DETAILED ACTION
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 .
Introduction
The following is a final Office Action in response to Applicants’ communications received on August 17, 2026. Claims 1, 3, 8, 10, 15 and 17 have been amended, claims 2, 9 and 16 have been canceled.
Currently claims 1, 3-8, 10-15 and 17-20 are pending. Claims 1, 8 and 15 are independent.
Response to Amendments
Applicants’ amendments necessitated the new ground(s) of rejection in this Office Action.
Applicants’ amendments to claims 1, 8 and 15 are NOT sufficient to overcome the 35 U.S.C. § 112(b) rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1, 3-8, 10-15 and 17-20 has been maintained.
Applicants’ amendments to claims 1, 3, 8, 10, 15 and 17 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1, 3-8, 10-15 and 17-20 has been maintained.
Response to Arguments
Applicants’ arguments filed on August 17, 2026 have been fully considered but are not persuasive.
In the Remarks on page 8, Applicants’ arguments regarding the 35 U.S.C. § 112(b) rejection that the Examiner has misapprehended the nature of the recited limitation. Claim 1 recites, among other things, “sort, aggregate, and organize the identified data in a social media data repository according to a product attribute hierarchy based on the attributes identified in each image.”
In response to Applicants’ argument, the Examiner respectfully disagrees. Claim 1 recites "sort, aggregate and organize the identified data in a social media data repository according to a product attribute hierarchy based on the attributes identified in each image", and paragraph [0062] repeats the content “trend identification system 110 identifies the product and or product attributes in particular social media posts and sort, aggregates, and organizes identified data 408a-408c in social media data repository 228 according to a product attribute hierarchy based on the attributes identified in each image”. However, neither the claims nor the Specification explain how each function is executed. At best, paragraph [0063] states that “trend identification system” may sort, aggregate and organize identified data in the social media data repository using image ingester and validator module 202, but does not explain how multiple functions are executed together. Therefore, the scope of the claims is indefinite as it is unclear to one of ordinary skill in the art whether the functions are executed parallelly or separately. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed (In re Zletz, 13 USPQ2d 1320 (Fed. Cir. 1989)).
In the Remarks on page 11, Applicants’ arguments regarding the 35 U.S.C. § 101 rejection that Applicants’ claims are not directed to “abstract ideas” such as mathematical concepts, certain methods of organizing human activity, mental processes, laws of nature or natural phenomena. The Examiner has characterized the claim at an impermissibly high level of abstraction that strips away the claim’s actual character.
In response to Applicants’ argument, the Examiner respectfully disagrees. Before determining whether the claims at issue are directed to an abstract idea, we first determining to what the claims are directed. The Federal Circuit has explained that “the ‘directed to’ inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether ‘their character as a whole is directed to excluded subject matter.’” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an “abstract idea” for which computers are invoked merely as a tool. See id. at 1335–36. Here, the claim recites “identify one or more products and one or more product attributes in on or more image”, “analyze the one or more identified products…to identify one or more trends and a social affinity score”; the Specification describes the invention relates to a system and method for calculating social affinity score for assortment planning (see ¶ 2); and paragraph [0016] discloses that “For example, assortment planning system 140 may identify one or more products which are forecasted to sell well during an upcoming planning period based on past sales of that product…select one or more trending products to include in the product assortment. A trending product comprises a combination of product attribute values (such as, for example, particular color, styles, or patterns), wherein one or more3 of the product attribute values are associated with an identified trend that is predicted to lead to an increase in consumer demand for the trending product.” It is clear from the Specification, including the claim language, that the claims focus on an abstract idea, and not on any improvement to technology and/or a technical field.
In the Remarks on page 11, Applicants’ arguments regarding the 35 U.S.C. § 101 rejection that claim 1, as amended, that the computer is configured to “identify one or more products and one or more product attributes…” This limitation cannot, as a practical matter, be performed in the human mind or by a human using pen and paper. The limitation requires “inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion”, which is an inherently computerized operation that is not amenable to mental performance; a convolutional neural network that learns model parameters in an unsupervised fashion is a specific technical mechanism that a human cannot perform mentally or with pen and paper.
