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 encoder neural network (NN) configured”, “a compute module configured to”, “a decoder NN configured to”, and “a controller configured to” in claim 1, and “wherein the distorted signature is configured to” in claim 5.
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. According to the specification on page 4, paragraph 20-21 the encoder and decoder NN are interpreted as [0020] Figs. 2A-B are diagrams of an encoder and decoder network 200 in one embodiment.
The network 200 can be decomposed into four components, each solving a specific task. Each
message may be a unique identifier (ID) corresponding to a respective tag. An encoder 210
maps random messages (in this example, bitstrings) r to the tags t. This set of tag configurations is then passed to the forward function block 215 that has been learned using the data to mimic the measurement function. This block 215 outputs a signature s of a signature array 216, to
which noise r may be added to produce a distorted signature s of a distorted signature array 218,
wherein s = s + [0021] A decoder 240 may receive s and produces i to match the original input random
bitstring r. An inverter 250 may then mirror the operation of the decoder 240, inverting i back to
the signature s. The network 200 altogether may be referred to as a decoder+inverter, which
operates as an auto-encoder that allows learning the latent structure of the signature space. With
this auto-encoder, the string r can be considered as a "latent embedding" of the value s from-4-
signature space. This latent embedding has enough information for the neural network to
correctly handle encoding and decoding even in the presence of errors.
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
Claim limitations “ a compute module configured to”, “a controller configured to”, and “wherein the distorted signature is configured to” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. the disclosure is devoid of any structure that performs the function in the claim. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to
an abstract idea (Abstract Idea) without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites a method for encoding messages. A method is one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, covers
a mental process but for recitation of generic computer components:
an encoder neural network (NN) configured to generate a tag description based on an input message; (one can mentally determine a description of data as a process of simply evaluating the data and making a judgement on the data.)
a decoder NN configured to generate an output message based on the distorted signature (one can mentally determine a message based on data as a process of simply evaluating the data and making a judgement on the data.)
and a controller configured to 1) detect an error based on a comparison of the input message and the output message (one can mentally determine the difference between two messages as a process of simply evaluating the data and making a judgement on the data.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A network for encoding messages, comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))
a compute module configured to generate a distorted signature based on the tag description and a noise model (In step 2A prong 2 generating data is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
and 2) update the encoder NN based on the error. (In step 2A prong 2 updating a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional element (iii) recites generally linking the use of the judicial exception to a particular technological environment or field of use, (iv) recites mere data gathering, and (v) recites a mere application of a computer tool, which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites The network of claim 1, wherein the compute module is further configured to: generate a signature based on the tag description; (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) and apply the noise model to the signature to generate the distorted signature. (In step 2A prong 2 applying noise is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites The network of claim 1, wherein the controller is further configured to update the decoder NN based on the error. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites The network of claim 1, wherein the tag description includes instructions for generating a tag, the tag being a coded physical representation of the input message. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Further, claim 5 recites The network of claim 4, wherein the distorted signature is configured to represent an output of the tag generated by a tag scanning device. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis applied to claim 5. Further, claim 6 recites The network of claim 5, wherein the output represented by the distorted signature is one of an image, a digital signal, and a spectrum. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites The network of claim 1, wherein the noise model is one of an additive white gaussian noise model, a bit-flip model, and a Hamming noise model. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 8 recites The network of claim 1, wherein the controller updates the encoder NN by modifying a size of a message corresponding to the tag description. (In step 2A prong 2 applying noise is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 9, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 9 recites The network of claim 1, wherein the tag description corresponds to one of a matrix barcode, a radio-frequency identification (RFID) tag, a DNA code, an electronic ink code, a magnetic microwires tag, an optochemical ink tag, and a datacules code. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 10-18, they comprise of limitations similar to those of claim 1-9 and are therefore rejected for similar rationale.
Claim Rejections - 35 USC § 103
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 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 1-4, 7, 9, 10-13, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over GARBACEA (U.S. Pub. No. US 11257507 B2) in view of SHARMA (U.S. Pub. No. US 20190306385 A1)
Regarding claim 1, GARBACEA substantially teaches the claim, including:
A network for encoding messages, comprising: an encoder neural network (NN) configured to generate a tag description based on an input message; ((col 1, line 56 - col 2 line 11) Thus in one aspect there is described a system comprising a memory for storing a set of content latent embedding vectors, and optionally a set of speaker latent embedding vectors. One or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement an encoder neural network configured to receive input audio data, e.g., digitized speech data in the time and/or frequency domain, and process the input audio data to generate an encoder output that comprises a respective encoded vector corresponding to each latent variable in a sequence of latent variables (which defines a discrete latent representation). The one or more computers also implement a subsystem configured to: provide the input audio data as input to the encoder neural network to obtain the encoder output for the input audio data, and generate a discrete latent representation of the input audio data from the encoder output.)
