Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-19 and 21 are pending. Claims 1, 8, and 15 are independent.
This Application was published as US 20250307562.
Apparent priority is 28 March 2024.
The instant Application is directed to a method of grouping content by location and by topic.
Applicant’s amendments and arguments are considered but are either unpersuasive or moot in view of the new grounds of rejection that, if presented, were necessitated by the amendments to the Claims.
This action is Final.
Response to Arguments
35 USC 101
Applicant's amendments to include “non-transitory” have overcome the rejection under 35 USC 101.
35 USC 102/103
Applicant’s arguments with respect to 35 USC 102/103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ivry et al. (US 20220292154 A1) in view of Nogueira et al. ("Fuzzy cluster descriptor extraction for flexible organization of documents") and of Xomnia ("An Introduction to Vector Databases for Beginners").
Regarding claim 1, Ivry discloses: 1. An apparatus comprising: a memory; and ("Memory 106" Fig. 1) a processor coupled to the memory, ("Processing unit(s) 102" Fig. 1)
the processor configured to: receive communication content from an interaction session between participants of an organization, ("[0072] Computing device 104 assigns automatically created geo-location tags 108B to user postings (i.e., user generated content items) to a social network 152A…" - posters of content on a social network are participants of an organization, and posts read on an interaction session.)
identify a plurality of subsets of content within the communication content that correspond to a plurality of geographic locations ("[0046] An aspect of some embodiments of the present invention relates to systems, methods, an apparatus, and/or code instructions (e.g., stored on a memory and executable by one or more processors) for automatically tagging social network posts with geo-location tags..."; see also: "[0046]...The multiple geographic region mapping datasets may correspond, for example, to different cities. Each specific geographic region dataset corresponds to a specific geographic region…" )
based on execution of a machine learning (ML) model on the communication content, ("[0072] Computing device 104 assigns automatically created geo-location tags 108B to user postings (i.e., user generated content items) to a social network 152A stored on a server(s) 152 and/or performs a sentiment analysis from the user postings optionally with automatically generated geo-tags 108B and/or from data obtained from sensor(s) 154, optionally using ML model(s) 108E trained on training dataset(s) 108F, as described herein." )
identify, based on the execution of the ML model, partially-overlapping subsets of chat content within the interaction session corresponding to different geographic locations and different topics, (“[0005]… clustering the user generated content items according to geo-location tags and topics into a plurality of topics,…” – each cluster reads on a subset. Ivry does not explicitly disclose that clusters overlap.)
convert the partially-overlapping subsets of chat content into a plurality of vectors and ("[0134] ...Topic clustering may be based on word similarity, for example, using word vectors and/or word embeddings, and/or based on taxonomy. In one example, a cluster of a topic of side-walk is created, which includes issues related to side-walks, such as drainage, street cleaning, noise, air quality, police, and busses." )
add labels to the plurality of vectors which include a first identifier that identifies one of the different geographic locations, and a second identifier that identifies one of the different topics, and ("[0124]... A geo-tag indicating the specific geographical location is automatically generated and assigned to the content item, for generating content item annotated with location 406…"; see also:
“[0008] In a further implementation of the first and third aspects, further comprising creating the specific geographic region mapping dataset by: obtaining a plurality of content items each with existing geotags, clustering the plurality of content items into a plurality of clusters according to terms mentioned in the content items, wherein each respective cluster represents a respective generic term,…” – the geo-tags and generic terms read on labels)
arrange the plurality of vectors within a vector database based on the labels. (not explicitly disclosed)
Ivry does not disclose overlapping subsets or arranging vectors in a vector database.
Nogueira discloses: partially-overlapping subsets (“Using fuzzy clustering algorithms, documents are assigned to multiple clusters simultaneously and relationships among the domains can be found, which are, otherwise, ignored by crisp clustering [6].” Pg. 529, para 6)
Ivry and Nogueira are considered analogous art to the claimed invention because they disclose clustering text data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Ivry with fuzzy clustering as taught by Nogueira to create overlapping clusters. Doing so would have been beneficial so that multiple relationships among domains can be found. (Nogueira pg. 529, para 6). This combination falls under simple substitution of one known element for another to obtain predictable results or use of known technique to improve similar devices (methods, or products) in the same way. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Nogueira does not disclose arranging vectors in a vector database.
