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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/15/2026 has been entered. Claims 1 and 12 are amended and hereby entered. No claims are allowed.
Response to Arguments
Applicant's arguments filed 1/15/2026 regarding 35 USC 101 and 103 are fully considered but are not persuasive.
Regarding 35 USC 101:
The applicant submits the claims reflect a technical improvement in light of the Ex Parte Desjardins decision. The applicant highlights the technical improvement is computational efficiency and weighting accuracy, which is reflected in the claims by use of an autoencoder neural network to generate a vector representation and the application of separate weights. However, there is no technical improvement to the underlying technology itself. Generating a representation by using an autoencoder neural network, weighting the vectors, and inputting weighted vectors into a model, merely uses the technology to perform the abstract idea. In other words, analyzing relevant products by identifying features, weighting features, and ranking products describes a commonplace business method. Further, this commonplace business method is being performed by general purpose computing components, autoencoder neural networks, and machine learning. Therefore, the claims as a whole recite an abstract idea being performed by the underlying computing technology with no improvement to the underlying technology, see MPEP 2106.05(a)(II) discussing Alice Corp. The improvements cited by the applicant (computational efficiency and accuracy) are not a caused by an improvement in the technology itself. For example, the claimed system preprocesses the data before inputting the data into a machine learning model. An intended result is to allow the model to run more efficiently because the data is already processed, but there is no technical improvement in the machine learning model technology. Therefore, the examiner respectfully disagrees, and the rejection is maintained.
Regarding 35 USC 103:
The applicant’s amendments necessitate new grounds for rejection relying on a different combination of prior art, rendering the applicant’s arguments moot.
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, 3-9, 11-12, and 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claims (1 and 12) recite a method and systems to process data associated with products and give an evaluation based on the processed data. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using data to evaluate products can equivalently be achieved by human observation and evaluation of product information. For example, analyzing data and other information related to products and ranking products can be done by a human. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of receiving information, processing information, and displaying the result of the analysis (i.e. processing product information to evaluate products) on a generically claimed computer structure. The claims do not include additional elements that integrate the judicial exception into practical application or amount to significantly more than the judicial exception. The independent claims recite the additional elements “one or more processors”, “computer readable media”, “an autoencoder neural network”, and “first machine learning model”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims
Claim 3 and 14 introduce the additional element “second machine learning model”. However, simply using a machine learning model is not indicative of integration into a practical application. The “machine learning” merely acts as a modality to implement the abstract idea (i.e., the additional element is simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
Claim 15 introduces the additional elements a “computer readable media” and “one or more processors”. However, the general-purpose computing components merely act as a modality to implement the abstract idea (i.e., the additional element is simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
Claims 4-9 and 11 are directed to further embellishments of the abstract idea identified above. They introduce no new abstract ideas or new additional elements, and do further impact analysis under 35 USC 101.
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, 3-9, 11-12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Takawale (US 20240070680 A1) in view of Rama (US 20230316045 A1).
