between the instant application and U.S. Patent No. 12,165,073.
18/917,709
12,165,073
Claim 1
Claim 1
Claim 2
Claim 2
Claim 3
Claim 3
Claim 4
Claim 4
Claim 5
Claim 5
Claim 6
Claim 9
Claim 7
Claim 10
Claim 8
Claim 11
Claim 9
Claim 12
Claim 10
Claim 13
Claim 11
Claim 14
Claim 12
Claim 16
Claim 13
Claim 17
Claim 14
Claim 18
Claim 15
Claim 19
Claim 16
Claim 20
Claim 17
Claim 21
Claim 18
Claim 22
Claim 19
Claim 24
Claim 20
Claim 25
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 - 4 and 11 - 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kai-yuh Hsiao, Steganie Tellex, Soroush Vosoughi, Rony Kubat and Deb Roy, “Object schemas for grounding language in a responsive robot”, Connection Science, Vol. 20, No. 4, Dec. 2008, pages 253 - 276, herein referred to as “Hsiao et al.”.
- With regards to claim 1, Hsiao et al. disclose a computer vision learning system (Hsiao et al., Pg. 258 § 3 - Pg. 259 First-Full Paragraph, Pg. 262 Subsection “Completion statistics”, Pg. 265 § 3.5 - Pg. 266 § 4.1 ¶ 2) comprising: at least one image sensor (Hsiao et al., Pg. 260 Subsection “Sensory processes”, Pg. 266 § 4.1 ¶ 1 - 2) configured to transform light sensed from an environment of the computer vision learning system into image data; (Hsiao et al., Pg. 260 Subsection “Sensory processes”, Pg. 266 § 4.1 ¶ 1 - 2, Pg. 267 Subsection “Visual tracking”, Pg. 268 Fig. 9, Pg. 269 ¶ 1 - 2, Pg. 274 Subsection “Multimodal tracking”) and a digital computational learning system (Hsiao et al., Pg. 258 § 3 - Pg. 259 First-Full Paragraph, Pg. 262 Subsection “Completion statistics”, Pg. 265 § 3.5 - Pg. 266 § 4.1 ¶ 2) configured to determine activity-related information from the image data based on a network of actor perceiver predictor (APP) nodes, (Hsiao et al., Pg. 258 § 3 - Pg. 259 First-Full Paragraph, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pgs. 262 - 265 § 3.3, Pg. 263 Fig. 7, Pg. 264 Fig. 8, Pg. 273 Fig. 12) the activity-related information associated with an activity in the environment, (Hsiao et al., Pg. 258 § 3 - Pg. 259 First-Full Paragraph, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pg. 262 Subsection “Completion statistics” - Pg. 263 ¶ 3, Pg. 263 Fig. 7, Pg. 264 Fig. 8, Pg. 273 Fig. 12) the digital computational learning system further configured to determine the activity-related information based on identifying at least one APP node of the APP nodes in the network as active based on a context and result of the at least one APP node being satisfied, the context and result associated with the activity. (Hsiao et al., Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pgs. 262 - 265 § 3.3, Pg. 263 Fig. 7, Pg. 264 Fig. 8, Pg. 273 Fig. 12)
- With regards to claim 2, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to: determine that the context of the at least one APP node has been satisfied based on the image data; (Hsiao et al., Pg. 259 First-Full Paragraph, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pg. 260 Fig. 5, Pg. 262 Fig. 6, Pg. 263 Fig. 7, Pg. 266 § 4.1 ¶ 1 - 2, Pg. 269 ¶ 1 - 3, Pg. 269 Fig. 10, Pg. 273 Fig. 12, Pg. 274 Subsection “Multimodal tracking”) and subsequent to a determination that the context of the at least one APP node has been satisfied, determine that the result of the at least one APP node has been satisfied, (Hsiao et al., Pg. 259 First-Full Paragraph, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 266 § 4.1 ¶ 1 - 2, Pg. 269 ¶ 1 - 3, Pg. 273 Fig. 12, Pg. 274 Subsection “Multimodal tracking”) wherein, to determine that the result has been satisfied, the digital computational learning system is further configured to identify, from the image data, that an action of the at least one APP node has been performed. (Hsiao et al., Pg. 259 First-Full Paragraph, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 266 § 4.1 ¶ 1 - 2, Pg. 269 ¶ 1 - 3, Pg. 273 Fig. 12, Pg. 274 Subsection “Multimodal tracking”)