In response to Applicants’ argument, the Examiner respectfully disagrees. It is noted that “identifying objects in the human brain is fundamentally a mental process that combines perception, memory, and cognitive interpretation. As such, it is an abstract idea. In fact, using a machine learning model (neural network model) is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning.
In the Remarks on page 12, Applicants’ arguments regarding the 35 U.S.C. § 101 rejection that even if Applicants’ claims recites a judicial exception…the claims as a whole integrates any alleged judicial exception into a practical application of that exception.
In response to Applicants’ argument, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. Here, the claims recite the additional elements of “a computer comprising a processor and a memory”, “a convolutional neural network model”, “a social media data repository”, “a user interface”, and the term “automatically”. The Specification describes that “Computer 180 may include one or more processors 186 and associated memory to execute instructions and manipulate information according to the operation of supply chain network 100…executing the instructions on computer 180 that cause computer to perform functions of the methods” (see ¶ 25); and “Inventory system 150 comprises a server and a database. Server 152 stores and retrieves item data from database or from one or more locations in supply chain network 100, and database 154 of inventory system 150 is configured to receive and transmit item data including item identifiers, pricing data, attribute data, inventory levels, and other like data” (see ¶ 22). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In the Remarks on page 13, Applicants’ arguments regarding the 35 U.S.C. § 101 rejection that even if claim 1 were found not to integrate any alleged exception into a practical application, the additional elements of claim 1, viewed individually and as an order combination, amount to significantly more than any alleged abstract idea itself. Specifically, “identify…by inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion to identify a product category or product attributes” is no a generic computer function performed at a high level of generality, nor is it a well-understood, routine, and conventional activity.
In response to Applicants’ argument, the Examiner respectfully disagrees. Step 2B is to determine whether any “inventive concept” which can transform the abstract idea into a patent-eligible invention. The “inventive concept” may arise in one or more of the individual claim limitations or in the ordered combination of the limitations. Alice, 134 S. Ct. at 2355. An “inventive concept” that transforms the abstract idea into a patent-eligible invention must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer. Id. at 2358.
In the present case, beyond the abstract idea, the claims recite the additional elements as discussed above. They are recited at a high level of generality and merely invoked as tools to perform generic computer functions. As to the use of “convolutional neural network model”, such generic use is conventional and will not confer eligibility. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to the term automatically, the courts have held that “Automating manual and mental processes on generic computers does not make an abstract idea patent eligible.” See Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“[A]utomation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”). With respect to the additional elements of “a computer comprising a processor and a memory”, “a social media data repository”, and “a user interface”, they are no more than generic computer components that perform generic computer functions including receiving, manipulating, and transmitting information over a network. However, using generic computer components to perform generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea.
In the Remarks on page 15, Applicants argue that neither Boal nor Fleischman, whether considered individually or in combination, teaches or suggests the amended limitation “identify one or more products and one or more product attributes in one or more images from one or more social media entities by inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion to identify a product category or product attributes”. However, Applicants’ arguments are directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Regarding claims 1, 8 and 15, they recite the limitations of "sort[ing], aggregate[ing] and organiz[ing], by the computer" render the claims indefinite because it is unclear how the functions are executed together in a single step by the computer. Applicants are required to particularly point out and distinctly claim the subject matter which applicants regard as the invention.
Dependent claims 2-7, 9-14 and 16-20 are also rejected for the same reasons as each depends on the rejected claims.
