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 .
DETAILED ACTION
Claims 1-20 are pending and are considered in this Non-Final Office action.
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 therefore, subject to the
conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In accordance with Step 1, it is first noted that the claimed non-transitory computer-readable medium in claims 1-15; claimed method in claims 16-19 and a device in claim 20 are directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 1-20.
In accordance with Step 2A, Prong One, claims 1-20, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements recited in bold):
Claim 1:
A non-transitory computer-readable medium having instructions recorded thereon that, in response to execution by one or more processors, cause performance of operations comprising: preparing a plurality of product webpage datasets, each product webpage dataset including a plurality of attribute values extracted from a product webpage among a plurality of product webpages and a plurality of attribute types, wherein each attribute value among the plurality of attribute values is associated with a corresponding attribute type among the plurality of attribute types;
applying a quality determining model to the plurality of product webpage datasets to produce a quality value associated with each attribute value among the plurality of attribute values included in a target product webpage dataset among the plurality of product webpage datasets; and
applying an attribute value predicting model to the plurality of product webpage datasets to produce a predicted attribute value for a target attribute value associated with a quality value lower than a threshold quality value, wherein the target attribute value is among the plurality of attribute values included in the target product webpage dataset; wherein a featured product is described by each attribute value among the plurality of attribute values included in each product webpage dataset among the plurality of product webpage datasets.
Claim 16:
A method comprising: preparing a plurality of product webpage datasets, each product webpage dataset including a plurality of attribute values extracted from a product webpage among a plurality of product webpages and a plurality of attribute types, wherein each attribute value among the plurality of attribute values is associated with a corresponding attribute type among the plurality of attribute types; applying a quality determining model to the plurality of product webpage datasets to produce a quality value associated with each attribute value among the plurality of attribute values included in a target product webpage dataset among the plurality of product webpage datasets; and applying an attribute value predicting model to the plurality of product webpage datasets to produce a predicted attribute value for a target attribute value associated with a quality value lower than a threshold quality value, wherein the target attribute value is among the plurality of attribute values included in the target product webpage dataset; wherein a featured product is described by each attribute value among the plurality of attribute values included in each product webpage dataset among the plurality of product webpage datasets.
Claim 20:
A device comprising: a controller including circuitry configured to perform operations including preparing a plurality of product webpage datasets, each product webpage dataset including a plurality of attribute values extracted from a product webpage among a plurality of product webpages and a plurality of attribute types, wherein each attribute value among the plurality of attribute values is associated with a corresponding attribute type among the plurality of attribute types, applying a quality determining model to the plurality of product webpage datasets to produce a quality value associated with each attribute value among the plurality of attribute values included in a target product webpage dataset among the plurality of product webpage datasets, and applying an attribute value predicting model to the plurality of product webpage datasets to produce a predicted attribute value for a target attribute value associated with a quality value lower than a threshold quality value, wherein the target attribute value is among the plurality of attribute values included in the target product webpage dataset, and wherein a featured product is described by each attribute value among the plurality of attribute values included in each product webpage dataset among the plurality of product webpage datasets.
The above-recited italicized limitations viewed as an abstract idea are certain methods of organizing
human activity (i.e., fundamental economic principles or practices (including hedging, insurance,
mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations); managing
personal behavior or relationships or interactions between people (including social activities,
teaching, and following rules or instructions)) and mental processes (i.e., concepts performed in the
human mind (including an observation, evaluation, judgment, opinion). The claimed invention is
directed to a process of evaluating product webpage data for attribute values with an attribute value predicting model for determining attribute value quality, which is a mental process. Applicant’s Specification, in paragraph ¶0002, identifies that the “in marketplace websites, many individual product webpages are managed. Product webpages are constantly being created in response to new products entering the market. As products are modified, corresponding webpages are updated. Many marketplace websites operate in a similar manner, with product webpages having similar product attributes.” It is clear that the claimed process of determining and predicting product webpage attribute value quality is to maintain a marketplace website, which is a method of organizing human activity. Accordingly, the claims recite mental processes and certain methods of organizing human activity.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application
because the use of bolded additional elements for receiving/transmitting data (e.g., preparing a plurality of product webpage datasets, each product webpage dataset including a plurality of attribute values extracted from a product webpage among a plurality of product webpages and a plurality of attribute types, wherein each attribute value among the plurality of attribute values is associated with a corresponding attribute type among the plurality of attribute types; etc.); processing data (e.g., applying a quality determining model to the plurality of product webpage datasets to produce a quality value associated with each attribute value among the plurality of attribute values included in a target product webpage dataset among the plurality of product webpage datasets, and applying an attribute value predicting model to the plurality of product webpage datasets to produce a predicted attribute value for a target attribute value associated with a quality value lower than a threshold quality value, wherein the target attribute value is among the plurality of attribute values included in the target product webpage dataset, and wherein a featured product is described by each attribute value among the plurality of attribute values included in each product webpage dataset among the plurality of product webpage datasets; etc.); storing data; displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims.