Xomnia discloses: arrange the plurality of vectors within a vector database based on the labels. (“A vector database specializes in storing, indexing, and retrieving data represented as vectors or vector embeddings. These databases are designed to manage large volumes of unstructured and semi-structured data, offering features like metadata storage, filtering, scalability, dynamic updates, and security. The use of embedding models allows vector databases to measure and understand the similarity between data objects, facilitating advanced search capabilities across high-dimensional vector spaces. A representation of how vector embeddings might be distributed in such a space is illustrated in Figure 2.” Pg. 3, last para – Fig. 2 shows that similar vectors are arranged in a similar location.)
Ivry and Xomnia are considered analogous art to the claimed invention because they disclose storing information in vectors. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to use a vector database as taught by Xomnia to store the vectors. Doing so would have been beneficial to manage features including metadata storage, filtering, scalability, dynamic updates, and security, and to facilitate advanced search capabilities. (Xomnia pg. 3, last para). This combination falls under simple substitution of one known element for another to obtain predictable results or use of known technique to improve similar devices (methods, or products) in the same way. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Regarding claim 2, Ivry discloses: 2. The apparatus of claim 1, wherein the processor is configured to identify a first subset based on a first geographic location and identify a second subset based on a second geographic location ("[0133] Geo-tagged user generated content items of the (e.g., each) respective geographic region are further sub-clustered into topic clusters each denoting a respective topic. The topic clusters may be for topics that are common for each of the geographic clusters, for example, common issues that arise in each of the cities, for example, noise levels, parking issues, cultural events, and police presence." )
and convert the first subset and the second subset into a first vector and a second vector, respectively. (“[0110]… The clustering may be performed, for example, by converting terms into vectors, and clustering the vectors in a multi-dimensional space. The center of each cluster corresponds to the generic term of that cluster…” – the center of each geographic cluster reads on the first and second vector.)
Regarding claim 3, Ivry does not disclose the additional limitations. Neither does Nogueira.
Xomnia discloses: 3. The apparatus of claim 2, wherein the processor is further configured to store the first(Pg. 13, para 3, discloses location as a vector embedding, and spatial queries, which show that the data is stored so that it can be queried based on the location. Storing the first vector with a group that is separate from the second vector is understood to mean that they are in separate clusters of the vector database, or separate locations in vector space.)
See claim 1 for motivation statement.
Regarding claim 4, Ivry discloses: 4. The apparatus of claim 1, wherein the processor is configured to aggregate a subset of chat content into a block of text and generate a vector from the subset of chat of content based on execution of a second ML model on the block of text. ("[0087]...The user generated content items may be, for example, text written by a user…"; - under the broadest reasonable interpretation, subsets of chat content could refer to words in a post, and Ivry discloses that content posts are vectorized by ML: “[0161]… The outcomes of the sensor ML model and/or content item ML model that are fed into the main ML model may be embeddings extracted from hidden neural layers of a neural network implementation, for example, as embedded feature vectors…”. One of ordinary skill in the art would understand that text content written by a user would contain multiple words. For at least one explicit example, [0098] discloses that the user discusses the mayor “John Smith” which is an aggregation of two words.)
Regarding claim 5, Ivry discloses: 5. The apparatus of claim 1, wherein the processor is configured to store an identifier of a geographic location corresponding to a subset of chat content within a metadata section of a vector of the subset of chat content, wherein the identifier of the geographic location comprises a text-based description of the geographic location. ("[0094] In another implementation, an existing geotag for a geographic region, but not for a specific geographic region may be accessed. For example, when the geotag is for New York City without more specific details, the geographic region is known." – “New York City” is a text-based description; see also “See also: "[0054] In at least some implementations, the improvement in the ability to automatically create geotags is by using a specific geographic region mapping dataset, for example, a respective mapping dataset for each city. The mapping dataset maps generic terms in each post to specific locations within the geographic region, for example, to specific addresses and/or global positioning (GPS) locations within a city." - an address is also a text-based description” – specific addresses are text-based descriptions.)
Ivry does not disclose that the geotag is stored in a metadata section of a vector. Neither does Nogueira.
