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 .
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: an acquirer, and a learning section; a detector; an acquirer, an extractor, an output section, in claims 12, 19, and 24 respectively.
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.
Notice re prior art available under both pre-AIA and AIA
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 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.
Examiner's Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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.
Claims 1-3, 12-13, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Bellala, et al. (WO 2016/122591 A1) in view of Jain (US 2023/0045487 A1).
With regard to claim 1, Bellala, et al. (hereinafter “Bellala”) discloses a machine learning method for generating a learning model for extracting a feature of a target, i.e., entity, vehicle or driver of a vehicle (See for example, Fig. 1 “system 100” and the associated text ) , the machine learning method comprising: (a) acquiring sequential data (See for example, item 104 in Fig. 1 “training time series data”; and paragraphs 0029-0031); (b) performing preprocessing, i.e., editing/scaling, pre-processing, and/or removing redundant or irrelevant features of the time series data, among others, for size adjustment in a sequence direction on the sequential data based on a predetermined condition, i.e., criteria, variance error, asynchronous data sampling, etc., to generate a plurality of pieces of adjusted sequential data, i.e., edited horizontal and/or vertical scales of the time series data, having different intervals, i.e., distinct internally homogenous sections or categorizing segments into various bins based on their respective length of segments, in the sequence direction from one piece of the sequential data (See for example, 0032, 0035, and 0040-0042) ; and (c) performing supervised learning using the plurality of pieces of adjusted sequential data generated in (b) to generate the learning model (See for example, paragraphs 0041-0042; and Figs. 1-2, and the associated text). While the system of Bellala does mention features sub-selection or validation may be performed in a supervised or semi-supervised manner (See for example, paragraphs 0041-0042), Bella does not expressly call for performing supervised learning using the plurality of pieces of adjusted sequential data. However, Jain (See for example paragraph 0024) teaches this feature. Before the effective filing date of the claimed invention, it would have been obvious to incorporate the teaching as taught by Jain into into the system of Bellala, and to do so would at least allow performing supervised learning using compressed representation of the sequence/time-series data, and as a result it may learn important patterns that might be present in the time-series data (See for example, paragraph 0024). Therefore, it would have been obvious to combine Bellala with Jain to obtain the invention as specified in claim 1.
With regard to claim 2, the machine learning method according to claim 1, wherein in (a), labels, i.e., categories, scores, and/or labels, of the sequential data are acquired together with the sequential data, and in (c), the supervised learning is performed by applying one of the labels of the sequential data to the plurality of pieces of adjusted sequential data (See for example, paragraph 0039 of Bellala; and paragraph 0049 of Jain).
With regard to claim 3, the machine learning method according to claim 1, wherein in (b), a condition for the size adjustment is automatically set based on the predetermined condition (See for example, paragraphs 0032 and 0042-0043 of Bellala).
Claim 12 is rejected the same as claim 1 except claim 12 is an apparatus claim. Thus, argument analogous to that presented above for claim 1 is applicable to claim 12.
Claims 13 and 14 are rejected the same as claims 2 and 3 respectively except claims 13 and 14 are apparatus claims. Thus, arguments similar to those presented above for claims 2 and 3 are respectively applicable to claims 13 and 14.
Claim 24 is rejected the same as claim 12. Thus, argument similar to that presented above for claim 12 is applicable to claim 12. Claim 24 distinguishes from claim 12 only in that it recites an extractor that extracts a feature of a target by using a learning model trained by the machine learning method according to claim l ; and an output section that outputs a result of the extraction. Fortunately, Bellala (See for example, Fig. 1: 108A, and the associated text) teaches these features.
Claims 4-7 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bellala in view of Jain as applied to claims 1-3, 12-13, and 24 above, and further in view of Namiki, et al. (US 2024/0153061 A1).
With regard to claim 4, Bellala (as modified by Jain) discloses all of the claimed subject matter as already addressed above in paragraph 9, and incorporated herein by reference. the machine learning method according to claim 1, One having ordinary skill may interpret the teaching as taught by Bella (See for example paragraph 0030 and 0040) as disclosing wherein the sequential data acquired in (a) is time-series image data obtained by imaging a target object in an imaging region, and the learning model is a learning model for extracting a feature of the target object. Nonetheless, Namiki, et al. (See for example, Fig. 6) teach this feature. Before the effective filing date of the claimed invention, it would have been obvious to incorporate the teaching as taught by Namiki, et al. into the system of Bellala (as modified by Jain), if for no other reason than to acquire the image sequence of a target object captured by a camera and outputting the image to a learning unit wherein the learning unit learns using the target object image sequence. Therefore, it would have been obvious to combine Bellala (as modified by Jain) with Namiki, et al. to obtain the invention as specified in claim 4.
With regard to claim 5, the machine learning method according to claim 4, wherein in (b), a condition for the size adjustment is set as the predetermined condition in accordance with a sampling rate of the sequential data or the number of frames (See for example, paragraph 0040 of Bellala).
With regard to claim 6, the machine learning method according to claim 4, further comprising (d) acquiring external information, i.e., speed, regarding an imaging environment, wherein in (b), a condition for the size adjustment is set as the predetermined condition based on the external information (See for example, paragraph 0031, and 0057-0059 of Bellala).
With regard to claim 7, the machine learning method according to claim 6, wherein the external information is information regarding a movement speed of the object, i.e., vehicle speed “mph”, or a specification of a camera that captures an image of the imaging region (See for example, paragraphs 0057-0059 of Bellala).
Claims 15, 16, and 17 are rejected the same as claims 4, 5, and 6 respectively except claims 15, 16, and 17 are apparatus claims. Thus, arguments similar to those presented above for claims 4, 5, and 6 are respectively applicable to claims 15, 16, and 17.
Allowable Subject Matter
Claims 8-11 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent Application Publication Number 2022/0108170 (See for example, Figs. 5-8 and the associated text); and Chinese Patent Number CN109178831 (See entire document).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL G MARIAM whose telephone number is (571)272-7394. The examiner can normally be reached M-F 7:30-5:00 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, Mathew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL G MARIAM/ Primary Examiner, Art Unit 2675
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