DETAILED ACTION
This office action is in response to the communication filed on March 16, 2026. Claims 1, 4, 8 , 9, 12, and 16 are currently pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/16/26 has been entered.
Response to Declaration under 37 CFR 1.132
The declaration under 37 CFR 1.132 filed on 03/16/26 is insufficient to overcome the rejection of claims 1, 4, 8 , 9, 12, and 16 based upon 35 U.S.C. 101 as set forth in the last Office action because:
Applicant in Section 6 on Page 3 of the Declaration argues that the limitations of the independent claims, read in light of the specification, could not have been practically performed in the human mind at least because they involve algorithm based preprocessing of colloquial place name data, generation and manipulation of fixed dimension vector representations, database-based storage and querying of those representations, and similarity determinations performed using vector-space distance computations.
Examiner respectfully disagrees.
Examiner points out that the 101 rejection never asserted that the claimed features “receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts; and a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which involves the argued “algorithm based preprocessing of colloquial place name data” limitation, is a mental process, it was identified as additional elements. So any assertion made by the applicant that this limitation does not recite a mental process that could not have been practically performed in the human mind is moot, as it was not treated in that fashion.
Examiner points out that the 101 rejection never asserted that the claimed features “storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space”, “receiving geographic location information corresponding to a geographic location wherein the geographic location information comprises a colloquial place name or a non- colloquial geographic location”, “querying the relational database with the geographic location information vector”, and “receiving, in response to the query, one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to the geographic location information vector”, which involves the argued “database-based storage and querying of those representations”, are mental processes, they were identified as additional elements. So any assertion made by the applicant that these limitations do not recite a mental process that could not have been practically performed in the human mind is moot, as they were not treated in that fashion.
Examiner asserts that the claimed features “transforming, using a word-embedding algorithm, the plurality of preprocessed colloquial place names into a vector space comprising a plurality of fixed-dimension real- valued vectors corresponding to the plurality of colloquial place names, wherein distance between the vectors in the vector space corresponds to semantic similarity among the colloquial place names”, “embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location”, and “identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the-one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector”, which involves the argued “generation and manipulation of fixed dimension vector representations” and “similarity determinations performed using vector-space distance computations”, are mental processes, and any assertion made by the applicant that these limitations could not have been practically performed in the human mind is not persuasive.
With respect to the claimed feature “transforming, using a word-embedding algorithm, the plurality of preprocessed colloquial place names into a vector space comprising a plurality of fixed-dimension real-valued vectors corresponding to the plurality of colloquial place names, wherein distance between the vectors in the vector space corresponds to semantic similarity among the colloquial place names” a person can mentally or using a pen and paper transform, using a word-embedding algorithm, already preprocessed information into a vector space comprising a plurality of fixed-dimension real-valued vectors, and the person can mentally or using a pen and paper determine that distances between the vectors in the vector space corresponds to semantic similarity.
As admitted by the applicant in this declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
With respect to the claimed feature “embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location” a person can mentally or using a pen and paper embed, using a word-embedding algorithm, a geographic location information into a vector that corresponds to the geographic location.
As admitted by the applicant in this declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
With respect to the claimed feature "identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector” a person can mentally or using a pen and paper identify social media data corresponding to a geographic location information by mentally or using a pen and paper determining that the social media data is geotagged with one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to a geographic location information vector, wherein the determination is based on the person mentally or using a pen and paper mapping each colloquial place name to a corresponding colloquial place name vector.
As admitted by the applicant in this declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
The limitations above, as recited in independent claims 1 and 9, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper and/or using a mathematical calculation, but for recitation of generic computer components.
Therefore, the limitations fall within the “Mental Processes” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Applicant in Section 6 on Page 3 of the Declaration further argues that the claimed invention represents an improvement to social media data identification technology by preprocessing colloquial place names, embedding both place names and geographic location information into a shared vector space, performing similarity-based retrieval using vector distance computations, and identifying social media data geotagged with colloquial place names corresponding to a received geographic location.