- With regards to claim 3, Hsiao et al. disclose the computer vision learning system of Claim 2, wherein the action is a sub-action of an overall action associated with an overall goal of the activity, (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12) wherein the overall action represents the activity, (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12) wherein the sub-action is associated with a sub-goal of the overall goal, (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12) and wherein the sub-goal is associated with the at least one APP node. (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12)
- With regards to claim 4, Hsiao et al. disclose the computer vision learning system of Claim 3, wherein the sub-action is among a plurality of sub-actions of the overall action, (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12) wherein the plurality of sub-actions is associated with at least one ordered sequence, (Hsiao et al., Pgs. 262 - 265 § 3.3, Pg. 265 § 3.5 ¶ 1, Pg. 273 Fig. 12) and wherein the digital computational learning system is further configured to: identify, based on the image data and network of APP nodes, at least a portion of the at least one ordered sequence; (Hsiao et al., Pg. 260 Subsection “Process classes” - Pg. 261 Subsection “Object expectations”, Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 265 § 3.5 ¶ 1, Pg. 269 ¶ 1 - 3, Pg. 272 Fig. 11, Pg. 273 Fig. 12) and determine status of the activity based on the at least a portion of the at least one ordered sequence identified, the status indicating whether the activity is in progress or has completed, the activity-related information representing the status. (Hsiao et al., Pg. 259 ¶ 1 - Subsection “Interaction histories”, Pg. 259 Fig. 4, Pg. 260 Fig. 5, Pg. 262 Fig. 6, Pg. 263 Fig. 7, Pg. 264 Fig. 8, Pg. 273 Fig. 12)
- With regards to claim 11, Hsiao et al. disclose the computer vision learning system of Claim 1, further comprising an audio sensor configured to transform audio from the environment to audio data (Hsiao et al., Pg. 265 § 3.4, Pg. 267 ¶ 3, Pg. 270 Subsection “Behaviour 2” - Pg. 271 Subsection “Behaviour 3”) and wherein the digital computational learning system is further configured to determine the activity-related information based on the audio data. (Hsiao et al., Pg. 265 § 3.4, Pg. 267 ¶ 3, Pg. 270 Subsection “Behaviour 2” - Pg. 271 Subsection “Behaviour 3”)
- With regards to claim 12, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the activity-related information determined includes status of the activity, the status indicating that the activity has started, stopped, or completed. (Hsiao et al., Pg. 259 ¶ 1 - 3, Pg. 259 Fig. 4, Pg. 260 Fig. 5, Pg. 263 Fig. 7, Pg. 264 Fig. 8, Pg. 273 Fig. 12)
- With regards to claim 13, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to compute a length of time taken to complete the activity (Hsiao et al., Pg. 259 ¶ 2 - 3, Pg. 260 Fig. 5, Pg. 262 Fig. 6, Pg. 263 Fig. 7, Pg. 272 Fig. 11) and wherein the activity-related information determined includes the length of time computed. (Hsiao et al., Pg. 259 ¶ 2 - 3, Pg. 260 Fig. 5, Pg. 262 Fig. 6, Pg. 263 Fig. 7, Pg. 272 Fig. 11)
- With regards to claim 14, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the activity-related information determined indicates that a new activity has begun, the new activity different from the activity. (Hsiao et al., Pg. 259 ¶ 1 - 3, Pg. 259 Fig. 4, Pgs. 260 - 261 Subsection “Process classes”, Pg. 260 Fig. 5, Pgs. 262 - 265 § 3.3, Pg. 264 Fig. 8, Pg. 273 Fig. 12)
