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 .
Claim Rejections – 35 U.S.C. § 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 1-6 and 20-33 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
At step 1: claim 1 is directed to a “method” and thus directed to a statutory category, and claim 25 is directed to a “system” claim thus directed to a statutory category.
At step 2a prong 1: claims 1 and 25 recite the limitation that is directed to an abstract idea: “causing, …, a search engine to use at least one entity resolution machine learning model to obtain the at least one matching entity matching to the at least one searched entity characteristic, the at least one entity resolution machine learning model being configured to map the at least one searched entity characteristic to at least one cluster of a plurality of clusters indexed in a database; wherein the at least one entity resolution machine learning model is configured to map a plurality of entity records to clusters based on a plurality of data items associated with the plurality of entity records, each cluster representing a particular entity; and returning, via the search engine, search results in response to the at least one search query; wherein the search results comprise at least one indication of at least one entity associated with the at least one cluster as the at least one matching entity; wherein the at least one indication comprises the plurality of data items associated with each entity record of the at least one cluster”, as drafted recites a mentally performable process as one can map and associate entity features/characteristics to clusters/classifications using mental processes.
At step 2a prong 2: Independent claims 1 and 25 recite the following additional elements: "querying” , which is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. In addition, “returning … search results” (i.e., transmitting/returning data) is a post solution activity. See MPEP 2106.05(g).
Furthermore, the recited hardware (claim 1 – “computer implemented”, and claim 25 – “processor” and “medium”) represent a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. These claims are a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claims as a whole, nothing provides integration into a practical application.
At step 2b: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “querying” and “returning … search results” – these receiving (e.g., “querying”) and transmitting (e.g., “returning … search results”) limitations are identified as insignificant extra-solution activity above, and when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);". Thus these limitations remain insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims taken as a whole does not change this conclusion and the claim is ineligible.
Claims 2-6 and 20-24 depend upon claim 1, and claims 26-33 depend upon claim 25, respectively, and do not correct the issues set forth above. These claims essentially further recite data manipulation (e.g., augmenting/merging) and similarity matching, and further uses of the query/suggestions display. Therefore, these claims are likewise rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6 and 20-33 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al. (US Patent Application Publication No. 2021/0311952, hereafter referred to as “Jain”).
Regarding independent claim 1: Jain discloses A computer-implemented method comprising: (See Jain Figure 1 showing an exemplary computing environment, including processors and storage.) causing utilizing, by at least one processor, in response to at least one searched entity characteristic associated with at least one search query for at least one matching entity, a search engine to use at least one entity resolution machine learning model to obtain the at least one matching entity matching to the at least one searched entity characteristic, (See Jain paragraphs 0057-0062 discussing the use of an entity resolution engine utilizing a machine learning model to classify entities and correlate feature vectors in order to establish matches.) the at least one entity resolution machine learning model being configured to map the at least one searched entity characteristic to at least one cluster of a plurality of clusters indexed in a database; (See Jain paragraphs 0057 and 0062 in the context of 0058 discussing the mapping/matching entity records based upon feature vectors / characteristics.) wherein the at least one entity resolution machine learning model is configured to map a plurality of entity records to clusters based on a plurality of data items associated with the plurality of entity records, each cluster representing a particular entity; (See Jain paragraphs 0056-0058 discussing the classification/clustering of records matching based upon feature vectors, producing matching clusters, and that these groups of matching entities are clustered together.) and returning, by the at least one processor, via the search engine, search results in response to the at least one search query; (See Jain paragraphs 0032-0034 discussing the use of searches to find matching records, in the context of Figure 1 #103 showing a display.) wherein the search results comprise at least one indication of at least one entity associated with the at least one cluster as the at least one matching entity; (See Jain paragraph 0058 discussing the matching of entities by clustering and representing then in a table or other entity resolution data structure, in the context of paragraph 0019 discussing structures such as an entity index and entity record database [i.e., searchable data structures].) wherein the at least one indication comprises the plurality of data items associated with each entity record of the at least one cluster. (See Jain paragraphs 0018-0019 discussing the providing of secondary records / information associated with entities.)
Regarding claim 2: Jain discloses wherein the clusters are based on pre-determined categories. (See Jain paragraphs 0046, 0050 sand 0054 discussing the use of predetermined quality index or rank ranges that reflect categories/classifications/clusters.)