Regarding Claims 1 and 12, (substantially similar in scope and language) Takawale teaches using weighted machine learning to score and rank products based on environmental characteristics:
A system comprising: one or more processors; and one or more computer readable media storing computer executable instructions that, when executed, cause the one or more processors to perform operations comprising: [see at least Takawale: (Para 0006, 0031, 0063)]
generating a representation of one or more environmental characteristics relating to a first product, wherein the one or more environmental characteristics comprise one or more of the following: a carbon footprint metric associated with the first product; a manufacturing location of the first product; and a transport distance associated with the first product; [see at least Takawale: (Para 0035) “The sustainable metrics from suppliers and/or manufacturers typically include metrics related to: the raw materials used to make products, the design size/weight of a product, the location in which products are manufactured, etc”, (Para 0033) “Nonlimiting examples of sustainability attributes may further include the amount of energy used in manufacturing, the waste footprint, the water footprint, the downstream distribution distance, the percentage of hazardous material as input, and hazardous material byproduct while manufacturing”]
configuring a first machine learning model to perform operations comprising: applying a first weight to the representation of one or more environmental characteristics relating to the first product; [see at least Takawale: (Para 0005) “The method may include applying, by the product sustainability score predictor, machine learning to generate individual sustainability attribute scores for each product of the set of products based upon the inputted sustainability metrics, weighting the individual sustainability attribute scores based on the user's preferences”, (Para 0051-0054), (Equation 1 )]
applying a second weight to the vector representation of the first product, wherein the second weight is different to the first weight; [see at least Takawale, showing multiple distinct weights being applied to a product: (Equation 1), (Para 0051-0054); also see at least Takawale, showing a second separate weighting: (Para 0051) “For example, if a user selects life span as a metric that they prioritize, then the output from an attribute model for life span can be given more weight by the product sustainability score predictor…”
processing, the representation of one or more environmental characteristics relating to the first product and the vector representation of the first product according to their respective weights; and generating, a grading metric associated with the first product, based at least in part on the processing; [see at least Takawale (Para 0005) “The method may include applying, by the product sustainability score predictor, machine learning to generate individual sustainability attribute scores for each product of the set of products based upon the inputted sustainability metrics, weighting the individual sustainability attribute scores based on the user's preferences, and combining the weighted individual sustainability attribute scores to generate a sustainability score for each product of the set of products”]
providing, as a first input to the first machine learning model, the representation of one or more environmental characteristics relating to the first product; [see at least Takawale (Para 0005) “The method may include applying, by the product sustainability score predictor, machine learning to generate individual sustainability attribute scores for each product of the set of products based upon the inputted sustainability metrics, weighting the individual sustainability attribute scores based on the user's preferences,”]
providing, as a second input to the first machine learning model, separate from the first input, the vector representation of the first product; [see at least Takawale: (Para 0047) “The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another. Each cluster (or segment) may be identified based on the common/unifying attribute of the clustered data points. For example, in some embodiments, the data (i.e., sustainability metrics) may be clustered to identify clusters belonging to segments, such as, Scope 1, 2, or 3 data and/or the product type to which the data pertains. Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models”, (Para 0050) “Once all data is collected, segmented, and identified (or classified), a sustainability attribute model training engine 614 may train sustainability attribute models to use the sustainability metrics of a product as input to generate (or calculate) sustainability scores… After training, the model specific to waste can take a product as input and calculate a sustainability score specific to waste” ]
weighting, by the first machine learning model, the one or more environmental characteristics independently from the vector representation of the first product; [see at least Takawale, showing independent weighting: (Para 0051) “For example, if a user selects life span as a metric that they prioritize, then the output from an attribute model for life span can be given more weight by the product sustainability score predictor...”;
receiving, as an output of the first machine learning model, the grading metric associated with the first product; and [see at least Takawale: (Para 0041) “Product sustainability score predictor 406 may further receive product sustainability metrics from product sustainability metrics data sources 404. In response to receiving the inputs, product sustainability score predictor 406 may automatically apply machine learning to the inputs to generate a product specific sustainability score that indicates the sustainability of the product, as well as the user's specific preferences with respect to sustainability”]
determining, a ranking associated with the first product based on the associated grading metric relative to other products and respective grading metrics thereof. [see at least Takawale: (Para 0005) “The method may include presenting, via a display of a user interface, the subset of products ranked in order of sustainability scores from highest to lowest.”]
While Takawale teaches using weighted machine learning to score and rank products based on environmental characteristics, it does not explicitly teach use of an autoencoder to generate a vector representation of data. However, Rama teaches:
generating, by an embedding layer of an autoencoder neural network, a vector representation of a first product based on a plurality of characteristics of the first product; [see at least Rama: (Figure 2), (Para 0032) “The encoder may be represented by nodes 202-220. Nodes 202, 204, 206, 208, 210, and 212 may represent an input layer by which input data (e.g., input feature vector(s) 108, as shown in FIG. 1) are received by autoencoder 200. The encoder (or encoder network) encodes the input data (i.e., input feature vector(s) 108) into increasingly lower dimensions. That is, the encoder is configured to compress the input data (i.e., input feature vector(s) 108) into an encoded representation that is typically several orders of magnitude smaller than the input data… The bottleneck is configured to restrict the flow of data to the decoder from the encoder to force a compressed knowledge representation of input feature vector(s) 108…”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that ranks products (Takawale), with an auto encoder to generate vector representations of data (Rama). One of ordinary skill would have recognized the benefits of representing data in vectorized format in machine learning to standardize inputs. Using a machine learning model to create vector representations of data in combination with machine learning to score and rank products, yields predictable results to one of ordinary skill. See Takawale paragraph 0047 regarding vector representations in clustering for use in machine learning models: (Para 0047) “In some embodiments, the method may include segmenting the collected data by applying unsupervised learning, such as, for example, clustering. Nonlimiting examples of clustering include k-means clustering or density based clustering. The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another. Each cluster (or segment) may be identified based on the common/unifying attribute of the clustered data points. For example, in some embodiments, the data (i.e., sustainability metrics) may be clustered to identify clusters belonging to segments, such as, Scope 1, 2, or 3 data and/or the product type to which the data pertains. Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models”.