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, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1 and 3-7 are directed to a system comprising a computer processor and a memory, which falls within the statutory category of a machine. Claims 8 and 10-14 are directed to a method for trend aggregation, which falls within the statutory category of a process. Claims 15 and 17-20 are directed to a non-transitory computer-readable medium embodied with software, which falls within the statutory category of a product.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking claim 1 as representative, the claim recites limitations of “identify one or more products and one or more product attributes in one or more images by inputting the one or more images to a convolutional neural network model, sort, aggregate and organize the identified data in a social media data repository, analyze the one or more identified products, the one or more identified product attributes, and associated contextual data from the one or more data feeds to identify one or more trends and social affinity score, and present the social affinity score and the one or more identified trends”; dependent claims 3-7 further narrowing and describing the attributes of claim 1 including “one or more product attributes, the one or more trends are identified as positive or negative, store the one or more identified trends and the identified social affinity score with associated images in the social media data repository, one or more data feed configuration parameters, sort and narrow retrieved data”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a computer comprising a processor and a memory” configured to perform the steps, nothing in the claim elements precludes the steps from practically being performed in the mind, or by a human using a pen and paper. For example, the claim encompasses a person can manually identify one or more products and one or more product attributes in one or more images, sort, aggregate and organize the identified data in a social media data repository, analyze the one or more identified products, and present the identified social affinity score and the one or more identified trends in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper, which fall within the “mental processes” grouping. The mere nominal recitation of “a computer comprising a processor and a memory” do not take the claim out of the mental processes grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Further, the claim recites a concept similar to the claims discussed in Electric Power Group (e.g., collecting information, analyzing it, and displaying certain result of the collection and analysis, see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016)). Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 1 recites the additional elements of “a computer comprising a processor and a memory”, “a convolutional neural network model”, “a social media data repository”, “a user interface”, and the term “automatically”. The Specification describes that “Computer 180 may include one or more processors 186 and associated memory to execute instructions and manipulate information according to the operation of supply chain network 100…executing the instructions on computer 180 that cause computer to perform functions of the methods” (see ¶ 25); and “Inventory system 150 comprises a server and a database. Server 152 stores and retrieves item data from database or from one or more locations in supply chain network 100, and database 154 of inventory system 150 is configured to receive and transmit item data including item identifiers, pricing data, attribute data, inventory levels, and other like data” (see ¶ 22). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effect designed to monopolize the exception. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 1 recites the additional elements of “a computer comprising a processor and a memory”, “a convolutional neural network model”, “a social media data repository”, “a user interface”, and the term “automatically”. The Specification describes that “Computer 180 may include one or more processors 186 and associated memory to execute instructions and manipulate information according to the operation of supply chain network 100…executing the instructions on computer 180 that cause computer to perform functions of the methods” (see ¶ 25); and “Inventory system 150 comprises a server and a database. Server 152 stores and retrieves item data from database or from one or more locations in supply chain network 100, and database 154 of inventory system 150 is configured to receive and transmit item data including item identifiers, pricing data, attribute data, inventory levels, and other like data” (see ¶ 22). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and merely invoked as tools to perform the generic computer functions including receiving, manipulating, and transmitting data over a network. However, implementing on a generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-7 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 8-14 and 15-20 parallel claims 1-7—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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 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 of this title, 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, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Boal, (US 2021/0264467), and in view of Pollak et al., (US 2023/0177584, hereinafter: Pollak), and further in view of Fleischman et al., (US 2014/0052740, hereinafter: Fleischman).
Regarding claim 1, Boal discloses a trend aggregation system, comprising:
computer comprising a processor and a memory (see ¶ 59, ¶ 738, ¶ 744), the computer configured to:
configure one or more data feeds (see ¶ 128, ¶ 169, ¶ 528);
analyze the one or more identified products, the one or more identified product attributes, and associated contextual data from the one or more data feeds to identify one or more trends and a social affinity score (see ¶ 75, ¶ 234, ¶ 367, ¶ 394-396); and
present the identified social affinity score and the one or more identified trends by automatically populating one or more visual elements of a user interface (see ¶ 143, ¶ 495, ¶ 801, ¶ 871).
Boal discloses identifying one or more products, or categories of products (see 872); and the data sources include transaction data, customer preferences, product taxonomy and product attributes (see ¶ 697).
Boal does not explicitly disclose the following limitations; however, Pollak in an analogous art for ring attributes using a predictive model discloses
identify one or more products and one or more product attributes in one or more images from one or more social media entities by inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion to identify a product category or product attributes (see ¶ 41-42, ¶ 72, ¶ 80, ¶ 107, claim 28);
sort, aggregate and organize the identified data in a social media data repository according to a product attribute hierarchy based on the attributes identified in each image (see ¶ 66, ¶ 86, ¶ 111, ¶ 121, ¶ 128, ¶ 151, Claim 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal to include teaching of Pollak in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Boal discloses that the consumer records are associated with trust scores and/or confidence scores to indicate the reliability (see ¶ 277, ¶ 291).