Regarding claim 4: Jain discloses wherein an entity record is augmented based on a classification of the entity record. (See Jain paragraph 0058 discussing the merging/augmenting of entity records based upon matching classifications/clusters.)
Regarding claim 5: Jain discloses wherein the augmentation comprises at least one sentence of text. (See Jain paragraph 0086 discussing the ability to generate embeddings, such as word2vec sentences, and paragraph 0061 discussing generate features such as “descriptions”.)
Regarding claim 6: Jain discloses wherein the augmentation comprises at least one numeric character. (See Jain paragraphs 0061 and 0063 discussing generation and extraction of quantitative features such as dates, measurements and phone numbers, and the merging of entity records.)
Regarding claim 20: Jain discloses further comprising: utilizing, by the at least one processor, the at least one entity resolution machine learning model to map at least one new entity record to one or more of the plurality of clusters based on at least one new entity characteristic associated with the at least one new entity record; and merging, by the at least one processor, the at least one new entity characteristic of the at least one new entity record with at least one entity record associated with the one or more of the plurality of clusters to form at least one merged entity record. (See Jain paragraph 0149 discussing the exemplary use of a map structure. See also, paragraphs 0057-0062 discussing the use of similarity determinations [e.g., a match probability threshold] between record characteristics/features.)
Regarding claim 21: Jain discloses wherein the at least one entity resolution machine learning model comprises at least one similarity measure. (See Jain paragraphs 0061-0062 discussing the use of similarity and performing an operation of matching similarity records.)
Regarding claim 22: Jain discloses further comprising: determining, by the at least one processor, at least one similarity between the at least one searched entity characteristic and each entity record of the plurality of entity records based at least in part on the at least one similarity measure; and determining, by the at least one processor, the at least one matching entity record from amongst the plurality of entity records based at least in part on the at least one similarity associated with each entity record and a similarity threshold. (See Jain paragraphs 0057-0062 discussing the use of similarity determinations [e.g., a match probability threshold] between record characteristics/features.)
Regarding claim 23: Jain discloses further comprising extracting, by the at least one processor, the at least one searched entity characteristic from at least one searched entity record. (See Jain paragraph 0022 discussing feature extraction to characterize possible matches between identified records.)
Regarding claim 24: Jain discloses wherein the at least one searched entity characteristic is input by at least one user into the search engine. (See Jain figure 1 #102 showing a user input device, in the context of paragraph 0073 discussing the use of a variety of languages for inputting queries.)
Claim 25 is substantially similar to claim 1, and therefore likewise rejected.
Regarding claim 26: Jain discloses wherein an entity record is augmented based on a classification of the entity record. (See Jain paragraphs 0057-0059 discussing the use of a classifier to aid in the identification of matches for the exemplary purpose of augmenting a record via a merging process.)
Regarding claim 27: Jain discloses wherein the augmentation comprises at least one sentence of text. (See Jain paragraph 0086 discussing the ability to generate embeddings, such as word2vec sentences, and paragraph 0061 discussing generate features such as “descriptions”.)
Regarding claim 28: Jain discloses wherein the augmentation comprises at least one numeric character. (See Jain paragraphs 0061 and 0063 discussing generation and extraction of quantitative features such as dates, measurements and phone numbers, and the merging of entity records.)
Claims 29-33 are substantially similar to claims 20-24, respectively, and therefore likewise rejected.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 3 is rejected under 35 U.S.C. §103 as being unpatentable over Jain et al. (US Patent Application Publication No. 2021/0311952, hereafter referred to as “Jain”) in view of Michaeli et al (US Patent Application Publication No. 2019/0066151, hereafter referred to as “Michaeli”).
Regarding claim 3, which depends upon claim 1: Jain does not explicitly teach the additional limitation(s) as claimed. Michaeli, though, teaches wherein the clusters are based on a randomly sampled subset of at least one data item of the plurality of entity records; (See Michaeli paragraph 0091 in the context of 0085 teaching the original assignment of entities randomly to a cluster of target entities.)
It 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 to apply the teachings of Michaeli for the benefit of Jain, because to do so provided a designer with options to implement a system for managing the clustering of target entities, as taught by Michaeli in paragraphs 0093-0095. These references were all applicable to the same field of endeavor, i.e., the management of entity data structures / entity features.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevance is provided in at least the Abstract of each cited document.
Non-Patent Literature
Christophides, Vassilis, et al., “An Overview of End-to-End Entity Resolution for Big Data”, ACM Computing Surveys, Volume 53, No. 6, Article 127, December 2020, pp. 1-42.