In accordance with Step 2B, the claims only recite the above bolded additional elements. The
additional elements are recited at a high-level of generality (i.e., as a generic computer for evaluating product webpage data for attribute values with an attribute value predicting model for determining attribute value quality) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification, ¶0054-0056, “device 660 includes processor 662, memory 663, storage component 664, input component 666, output component 667, communication interface 668, and bus 669. The processor 662, as used herein, means any type of computational circuit that may comprise hardware elements and software elements. The processor 662 may be embodied as a multi-core processor, a single core processor, or a combination of one or more multi-core processors and/or one or more single core processors, a distributed processing system, or the like. The processor 662 may be a Central Processing Unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), or another type of processing component. Memory 663 includes a non-transitory computer readable medium. Memory 663 includes a random-access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by processor 662. The memory 663 comprises machine-readable instructions which are executable by the processor 662. These machine-readable instructions when executed by the processor 662 cause the processor 662 to perform one or more method steps of an embodiment described above.” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that “receiving or transmitting data over a network, e.g., using the Internet to gather data” (See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and transmits attribute value data) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
Dependent claims 2 and 17 recite limitations that describe the organization of product attribute data in a report. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claims 3-5 and 18-19 recite limitations that further narrow the metes and bounds of the abstract idea, such that they describe observing through detection of the attribute type and modifying a webpage dataset. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claims 6-7 recite limitations that further narrow the metes and bounds of the abstract idea, such that they describe evaluating attribute types through applying a mathematical weight. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claims 8-10 recite limitations that further narrow the metes and bounds of the abstract idea, such that they describe how the attribute data is gathered for datasets by extracting values from a webpage. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claims 11-12 recite limitations that further narrow the metes and bounds of the abstract idea, such that they describe how the attribute data is gathered for datasets by extracting values from a webpage. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claims 13-15 recite limitations that further narrow the metes and bounds of the abstract idea, such that they describe the type of data included in the dataset. These limitations further narrow the abstract idea, but do not recite additional elements that practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy these deficiencies.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Balakrishnan et al. (United States Patent Application Publication, 2022/0058227, hereinafter referred to as Balakrishnan).
As per claim 1, Balakrishnan discloses a non-transitory computer-readable medium having instructions recorded thereon that, in response to execution by one or more processors, cause performance of operations comprising:
preparing a plurality of product webpage datasets, each product webpage dataset including a plurality of attribute values extracted from a product webpage among a plurality of product webpages and a plurality of attribute types, wherein each attribute value among the plurality of attribute values is associated with a corresponding attribute type among the plurality of attribute types (Balakrishnan: Fig. 2A-2B and ¶0054-0055 and 0058: Attributes may be extracted from multiple websites to create structured datasets of product data, whereas the extracted data includes raw attribute values. See ¶0106 where the product data record with the associated product attributes are also associated with a corresponding attribute type/category.);
applying a quality determining model to the plurality of product webpage datasets to produce a quality value associated with each attribute value among the plurality of attribute values included in a target product webpage dataset among the plurality of product webpage datasets (Balakrishnan: ¶0106-0107: where the attributes are predicted by a machine learning model operated by a Predictor. The machine learning model determines an estimated attribute value used to updated the target product data record in the dataset. See ¶0111 where it is trained to facilitate the ML models in predicting accurate product information.); and
applying an attribute value predicting model to the plurality of product webpage datasets to produce a predicted attribute value for a target attribute value associated with a quality value lower than a threshold quality value, wherein the target attribute value is among the plurality of attribute values included in the target product webpage dataset; wherein a featured product is described by each attribute value among the plurality of attribute values included in each product webpage dataset among the plurality of product webpage datasets (Balakrishnan: ¶0067-0069: A machine learning model is trained to identify a product attribute based on its features. The machine learning model may be trained with training examples comprising feature sets of HTML elements and their corresponding output labels predicting what product attribute they correspond to, or whether they do not correspond to a product attribute. See ¶0093-0094 where a similarity score is used by the system that applies a threshold with the product data records in the webpage datasets to determine if the predictive attribute corresponds to an accurate product attribute value. The output of the model is an enriched product data record that represents a featured product, via product code, represented by its plurality of attribute values. Examiner notes that Applicant’s Specification, in ¶0034, recites that an “attribute value predicting model 306 is trained to predict an attribute value in the target product webpage dataset based on attribute values of the same attribute type in other datasets in product webpage datasets of featured product.”).