Boal and Pollak do not explicitly disclose a social media affinity score; however, Fleischman in an analogous art for correlating social media content discloses
an affinity score is determined that indicates the affinity of social media user (see Abstract; ¶ 32, ¶ 41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal and in view of Pollak to include teaching of Fleischman in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Boal discloses the trend aggregation system of Claim 1, wherein the one or more identified product attributes are organized in the social media data repository according to the product attribute hierarchy (see ¶ 74-75, ¶ 151, ¶ 218, ¶ 277, ¶ 357, ¶ 505, ¶ 701).
Regarding claim 4, Boal discloses the trend aggregation system of Claim 1, wherein the one or more identified trends are identified as positive or negative (see ¶ 652-654, ¶ 668).
Regarding claim 5, Boal discloses the trend aggregation system of Claim 1, wherein the computer is further configured to:
store the one or more identified trends and the identified social affinity score with associated images in the social media data repository (see ¶ 71-72, ¶ 130, ¶ 397, ¶ 875).
Regarding claim 6, Boal discloses the trend aggregation system of Claim 1, wherein the one or more data feeds are configured using one or more data feed configuration parameters (see ¶ 128, ¶ 137, ¶ 403-406, ¶ 528).
Regarding claim 7, Boal discloses the trend aggregation system of Claim 1, wherein the one or more data feeds are configured to sort and narrow retrieved data (see ¶ 152, ¶ 371, ¶ 393, ¶ 496, ¶ 528).
Regarding claim 8, Boal discloses a computer-implemented method of trend aggregation, comprising:
configuring, using a computer comprising a processor and a memory (see ¶ 59, ¶ 738, ¶ 744), one or more data feeds (see ¶ 128, ¶ 169, ¶ 528);
analyzing, by the computer, the one or more identified products, the one or more identified product attributes, and associated contextual data from the one or more data feeds to identify one or more trends and a social affinity score (see ¶ 75, ¶ 234, ¶ 367, ¶ 394-396); and
presenting, by the computer, the identified social affinity score and the one or more identified trends by automatically populating one or more visual elements of a user interface (see ¶ 143, ¶ 495, ¶ 801, ¶ 871).
Boal discloses identifying one or more products, or categories of products (see 872); and the data sources include transaction data, customer preferences, product taxonomy and product attributes (see ¶ 697).
Boal does not explicitly disclose the following limitations; however, Pollak in an analogous art for ring attributes using a predictive model discloses
identifying, by the computer, one or more products and one or more product attributes in one or more images from one or more social media entities by inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion to identify a product category or product attributes (see ¶ 41-42, ¶ 72, ¶ 80, ¶ 107, claim 28);
sorting, aggregating and organizing, by the computer, the identified data in a social media data repository according to a product attribute hierarchy based on the attributes identified in each image (see ¶ 66, ¶ 86, ¶ 111, ¶ 121, ¶ 128, ¶ 151, Claim 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal to include teaching of Pollak in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Boal discloses that the consumer records are associated with trust scores and/or confidence scores to indicate the reliability (see ¶ 277, ¶ 291).
Boal and Pollak do not explicitly disclose a social media affinity score; however, Fleischman in an analogous art for correlating social media content discloses
an affinity score is determined that indicates the affinity of social media user (see Abstract; ¶ 32, ¶ 41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal and in view of Pollak to include teaching of Fleischman in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10, Boal discloses the computer-implemented method of Claim 8, wherein the one or more identified product attributes are organized in the social media data repository according to a product attribute hierarchy (see ¶ 74-75, ¶ 151, ¶ 218, ¶ 277, ¶ 357, ¶ 505, ¶ 701).
Regarding claim 11, Boal discloses the computer-implemented method of Claim 8, wherein the one or more identified trends are identified as positive or negative (see ¶ 652-654, ¶ 668).
Regarding claim 12, Boal discloses the computer-implemented method of Claim 8, further comprising:
storing, by the computer, the one or more identified trends and the identified social affinity score with associated images in the social media data repository (see Fig. 28, #2830; ¶ 71-72, ¶ 130, ¶ 394-397, ¶ 875).