One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity. Despite several decades of research, ER remains a challenging problem. In this survey, we highlight the novel aspects of resolving Big Data entities when we should satisfy more than one of the Big Data characteristics simultaneously (i.e., Volume and Velocity with Variety). We present the basic concepts, processing steps, and execution strategies that have been proposed by database, semantic Web, and machine learning communities in order to cope with the loose structuredness, extreme diversity, high speed, and large scale of entity descriptions used by real-world applications. We provide an end-to-end view of ER workflows for Big Data, critically review the pros and cons of existing methods, and conclude with the main open research direction. (page 1, Abstract). ER aims to classify pairs of descriptions that are assumed to correspond to the same (vs. different) entity into matches (vs. non-matches). An ER process usually encompasses several tasks, including Indexing (a.k.a., Blocking), which reduces the number of candidate descriptions to be compared in detail, and Matching, which assesses the similarity of pairs of candidate descriptions using a set of functions. Several ER frameworks and algorithms for these tasks have been proposed during the last three decades in different research communities. In this survey, we present the latest developments in ER, explaining how the Big Data characteristics call for novel ER frameworks that relax a number of assumptions underlying several methods and techniques proposed in the context of the database [34, 50, 58, 106, 124], machine learning [72] and semantic Web communities [127] (page 2, 2nd full paragraph). Record linkage and deduplication techniques for structured data in data warehouse settings are the subject of numerous surveys and benchmarking efforts [34, 35, 54, 58, 80, 87, 106, 124]. Approximate instance matching is surveyed in [50], link discovering algorithms in [127], and uncertain ER in [69]. Recent efforts to enhance scalability of ER methods by leveraging distribution and parallelization techniques are surveyed in [29], while overviews of blocking and filtering techniques are presented in [132, 140]. In contrast, our goal is to present an in-depth survey on all tasks required to implement complex ER workflows, including Indexing, Matching, and Clustering. To the best of our knowledge, this is the first survey that provides an end-to-end view of ER workflows for Big Data entities and of the new entity methods addressing the Variety in conjunction with the Volume or the Velocity of Big Data Entities. Throughout this survey, we present the basic concepts, processing tasks, and execution strategies required to cope with the loose structuredness, extreme structural diversity, high speed, and large scale of entity descriptions actually consumed by Big Data applications. This survey is intended to provide a starting point for researchers, students, and developers interested in recent advances of schema-agnostic, budgetaware, and incremental ER techniques that resolve nearly similar entity descriptions published by numerous Big Data sources. The remaining of this survey is organized as follows. Section 2 presents the core concepts and tasks for building end-to-end ER workflows. Each workflow task is then examined in a separate section: Blocking in Section 3, Block Processing in Section 4, Matching in Section 5, and Clustering in Section 6. All these sections study methods for batch ER, while budget-aware and incremental ER are described in Sections 7 and 8, respectively. Section 9 covers complementary ER methods along with the main systems for end-to-end ER, while Section 10 elaborates on the most important directions for future work. Finally, Section 11 summarizes the current status of ER research. (page 4, paragraphs 1-3 of the section labeled “Contributions.”). Learning-based methods. Hetero [100] is an unsupervised approach that maps every dataset to a normalized TF vector, and applies an efficient adaptation of the Hungarian algorithm to produce positive and negative feature vectors. Then, it applies FisherDisjunctive [99] with bagging to achieve robust performance. Extended DNF BSL [101] combines an established instance-based schema matcher with weighted set covering to learn supervised blocking schemes in Disjunctive Normal Form (DNF) with at most k attributes (page 8, section labelled “Learning-based methods”). A merging-based collective ER example and (b) a relationship-based collective ER example. (c) Two different descriptions of the movie A Clockwork Orange and its cast in XML. (page 13, Figures 4a, 4b, 4c).
US Patent Application Publications
Faruquie 2021/0224237
In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record. (Abstract).
Trifunovic 2017/0255693
Techniques and technologies for providing images for search queries are described. In at least some embodiments, a system includes a scraping query component, a search component, and a search results analysis component. The scraping query component provides a scrape query based on textual information associated with an entity of interest. The search component conducts an electronic search via one or more networks to obtain search results based at least partially on the scrape query, the search results including at least a search result image and image metadata associated with the search result image. The search results analysis component determines a similarity between at least part of the image metadata associated with the search result image and at least part of the textual information associated with the entity of interest, and determines whether to store, provide, or discard the search result image based at least partially on the determined similarity. (Abstract).
