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 .
Response to Amendment
This Action is in response to the Amendment filed September 16, 2025. Claims 23-24 are added. Claims 1, 9 and 16 have been amended. Claims 1, 3, 5, 9, 11-12, 16, 18 and 21-24 are pending and have been examined.
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, 5, 9, 11-12, 16, 18 and 21-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, claims 1, 3, 5, 9, 11-12, 16, 18 and 21-24 are directed toward at least one judicial exception without significantly more. In accordance with MPEP § 2106, the rationale for this determination is explained below:
Representative claim 1 is directed towards a method, claim 9 is directed towards a device, claim 16 is directed towards a non-transitory storage medium, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim, however is directed towards abstract ideas. The limitations that set forth the abstract ideas recite: logging in to a social platform by a User Identification (UID); obtaining a target post on a social platform, comprising: obtaining all posts on the social platform according to the UID;
obtaining advertising contents; searching the posts by key information of the advertising contents, and taking a post with key information of the advertising contents as the target post, and the target post comprising image information and text information; obtaining a second analysis result based on the text information; generating advertising message information corresponding to the target post according to the target content and the text description of the target content and/or the second analysis result, comprising: creating a corpus, which comprises a plurality of first vocabulary groups and a plurality of text replies corresponding to each of the plurality of first vocabulary groups; performing a word segmentation processing on the target content and the text description of the target content and/or the second analysis result, and obtaining a second vocabulary group; searching a target text reply corresponding to the target vocabulary group in the corpus, and setting the target text reply as the advertising message information; searching a target vocabulary group in the corpus, the target vocabulary group having a highest similarity with the second vocabulary group; and publishing the advertising message information at a message position of the target post. These limitations entail commercial interactions including, advertising, marketing or sales activities; business relations; as well as managing personal behavior including social activities; and are thus, directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (see MPEP 2106.04(d)), the additional elements provided by the claim amount to insignificant extra-solution activity. In particular the claim recites the additional element(s) of, inputting the image information into a pre-trained Convolutional Neural Network (CNN), obtaining target content by using the CNN to perform a feature extraction on the image information; inputting the target content into a pre-trained Recurrent Neural Network (RNN), obtaining text description of the target content by using the RNN to process the target content, which amounts to pre-solution data gathering and obtaining a particular data source or type of data to be manipulated in implementing the judicial exception. See MPEP 2106.05(g). Simply adding insignificant extra-solution activities, albeit applied by a neural network algorithm, is not a practical application of the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional limitations of inputting image information into a pre-trained Convolutional Neural Network (CNN), obtaining target content by using the CNN to perform a feature extraction on the image information; inputting the target content into a pre-trained Recurrent Neural Network (RNN), obtaining text description of the target content by using the RNN to process the target content. The limitation viewed individually, it is taken as insignificant pre-solution activity because such activities are used for providing the data source or type of data, incidental to the main advertisement process, see MPEP 2106.05(g). Furthermore, it is well understood and conventional to use Neural Networks algorithms for obtaining feature extraction or image features, and using image features to obtain a text description of the image features. See at least Saraee et al. (US 20220198779 A1); Pinnamaneni et al. (US 20210141997 A1); Mohanty et al. (US 20220222481 A1); Hohwald et al. (US 11017019 B1); Kuroki et al. (US 20130051687 A1); Choi et al. (US 20160048298 A1); Sikka et al. (US 20190325342 A1).
Claims 9 and 16 recitations include a processor and storage medium of an electronic device, which viewed individually, does not constitute significantly more because they simply amount to limiting the abstract idea to a particular technological environment1. Merely applying an exception using generic computer components cannot provide an inventive concept. Therefore, the limitations of the claims as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract ideas.
A review of dependent claims 3, 5, 21, 23, likewise do not recite any limitations that would remedy the deficiencies outlined above as they do not add any elements which integrate the abstract idea into a practical application or constitute significantly more. For instance, claim 23 is directed the abstract grouping of Mathematical Concepts. Thus, while they may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claims 9, 11-12, 16, 18, 22 and 24 suffer from substantially the same deficiencies as outlined with respect to claims 1, 3, 5, 21, 23 and are also rejected accordingly.
Claim Rejections - 35 USC § 103
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, 9, 11, 16 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Luciani (US Publication 2020/0357074) in view of Khoury (US Publication 2021/0224858) in further view of Rathod (US Publication 2016/0132608) and Crossley (US Publication 2020/0089769) and Saraee (US Publication 2022/0198779).
A. In regards to Claims 1, 9 and 16, Luciani teaches method, device and storage medium, comprising:
a processor; Luciani [0028];
and a non-transitory storage medium coupled to the processor and configured to store a plurality of instructions, Luciani [0087];
obtaining a target post on a social platform, comprising: obtaining advertising contents; and the target post comprising image information and text information; Luciani [0013: server accesses activities/communications from one or more social media platforms to obtain various content shared within the one or more social media platforms. This content can include posts, comments, etc.; 0067: targeted advertisements may include one or more links to a webpage or other content provided by an insurance provider; 0074: targeted advertisements may be presented to users. server may transmit targeted advertisements to each user by utilizing contact information available for each user; 0013: access activities/communications from one or more social media platforms to obtain various content shared within the one or more social media platforms. This content can include text data and image data]
obtaining a second analysis result based on the text information; Luciani [0058: dataset of social media posts, comments may be analyzed using a clustering algorithm];
generating advertising message information corresponding to the target post according to the target content and the text description of the target content and/or the second analysis result; Luciani [0038: a dataset of social media posts, comments, and other content (e.g., images, memes, etc.) may be analyzed using a clustering algorithm to identify one or more ad hoc groups that include users that share one or more characteristics; 0049: matching system generate a post within the predefined group maintained by the social media platform to advertise insurance products selected for the ad hoc group; 0060: generate targeted advertisements that may be posted to the pre-defined group within the social media platform environment];
Luciani does not specifically disclose, logging in to a social platform by a User Identification (UID); this is old and well known and disclosed by Khoury [0343: data determined when identified audience interacts online, e.g., when they logon; 0384: the system is configured for evaluating online communications, user engagement with those communications, and based on those engagements determining connections and patterns in the behaviors of the users of various social media platforms];
obtaining all posts on a social platform according to the UID; this is disclosed by Khoury [0330: tag may be generated for each content element of each post whereby a unique identifier may be associated with each piece of specific content; 0262: tracking may be performed from an initial message being crafted and distributed to a consumer interacting with the content to making a purchase of a good or service, e.g., point of sales or other customer resource/relations management data, to posting one or more comments about the good purchased and/or receiving further engagement from the seller of the goods or services; 0289: data that may be tracked may include customer identification, interaction, and/or previous purchase data, which may tracked by a RSS feed, such as with respect to their location and community demographics that may be relevant to the identified consumer to be communicated with. CRM and/or POS module may track, collect, and store personal information related to consumers, their online activities, prior engagements, purchases, demographics, social circles, social networks, and the like; 0429: information derived from the user's social media, include, user posted information, commentary they post, photos they upload, comments they respond to about the company, likes they make, up or down votes they make, purchases they make, videos or blogs they view, searches they perform, and the like; user may be tracked by their online ID, handle, phone number, computer ID, user ID, their cellular ID, etc.]
searching the posts by key information of the advertising contents, and taking a post with key information of the advertising contents as the target post; this is disclosed by Khoury [0335: scan for words, e.g., from a list of hot or active words, used in posting material by the system or its users; 0416: for example, one or more databases may be searched for content such as by employing a filter for one or more keywords, factors evidencing consumer's interests in a given subject business; 0121: for example, a template reflecting the universal look and feel of a national brand can be employed, while information pertaining to each of the localized franchisees and/or the products and services they offer that have been determined to be of interest to one or more target recipients, as well as their parameters for servicing a localized market, can be retrieved and inserted into the communication template, e.g., at one or more defined containers of one or more layers of the template, so as to generate a variety of advertisements that all have the same look and feel, but where each is uniquely catered to the local audience and/or recipients to which they are to be distributed, such as by posting on one or more social media];
before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify the teachings of Luciani with the teachings from Khoury with the motivation to provide a workflow system that can scan, collect, and evaluate content for positive and negative trends, and where one or more actions in response thereto can be suggested and/or implemented. Khoury [0375];
Luciani does not specifically disclose, logging in to a social platform by a User Identification (UID); this is old and well known and is disclosed by Rathod [0097: enables the end-user to log into an end-user account that may be managed by a network application; 0140: registered users can log-in with the Anybody to Anybody Connections, Messaging & Communication Servers from/via one or more web sites, networks; 0021: network(s) comprises communication, messaging & social network(s)];
and publishing the advertising message information at a message position of the target post; this is disclosed by Rathod [0052: advertisers prepare and providing in-line or in-text advertisements based on message(s)];
before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to modify the teachings of Luciani with the teachings from Rathod with the motivation of enabling advertisers to present advertisement to users or customers or prospective customers for particular keywords, categories at one or more advertisement spaces or in-line with messages, wherein server matches advertisement criteria including keywords, phrases, categories, location, language with user's data. Rathod [0059];
Luciani does not specifically disclose, creating a corpus, which comprises a plurality of first vocabulary groups and a plurality of text replies corresponding to each of the plurality of first vocabulary groups; this is disclosed by Crossley [0040: generate k words that each approximates a representation of a relationship between two concepts from a computing device. The request may include two input n-grams that each represents one of the two concepts. In such a case, the social-networking system may train the word embedding model with a corpus of text collected from content objects created by a group of users who satisfy the one or more conditions];
performing a word segmentation processing on the target content and the text description of the target content and/or the second analysis result, and obtaining a second vocabulary group; this is disclosed by Crossley [0027: analyze public sentiments based on word embeddings generated by a word embedding model that is trained with a large corpus of text collected from content objects created by the online social network users; 0063: social-networking system may determine a directional vector in the d-dimensional embedding space that connects from a point represented by the entity word vector to a point represented by the target attribute word vector. The social-networking system may identify k points on the directional vector that evenly split the directional vector into k+1 segments];
searching a target vocabulary group in the corpus, the target vocabulary group having a highest similarity with the second vocabulary group; this is disclosed by Crossley [0035: system may access table of word vector relationships; may look up a first word vector corresponding to the input n-gram using the table; may, for each n-gram in the table, calculate a similarity metric to the first word vector; may select k word vectors from the word vectors in the table closest to the first word vector in the embedding space based on the calculated similarity metric; 0052: select a target word vector closest to the target vector (largest cosine value) in the embedding space based on the calculated similarity metrics; the similarity metric being a cosine similarity]
searching a target text reply corresponding to the target vocabulary group in the corpus, and setting the target text reply as the advertising message information; this is disclosed by Crossley [0056: system may send a response message comprising the target n-gram. As an example send a response with ‘impromptu,’ ‘excursion,’ ‘oasis,’ and ‘luau’ as four n-grams closest to the target concept that is in an analogous relationship from ‘staycation’ to a relationship from ‘travel’ to ‘adventure’. Based on the n-grams approximating the target concept that is in an analogous relationship from ‘staycation’ to a relationship from ‘travel’ to ‘adventure,’ supermarket company may send out an ad that “have a staycation on this mothers-day in an impromptu intimate way”];
before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to modify the teachings of Luciani with the teachings from Crossley with the motivation to provide word vector concepts that a third party can evaluate so as to determine whether their campaign theme is decided suitably for the target audience and to develop their promotional campaign further. Crossley [0044];
Luciani does not specifically disclose, inputting the image information into a pre-trained Convolutional Neural Network (CNN), obtaining target content by using the CNN to perform a feature extraction on the image information; inputting the target content into a pre-trained Recurrent Neural Network (RNN), obtaining text description of the target content by using the RNN to process the target content; this is disclosed by Saraee [0023: executing neural network to generate performance score for the first image comprises generating, a feature vector comprising extracted features of the first image, each of the one or more extracted features comprising a descriptor of the first image, and applying, the generated feature vector to the neural network; 0231: various algorithms may be used for text, and other analysis to analyze data, content, etc.; example, algorithms utilizing machine learning, descriptive statistics, sequencing algorithms, numerical algorithms, optimization algorithms, deep learning, artificial intelligence, etc. may all be used in various embodiments; 0832: the content evaluation system may input an image into a feature extraction neural network; the feature extraction neural network automatically extracts features from the image; such features include descriptors of the image such as identifications of objects within the image, colors within the image, scenery within the image, etc.].
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to modify the teachings of Luciani with the teachings from Saraee with the motivation to provide a system that uses artificial intelligence and machine learning algorithms to extract features from a large corpus of content items, such as images, to figure out the aspects of the content that matter the most to an audience; Saraee [0265]; and since CNN and RNN are common and well known types of neural networks it would have been predictable and reasonable for one of ordinary skill in the art to use them, see Saraee [0421].
B. In regards to Claims 3 and 11, Luciani discloses, further comprising: extracting key information from the advertisement contents; Luciani [0022: system may receive specific activities or other information about the group; the advertisements targeted to the group provide useful data for better understanding the risk classification and categorization];
determining the post comprising the key information as the target post. Luciani [0056: data obtained by the data crawler facilitate classification of the data and identification of ad hoc groups; 0060: targeted advertisements may be posted to the pre-defined group within the social media platform environment].
C. In regards to Claims 21 and 22, Luciani does not specifically discloses, when detecting that any comment information commented on the advertising message information, publishing the advertising contents at the message position of the comment information. This is disclosed by Rathod [0052: advertisers prepare and providing in-line or in-text advertisements based on user data]. The motivation being the same as stated in claim 1.
D. In regards to Claims 23 and 24, Luciani does not specifically discloses, constructing a first word frequency vector corresponding to the first word group based on any first word group and the second word group in the corpus, and obtaining a plurality of first word frequency vectors; this is disclosed by Crossley [0035: system may access the table of word vector relationships; may look up a first word vector corresponding to the input n-gram using the table; may, for each n-gram in the table, calculate a similarity metric to the first word vector; may select k word vectors from the word vectors in the table closest to the first word vector in the embedding space based on the calculated similarity metric];
constructing a plurality of second word frequency vectors corresponding to the second word group based on the first word group and the second word group; this is disclosed by Crossley [0035: system may access the table of word vector relationships; may look up a first word vector corresponding to the input n-gram using the table; may, for each n-gram in the table, calculate a similarity metric to the first word vector; may select k word vectors from the word vectors in the table closest to the first word vector in the embedding space based on the calculated similarity metric; 0042: calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams. social-networking system may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram; 0051: generate, using a word embedding model, a table comprising unique n-grams in the list and their corresponding word vectors, wherein the word embedding model was trained using a second corpus of text collected from a plurality of user-created content objects];
calculating a plurality of cosine values between the plurality of the first word frequency vectors and the plurality of the second word frequency vectors, and obtaining a plurality of cosine values; this is disclosed by Crossley [0052: determine a target vector such that a first directional vector from a point represented by the first reference word vector to a point represented by the second reference word vector is equal to a second directional vector from a point represented by the particular word vector to a point represented by the target vector; 0027: each vector may comprise coordinates corresponding to a particular point in the embedding space, where each coordinate corresponds to a particular feature. A similarity metric of two vectors in the embedding space can be calculated; similarity metric may be a cosine similarity];
setting the first vocabulary group corresponding to the largest cosine value among the plurality of the cosine values to be the target vocabulary group. This this is disclosed by Crossley [0052: select a target word vector closest to the target vector (largest cosine value) in the embedding space based on the calculated similarity metrics; the similarity metric being a cosine similarity]. The motivation being the same as stated in claim 1.
Claims 5, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Luciani (US Publication 2020/0357074) in view of Khoury (US Publication 2021/0224858) in further view of Rathod (US Publication 2016/0132608) and Crossley (US Publication 2020/0089769) and Saraee (US Publication 2022/0198779) and Chan (US Publication 2020/0160385).
A. In regards to Claim 5, 12 and 18, Luciani discloses, further comprising: extracting key information of the text information by using a keyword extraction algorithm; Luciani [0038: dataset of social media posts, comments may be analyzed using a clustering algorithm];
Luciani does not specifically disclose, extracting emotional information of the text information by using a text emotion analysis algorithm; this is disclosed by Chan [0052: emotional information can be determined by monitoring the text entered on a user device by filtering the text for keywords such as performing the sentimental analysis];
determining the second analysis result according to the key information of the text information and the emotional information of the text information. This is disclosed by Chan [0052: sentiment analysis module can process natural language to incorporate both a linguistic and statistical analysis in evaluating the context of a communication. In text analysis, the sentiment is the attitude or opinion expressed toward something. Sentiment can be calculated based on keywords extracted and evaluated at a keyword level].
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to modify the teachings of Luciani with the teachings from Chan with the motivation to identify user behaviors during a specific emotional state and determine patterns of how the user responds to various types of advertisements in order to push advertisements to the user in a targeted manner. Chan [0045].
Response to Arguments
Applicant's filed arguments have been fully considered but have not been found persuasive.
A. Applicant argues regarding the 35 U.S.C. § 101 rejection that amended claim 1 is not directed to a judicial exception. The Examiner respectfully disagrees. The claim is directed to abstract ideas with no additional elements that integrate the abstract idea into a practical application or constitute significantly more. That the claim achieves the effect of urging potential consumers to pay active attention to advertising information, realizing precise delivery of the advertising information and improving advertising delivery efficiency, is directed to the abstract grouping of Certain Methods of Organizing Human Activity because it entails commercial interactions including, advertising, marketing or sales activities; business relations (generating advertising message information corresponding to target post); also, managing personal behavior including social activities (user logging into social platform and posting content). The proffered limitations are in and of themselves abstract ideas; and/or extra-solution activity directed to data creation for targeted advertising. See 101 analysis above. The advertising message information determined by the present application addresses an entrepreneurial problem rather than a technological one.
As such, the claims as a whole in view of Alice, do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the 35 U.S.C. § 101 rejection is maintained.
B. Applicant submits regarding the 35 U.S.C. § 103 rejection, that in Luciani, a purpose of processing the dataset of content is to identify ad hoc groups, but in amended claim 1, a purpose of processing the image information of the target post is to determining user's interest from the target post. This, however, is not cogent, as the intended use of a limitation carries no patentable weight. For instance, regarding “wherein/whereby” clauses, according to the MPEP, a “whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited” (Minton v. Nat'l Ass'n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003); MPEP 2111.04). Also, a clause that merely states the result of the limitations in the claim adds nothing to the patentability or substance of the claim (Texas Instruments Inc. v. International Trade Commission 26, USPQ2d 1018 (Fed. Cir. 1993); Griffin v. Bertina, 62 USPQ2d 1431 (Fed. Cir. 2002); Amazon.com Inc. v. Barnesandnoble.com Inc., 57 USPQ2d 1747 (CAFC 2001)). Thus, such limitations do not serve to differentiate the claims from the prior art.
Applicant’s other arguments that the references do not disclose “inputting the image information into a pre-trained Convolutional Neural Network (CNN), obtaining target content by using the CNN to perform a feature extraction on the image information; inputting the target content into a pre-trained Recurrent Neural Network (RNN), obtaining text description of the target content by using the RNN to process the target content” are moot in light of the new grounds of rejection. Applicant’s arguments relating to the dependent claims are rejected accordingly to independent claims 1, 9 and 16.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571- 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).
Prosecution Insights