Examiner respectfully disagrees.
It is important to note that the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements (MPEP 2106.05(a)).
The features of the claimed invention that applicant argues as representing an improvement to social media data identification technology, such as “embedding both place names and geographic location information into a shared vector space”, “performing similarity-based retrieval using vector distance computations”, and “identifying social media data geotagged with colloquial place names corresponding to a received geographic location” are recited in the transforming, embedding, and identifying steps in claims 1 and 9, which, as shown above, recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps.
The claims do not provide any limitations that are directed to a specific improvement in computer technology because the transforming, embedding, and identifying steps in claims 1 and 9, as argued by the applicant as being directed to a specific improvement in social media data identification technology, are all recited in the claims as limitations that have been identified as abstract ideas.
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, and are recognized as a well understood, routine, and conventional activity within the field of computer functions, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
For example, the claimed limitation “receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts; and a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which involves the argued “preprocessing colloquial place names” feature, recites a step of receiving data, which is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Section 8 on Pages 3-4 of the Declaration argues that a human mind could not practically "receiv[e] colloquial place names from social media user accounts" that are preprocessed using "a bloom filter configured to identify and remove duplicate colloquial place names" and "a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names" as recited by claim 1.
Examiner respectfully disagrees.
Examiner points out that the rejection never asserted the limitations, such as receiving, preprocessing, database-based storage, and querying as a mental process, they were identified as an additional element. So any assertion that these limitations do not recite a mental process is moot as they was not treated in that fashion.
Examiner notes that applicant asserts aspects of the preprocessing being done with a “bloom filter” and “a collocation-detection algorithm”, however, the claim recites preprocessing based on “at least one of” those.
Applicant in Section 8 on Pages 3-4 of the Declaration further argues that as described in the specification, duplicate removal and phrase identification are performed using algorithmic preprocessing applied to user-provided location strings, including identifying and eliminating repeated colloquial place names and detecting multi-word phrases that form colloquial place names, and a person of skill in the art would have understood that bloom filters and collocation-detection algorithms involve systematic evaluation of input data against stored representations and statistical criteria, involving precise and repeatable operations that cannot be reliably or practically carried out by a human mind, whether unaided or using pen and paper.
Examiner respectfully disagrees.
The rejection never asserted the limitations, such as receiving, preprocessing, database-based storage, and querying as a mental process, they were identified as an additional element. So any assertion that these limitations do not recite a mental process is moot as they was not treated in that fashion.
Examiner states that limitations such as “identify multi-word phrases” can be done mentally and would be a mental process. Even “systematic evaluations of input data against stored representation and statistical criteria” can be done mentally, but the recited claim does not require any of those specifics.
Applicant in Section 9 on Page 4 of the Declaration argues that that bloom filters and collocation-detection algorithms involve systematic evaluation of input data against stored representations and statistical criteria, involving precise and repeatable operations that cannot be reliably or practically carried out by a human mind, whether unaided or using pen and paper, as described in the specification, the preprocessing techniques are applied to user-provided location strings obtained from social media data, including identifying duplicate colloquial place names and detecting multi-word phrases within such inputs, and a person of skill in the art would have understood that systems designed to process such user-generated social media location data necessarily operate on large collections of textual inputs and therefore require automated computational processing to evaluate those inputs against stored data structures and statistical criteria, rendering mental or manual performance infeasible.
Examiner respectfully disagrees.
The rejection never asserted the limitations, such as receiving, preprocessing, database-based storage, and querying as a mental process, they were identified as an additional element. So any assertion that these limitations do not recite a mental process is moot as they was not treated in that fashion.
Examiner notes that the claimed process is not as specific as the portions cited in the specification, and the claimed process does not require large collections of textual inputs or data.
Examiner states that limitations such as “evaluation of input data against stored representations and statistical criteria” can be done mentally and would be a mental process.