- With regards to claim 15, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to produce a prediction that a sub-activity of the activity is expected to be performed in the environment, (Hsiao et al., Pgs. 261 - 262 § 3.2, Pg. 263 ¶ 1 - Pg. 265 ¶ 1, Pg. 263 Fig. 7, Pg. 271 Subsection “Plan hierarchy construction” - Subsection “Affordance monitoring and plan revisions”, Pg. 272 Fig. 11, Pg. 273 Fig. 12) wherein the activity-related information determined includes the prediction produced, (Hsiao et al., Pgs. 261 - 262 § 3.2, Pg. 263 ¶ 1 - Pg. 265 ¶ 1, Pg. 263 Fig. 7, Pg. 271 Subsection “Plan hierarchy construction” - Subsection “Affordance monitoring and plan revisions”, Pg. 272 Fig. 11, Pg. 273 Fig. 12) wherein the predication produced is based on a sub-activated value of a given APP node of the APP nodes, (Hsiao et al., Pg. 259 Subsection “Interaction histories”, Pgs. 261 - 262 § 3.2, Pg. 263 Fig. 7, Pg. 264 Lines 1 - 3, Pg. 265 Lines 1 - 6, Pg. 271 Subsection “Plan hierarchy construction” - Subsection “Expected affordances”, Pg. 272 Fig. 11) the sub-activated value representing a simulated value of an actual value of the given APP node if the given APP node were to become active based on the image data. (Hsiao et al., Pg. 259 Subsection “Interaction histories”, Pgs. 261 - 262 § 3.2, Pg. 263 Fig. 7, Pg. 264 Lines 1 - 3, Pg. 265 Lines 1 - 6, Pg. 271 Subsection “Plan hierarchy construction” - Subsection “Expected affordances”, Pg. 272 Fig. 11)
- With regards to claim 16, Hsiao et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to access a rule memory, the rule memory including safety rules, compliance rules, or a combination thereof, (Hsiao et al., Pg. 258 § 3.1 ¶ 1, Pg. 270 Subsection “Primary motivations”, Pgs. 265 - 266 § 3.5, Pgs. 272 - 273 Subsection “Behaviour 4”) and wherein the activity-related information is determined based on (i) matching the activity or sub-components thereof with the safety rules, compliance rules, or the combination thereof, (ii) matching the safety rules, compliance rules, or the combination thereof with a manner in which the activity or sub-components thereof are performed in the image data, or (i) and (ii). (Hsiao et al., Pg. 270 Subsection “Primary motivations”, Pgs. 265 - 266 § 3.5, Pgs. 272 - 273 Subsection “Behaviour 4”)
- With regards to claim 17, Hsiao et al. disclose the computer vision learning system of Claim 1, further comprising a plurality of sensors configured to produce multi-sensor data from input from the environment, (Hsiao et al., Pg. 260 Subsection “Sensory processes”, Pgs. 266 - 267 § 4.1, Pg. 274 Subsection “Multimodal tracking”) the plurality of sensors including the image sensor, (Hsiao et al., Pg. 260 Subsection “Sensory processes”, Pgs. 266 - 267 § 4.1, Pg. 274 Subsection “Multimodal tracking”) wherein the multi-sensor data produced includes the image data, (Hsiao et al., Pg. 260 Subsection “Sensory processes”, Pg. 266 § 4.1 ¶ 1 - 2, Pg. 267 Subsection “Visual tracking” ¶ 1 - 2, Pg. 268 Fig. 9, Pg. 269 ¶ 1 - 3, Pg. 274 Subsection “Multimodal tracking”) and wherein the activity-related information is further determined based on the multi-sensor data produced. (Hsiao et al., Pgs. 260 - 261 Subsection “Process classes”, Pg. 266 § 4.1 - Pg. 267 Subsection “Visual tracking”, Pg. 273 Fig. 12, Pg. 274 Subsection “Multimodal tracking” - § 5)
Claims 1 and 18 - 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Suchkov et al. U.S. Publication No. 2021/0334758 A1.