Regarding Claims 3 and 14, the combination of Takawale and Rama teach the limitations of claim 1. While Takawale teaches weighted machine learning to score and rank products based on environmental characteristics, it does not explicitly teach a second machine model that inputs features and outputs vector representations of data. However, Rama teaches:
wherein the operations further comprise: providing, as a first input to a second machine learning model, a plurality of characteristics of the first product; and receiving, as a first output of the second machine learning model, the vector representation of the first product. [see at least Rama: (Figure 2), (Para 0032) “That is, the encoder is configured to compress the input data (i.e., input feature vector(s) 108) into an encoded representation that is typically several orders of magnitude smaller than the input data. The encoder may perform a set of convolutional and pooling operations that compress the input data into the bottleneck (which is represented by nodes 222 and 224). The bottleneck is configured to restrict the flow of data to the decoder from the encoder to force a compressed knowledge representation of input feature vector(s) 108”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that ranks products (Takawale), with a machine learning model to create vector representations of data (Rama). One of ordinary skill would have recognized the benefits of representing data in vectorized format in machine learning to standardize inputs. Using a machine learning model to create vector representations of data in combination with machine learning to score and rank products, yields predictable results to one of ordinary skill. See Takawale paragraph 0047 regarding vector representations in clustering for use in machine learning models: (Para 0047) “In some embodiments, the method may include segmenting the collected data by applying unsupervised learning, such as, for example, clustering. Nonlimiting examples of clustering include k-means clustering or density based clustering. The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another. Each cluster (or segment) may be identified based on the common/unifying attribute of the clustered data points. For example, in some embodiments, the data (i.e., sustainability metrics) may be clustered to identify clusters belonging to segments, such as, Scope 1, 2, or 3 data and/or the product type to which the data pertains. Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models”.
Regarding Claim 4, the combination of Takawale and Rama teach the limitations of claim 3. Takawale further teaches:
wherein the plurality of characteristics of the first product comprise M dimensions and the vector representation of the first product comprises N dimensions, where N is greater than or equal to M. [see at least Takawale: (Para 047) “The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space”]
Regarding Claim 5, the combination of Takawale and Rama teach the limitations of claim 1. Takawale further teaches:
wherein the operations further comprise: providing, as an additional input to the first machine learning model, a vector representation of a first user. [see at least Takawale: (Pare 0020) “The disclosed system and method apply machine learning to evaluate features of products, as well as to curate a display of recommendations of products based on the evaluated features and user preferences related to the features, such that the recommendations are customized to the user preferences related to the features”, (Para 0041) “User preferences may additionally be input into product sustainability score predictor 406. As discussed above, in some cases, the user preferences may be explicit preferences the user has selected. In other cases, the user preferences may be extracted from past purchase history of the user”, (Para 0047) “ The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another… Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models.”]