Applicant in Section 10 on Page 4 of the Declaration argues that a person of skill in the art would have understood that generating fixed-dimension real-valued vectors based on learned semantic relationships involves performing numerous multidimensional numerical computations and maintaining precise relationships among vector values, operations that are beyond the ability of a human mind to carry out manually, whether or not aided by pen and paper.
Examiner respectfully disagrees.
Examiner does not find this assertion persuasive, as nothing in the claims recites numerous multi-dimensional numerical computations.
As discussed above, with respect to the claimed transforming limitation, a person can mentally or using a pen and paper transform, using a word-embedding algorithm, already preprocessed information into a vector space comprising a plurality of fixed-dimension real-valued vectors, and the person can mentally or using a pen and paper determine that distances between the vectors in the vector space corresponds to semantic similarity, which are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper and falls within the “Mental Processes” grouping of abstract ideas.
Moreover, the assertion of facts made by the applicant, that the feature involves performing numerous multidimensional numerical computations, seems to be an admission that the claimed limitation involves numerical computations, which would cover steps that can be performed using a mathematical calculation, therefore, the limitations also falls within the “Mathematical Concepts” grouping of abstract ideas.
Applicant in Section 11 on Pages 4-5 of the Declaration argues that with respect to the claimed feature "storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space" a person of skill in the art would have understood that maintaining a relational database of vectorized data and mappings involves systematic data storage, indexing, and retrieval operations that cannot be practically implemented or managed by a human mind.
Examiner respectfully disagrees.
The rejection never asserted the limitation "storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space" as a mental process, it was identified as an additional element. So any assertion that the operations recited in this limitation cannot be practically implemented or managed by a human mind to be considered as a mental process is moot, as the limitation was not treated in that fashion.
Applicant in Section 12 on Page 5 of the Declaration argues that with respect to the claimed feature "embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location" a person of skill in the art would have understood that applying a word embedding algorithm to generate numerical vector representations within a vector space involves precise multi-dimensional numerical computations and determination of exact relationships among vector values, operations that cannot be practically performed or tracked by a human mind, whether mentally or using pen and paper.
Examiner respectfully disagrees.
Again applicant seems to admit that the claimed limitations involve numerical computations, which would cover steps that can be performed using a mathematical calculation, therefore, the limitations also fall within the “Mathematical Concepts” grouping of abstract ideas.
Additionally Examiner disagrees that the operations recited in the argued feature cannot be practically performed or tracked by a human mind, because the claims do not require any specific complexity that would be beyond the capabilities of a human mind.
Applicant in Section 13 on Page 5 of the Declaration argues that with respect to the claimed features "querying the relational database with the geographic location information vector" and "receiving, in response to the query, one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to the geographic location information vector” a person of skill in the art would have understood that calculating vector similarities and ranking results based on distance metrics involves numerical comparison across multiple dimensions with a level of accuracy and consistency that is not feasible through mental or manual effort without automated systems.
Examiner respectfully disagrees.
The rejection never asserted the querying and receiving limitations as a mental process, they were identified as an additional element. So any assertion that the operations recited in these limitations are not feasible through mental or manual effort without automated systems is not persuasive, as the limitations were not treated in that fashion.
Again applicant seems to admit that the claimed limitations involve numerical computations, which would cover steps that can be performed using a mathematical calculation, therefore, the limitations also fall within the “Mathematical Concepts” grouping of abstract ideas.
Examiner states that limitations such as “calculating vector similarities and ranking results based on distance metrics involves numerical comparison across multiple dimensions with a level of accuracy and consistency” is feasible through mental or manual effort, can be done mentally, and would be a mental process, especially since the claim does not recite a size for the data involved.
Applicant in Section 14 on Pages 5-6 of the Declaration argues that with respect to the claimed feature "identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the one or more colloquial place names" where "the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector" a person of skill in the art would have understood that correlating social media data to geographic location information using stored vector-based mappings and relational database records involves automated data association, lookup, and retrieval operations that depend on precise numerical and structural relationships, and that such operations cannot be practically performed or managed by a human mind, whether mentally or using pen and paper.
Examiner respectfully disagrees.