- With regards to claim 1, Suchkov et al. disclose a computer vision learning system (Suchkov et al., Abstract, Figs. 1 & 4, Pg. 1 ¶ 0008, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0032 and 0039 - 0045, Pg. 5 ¶ 0058) comprising: at least one image sensor configured to transform light sensed from an environment of the computer vision learning system into image data; (Suchkov et al., Abstract, Figs. 1 & 4, Pg. 1 ¶ 0008, Pg. 2 ¶ 0016, Pg. 3 ¶ 0040 - 0042, Pg. 4 ¶ 0048 - 0050) and a digital computational learning system (Suchkov et al., Abstract, Figs. 1 & 4, Pg. 1 ¶ 0008, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0032 and 0039 - 0045, Pg. 5 ¶ 0058) configured to determine activity-related information from the image data based on a network of actor perceiver predictor (APP) nodes, (Suchkov et al., Fig. 4, Pg. 4 ¶ 0054 - Pg. 5 ¶ 0057, Pg. 5 ¶ 0059 - 0060, Pg. 7 ¶ 0082 - 0086 [“the video and metadata of the objects received in a certain way are analyzed by the data processing unit using at least one artificial neural network (ANN) for (a) distinguishing employees and visitors by the presence or absence of uniforms, (b) identifying each detected employee, as well as for (c) further analyzing the location and interaction between employees and visitors in accordance with user-defined system operation parameters”, “at least one data processing device first detects each person in the frame, then recognizes the clothing on the person, and then analyzes a set of images of each type of uniform, to identify a match. When a person's clothing matches a store uniform, the system detects that the person is an employee, then recognizes the face of the person in the uniform, and sequentially analyzes a set of photos of each employee's face to identify a match and, therefore, identify the employee” and “To perform the next stage, namely, analysis of (c) location and interaction between employees and visitors, the system user should set specific system operation parameters, based on which the video data, the corresponding metadata, and the data received after distinguishing the employees and visitors, as well as after identifying the employees will be analyzed”]) the activity-related information associated with an activity in the environment, (Suchkov et al., Abstract, Fig. 4, Pg. 4 ¶ 0048 and 0054, Pg. 5 ¶ 0059 - 0063, Pg. 7 ¶ ¶ 0080 - 0086) the digital computational learning system further configured to determine the activity-related information based on identifying at least one APP node of the APP nodes in the network as active based on a context and result of the at least one APP node being satisfied, (Suchkov et al., Fig. 4, Pg. 4 ¶ 0054 - Pg. 5 ¶ 0057, Pg. 5 ¶ 0059 - 0060, Pg. 7 ¶ 0082 - 0086 [“the video and metadata of the objects received in a certain way are analyzed by the data processing unit using at least one artificial neural network (ANN) for (a) distinguishing employees and visitors by the presence or absence of uniforms, (b) identifying each detected employee, as well as for (c) further analyzing the location and interaction between employees and visitors in accordance with user-defined system operation parameters”, “at least one data processing device first detects each person in the frame, then recognizes the clothing on the person, and then analyzes a set of images of each type of uniform, to identify a match. When a person's clothing matches a store uniform, the system detects that the person is an employee, then recognizes the face of the person in the uniform, and sequentially analyzes a set of photos of each employee's face to identify a match and, therefore, identify the employee” and “To perform the next stage, namely, analysis of (c) location and interaction between employees and visitors”]) the context and result associated with the activity. (Suchkov et al., Fig. 4, Pg. 4 ¶ 0054 - Pg. 5 ¶ 0057, Pg. 5 ¶ 0059 - 0060, Pg. 7 ¶ 0082 - 0086)
- With regards to claim 18, Suchkov et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to generate an electronic report including the activity-related information determined. (Suchkov et al., Abstract, Fig. 4, Pg. 2 ¶ 0016, Pg. 2 ¶ 0022 - Pg. 3 ¶ 0031, Pg. 5 ¶ 0062 - Pg. 6 ¶ 0067, Pg. 7 ¶ 0071 - 0075 and 0079 - 0080)
- With regards to claim 19, Suchkov et al. disclose the computer vision learning system of Claim 1, further comprising a user interface (Suchkov et al., Figs. 1 & 2, Pg. 2 ¶ 0016, Pg. 3 ¶ 0030 - 0031, 0040 and 0044) and wherein the digital computational learning system is further configured to output, via the user interface, natural language representing the activity-related information determined. (Suchkov et al., Figs. 1 - 4, Pg. 1 ¶ 0009, Pg. 3 ¶ 0026 - 0028, Pg. 5 ¶ 0062 - Pg. 6 ¶ 0067, Pg. 7 ¶ 0071 - 0075)
- With regards to claim 20, Suchkov et al. disclose the computer vision learning system of Claim 1, wherein the context includes a neural network. (Suchkov et al., Abstract, Fig. 4, Pg. 1 ¶ 0008, Pg. 4 ¶ 0054 - Pg. 5 ¶ 0057, Pg. 7 ¶ 0081 - 0084)
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.