Claims 16 and 20 recite limitations already rejected by the rejection of claim 1; therefore, the same rejection applies.
As per Claim 2, Balakrishnan discloses the computer-readable medium of claim 1, wherein the operations further comprise generating a report for the target product webpage, the report including the target attribute value and the predicted attribute value (Balakrishnan: See ¶0151 where the product target value is placed with the predicted attribute value and determined in the output of the machine learning model. See ¶0103 where the display of the reported output is published to the user interface.)
Claim 17 recites limitations already rejected by the rejection of claim 2; therefore, the same rejection applies.
As per Claim 3, Balakrishnan discloses the computer-readable medium of claim 1, wherein the operations further comprise modifying the target product webpage to replace the target attribute value and the predicted attribute value (Balakrishnan: ¶0119-0120: Predictor estimates product data, where the estimated product data parameters replace null data fields in the product data record. See ¶0151 where the product target value is placed with the predicted attribute value.).
Claim 18 recites limitations already rejected by the rejection of claim 3; therefore, the same rejection applies.
As per Claim 4, Balakrishnan discloses the computer-readable medium of claim 1, wherein the operations further comprise detecting an attribute type among the plurality of attribute types of at least one product webpage dataset that is not included in the plurality of attribute types of the target webpage dataset (Balakrishnan: ¶0110: The predictor may determine similarity between attribute type applying weight values to attributes according to a ranked list of isolated importance of attributes. See ¶0119-0121 where the similarity score is used to detect matching product data. If matching information is found in the databases, it is determined if the matching information is itself available product data. If so, the product data record is augmented with the available product data).
Claim 19 recites limitations already rejected by the rejection of claim 4; therefore, the same rejection applies.
As per Claim 5, Balakrishnan discloses the computer-readable medium of claim 1, wherein the operations further comprise adding the detected attribute type and corresponding attribute value included in the at least one product webpage dataset to the target product webpage dataset (Balakrishnan: ¶0110: The predictor may determine similarity between attribute type applying weight values to attributes according to a ranked list of isolated importance of attributes. See ¶0119-0121 where the similarity score is used to detect matching product data. If matching information is found in the databases, it is determined if the matching information is itself available product data. If so, the product data record is augmented with the available product data).
As per Claim 6, Balakrishnan discloses the computer-readable medium of claim 1, wherein applying the quality determining model includes applying one or more weight values to the plurality of attribute types (Balakrishnan: ¶0110: The predictor may determine similarity between attribute type applying weight values to attributes according to a ranked list of isolated importance of attributes.).
As per Claim 7, Balakrishnan discloses the computer-readable medium of claim 1, wherein the one or more weight values are included in a weight set corresponding to a target attribute type among the plurality of attribute types (Balakrishnan: ¶0110: The predictor may determine similarity between attribute type applying weight values to attributes according to a ranked list of isolated importance of attributes. This method of ranking importance of targets valuable attribute types over less desirable/valuable types.)
As per Claim 8, Balakrishnan discloses the computer-readable medium of claim 1, wherein the preparing includes extracting, from each product webpage among the plurality of product webpages, the plurality of attribute values (Balakrishnan: ¶0125: The retriever module crawls product webpages to obtain product data and extract product attributes and product attribute values.).
As per Claim 9, Balakrishnan discloses the computer-readable medium of claim 1, wherein the preparing further includes associating, with each attribute value among the plurality of attribute values extracted from each product webpage among the plurality of product webpages, the corresponding attribute type (Balakrishnan: ¶0069-0071: The machine learning model may be used according to attribute type (e.g. color attribute, size attribute, etc.). Therefore, the extraction of attribute values may correspond to attribute type through meta-tags.).
As per Claim 10, Balakrishnan discloses the computer-readable medium of claim 1, wherein the preparing further includes assembling the plurality of product webpage datasets (Balakrishnan: ¶0081, 0083 and 0085: Attributes and product data are extracted from product webpages and assembled, mapped and standardized in a dataset of a product database.).