Regarding Claim 6, the combination of Takawale and Rama teach the limitations of claim 5. Takawale further teaches:
wherein the operations further comprise: prior to the vector representation of the first user being provided as the additional input to the first machine learning model, combining the vector representation of the first user with a vector representation of the first product. [see at least Takawale: (Para 0047) “In some embodiments, the method may include segmenting the collected data by applying unsupervised learning, such as, for example, clustering… The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another. Each cluster (or segment) may be identified based on the common/unifying attribute of the clustered data points… Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models”]
Regarding Claim 7, the combination of Takawale and Rama teach the limitations of claim 5. While Takawale teaches weighted machine learning to score and rank products based on user characteristics, it does not explicitly teach a second machine model that inputs features and outputs vector representations of data. However, Rama teaches:
wherein the operations further comprise: providing, as a second input to the second machine learning model, a plurality of characteristics of the first user; and receiving, as a second output of the second machine learning model, the vector representation of the first user. [see at least Rama: (Figure 2), (Para 0032) “That is, the encoder is configured to compress the input data (i.e., input feature vector(s) 108) into an encoded representation that is typically several orders of magnitude smaller than the input data. The encoder may perform a set of convolutional and pooling operations that compress the input data into the bottleneck (which is represented by nodes 222 and 224). The bottleneck is configured to restrict the flow of data to the decoder from the encoder to force a compressed knowledge representation of input feature vector(s) 108”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that ranks products (Takawale), with a machine learning model to create vector representations of data (Rama). One of ordinary skill would have recognized the benefits of representing data in vectorized format in machine learning to standardize inputs. Using a machine learning model to create vector representations of data in combination with machine learning to score and rank products, yields predictable results to one of ordinary skill. See Takawale paragraph 0047 regarding vector representations in clustering for use in machine learning models: (Para 0047) “In some embodiments, the method may include segmenting the collected data by applying unsupervised learning, such as, for example, clustering. Nonlimiting examples of clustering include k-means clustering or density based clustering. The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space and the vectors may be clustered according to proximity to one another. Each cluster (or segment) may be identified based on the common/unifying attribute of the clustered data points. For example, in some embodiments, the data (i.e., sustainability metrics) may be clustered to identify clusters belonging to segments, such as, Scope 1, 2, or 3 data and/or the product type to which the data pertains. Clustering the data into segments cleans the data and makes the data ready for use as input to the sustainability attribute models”.
Regarding Claim 8, the combination of Takawale and Rama teach the limitations of claim 7. Takawale further teaches:
wherein the plurality of characteristics of the first user comprise P dimensions and the vector representation of the first user comprises Q dimensions, where Q is greater than or equal to P. [see at least Takawale: (Para 047) “The collected data may be converted into vectors (or data points) that are plotted in a multidimensional space”]
Regarding Claim 9, the combination of Takawale and Rama teach the limitations of claim 1. Takawale further teaches:
wherein the operations further comprise: compiling a list of products based on their associated rankings; and [see at least Takawale: (Para 0005) “The method may include presenting, via a display of a user interface, the subset of products ranked in order of sustainability scores from highest to lowest”]
generating a recommendation for one or more products based on list. [see at least Takawale: (Para 0022) “The subset of products may include a first set of recommendations for brands and/or models of products the user may be interested in”]
Regarding Claim 11, the combination of Takawale and Rama teach the limitations of claim 1. Takawale further teaches:
wherein the operations further comprise: receiving a user query comprising one or more search parameters; and [see at least Takawale: (Para 0022) “For example, as shown in FIG. 1, the user may input “smartphone” into a search bar 102.”]
identifying a plurality of products that satisfy the one or more search parameters, wherein the plurality of products comprises the first product. [see at least Takawale: (Para 0022) “Based on the product category selection, the disclosed system and method may generate sustainability scores for a set of products categorized in the selected product category… In response to identifying the subset of products, the system may automatically present, via a display of a user interface, the subset of products ranked in order of sustainability scores from highest to lowest. The subset of products may include a first set of recommendations for brands and/or models of products the user may be interested in”]
Regarding Claim 15, the combination of Takawale and Rama teach the limitations set forth above. Takawale further teaches:
A computer readable media storing computer executable instructions that, when executed, cause the one or more processors to perform operations according to method claim 12 [see at least Takawale: (Para 0031) “Embodiments may include a non-transitory computer-readable medium (CRM) storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the disclosed methods”, (Para 0063) “Embodiments may include a non-transitory computer-readable medium (CRM) storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the disclosed methods”]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST)
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/B.L.T./
Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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