Claims 1, 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Filipo Studzinski Perotto, Jean-Christophe Buisson, Luis Otávio Alvares, “Constructivist Anticipatory Learning Mechanism (CALM) - dealing with partially deterministic and partially observable environments”, Proceedings of the Seventh International Conference on Epigenetic Robotics, 2007, pages 1 - 8, herein referred to as “Perotto et al.”, in view of Clune et al. U.S. Publication No. 2020/0166896 A1.
- With regards to claim 1, Perotto et al. disclose a learning system (Perotto et al., Pg. 1 Abstract, Pg. 1 § 2 ¶ 3 - 4, Pg. 3 § 5 - Pg. 4 Left-Hand Column Second-Full Paragraph, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1) comprising: determining activity-related information from the sensor data based on a network of actor perceiver predictor (APP) nodes, (Perotto et al., Pg. 1 § 2 - Pg. 2 Left-Hand Column Second-Full Paragraph, Pg. 3 § 4, Pg. 3 Fig. 1, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1, Pg. 8 § 9 ¶ 3 and 6) the activity-related information associated with an activity in the environment, (Perotto et al., Pg. 1 § 2 - Pg. 2 Left-Hand Column Third-Full Paragraph, Pg. 3 § 4, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1) determining the activity-related information based on identifying at least one APP node of the APP nodes in the network as active based on a context and result of the at least one APP node being satisfied, (Perotto et al., Pg. 3 § 4, Pg. 3 Fig. 1, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1) the context and result associated with the activity. (Perotto et al., Pg. 3 § 4, Pg. 3 Fig. 1, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1) Perotto et al. fail to disclose a computer vision learning system comprising: at least one image sensor configured to transform light sensed from an environment of the computer vision learning system into image data; and a digital computational learning system configured to determine activity-related information from the image data. Pertaining to analogous art, Clune et al. disclose a computer vision learning system (Clune et al., Abstract, Figs. 1 - 3 & 7, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0014, Pg. 5 ¶ 0043 - 0045 and 0048, Pg. 6 ¶ 0054) comprising: at least one image sensor configured to transform light sensed from an environment of the computer vision learning system into image data; (Clune et al., Abstract, Pg. 3 ¶ 0030 - 0032, Pg. 4 ¶ 0034 and 0040, Pg. 8 ¶ 0067) and a digital computational learning system (Clune et al., Abstract, Figs. 1 - 3 & 7, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0014, Pg. 5 ¶ 0043 - 0045 and 0048, Pg. 6 ¶ 0054, Pg. 8 ¶ 0070 - 0073) configured to determine activity-related information from the image data, the activity-related information associated with an activity in the environment. (Clune et al., Abstract, Pg. 3 ¶ 0030 - 0031, Pg. 4 ¶ 0033 - 0034 and 0038 - 0041, Pg. 5 ¶ 0043, Pg. 7 ¶ 0065, Pg. 8 ¶ 0069) Perotto et al. and Clune et al. are combinable because they are both directed towards methods and systems for determining an action to be carried out based upon sensor data acquired from an environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perotto et al. with the teachings of Clune et al. A first modification would have been prompted in order to enhance the base device of Perotto et al. with the well-known and applicable technique Clune et al. applied to a comparable device. Utilizing computer architecture, such as at least one processor and one or more memories, to implement a learning system, as taught by Clune et al., would enhance the base device of Perotto et al. by facilitating its ability to autonomously construct