As per Claim 11, Balakrishnan discloses the computer-readable medium of claim 1, wherein the quality determining model is trained for a product category of the featured product (Balakrishnan: ¶0106-0107: where the attributes are predicted by a machine learning model operated by a Predictor for categories. See ¶0111 where it is trained to facilitate the ML models in predicting accurate product information.).
As per Claim 12, Balakrishnan discloses the computer-readable medium of claim 1, wherein the attribute predicting model is trained for the product category of the featured product (Balakrishnan: ¶0067-0069: A machine learning model is trained to identify a product attribute based on its features. The machine learning model may be trained with training examples comprising feature sets of HTML elements and their corresponding output labels identifying what product attribute they correspond to, or whether they do not correspond to a product attribute. See also ¶0088 where the training of the model pertains to a specific brand or category of products. Examiner notes that Applicant’s Specification, in ¶0034, recites that an “attribute value predicting model 306 is trained to predict an attribute value in the target product webpage dataset based on attribute values of the same attribute type in other datasets in product webpage datasets of featured product.”).
As per Claim 13, Balakrishnan discloses the computer-readable medium of claim 1, wherein the plurality of product webpages are in HTML (Balakrishnan: ¶0064 and 0070: Product webpages where the elements retrieved are HTML.).
As per Claim 14, Balakrishnan discloses the computer-readable medium of claim 1, wherein a format of each product webpage dataset among the plurality of product webpage datasets is one of JSON, XML, or YAML (Balakrishnan: ¶0070: The format of the product webpage dataset is JSON.).
As per Claim 15, Balakrishnan the computer-readable medium of claim 1, wherein the target product webpage dataset includes a product image (Balakrishnan: ¶0073: Webpage elements included in the dataset include a retrieved image.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Geva (US 10,042,895): A product matching system, comprising a memory configured to store a database of first product records, each record associated with values of one or more first attributes describing a respective product corresponding to the record. In addition, the system includes an input interface configured to receive a second product record associated with values of one or more second attributes describing a given product. A processing unit adapted to compare the values of the second attributes of second product records received through the input interface to the attributes associated with first product records in the database, to link the second attributes of second records determined to match a first record with the matching first database record, and to use the second attributes of the second records determined to match a first record, in comparing the first database record to further product records that are subsequently received through the input interface.
Wang et al. (US 12,541,782): Embodiments of the specification provide a product object publishing method and a system. The method includes: obtaining an image of a product object; sending the image to a server; receiving a plurality of types of product attribute information obtained by image recognition on the image of the product object performed by an image recognizer set; displaying the plurality of types of product attribute information for selection by a user; generating structured information of the product object according to selection of the product attribute information; and publishing the product object, wherein the publishing comprises publishing the image and the structured information of the product object. Accuracy and efficiency of product object publishing can be improved.
Ripley (US 2020/0286045): A system may include an interface communicatively coupled to a network, a processor, and a memory to store instructions that cause the processor to receive data about a retail product from one of a retailer system and a supplier system through the network. The instructions may further cause the processor to automatically parse and index attributes from the received data, determine one or more classifications based on the indexed attributes, and provide retail product data including the one or more classifications to the retailer system via the network.
Fang et al. (US 2025/0245246): Systems and methods of attribute extraction and labelling are disclosed. An input dataset is received and a plurality of preliminary attribute labels are generated for at least a first attribute of a first element in the input dataset. Each preliminary attribute label in the plurality of preliminary attribute labels is generated by one of a plurality of large language models (LLM). A final attribute label for the first attribute is generated based on a weighted combination of the plurality of preliminary attribute labels for the first attribute and a data structure representative of the first element is updated to include the final attribute label for the first attribute.
Chandrasekhar et al. (US 2022/0284392): Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automated extraction, inference and normalization of structured attributes for a Product Category Normalizer to access product records from external data sources. The Product Category Normalizer defines a product-description taxonomy of product categories represented by a classification tree. The Product Category Normalizer employs product attribute data and machine learning techniques to analyze the product data of an input product record. Based on the product data of the input product record, the Product Category Normalizer extracts and infers appropriate product data for a relevant product category in the classification tree for the item described by the input product record. The Product Category Normalizer normalizes the product data of the input product record. The Product Category Normalizer provides an output normalized product record related to a product category and product attributes of the classification tree.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
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, Brian Epstein can be reached at 5712705389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625
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