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 of the following informalities:
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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: “unit” in claims 1-19.
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 § 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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipate by WO2021241261 A1 (ISHIDAABE).
Regarding claim 1, An information processing apparatus (ISHIDAABE discloses an “information processing apparatus” and an “information processing system 101.” See ¶ [0007], [0022]) comprising: a learning execution unit (ISHIDAABE discloses a processing unit 121 that performs training and implements the cross-fusion CNN. See ¶ [0029], [0073], [0101]) that generates a neural network (ISHIDAABE discloses generation of cross-fusion CNN121 by connecting HVC131 and MVC132. See ¶ [0030], [0102], [0110]) which has a visual pathway layer (ISHIDAABE discloses an HVC131 whose functional modules reflect the human vision system, including V1 to V5-like layers. See ¶ [0041], [0043]) generated by executing learning based on a configuration of a visual pathway in a visual cortex (ISHIDAABE discloses training HVC131 to implement processes and functions similar to a human vision system and to reflect visual-field areas V1–V5. See ¶ [0016], [0039], [0041], [0074], [0075]); and which takes an image as input (ISHIDAABE expressly states that image data is input to HVC131 and MVC132. See ¶ [0058], [0113], [0114]); and provides information related to an object included in the image as output (ISHIDAABE discloses outputs including labeled image data, object classification, and HVC sensitized output related to the image. See ¶ [0050], [0060], [0104]-[0106]); and a storage unit (ISHIDAABE discloses a storage structure including a learning result storage unit. See ¶ [0042]) that stores the neural network generated by the learning execution unit (ISHIDAABE states that the generated neural network is stored in the learning result storage unit 118. See ¶ [0049], [0068]).
Regarding claim 2, the information processing apparatus according to claim 1, wherein the learning execution unit generates the neural network which has the visual pathway layer (see claim 1 mapping) including a primary visual cortex layer which has learned to reproduce orientation selectivity in a primary visual cortex (ISHIDAABE discloses a human-vision-inspired neural network architecture in which the HVC131 includes functional modules reflecting the human visual system of visual fields V1-V5 where V1 is depicts primary visual cortex, and wherein only certain portions of the human visual field is activities i.e., orientation selectivity. See ¶ [0041], [0043-0045].)
Regarding claim 3, The information processing apparatus according to claim 2, wherein the learning execution unit generates the primary visual cortex layer in which a neuron corresponding to an edge included in an input image fires, by using learning data in which angles of edges included in an image are correspondingly registered with neurons that should fire for each of the angles. (ISHIDAABE discloses that the learning data used for generating the primary visual cortex layer includes learning data in which angles of edges included in an image are correspondingly registered with neurons that should fire for each of the angles. See ISHIDAABE, ¶ [0044]. The reference further discloses that the learning execution unit generates the primary visual cortex layer in which a neuron corresponding to an edge included in an input image fires using such learning data. See ¶ [0051].)
Regarding claim 4, The information processing apparatus according to claim 3, wherein the learning execution unit generates the primary visual cortex layer by executing, on a moving image, reinforcement learning which modifies a weight according to a response speed of the neuron which differs for each of the angles of the edges. (ISHIDAABE discloses that the learning execution unit may generate the primary visual cortex layer by reinforcement learning with time-delay rewards. See ISHIDAABE, ¶¶ [0054]–[0056]. The reference further teaches that reward timing is based on the response speed of the neuron, which differs depending on the angle of the edge, and that the learning execution unit gives reward according to that relationship. See ¶¶ [0055]–[0057].)
Regarding claim 5, The information processing apparatus according to claim 2, wherein the learning execution unit generates the neural network which has the visual pathway layer including a secondary visual cortex layer which has learned to reproduce orientation selectivity in a secondary visual cortex which is finer than the orientation selectivity in the primary visual cortex. (ISHIDAABE expressly discloses that the learning execution unit generates the neural network with the visual pathway layer including a secondary visual cortex layer 324 which has learned to reproduce orientation selectivity in a secondary visual cortex. See ISHIDAABE, ¶¶ [0032]–[0033], [0058]–[0060]. The reference further discloses that the orientation selectivity in the secondary visual cortex is finer than that in the primary visual cortex. See ¶ [0032].)
Regarding claim 6, The information processing apparatus according to claim 3, wherein the learning execution unit generates the neural network which has the visual pathway layer including a secondary visual cortex layer which has learned to reproduce orientation selectivity in a secondary visual cortex which is finer than the orientation selectivity in the primary visual cortex. (ISHIDAABE discloses the secondary visual cortex layer 324 and teaches that it is generated by learning to reproduce the orientation selectivity of the secondary visual cortex, which is finer than that of the primary visual cortex. See ISHIDAABE, ¶¶ [0032]–[0033], [0058]–[0060].)
Regarding claim 7, The information processing apparatus according to claim 4, wherein the learning execution unit generates the neural network which has the visual pathway layer including a secondary visual cortex layer which has learned to reproduce orientation selectivity in a secondary visual cortex which is finer than the orientation selectivity in the primary visual cortex. (ISHIDAABE discloses reinforcement-learning-based training of the primary visual cortex layer, and separately discloses the secondary visual cortex layer 324 having finer orientation selectivity than the primary visual cortex. See ISHIDAABE, ¶¶ [0032]–[0033], [0054]–[0060].)