policies, learn schemas and form action sequences, by allowing for it to refer back to any previously constructed policies, learned schemas and formed action sequences for evaluation and/or modification, and by ensuring that its techniques are carried out accurately and efficiently at high computational speed on computer hardware. In addition, a second modification would have been prompted in order to substitute the undisclosed sensor of Perotto et al. for the image sensor of Clune et al. The image sensor of Clune et al. could be substituted in place of the undisclosed sensor of Perotto et al. utilizing well-known techniques in the art and would likely yield predictable results, in that in the combination an image sensor would be utilized to observe the environment and activity-related information would be determined from image data obtained via the image sensor. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that the base device of Perotto et al. would be implemented utilizing computer architecture, such as at least one processor and one or more memories, so as to ensure that its techniques are carried out accurately and efficiently at high computational speed on computer hardware, in that an image sensor would be utilized to perceive the environment by obtaining image data and in that the activity-related information would be determined based on the obtained image data obtained. Therefore, it would have been obvious to combine Perotto et al. with Clune et al. to obtain the invention as specified in claim 1.
- With regards to claim 5, Perotto et al. in view of Clune et al. disclose the computer vision learning system of Claim 1, wherein the network is a knowledge graph, (Perotto et al., Pg. 1 § 2 ¶ 3 - Pg. 2 Left-Hand Column Second-Full Paragraph, Pg. 3 § 4, Pg. 3 Fig. 1, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1, Pg. 8 § 9 ¶ 6) and learning, automatically, the APP nodes of the knowledge graph, (Perotto et al., Pg. 1 § 2 - Pg. 2 Left-Hand Column Second-Full Paragraph, Pg. 3 § 4 - § 5 ¶ 2, Pg. 7 Left-Hand Column First-Full Paragraph - Right-Hand Column Table 1, Pg. 8 § 9 ¶ 6) each APP node of the APP nodes associated with a respective context, respective action, and respective result, (Perotto et al., Pg. 3 § 4, Pg. 3 Fig. 1) the respective result expected to be achieved in response to the action being taken as a function of the context having been satisfied. (Perotto et al., Pg. 3 § 4, Pg. 3 Fig. 1) Perotto et al. fail to disclose explicitly wherein the digital computational learning system includes at least one processor. Pertaining to analogous art, Clune et al. disclose wherein the digital computational learning system includes at least one processor. (Clune et al., Abstract, Fig. 7, Pg. 3 ¶ 0028, Pg. 8 ¶ 0070 - 0073)
- With regards to claim 10, Perotto et al. in view of Clune et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system is further configured to maintain synthetic state items, the synthetic items representing perceived latent state of the environment computed by the APP nodes in the network based on sensor data. (Perotto et al., Pgs. 5 - 6 § 6, Pg. 8 § 9) Perotto et al. fail to disclose explicitly image data. Pertaining to analogous art, Clune et al. disclose computing based on the image data. (Clune et al., Abstract, Pg. 3 ¶ 0031 - Pg. 4 ¶ 0034, Pg. 4 ¶ 0039 - 0042, Pg. 5 ¶ 0044)
Claims 1 and 6 - 9 are rejected under 35 U.S.C. 103 as being unpatentable over Garbis Salgian and Dana H. Ballard, "Visual Routines for Vehicle Control", Springer, The confluence of vision and control, 1998, pages 244 - 256, herein referred to as "Salgian et al.", in view of Clune et al. U.S. Publication No. 2020/0166896 A1.