Examiner states that applicant’s arguments are not persuasive because the claimed identifying step does not require any automated data association, lookup, and retrieval operations that depend on precise numerical and structural relationships, and that claimed operations in the identifying step can be practically performed or managed by a human mind, whether mentally or using pen and paper.
The claimed limitation states "identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector”.
A person can mentally or using a pen and paper identify social media data corresponding to a geographic location information by mentally or using a pen and paper determining that the social media data is geotagged with one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to a geographic location information vector, wherein the determination is based on the person mentally or using a pen and paper mapping each colloquial place name to a corresponding colloquial place name vector.
Therefore, the argued limitation can be practically performed or managed by a human mind or using a pen and paper.
Applicant in Section 17 on Pages 6-7 of the Declaration, with respect to the claimed “receiving a plurality of colloquial place names…” step, argues that applicant’s specification in [0003]-[0006], [0028], [0054], [0055], and [0076]-[0078] explains that location information in social media data may be noisy, inconsistent, and/or redundant, known techniques relied on categorical matching or manual mappings that were thus not well suited to handling such data, that the invention improves this aspect of the technology by preprocessing colloquial place names using duplicate-removal and phrase-identification algorithms to normalize and structure the input data prior to further analysis, which enables more efficient downstream analysis by reducing comparisons among redundant inputs, and that a person of skill in the art would have recognized this preprocessing as a technical improvement reflected in claim 1 's recitation of preprocessing colloquial place names using "a bloom filter configured to identify and remove duplicate colloquial place names" and "a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names," thereby improving the quality and usability of the input data for subsequent vector-based processing and retrieval operations.
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how preprocessing data is an improvement to the functioning of the computer or technology of social media data identification. If anything, it seems to indicate an improvement to the abstract idea itself, as reducing comparisons among redundant inputs by preprocessing would also improve the mental process of transforming, embedding, and identifying.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
Applicant in Section 18 on Page 7 of the Declaration, with respect to the claimed transforming step, argues that applicant’s specification in [0005], [0006], [0008], [0054], [0058], [0059], and [0079] explains that known systems grouped colloquial place names categorically based on terminology rather than geographic meaning, which failed to capture semantic relationships between different colloquial terms referring to the same geographic location, that the invention improves this aspect of the technology by transforming colloquial place names into vector representations such that semantic similarity is encoded as distance within a vector space, which improves the functioning of a computer by allowing it to retrieve related colloquial place names and geographic location information even in the absence of an explicit mapping between terms, and that a person of skill in the art would have recognized this transformation as a technical improvement reflected in claim 1' s recitation of "transforming, using a word-embedding algorithm," colloquial place names into a vector space in which "distance between the vectors ... corresponds to semantic similarity," thereby enabling more accurate and flexible association of colloquial place names with geographic locations.
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how this is an improvement to the functioning of the computer or technology. If anything, it seems to indicate that features recited in the abstract recite is the improvement. Because if the claimed limitations of transforming, embedding, and identifying are directed to abstract idea, claiming an efficiency when that is used on a computer is not an improvement that provides eligibility. That is an improvement in the abstract idea.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
Applicant in Section 19 on Pages 7-8 of the Declaration, with respect to the claimed storing step, argues that applicant’s specification in [0005], [0006], [0054], [0059], [0067], [0063], [0067], and [0084] explains that known systems associated colloquial place names categorically based on terminology rather than semantic or geographic similarity, which prevented efficient retrieval of related colloquial place names, that the invention improves this aspect of the technology by storing mappings between colloquial place names and their corresponding vector representations in a relational database, enabling similarity-based retrieval and comparison operations, by storing vector representations rather than relying on categorical associations, a system corresponding to the invention may reduce the amount of data that is searched or compared during retrieval, and a person of skill in the art would have recognized this database storage as a technical improvement reflected in claim 1' s recitation of "storing, in a relational database," both the colloquial place name vectors and their mappings, thereby enabling efficient vector-based retrieval of colloquial place names.