Regarding claim 13, Boal discloses the computer-implemented method of Claim 8, wherein the one or more data feeds are configured using one or more data feed configuration parameters (see ¶ 128, ¶ 137, ¶ 403-406, ¶ 528).
Regarding claim 14, Boal discloses the computer-implemented method of Claim 8, wherein the one or more data feeds are configured to sort and narrow retrieved data (see ¶ 152, ¶ 371, ¶ 393, ¶ 496, ¶ 528).
Regarding claim 15, Boal discloses a non-transitory computer-readable medium embodied with software for trend Specification aggregation, the software when executed by a processor (see ¶ 744), cause the processor to:
configure one or more data feeds (see ¶ 128, ¶ 169, ¶ 528);
analyze the one or more identified products, the one or more identified product attributes, and associated contextual data from the one or more data feeds to identify one or more trends and a social affinity score (see ¶ 75, ¶ 234, ¶ 367, ¶ 394-396); and
present the identified social affinity score and the one or more identified trends by automatically populating one or more visual elements of a user interface (see ¶ 143, ¶ 495, ¶ 801, ¶ 871).
Boal discloses identifying one or more products, or categories of products (see 872); and the data sources include transaction data, customer preferences, product taxonomy and product attributes (see ¶ 697).
Boal does not explicitly disclose the following limitations; however, Pollak in an analogous art for ring attributes using a predictive model discloses
identify one or more products and one or more product attributes in one or more images from one or more social media entities by inputting the one or more images to a convolutional neural network model that learns model parameters in an unsupervised fashion to identify a product category or product attributes (see ¶ 41-42, ¶ 72, ¶ 80, ¶ 107, claim 28);
sort, aggregate and organize the identified data in a social media data repository according to a product attribute hierarchy based on the attributes identified in each image (see ¶ 66, ¶ 86, ¶ 111, ¶ 121, ¶ 128, ¶ 151, Claim 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal to include teaching of Pollak in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Boal discloses that the consumer records are associated with trust scores and/or confidence scores to indicate the reliability (see ¶ 277, ¶ 291).
Boal and Pollak do not explicitly disclose a social media affinity score; however, Fleischman in an analogous art for correlating social media content discloses
an affinity score is determined that indicates the affinity of social media user (see Abstract; ¶ 32, ¶ 41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boal and in view of Pollak to include teaching of Fleischman in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17, Boal discloses the non-transitory computer-readable medium of Claim 15, wherein the one or more identified product attributes are organized in the social media data repository according to a product attribute hierarchy (see ¶ 74-75, ¶ 151, ¶ 218, ¶ 277, ¶ 357, ¶ 505, ¶ 701).
Regarding claim 18, Boal discloses the non-transitory computer-readable medium of Claim 15, wherein the one or more identified trends are identified as positive or negative (see ¶ 652, ¶ 654, ¶ 668).
Regarding claim 19, Boal discloses the non-transitory computer-readable medium of Claim 15, wherein the software when executed by a processor, further cause the processor to: store the one or more identified trends and the identified social affinity score with associated images in the social media data repository (see Fig. 28, #2830; ¶ 71-72, ¶ 130, ¶ 394-397, ¶ 875).
Regarding claim 20, Boal discloses the non-transitory computer-readable medium of Claim 15, wherein the one or more data feeds are configured using one or more data feed configuration parameters (see ¶ 128, ¶ 137, ¶ 403-406, ¶ 528).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Psota et al., (US 2014/0258032) discloses a method for aggregating the transactions and using the transactions as training set to predict a particular transaction with a type of supplier, a type of product, and product attribute.
Cecchi et al., (US 2018/0089739) discloses a method for predicting consumer response to a stimulus based on factory characteristics of the stimulus associated with a product.
Adato et al., (WO 2019/140091) discloses a system for automatically monitoring retail products based on captured images of products.
Sewak (US 2019/0318304) discloses a method for determining a plurality of f-score for a plurality of products in an inventory based on historical data associated with the plurality of products.
Garrity et al., (US 2024/0193526) discloses a social media processing system for identifying social streams from the social media associated with a particular client.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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