Regarding claim 8, The information processing apparatus according to claim 5, wherein the learning execution unit generates the neural network which has the visual pathway layer including a quinary visual cortex layer which has learned to reproduce direction selectivity in a quinary visual cortex. (ISHIDAABE discloses that the learning apparatus generates the quinary visual cortex layer 326 by learning to reproduce direction selectivity in the quinary visual cortex. See ISHIDAABE, ¶¶ [0034]–[0035]. The reference further discloses that the quinary visual cortex layer may be included in the visual pathway layer. See ¶¶ [0062]–[0063].)
Regarding claim 9, The information processing apparatus according to claim 6, wherein the learning execution unit generates the neural network which has the visual pathway layer including a quinary visual cortex layer which has learned to reproduce direction selectivity in a quinary visual cortex. (ISHIDAABE discloses a neural network including the visual pathway layer with a secondary visual cortex layer and a quinary visual cortex layer, each generated by learning to reproduce the corresponding visual cortex properties. See ISHIDAABE, ¶¶ [0032]–[0035], [0058]–[0063].)
Regarding claim 10, The information processing apparatus according to claim 7, wherein the learning execution unit generates the neural network which has the visual pathway layer including a quinary visual cortex layer which has learned to reproduce direction selectivity in a quinary visual cortex. (ISHIDAABE discloses the quinary visual cortex layer 326 and its direction selectivity, and also discloses that the neural network may include that layer as part of the visual pathway layer. See ISHIDAABE, ¶¶ [0034]–[0035], [0062]–[0063].)
Regarding claim 11, The information processing apparatus according to claim 1, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (ISHIDAABE expressly discloses that a spiking neural network may be applied to the HVC131. See ISHIDAABE, ¶ [0047]. The reference further states that the learning execution unit may generate the neural network with the visual pathway layer configured by a spiking neural network. See ¶ [0067].)
Regarding claim 12, The information processing apparatus according to claim 2, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (Rejected as stated in claim 11).
Regarding claim 13, The information processing apparatus according to claim 3, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (Rejected as stated in claim 11).
Regarding claim 14, The information processing apparatus according to claim 4, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (Rejected as stated in claim 11).
Regarding claim 15, The information processing apparatus according to claim 5, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (Rejected as stated in claim 11).
Regarding claim 16, The information processing apparatus according to claim 6, wherein the learning execution unit generates the neural network which has the visual pathway layer configured by a spiking neural network. (Rejected as stated in claim 11).
Regarding claim 17, The information processing apparatus according to claim 1, wherein the learning execution unit generates the neural network which has the visual pathway layer and a training layer which is generated by learning which uses annotated training data. (ISHIDAABE expressly discloses a training layer 330 generated by learning using annotated training data. See ISHIDAABE, ¶ [0036]. The reference further discloses that the neural network includes the visual pathway layer and the training layer generated by such annotated training data. See ¶ [0049].)
Regarding claim 18, The information processing apparatus according to claim 17, comprising: an image acquisition unit that acquires an image input; and a processing unit that inputs the image acquired by the image acquisition unit into a neural network stored in the storage unit and outputs information related to an object included in the image. (ISHIDAABE discloses an image acquisition function by way of camera 30, which captures an image and transmits the captured image. See ISHIDAABE, ¶ [0023]. The reference further discloses that the processing unit 136 inputs the image acquired by the image acquisition unit 134 into the neural network stored in the learning result storage unit 118 and outputs information related to the object included in the image. See ¶¶ [0068]–[0070].)
Regarding claim 19, A non-transitory computer readable storage medium storing a program that causes a computer to function as: a learning execution unit that generates a neural network which has a visual pathway layer generated by executing learning based on a configuration of a visual pathway in a visual cortex and which takes an image as input and provides information related to an object included in the image as output; and a storage unit that stores the neural network generated by the learning execution unit. (Rejected as stated in claim 1. This is a corresponding computer readable medium (CRM) claim. ISHIDAABE discloses that the described technique may be embodied as a program executed by a computer, and that the program may be recorded and provided on a removable medium and installed in a recording unit. See ISHIDAABE, ¶¶ [00230]–[00238]. The reference further discloses that a computer readable storage medium may include tangible media such as ROM, RAM, HDD, semiconductor storage, optical storage, and removable media. See ¶¶ [0082]–[0084].)
Regarding claim 20, An information processing method executed by a computer, the information processing method comprising; executing learning to generate a neural network which has a visual pathway layer generated by executing learning based on a configuration of a visual pathway in a visual cortex and which takes an image as input and provides information related to an object included in the image as output; and storing the neural network generated by the executing learning. (Rejected as stated in claim 1. This is a corresponding method claim. ISHIDAABE expressly discloses a learning method including training of the neural network based on the configuration of a visual pathway in a visual cortex and storing the generated neural network. See ISHIDAABE, ¶¶ [0010], [0065]–[0073], [0101]–[0110]. The reference further discloses that the neural network takes an image as input and provides information related to an object included in the image as output. See ¶¶ [0022], [0049], [0070].)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20230145616-A1; US-20080071710-A1; US-10452959-B1; US-20200086879-A1; US-11599981-B2
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VU LE whose telephone number is (571)272-7332. The examiner can normally be reached M-F 8:00 - 17:00.
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Vu Le can be reached at 2-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VU LE/ Supervisory Patent Examiner, Art Unit 2668
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