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how storing data is an improvement to the functioning of the computer or technology of social media data identification. If anything, it seems to indicate an improvement to the abstract idea itself, as reducing the amount of data that is searched or compared would also improve the mental process of transforming, embedding, and identifying.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
Applicant in Section 20 on Page 8 of the Declaration, with respect to the claimed “receiving geographic location information…” and embedding steps, argues that applicant’s specification in [0003], [0005], [0006], [0012], [0029], [0054], [0058], [0066], and [0093] explains that known approaches involved explicit mappings to relate geographic location information to colloquial place names, which limited accuracy and efficiency, the invention improves this aspect of the technology by embedding geographic location information into the same vector space as colloquial place names using the word-embedding algorithm, allowing both types of information to be encoded as fixed-dimension vector representations, such vector representations thereby reduce the need for explicit mappings between geographic location information and colloquial place names, and a person of skill in the art would have recognized this vectorization approach as a technical improvement reflected in claim 1 's recitation of "embedding, using the word-embedding algorithm," geographic location information into a corresponding vector.
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how storing data is an improvement to the functioning of the computer or technology of social media data identification. If anything, it seems to indicate an improvement to the abstract idea itself, as reducing the need for explicit mappings between geographic location information and colloquial place names would also improve the mental process of transforming, embedding, and identifying.
If the claimed limitations of transforming, embedding, and identifying are directed to abstract idea, claiming an efficiency when that is used on a computer is not an improvement that provides eligibility. That is an improvement in the abstract idea.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
Applicant in Section 21 on Pages 8-9 of the Declaration, with respect to the claimed querying step, argues that applicant’s specification in [0005], [0006], [0012], [0054], [0066], [0079], [0084], and [0085] explains that known techniques for associating colloquial place names with geographic locations involved categorical groupings based on terminology, which reduced the efficiency of retrieving related colloquial place names, the invention improves this aspect of the technology by performing retrieval within a relational database using vector distance computations to efficiently identify colloquial place names that are most similar to the geographic location information, by utilizing vector similarity rather than categorical groupings, a system associated with the invention may reduce computational overhead involved in searching and comparing location information, and person of skill in the art would have recognized this similarity-based querying as a technical improvement reflected in claim 1' s recitation of "querying the relational database with the geographic location information vector" and "receiving, in response to the query, one or more colloquial place names" whose corresponding vectors are "most similar" to the geographic location information vector.
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how storing data is an improvement to the functioning of the computer or technology of social media data identification. If anything, it seems to indicate an improvement to the abstract idea itself, as efficiently identifying colloquial place names that are most similar to the geographic location information, by utilizing vector similarity rather than categorical groupings would also improve the mental process of transforming, embedding, and identifying.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
Applicant in Section 22 on Page 9 of the Declaration, with respect to the claimed identifying step, argues that applicant’s specification in [0003], [0004], [0011], [0025], [0055], [0059], [0079], [0064], [[75], and [0085] explains that social media data was commonly geotagged using colloquial place names rather than the names of official geographic locations, which made conventional identification techniques unreliable or inefficient, the invention improves this aspect of the technology by identifying such social media data corresponding to given geographic location information based on stored mappings between colloquial place names and their corresponding vector representations, this in turn enables more efficient identification of social media data corresponding to a received geographic location, and a person of skill in the art would have recognized this identification process as a technical improvement reflected in claim 1' s recitation of "identifying social media data corresponding to the geographic location information," where "the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector."
Examiner respectfully disagrees. Nothing in these cited paragraphs clearly recite or explain how preprocessing data is an improvement to the functioning of the computer or technology of social media data identification. If anything, it seems to indicate an improvement to the abstract idea itself, as more efficient identification of social media data corresponding to a received geographic location based on stored mappings between colloquial place names and their corresponding vector representations would also improve the mental process of transforming, embedding, and identifying.
If the claimed limitations of transforming, embedding, and identifying are directed to abstract idea, claiming an efficiency when that is used on a computer is not an improvement that provides eligibility. That is an improvement in the abstract idea.