- With regards to claim 1, Salgian et al. disclose a computer vision learning system (Salgian et al., Pg. 244 § 1 ¶ 2 - 3, Pg. 245 ¶ 2, Pg. 245 Fig. 2.1, Pg. 246 § 3, Pgs. 254 - 255 § 5) comprising: image data; (Salgian et al., Pg. 245 ¶ 1 - 2, Pg. 245 Fig. 21, Pg. 246 § 3 ¶ 4, Pg. 247 § 3.1 - Pg. 248 ¶ 2, Pg. 249 § 3.2 - Pg. 250 ¶ 1) and a digital computational learning system (Salgian et al., Pg. 244 § 1 ¶ 2 - 3, Pg. 245 ¶ 1 - 2, Pg. 245 Fig. 2.1, Pg. 247 § 3.1 ¶ 1, Pgs. 254 - 255 § 5) configured to determine activity-related information from the image data based on a network of actor perceiver predictor (APP) nodes, (Salgian et al., Pg. 246 § 3 - Pg. 248 ¶ 2, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pgs. 251 - 252 § 3.3, Pg. 254 § 5 ¶ 1 - 2) the activity-related information associated with an activity in the environment, (Salgian et al., Pg. 244 § 1, Pg. 245 ¶ 1 - 2, Pg. 246 § 3 ¶ 1 - 4, Pg. 247 Fig. 3.1, Pgs. 251 - 252 § 3.3, Pg. 254 § 5 ¶ 1 - 2) the digital computational learning system further configured to determine the activity-related information based on identifying at least one APP node of the APP nodes in the network as active based on a context and result of the at least one APP node being satisfied, (Salgian et al., Pg. 246 § 3, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 251 § 3.3, Pg. 251 Fig. 3.4) the context and result associated with the activity. (Salgian et al., Pg. 246 § 3, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 251 § 3.3, Pg. 251 Fig. 3.4) Salgian et al. fail to disclose explicitly at least one image sensor configured to transform light sensed from an environment of the computer vision learning system into image data. Pertaining to analogous art, Clune et al. disclose a computer vision learning system (Clune et al., Abstract, Figs. 1 - 3 & 7, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0014, Pg. 5 ¶ 0043 - 0045 and 0048, Pg. 6 ¶ 0054) comprising: at least one image sensor configured to transform light sensed from an environment of the computer vision learning system into image data; (Clune et al., Abstract, Pg. 3 ¶ 0030 - 0032, Pg. 4 ¶ 0034 and 0040, Pg. 8 ¶ 0067) and a digital computational learning system (Clune et al., Abstract, Figs. 1 - 3 & 7, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0014, Pg. 5 ¶ 0043 - 0045 and 0048, Pg. 6 ¶ 0054, Pg. 8 ¶ 0070 - 0073) configured to determine activity-related information from the image data, the activity-related information associated with an activity in the environment. (Clune et al., Abstract, Pg. 3 ¶ 0030 - 0031, Pg. 4 ¶ 0033 - 0034 and 0038 - 0041, Pg. 5 ¶ 0043, Pg. 7 ¶ 0065, Pg. 8 ¶ 0069) Salgian et al. and Clune et al. are combinable because they are both directed towards image processing systems and methods for determining vehicle control actions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Salgian et al. with the teachings of Clune et al. This modification would have been prompted in order to enhance the base device of Salgian et al. with the well-known and applicable technique Clune et al. applied to a comparable device. Utilizing image data from at least one image sensor configured to transform light sensed from an environment of the computer vision learning system as the image data from which activity-related information is determined, as taught by Clune et al., would enhance the base device of Salgian et al. by allowing for it to take advantage of improvements in processing power and be utilized in real-world scenarios and applications thereby increasing the overall appeal and usefulness of the base device of Salgian et al. to potential end-users. Furthermore, this modification would enhance the base device of Salgian et al. by enabling it to be tested on real-world image data and thereby have its performance evaluated under a wider array of scenarios so as to help ensure it is ready to be implemented in real-world scenarios. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that image data from at least one image sensor configured to transform light sensed from an environment of the computer vision learning system would be utilized as the image data from which the activity-related information is determined so as to allow for the techniques of the base device of Salgian et al. to be applied to real-world scenarios and applications thereby increasing its overall appeal and usefulness to potential end-users. Therefore, it would have been obvious to combine Salgian et al. with Clune et al. to obtain the invention as specified in claim 1.