While GARBACEA does teach generating a tag description based on an input, it does not explicitly teach:
a compute module configured to generate a distorted signature based on the tag description and a noise model;
However, in analogous art that similarly handles tag descriptions and encoding, SHARMA teaches:
a compute module configured to generate a distorted signature based on the tag description and a noise model; ([0116] Different candidate plastic reference signals can be tested for potential confusion with the label reference signal by applying different random distortions to each candidate signal—such as tilt, rotation, and scaling, and additive Gaussian noise—and determining how frequently the reference signal detection stage of a point-of-sale watermark reader mistakes the distorted signal as the reference signal for a label watermark. )
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with SHARMA‘s signature generation and, with GARBACEA‘s tag description generation, with a reasonable expectation of success, a method for generating a distorted signature using a noise model and description, as in SHARMA, after generating a tag description using an encoder, as found in GARBACEA. A person of ordinary skill would have been motivated to increase the reliability of encoded signals (SHARMA [0040]).
GARBACEA further teaches:
a decoder NN configured to generate an output message based on the distorted signature; ((9) The one or more computers may also implement, locally or remotely, a decoder neural network. The decoder neural network may be configured to receive a decoder input derived from the discrete latent representation of the input audio data, and process the decoder input to generate a reconstruction of the input audio data. A or the subsystem may be further configured to generate the decoder input.) and a controller configured to 1) detect an error based on a comparison of the input message and the output message, and 2) update the encoder NN based on the error. (The method may further comprise processing the training decoder input through the decoder neural network in accordance with current values of the decoder network parameters of the decoder neural network to generate a training reconstruction of the training audio input, and determining a reconstruction update to the current values of the decoder network parameters and the encoder network parameters by determining a gradient with respect to the current values of the decoder network parameters and the encoder network parameters to optimize a reconstruction error between the training reconstruction and the training audio input.)
Regarding claim 2, SHARMA further teaches:
The network of claim 1, wherein the compute module is further configured to:generate a signature based on the tag description; ([0045] Despite the difference in watermark signaling protocols, the recycling system is desirably also configured with a watermark processing module adapted to read the retail watermark (as well as the recycling watermark), and to discern information from the retail watermark usable for plastic recycling purposes (commonly by reference to a database that associates retail watermark payload data to plastic information). Thus regardless of which watermark is read from an item by the recycling system, the system obtains information to control proper item sorting by plastic type.
[0046] As noted, the two watermarks' signaling protocols can differ in multiple manners, e.g., including the reference signals, and/or the encoding algorithms used. The reference signal of each watermark (sometimes termed a calibration signal, a synchronization signal, a grid signal, or a registration signal) serves as a synchronization component that enables the geometric pose of the watermark, as depicted within captured imagery, to be discerned, so that the payload can be extracted correctly. An exemplary reference signal is a constellation of plural peaks in the spatial frequency domain. A first of the two watermarks commonly includes a first reference signal, and the other watermark commonly lacks this first reference signal. (The latter watermark may include a different reference signal, e.g., comprised of different frequencies of peaks, different phases of peaks, and/or a different number of peaks.)
) and apply the noise model to the signature to generate the distorted signature. ([0116] Different candidate plastic reference signals can be tested for potential confusion with the label reference signal by applying different random distortions to each candidate signal—such as tilt, rotation, and scaling, and additive Gaussian noise—and determining how frequently the reference signal detection stage of a point-of-sale watermark reader mistakes the distorted signal as the reference signal for a label watermark.)
Regarding claim 3, GARBACEA further teaches:
The network of claim 1, wherein the controller is further configured to update the decoder NN based on the error. (The method may further comprise processing the training decoder input through the decoder neural network in accordance with current values of the decoder network parameters of the decoder neural network to generate a training reconstruction of the training audio input, and determining a reconstruction update to the current values of the decoder network parameters and the encoder network parameters by determining a gradient with respect to the current values of the decoder network parameters and the encoder network parameters to optimize a reconstruction error between the training reconstruction and the training audio input.)
Regarding claim 4, SHARMA further teaches:
The network of claim 1, wherein the tag description includes instructions for generating a tag, the tag being a coded physical representation of the input message. ([0040] Digital watermarks provide 2D optical code signals that enable machine vision in waste sorting systems, to ascertain the types of materials in each object and sort the waste stream accordingly. Encoded signals imparted into containers via 3D printed molds, laser textured molds, and etched molds, as discussed below, can be used to sort containers in various recycling environments.)