It is important to note, the judicial exception alone cannot provide the
improvement. The improvement can be provided by one or more additional elements.
In view of the foregoing, when all of the evidence is considered, the totality of the rebuttal evidence of eligibility fails to outweigh the evidence of non-eligibility.
Response to Arguments
Applicant's arguments filed on March 16, 2026 with respect to the 101 rejection of claims 1, 4, 8, 9, 12, and 16 have been fully considered but they are not persuasive for the following reasons:
Applicant in Pages 3-4 of the Remarks argues that none of the claimed limitations can be practically performed in the human mind at least because they involve algorithm-based preprocessing of colloquial place name data, generation and manipulation of fixed-dimension vector representations, database-based storage and querying of those representations, and similarity determinations performed using vector-space distance computations.
Applicant in Pages 4-5 of the Remarks argues that the eligibility rejection should be withdrawn at least because the claimed limitations do not fall within the "mental processes" grouping, the "mathematical concepts" grouping, or within any other abstract idea grouping.
Applicant in Pages 4-5 of the Remarks further argues that the claimed limitations, such as receiving colloquial place names from social media user accounts preprocessed using "a bloom filter" configured to "identify and remove duplicate colloquial place names" and "a collocation-detection algorithm" configured to "identify multi-word phrases forming colloquial place names"; "transforming, using a word- embedding algorithm," the preprocessed colloquial place names into "a vector space" including "fixed-dimension real-valued vectors", "storing, in a relational database," both the colloquial place name vectors and "a mapping of each colloquial place name" to a corresponding vector, "embedding, using the word-embedding algorithm," geographic location information into "a geographic location information vector", "querying the relational database with the a geographic location information vector", "receiving, in response to the query," colloquial place names corresponding to colloquial place name vectors that are most similar to the geographic location information vector, and "identifying social media data" by determining that it is geotagged with those colloquial place names based on the stored mapping cannot be practically performed in the human mind.
Applicant in Page 5 of the Remarks argues that a human mind, alone or with pen and paper, cannot practically implement a bloom filter to identify and remove duplicate colloquial place names, execute a collocation-detection algorithm to identify multi-word phrases, generate and manipulate fixed-dimension real-valued vectors in a vector space in which distance corresponds to semantic similarity, store and query those vectors and their mappings in a relational database, or determine which vectors are most similar to an embedded geographic location information vector using vector-space distance computations.
Applicant in Page 5 of the Remarks further argues that as explained in the accompanying declaration of Jeffrey Zarrella, the inventor of the present application, bloom filters, collocation-detection algorithms, vector-embedding operations, and/or vector-space similarity determinations involve systematic algorithmic evaluation of textual inputs, multi-dimensional numerical computations, and/or automated database operations that cannot practically be performed mentally or manually.
Examiner respectfully disagrees.
Examiner points out that the rejection never asserted the limitations, such as receiving, preprocessing, database-based storage, and querying as a mental process, they were identified as an additional element. So any assertion that these limitations do not recite a mental process is moot as they was not treated in that fashion.
Examiner notes that applicant asserts aspects of the preprocessing being done with a “bloom filter” and “a collocation-detection algorithm”, however, the claim recites preprocessing based on “at least one of” those.
Examiner points out that the 101 rejection never asserted that the claimed features “receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts; and a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which involves the argued “algorithm based preprocessing of colloquial place name data” limitation, is a mental process, it was identified as additional elements. So any assertion made by the applicant that this limitation does not recite a mental process that could not have been practically performed in the human mind is moot, as it was not treated in that fashion.
Examiner points out that the 101 rejection never asserted that the claimed features “storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space”, “receiving geographic location information corresponding to a geographic location wherein the geographic location information comprises a colloquial place name or a non- colloquial geographic location”, “querying the relational database with the geographic location information vector”, and “receiving, in response to the query, one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to the geographic location information vector”, which involves the argued “database-based storage and querying of those representations”, are mental processes, they were identified as additional elements. So any assertion made by the applicant that these limitations do not recite a mental process that could not have been practically performed in the human mind is moot, as they were not treated in that fashion.