Jain 2021/0311952
In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record. (Abstract). In some embodiments, a second database 107 may communicate with the dynamic graphing system 100 to provide second records 109 via, e.g., the communication bus 101. In some embodiments, the second records 109 may include entity records identifying entities, such as, e.g., commercial entities, including merchants, industrial entities, firms and businesses, as well as individuals, governmental organizations, or other entities that are the same or different from the first entities. In some embodiments, the second records 109 include records of data items identifying, e.g., each merchant in a geographic area, each merchant in a catalogue or database of business partners or business customers, or other database of merchants and associated records. In some embodiments, the data items may include, e.g., information related to an entity name or secondary name, address, a business owner, a geographic location (e.g., latitude and longitude), a zip code, telephone number, industry category or description (e.g., education, healthcare, food services, etc.), franchise indicator (e.g., a “1” to designate a franchise, or a “0” to designate not a franchise, or vice versa), among other information and combinations thereof. In some embodiments, the second records 109 are collected from, e.g., a consumer transaction database, web search results, an entity index, or other compilation of entity records into a database such as, e.g., the second database 107. (para 0019). In some embodiments, the pairs of second records that have a match probability, e.g., greater than or equal to 0.5, 0.6, or other suitable threshold, may be identified as matching entity records, and thus duplicative of a given entity. In some embodiments, the entity resolution engine 210 may merge groups of matching entities by clustering them together using, e.g., clustering algorithms, such as graph algorithms including, e.g., connected components algorithms, to produce matching clusters based on the probability scores of matching entity records. In some embodiments, each matching cluster may then be merged as merged entity records 211 that removes redundant data in the records forming the cluster. Accordingly, the sets of second records 209 may be resolved as related to a common entity and be represented in, e.g., a table, list, or other entity resolution data structure to produce a set of merged entity records 211 having reduced duplication of entities represented therein compared to the sets of second records 209. (para 0058). In some embodiments, the dynamic query module 341 may include, e.g., a suitable graph query mechanism, including a graph query language to search for and find nodes and edges according to specified properties. In some embodiments, the properties can include, e.g., scalar value types such as Boolean, string, number, integer, and floating-point numbers, temporal types like datetime, localdatetime, date, time, localtime, and duration, container types for maps and lists, graph types for node, relationship, and path, and a void type. For example, the dynamic query module 341 may employ, e.g., a Cypher query language, however other languages are contemplated, including, e.g., ArangoDB Query Language (AQL), GraphQL, Gremlin, SPARQL, among others. (para 0073).
Patil 2022/0004568
Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities. (Abstract).
Erenrich 2018/0330280
Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process. (Abstract).
US Patents
Guo 10,422,258
In an example embodiment, a fuzzy join operation is performed by, for each pair of records, evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records. Then a graph data structure is created, having a node for each record in the plurality of records and edges between each of the nodes, except, for each record pair having a similarity score that does not transgress a first threshold, causing no edge between the nodes for the record pair to appear in the graph data structure. (Abstract).
Erenrich 9,483,546
Computer implemented systems and methods are disclosed for associating records across lists, wherein the lists include a plurality of records and the plurality of records is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise grouping one or more records from a first list into a first group based on fields of the records in the first list, grouping one or more records from a second list into a second group based on fields of the records in the second list, pairing a record from the first group with a record from the second group, assessing each pair of records based on an evaluation of the respective pair according to fields of the pair, and associating records from the first group and records of the second group with an entity based on the assessment. (Abstract).
Li 8,792,905
According to one aspect, one of the entities in the cluster may be selected as a leader either randomly or based on a predefined metric and the location of the other entities in the cluster (e.g., the followers) may be calculated in relation to the leader. To reduce the number of updates to the location information of the entities and reduce latency, the system may update the back-end location database with the location information for a leader while location updates for followers may be converted into locations that are relative to the corresponding leader's and cached at the system (e.g., stored at on a local cache). The relative location of a follower may be calculated, in one aspect, as a displacement value (e.g., a distance from the leader) and a displacement direction (e.g., a direction away from the leader). (col. 4 lines 5-19).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner ROBERT STEVENS whose telephone number is (571) 272-4102. The examiner can normally be reached Mon - Fri 6:00 - 2:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT STEVENS/Primary Examiner, Art Unit 2164
February 6, 2026