Xomnia discloses: storing information in a metadata section of a vector. ("Metadata filtering is a technique that allows users to refine their search or query results beyond just vector similarities. This involves using additional information (metadata) associated with the vectors for filtering results according to specific criteria. This metadata can include various attributes like categories, tags, or any other descriptive information related to the vector data (as shown in Figure 4). By leveraging metadata filtering, our searches can consider specific characteristics or classifications that align with the query's intent beyond vector similarity." Pg. 6, para 2)
Ivry and Xomnia are considered analogous art to the claimed invention because they disclose storing information in vectors. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Ivry with vector metadata as taught by Xomnia to store the geotags. Doing so would have been beneficial so that searches can consider specific characteristics that align with the query’s intent. (Xomnia pg. 6, para 2)
Regarding claim 6, Ivry discloses: 6. The apparatus of claim 1, wherein the processor is further configured to identify additional contextual attributes of the plurality of subsets of chat content based on the execution of the ML model on the plurality of subsets of chat content, and label the plurality of vectors with the additional contextual attributes. ("[0178] Alternatively or additionally to using a baseline, two or more sentiment values computed for two or more groups of user types are compared to each another. Two groups of people may be compared to each other, to determine whether they have similar sentiments towards the event/topic the same, or different sentiments towards the event/topic. Optionally, the subset of user generated content items are clustered into at least two clusters according to a respective user type. Examples of user types include: fan of a certain spot club where two or more sport clubs are compared to each other, affiliation with a certain political party where different political parties are compared to each other, and home city where different cities are compared to each other" )
Regarding claim 7, Ivry discloses: 7. The apparatus of claim 1, wherein the processor is further configured to receive a query from a software application with an identifier of a geographic location, retrieve vectors from the vector database which are labeled with the geographic location, and generate a response to the query based on execution of an ML model on the query and the vectors. ("[0150] At 302, a query is received. The query may indicate a specific topic for a specific geographic region, for example, garbage pickup in a specific city, and/or a parade held in a neighborhood. The specific topic may be for a specific geographic location in the specific geographic region, for example, garbage pickup along 4th street in the specific city. The query may be for a specific spatiotemporal event defined for a specific time interval in a specific geographic location in the specific geographic region, for example, or a concert held on the 4th of July in a specific park in a specific city." – see further Fig. 3 which shows the process mapped in claim 1, (which includes use of vectors) and shows that the result is provided as the response to the query. Xomnia discloses the vector database as mapped in claim 1.)
Claim 8 is a method claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale.
Claim 9 is a method claim with limitations corresponding to the limitations of Claim 2 and is rejected under similar rationale.
Claim 10 is a method claim with limitations corresponding to the limitations of Claim 3 and is rejected under similar rationale.
Claim 11 is a method claim with limitations corresponding to the limitations of Claim 4 and is rejected under similar rationale.
Claim 12 is a method claim with limitations corresponding to the limitations of Claim 5 and is rejected under similar rationale.
Claim 13 is a method claim with limitations corresponding to the limitations of Claim 6 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of Claim 7 and is rejected under similar rationale.
Claim 15 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale.
Claim 16 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 2 and is rejected under similar rationale.
Claim 17 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 3 and is rejected under similar rationale.
Claim 18 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 4 and is rejected under similar rationale.
Claim 19 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 5 and is rejected under similar rationale.
Regarding claim 21, Ivry discloses: 21. The apparatus of claim 1, wherein the partially-overlapping subsets include at least two subsets with a chat message in common, and (See mapping for claim 1; Nogueira discloses fuzzy clustering which means that clusters partially overlap subsets of common content.
the processor is configured to generate at least two vectors that include content from the chat message. ("[0133] Geo-tagged user generated content items of the (e.g., each) respective geographic region are further sub-clustered into topic clusters each denoting a respective topic. The topic clusters may be for topics that are common for each of the geographic clusters, for example, common issues that arise in each of the cities, for example, noise levels, parking issues, cultural events, and police presence.". Generating vectors for a common topic includes content that is in a common chat message. The cluster centers for overlapping clusters read on this claim, and vectors for any two messages with overlapping topics also read on this claim.)
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JON C MEIS whose telephone number is (703)756-1566. The examiner can normally be reached Monday - Thursday, 8:30 am - 5:30 pm EST.
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/JON CHRISTOPHER MEIS/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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