Examiner asserts that the claimed features “transforming, using a word-embedding algorithm, the plurality of preprocessed colloquial place names into a vector space comprising a plurality of fixed-dimension real- valued vectors corresponding to the plurality of colloquial place names, wherein distance between the vectors in the vector space corresponds to semantic similarity among the colloquial place names”, “embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location”, and “identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the-one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector”, which involves the argued “generation and manipulation of fixed dimension vector representations” and “similarity determinations performed using vector-space distance computations”, are mental processes, and any assertion made by the applicant that these limitations could not have been practically performed in the human mind is not persuasive.
With respect to the claimed feature “transforming, using a word-embedding algorithm, the plurality of preprocessed colloquial place names into a vector space comprising a plurality of fixed-dimension real-valued vectors corresponding to the plurality of colloquial place names, wherein distance between the vectors in the vector space corresponds to semantic similarity among the colloquial place names” a person can mentally or using a pen and paper transform, using a word-embedding algorithm, already preprocessed information into a vector space comprising a plurality of fixed-dimension real-valued vectors, and the person can mentally or using a pen and paper determine that distances between the vectors in the vector space corresponds to semantic similarity.
As admitted by the applicant in the accompanying declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
With respect to the claimed feature “embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location” a person can mentally or using a pen and paper embed, using a word-embedding algorithm, a geographic location information into a vector that corresponds to the geographic location.
As admitted by the applicant in the accompanying declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
With respect to the claimed feature "identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector” a person can mentally or using a pen and paper identify social media data corresponding to a geographic location information by mentally or using a pen and paper determining that the social media data is geotagged with one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to a geographic location information vector, wherein the determination is based on the person mentally or using a pen and paper mapping each colloquial place name to a corresponding colloquial place name vector.
As admitted by the applicant in the accompanying declaration, this limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations.
The limitations above, as recited in independent claims 1 and 9, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper and/or using a mathematical calculation, but for recitation of generic computer components.
Therefore, the limitations fall within the “Mental Processes” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Applicant in Page 4 of the Remarks argues that the claimed method improves social media data identification technology by preprocessing colloquial place names, embedding both place names and geographic location information into a shared vector space, performing similarity-based retrieval using vector distance computations, and identifying social media data geotagged with colloquial place names corresponding to a received geographic location.
Applicant in Pages 5-6 of the Remarks further argues that assuming arguendo that claim 1 recites a mental process or mathematical concept, the eligibility rejection should be withdrawn at least because claim 1 as a whole integrates any alleged abstract idea into a practical application by improving the functioning of a computer and social media data identification technology.
Applicant in Pages 6-7 of the Remarks argues that claim 1 improves the functioning of a computer by enabling it to retrieve relevant colloquial place name vectors similar to an input geographic location information vector even without an explicit connection between the two, by reducing the need for geographic information-colloquial place name mappings and instead utilizing vector similarity computations, claim 1 reduces the amount of data to be searched and/or compared, thereby reducing the computational overhead involved in retrieving colloquial place names relevant to input geographic locations, claim 1 recites an improvement to social media data identification technology, which in conventional implementations may fail to efficiently associate colloquial place names with geographic locations, by receiving preprocessed colloquial place names, embedding the preprocessed names into a vector space, and applying vectorization and vector space computation to more efficiently identify social media data corresponding to a received geographic location, and as further explained in the accompanying declaration, these operations improve social media data identification technology by preprocessing colloquial place names, embedding both place names and geographic location information into a shared vector space, and performing similarity-based retrieval using vector distance computations to identify social media data associated with a geographic location.