- With regards to claim 6, Salgian et al. in view of Clune et al. disclose the computer vision learning system of Claim 1, wherein the digital computational learning system further comprises a library of visual methods for applying to the image data (Salgian et al., Pg. 246 § 3, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 254 § 5 ¶ 1 - 3) and wherein the APP nodes are configured to encode results expected after executing a sequence of the visual methods under different starting context conditions of the environment. (Salgian et al., Pg. 246 § 3, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 251 § 3.3 - Subsection “Stop sign behavior”, Pg. 251 Fig. 3.4)
- With regards to claim 7, Salgian et al. in view of Clune et al. disclose the computer vision learning system of Claim 6, wherein the digital computational learning system is further configured to automatically select a given visual method from the library and apply the given visual method selected to the image data, (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3 - Pg. 247 Subsection “Static features”, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 251 § 3.3 - Subsection “Stop sign behavior”, Pg. 254 § 5 ¶ 1 - 3) the given visual method selected, dynamically, by an action-controller of a given APP node of the APP nodes. (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3 - Pg. 247 Subsection “Static features”, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 251 § 3.3 - Subsection “Stop sign behavior”, Pg. 252 § 3.4, Pg. 254 § 5 ¶ 1 - 3)
- With regards to claim 8, Salgian et al. in view of Clune et al. disclose the computer vision learning system of Claim 6, wherein digital computational learning system further comprises an attention control system (Salgian et al., Pg. 245 ¶ 2, Pg. 245 Fig. 2.1, Pg. 247 § 3.1 ¶ 1, Pgs. 254 - 255 § 5) configured to place attention markers in the image data, (Salgian et al., Pg. 246 § 3 ¶ 4 - 5, Pg. 247 § 3.1 ¶ 1 - 2, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 249 Fig. 3.2) the attention markers identifying a respective location in the image data and respective visual-image processing method of the library of image-processing methods for the digital computational learning system to apply at the location. (Salgian et al., Pg. 246 § 3 - Pg. 247 § 3.1 ¶ 2, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 249 Fig. 3.2, Pg. 254 § 5 ¶ 1 - 3)
- With regards to claim 9, Salgian et al. in view of Clune et al. disclose the computer vision learning system of Claim 6, wherein the digital computational learning system is further configured to automatically select a plurality of visual methods from the library (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3 - Pg. 248 ¶ 2, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 254 § 5 ¶ 1 - 3) and apply the plurality of visual methods selected, sequentially, to the image data, (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3 - Pg. 248 ¶ 2, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 254 § 5 ¶ 1 - 3) the plurality of visual methods selected employed as actions of at least a portion of the APP nodes, (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3 - Pg. 248 ¶ 2, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pg. 254 § 5 ¶ 1 - 3) and wherein respective results of the at least a portion of the APP nodes resulting from taking the actions enable the digital computational learning system to determine the activity-related information. (Salgian et al., Pg. 245 ¶ 2, Pg. 246 § 3, Pg. 247 Fig. 3.1, Pg. 249 § 3.2 - Pg. 250 ¶ 1, Pgs. 251 - 252 § 3.3, Pgs. 254 - 255 § 5)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Banino et al. U.S. Publication No. 2020/0191574 A1; which is directed towards methods and systems for navigating through an environment, wherein a vision based neural network is utilized to select actions to be performed by a robot or vehicle to control its movement through the environment.
Lim et al. U.S. Publication No. 2008/0144937 A1; which is directed towards a system and method for recognizing activities in video, wherein an activity dynamic Bayesian network (ADBN), an object/action dictionary and an activity inference engine are utilized to detect activities and estimate a likely activity state for each frame of an input video stream.
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/ERIC RUSH/Primary Examiner, Art Unit 2677
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