Regarding claim 7, SHARMA further teaches:
The network of claim 1, wherein the noise model is one of an additive white gaussian noise model, a bit-flip model, and a Hamming noise model. [0116] Different candidate plastic reference signals can be tested for potential confusion with the label reference signal by applying different random distortions to each candidate signal—such as tilt, rotation, and scaling, and additive Gaussian noise—and determining how frequently the reference signal detection stage of a point-of-sale watermark reader mistakes the distorted signal as the reference signal for a label watermark. )
Regarding claim 9, SHARMA further teaches:
The network of claim 1, wherein the tag description corresponds to one of a matrix barcode, a radio-frequency identification (RFID) tag, a DNA code, an electronic ink code, a magnetic microwires tag, an optochemical ink tag, and a datacules code. ([0057] Digital watermarks (hereafter watermarks) are printed on packaging for many products, and commonly serve to encode a Global Trade Item Number, or GTIN, (much like the ubiquitous 1D UPC barcodes), but in a visually non-intrusive manner. A point of sale scanner in a retail store can detect and decode the watermark data, use it to look up the product's identity and price, and add same to a shopper's checkout tally. The watermark data is typically organized in square blocks that are redundantly tiled—edge to edge—spanning some or all of the printing on the product. Because the watermark data is spatially dispersed, the scanner can read the data from different views of the product (e.g., from front and back views of a drink bottle). This matches the description of a matrix barcode)
Regarding claims 10-13, 16, and 18, they comprise of limitations similar to those of claims 1-4, 7, and 9 and are therefore rejected for similar rationale.
Claims 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over GARBACEA (U.S. Pub. No. US 11257507 B2), SHARMA (U.S. Pub. No. US 20190306385 A1) in further view of PAUL (U.S. Pub. No. US 20160027271 A1)
Regarding claim 5, while GARBACEA, as modified by SHARMA, does teach claim 4, which claim 5 is dependent upon, it does not explicitly teach:
The network of claim 4, wherein the distorted signature is configured to represent an output of the tag generated by a tag scanning device.
However, in analogous art that similarly handles tags, PAUL teaches:
The network of claim 4, wherein the distorted signature is configured to represent an output of the tag generated by a tag scanning device. ([0031] As previously described, the transmitter is activated after the tag has experienced an interaction with the user, such as, but not exclusively, the user activating button 104. This results in a first visual indication being presented to the user immediately before or immediately after the tag transmits the output signal. Furthermore, in an embodiment, the output signal represents a unique tag identification. Thus, when the output signal is transmitted to the mobile device, in an embodiment, this results in the mobile device receiving the unique tag identification. Furthermore, in an embodiment, the interaction further includes the sending of the unique tag identification and a mobile identification to the administration system from the mobile device.)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with PAUL‘s distorted signature configuration and, with GARBACEA‘s, as modified by SHARMA, signature generation, with a reasonable expectation of success, a method for configuring the signature to represent a tag output, as in PAUL, after generating the distorted signature, as found in GARBACEA, as modified by SHARMA. A person of ordinary skill would have been motivated to improve the monitoring of the tag (PAUL [0006]).
Regarding claim 6, SHARMA further teaches:
The network of claim 5, wherein the output represented by the distorted signature is one of an image, a digital signal, and a spectrum. [0116] Different candidate plastic reference signals can be tested for potential confusion with the label reference signal by applying different random distortions to each candidate signal—such as tilt, rotation, and scaling, and additive Gaussian noise—and determining how frequently the reference signal detection stage of a point-of-sale watermark reader mistakes the distorted signal as the reference signal for a label watermark.)
Regarding claims 14-15, they comprise of limitations similar to those of claim 5-6 and are therefore rejected for similar rationale
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over GARBACEA (U.S. Pub. No. US 11257507 B2), SHARMA (U.S. Pub. No. US 20190306385 A1) in further view of NITTA (U.S. Pub. No. US 20220284238 A1)
Regarding claim 8, while GARBACEA, as modified by SHARMA, does teach claim 1, which claim 8 is dependent upon, it does not explicitly teach:
The network of claim 1, wherein the controller updates the encoder NN by modifying a size of a message corresponding to the tag description.
However, in analogous art that similarly handles encoders, NITTA teaches:
The network of claim 1, wherein the controller updates the encoder NN by modifying a size of a message corresponding to the tag description. ([0032] Next, an exemplary operation of the learning apparatus 10 according to one embodiment will be described with reference to FIG. 3 as a flowchart. Note that FIG. 3 assumes an exemplary case in which a trained model for executing an image classification task of determining whether or not an image shows a car is provided and loaded on a street surveillance camera as the subject device 21. It will also be assumed that, in this exemplary case, the trained model is generated through a training process on a scalable network which is adapted to change the size of input, data and the number of network layers (which may also be called “layer number”).)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with NITTA‘s model input manipulation and, with GARBACEA‘s, as modified by SHARMA, encoder NN, with a reasonable expectation of success, a method for configuring the input size of a model through updates, as in NITTA, to train the encoder NN, as found in GARBACEA, as modified by SHARMA. A person of ordinary skill would have been motivated to improve model accuracy (NITTA [0004]).
Regarding claim 17, it comprises of limitations similar to those of claim 8 and is therefore rejected for similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKIELER A KOWALIK whose telephone number is (571)272-1850. The examiner can normally be reached 8-5.
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, Mariela D Reyes can be reached at (571)270-1006. 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.
/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights