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 .
Election/Restrictions
1. Applicant’s election without traverse of Group I [claims 1-5] in the reply filed on 05/29/2026 is acknowledged.
Claims 6-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/29/2026.
Applicant's supplemental amendment filed 07/07/2026 is entered. Claims 6-20 are canceled and new claims 21-30 are added.
Currently claims 1-5, 21-30 are pending for examination.
2. Continuity: filed 09/09/2024 is a Continuation of 17827359, filed 05/27/2022, now U.S. Patent # 12112365, 17827359 is a Continuation of 15957822, filed 04/19/2018, now U.S. Patent # 11373230.
Examiner has reviewed and compared the limitations of the patents 12112365 and 11373230 with the current pending limitations of the instant application and they do not warrant a double patenting rejection.
Claim Rejections - 35 USC § 101
3. 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 1-5, and 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-5 are to a system /apparatus, clams 21-25 to a process comprising a series of steps, and claim 16-20 to manufacture, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1. (Original) A computing system, comprising:
one or more processors; and a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors to at least:
(i) obtain a plurality of textual content associated with a corpus of content collections;
(ii) generate, based at least in part on a plurality of key textual terms, a template feature vector having a plurality of feature elements, wherein the plurality of feature elements correspond to the plurality of key textual terms;
(iii) generate, based at least in part on content similarity of content included the corpus of content collections, a plurality of content collection groups by aggregating at least some content collections of the corpus of content collections;
(iv) for each content collection group of the plurality of content collection groups:
(a)determine a set of representative content collections that are representative of each content collection group and based at least in part on at least one of common text terms of content included in each content collection group or common content included in each content collection group;
(b)determine a plurality of similar feature elements among content items included in the set of representative content collections, the plurality of similar feature elements including common elements among content items of each content collection group; and
( c ) generate a respective feature vector by modifying the template feature vector to include the plurality of similar feature elements; and
(v) generate a taste graph for organizing the corpus of content collections, each node of the taste graph corresponding to a content collection group of the plurality of content collection groups and its respective feature vector.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-5, 21-20 recite abstract idea.
The high- lighted limitations in claim 1 comprising, " generate, based at least in part on a plurality of key textual terms, a template feature vector having a plurality of feature elements, wherein the plurality of feature elements correspond to the plurality of key textual terms; generate, based at least in part on content similarity of content included the corpus of content collections, a plurality of content collection groups by aggregating at least some content collections of the corpus of content collections; for each content collection group of the plurality of content collection groups: determine a set of representative content collections that are representative of each content collection group and based at least in part on at least one of common text terms of content included in each content collection group or common content included in each content collection group; determine a plurality of similar feature elements among content items included in the set of representative content collections, the plurality of similar feature elements including common elements among content items of each content collection group; and generate a respective feature vector by modifying the template feature vector to include the plurality of similar feature elements; and generate a taste graph for organizing the corpus of content collections, each node of the taste graph corresponding to a content collection group of the plurality of content collection groups and its respective feature vector", under their broadest reasonable interpretation, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. That is, other than reciting “by one or more processors” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “by the one or more processors” language, the claim encompasses a person looking at data collected [digital contents, such as textual content or images] and generating a template [guide] feature vector using key textual terms from the collection of contents, determine a representative collection based on common text terms, and determine similar feature elements from the content collections and based upon that modify the template feature vector. Further, the human operator can generate a taste graph [likeliness graph] for a user's preferences to organize the corpus of content collections. The mere nominal recitation of by one or more processors does not take the claim limitations out of the mental process grouping. Accordingly, the claim 1 and its dependent claims 205 recite a mental process. Since the other two independent claims 21 and 26 recite similar limitations as claim 1, they including with their dependent claims 22-25 and 27-30 respectively are analyzed on the same basis reciting mental process.
Thus, all pending claims 1-5, 21-30 recite an abstract idea [Step 2A=Yes).
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-5, 21-30: The judicial exception is not integrated into a practical application.
The claim 1 recites additional elements comprising generic one or more processors executing the steps of: (i) obtain a plurality of textual content associated with a corpus of content collections; (ii)generate, based at least in part on a plurality of key textual terms, a template feature vector having a plurality of feature elements, wherein the plurality of feature elements correspond to the plurality of key textual terms; (iii) generate, based at least in part on content similarity of content included the corpus of content collections, a plurality of content collection groups by aggregating at least some content collections of the corpus of content collections; (iv) for each content collection group of the plurality of content collection groups: (a)determine a set of representative content collections that are representative of each content collection group and based at least in part on at least one of common text terms of content included in each content collection group or common content included in each content collection group; (b)determine a plurality of similar feature elements among content items included in the set of representative content collections, the plurality of similar feature elements including common elements among content items of each content collection group; and ( c ) generate a respective feature vector by modifying the template feature vector to include the plurality of similar feature elements; and (v) generate a taste graph for organizing the corpus of content collections, each node of the taste graph corresponding to a content collection group of the plurality of content collection groups and its respective feature vector. ".
The limitations in step (i) “obtain a plurality of textual content associated with a corpus of content collections;” are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Further, this step is performed by a computer processor recited at a high level of generality and the processor is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). In limitations (II), (iii) (iv-a, b, c,) and (v) the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Accordingly, even in combination, the additional elements in claim 1 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim 1 is directed to an abstract idea. Since the limitations of the other independent claim 26 are similar to the limitations of claim 1, it is analyzed on the same basis as directed to an abstract idea.
Since the independent claim 21, as drafted, does not recite engagement of a computer or any other device to implement the recited steps, the additional element is step (i) of obtaining content associated with a corpus of content collections, which is a generic function which can be done by both using a computer or manually and as such do not add any meaningful limits on practicing the abstract idea. As such claim 21 is directed to the abstract idea.
Dependent claims 2 and 5 recite limitations are mere descriptive subject matter amounting to non-functional descriptive data, and the limitations of dependent claim 3 merely recites clustering textual content into textual clusters and claims 3 and 4 recite making determinations key textual terms from the textual clusters, based on one of many criteria, which, similar to claim 1 recite mental processes which do not impose any meaningful limits on practicing the abstract idea. Therefore, dependent claims 2-5, similar to claim 1, are directed to an abstract idea. Since the limitations of the dependent claims 22-25 and 27-30 are similar to the claims of 2-5, they are analyzed on the same bases as directed to the abstract idea.
Even when viewed individually and in combination, the additional elements in claims 1-5, 21-30 do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claims 1-5, 21-30 are directed to the judicial exception. (Step 2A: YES).
Step 2A=Yes. Claims 1-5, 21-30 are directed to abstract ideas.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-5, 21-30 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claims recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim. As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-5, 21-30 amount to no more than mere instructions to apply the exception using a generic -computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using generic- computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The additional element "obtaining a plurality of textual content…" in claims 1, 21, and 26 was considered extra-solution activity, or is well-understood, conventional and routine activity in step 2A. As per MPEP 2106, a conclusion that an additional element or elements is/are extra-solution activity, or are well-understood, conventional and routine activity in step 2A should be re-evaluated in step 2B. The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere receiving, acquiring, transmitting, and displaying steps using a generic computer is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered individually and in combination, the additional elements in claims 1-5, 21-30 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus, the limitations, as recited, in pending claims 1-5, 21-30 are not patent eligible.
4. Prior Art Discussion:
Reference independent claims 1, 21, and 26, the best prior art Anderson et al. [US 20150242750 A1 cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above [see FIGS 1-2, paras 0017, 0019, and 0021. Pars 0017-00Figures 1-2] teaches a computer-implemented system storing a user generated content collection of digital contents of different types such as a book, a movie, a picture or a collection of songs and the system identifies based on an analysis a first feature vector in the form of a popular jazz artist from the collection of songs, wherein Para 0019 describes that the feature vector is determined based on at least in part on a computer implemented analysis of the first plurality of digital items identifying, by the computing system, a first plurality of representative digital content collections from a plurality of representative digital content collections based at least in part on the feature vector and a plurality of representative feature vectors associated with the plurality of representative digital content collections, wherein the plurality of representative feature vectors are determined based at least in part on corresponding computer-implemented analyses of the plurality of representative digital content collections [See Anderson Fig. 4, Paras 0017, 0018, 0019, and 0025. Fig.4 and Para 0025 ] and determining, by the computing system, a first representative digital content collection from the first plurality of representative digital content collections based at least in part on a similarity between the feature vector and a first representative feature vector associated with the first representative digital content collection, wherein the first representative digital content collection includes a second plurality of digital content items having different digital content types [See Anderson Fig.4, paras 0019 and 0025], but fails to teach at least the limitations, as a whole, comprising, " generate, based at least in part on a plurality of key textual terms, a template feature vector having a plurality of feature elements, wherein the plurality of feature elements correspond to the plurality of key textual terms; generate a respective feature vector by modifying the template feature vector to include the plurality of similar feature elements; and generate a taste graph for organizing the corpus of content collections, each node of the taste graph corresponding to a content collection group of the plurality of content collection groups and its respective feature vector. ".
5. Discussion of the most relevant prior art: The following references have been identified as the most relevant prior art to the claimed invention.
(i) Adam et al. [US 20100290699 A1; see paras 0019, 0031, 0042, 0046] describes obtaining images and being posted on photo sharing websites or made digitally available through networks such as the Internet. The collected digital documents could include both text and images of landmarks. The system provides a template to be used in the automatic recognition of certain common features in images by using an appearance model which includes a feature vector comprising numerical scores for a set of predetermined image features. Well known feature vector generation can be used.
(ii) Pillai et al. [US 9367756 B2 ; See col.6, 37-54 and Fig.4 ] discloses an image clusterer 121 forming one or more clusters of the images of the entity of interest, and the image clusterer includes a pivot image selector 201, a feature vector determiner 202, and a similarity comparator 203, wherein the Pivot image selector 201 selects a set of pivot images so that a cluster will be created for each pivot image, the feature vector determiner 202 generates a feature vector for each image comprising a plurality of predetermined features for each image to be considered and the similarity comparator 203 compares two images based on a set of similarity features and determines a similarity score between two images based on the comparison of a plurality of features in the respective feature vectors.
(iii) Allison et al. cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above [US 20110082824 A1, paragraphs 0086 and 0209 cited in the Final Rejection mailed 12/08/2023] describes aggregating and maintaining collection of digital contents for users from which similar profile behaviors of users can be determined by vector comparison of the features values calculated in each profile.
(iv) Jia cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above [US10244286 A1 cited in the parent application 15/957822, now US Patent 11373230 and in the IDS filed 05/27/2022.; see claim 1, teaches a method and system wherein a server determines a user’s session context from his online interactions with digital contents , current and prior interactions, and based on the user’s rating data identifies one or more digital content objects with which the user has not previously interacted and for which one or more prior users have provided ratings, and adding the identified target digital content objects to the first set of objects, determines similarity using (i) the rating provided by the identified prior users for the digital content object, (ii) the rating provided by the user during the session for the digital content object, and (iii) the rating provided by prior users for the target digital content objects, generating the recommendation feature vector using the neighborhood matrix, the recommendation feature vector comprising (i) a number of ratings provided by the user for other digital content objects; (ii) a number of ratings provided by prior users for the target digital content objects; (iii) an estimated probability distribution of ratings provided by the user for other digital content objects; (iv) an estimated probability distribution of ratings provided by prior users for the target digital content objects; (v) an estimated probability distribution of ratings provided by prior users for digital content objects in the first set of objects; and (vi) a weighted sum of a random sample of (v); training, by the server computing device, a machine learning model using the generated recommendation feature vector, selecting, by the server computing device using the trained machine learning model, one or more target digital content objects that have a predicted rating of the user that is at or above a predetermined threshold, providing, indicia of the selected target digital content objects to the remote mobile computing device of the user as a recommendation, and in response from the user device transmitting at least one of the selected target digital content objects in the recommendation to the remote mobile computing device during the session.
(v) Walkingshaw cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above [US 20150248683A1; see Abstract and para 0087 with Fig.7] discloses a system and method wherein a magazine server facilitates a user to create digital magazines by including digital contents from one or more digital magazines. The digital magazine server stores several digital magazines 705, 710, 715, 720 and determines an influence score as shown determined influence scores based on digital magazine 705 in Fig.7, wherein each digital magazine or other collection of content is associated with a node in a graph, with connections between a pair of nodes having a direction based on which node in the pair of nodes a content item was first associated.
Foreign references:
(vi) JP 2016139229 A describes a content recommendation device comprising means for generating a graph by recursively grouping, tree structure graph generation means for generating a tree structure graph from the grouped graph on the basis of the position information of the grouped graph, a profile generation means for generating a personal profile by transmitting weight expressing personal preference to a node of the first group tree structure graph, and content recommendation means for determining whether or not to recommend content of a recommendation object to an individual person.
(vii) JP 2016110402 A cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above, describes assembling a digital content information graph that is coupled between nodes corresponding to digital content information which ensures collection of music information in large quantities.
NPL references:
(viii) Dmitry Bugaychenko, Alexandr Dzuba; "Musical recommendations and personalization in a social network"; arXiv:1310.7428 [cs.IR] Oct 28, 2013; pages 1-8; retrieved from IP. Com on 08062026; describes [See page 2, under "Taste Graph"] that in order to combine all the information mined from the system, including collaborative correlations, content information and social data a stochastic graph structure is used. Then this graph is analyzed by different algorithms in or der to construct recommendations and personalize output.
(ix) Article, “Shutterstock Inc Submits Patent Application for Selected Image Subset Based Search” Global IP News. Optics & Imaging Patent News [New Delhi] 31 Aug 2017 retrieved from Dialog on 07/27/2023; cited in the IDS filed 09/09/2024 and in the parent applications, now US Patents cited above discloses that patent application .20170249339 discloses an image retrieval system and method using a search engine to search features of a cropped raw image from images having similar content , wherein the feature vector of the cropped raw image is searched against images having similar content to obtain a prioritized listing of images.
6. Allowability note: If the independent claims 1, 21, and 26 are amended to overcome 35 USC 101 rejection, the application can be placed in condition for allowance. All amendments will be subject to reconsideration.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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, Maria-Teresa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YOGESH C GARG/Primary Examiner, Art Unit 3688
Prosecution Insights