Applicant in Pages 7-8 of the Remarks argues that as disclosed in the specification, to enact this technical improvement, the claimed invention first receives colloquial place names associated with social media user accounts, the colloquial place names having been preprocessed using at least one of a bloom filter to remove duplicates and a collocation-detection algorithm to identify multi-word phrases, the preprocessed colloquial place names are then transformed into a fixed-dimension vector space using a word-embedding algorithm (e.g., a machine learning-based algorithm as in Desjardins), producing vector representations of each colloquial place name, the resulting colloquial place name vectors are stored in a relational database together with a mapping of each colloquial place name to its corresponding vector, input geographic location information is likewise embedded into a corresponding vector using the word-embedding algorithm and used to query the relational database, the system returns the most similar colloquial place names and identifies social media data geotagged with them as corresponding to the input geographic location information, thus, by combining preprocessed colloquial place names with vector space computation for similarity-based retrieval, the claimed invention improves the efficiency of identifying social media data geotagged with colloquial place names instead of official geographic identifiers.
Applicant in Page 8 of the Remarks argues that the technical solution disclosed in the specification is reflected in the independent claims, for example, as described above, independent claim 1 recites a method for identifying social media data by preprocessing colloquial place names associated with social media user accounts to remove duplicates and identify multi-word phrases, and transforming the preprocessed place names into fixed-dimension vector representations using a word-embedding algorithm, claim 1 further recites storing the resulting vectors and corresponding mappings in a relational database and embedding received geographic location information into a corresponding vector using the word-embedding algorithm, the relational database is then queried using the geographic location information vector to identify colloquial place name vectors that are most similar based on vector-space distance, and social media data geotagged with those colloquial place names is identified as corresponding to the geographic location information.
Applicant in Page 8 of the Remarks argues that by making similarity determinations based on distances between vectors in a vector space generated using a word-embedding algorithm, the claimed method improves the efficiency and accuracy of identifying social media data associated with colloquial place names, thus, the invention is subject matter eligible at least because both the specification and claims demonstrate a specific, technology-focused improvement to the functioning of computer systems for processing, storing, and retrieving data.
Examiner respectfully disagrees.
It is important to note that the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements (MPEP 2106.05(a)).
The features of the claimed invention that applicant argues as representing an improvement to social media data identification technology, such as “embedding both place names and geographic location information into a shared vector space”, “performing similarity-based retrieval using vector distance computations”, and “identifying social media data geotagged with colloquial place names corresponding to a received geographic location” are recited in the transforming, embedding, and identifying steps in claims 1 and 9 which, as shown above, recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps.
The claims do not provide any limitations that are directed to a specific improvement in computer technology because the transforming, embedding, and identifying steps in claims 1 and 9, as argued by the applicant as being directed to a specific improvement in social media data identification technology, are all recited in the claims as limitations that have been identified as abstract ideas.
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, and are recognized as a well understood, routine, and conventional activity within the field of computer functions, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
For example, the claimed limitation “receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts; and a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which involves the argued “preprocessing colloquial place names” feature, recites a step of receiving data, which is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Page 5 of the Remarks further argues that although certain limitations of claim 1 may involve computational techniques, claim 1 does not recite any mathematical concept because no such concepts appear in the claim language itself.
Examiner respectfully disagrees.
Applicant in Page 5 of the Remarks states that as explained in the accompanying declaration of Jeffrey Zarrella, the inventor of the present application, bloom filters, collocation-detection algorithms, vector-embedding operations, and/or vector-space similarity determinations involve systematic algorithmic evaluation of textual inputs, multi-dimensional numerical computations, and/or automated database operations that cannot practically be performed mentally or manually.
The assertion of facts made by the applicant, that the claimed feature involves performing steps of multi-dimensional numerical computations, seems to be an admission that the claimed limitation involves numerical computations, which would cover steps that can be performed using a mathematical calculation, therefore, the limitations also falls within the “Mathematical Concepts” grouping of abstract ideas
For the above reasons, Examiner states that rejection of the current Office action is proper.
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, 4, 8, 9, 12, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1 and 9 respectively recite a method and a system, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claim 9 recite the limitations:
“transforming, using a word-embedding algorithm, the plurality of preprocessed colloquial place names into a vector space comprising a plurality of fixed-dimension real-valued vectors corresponding to the plurality of colloquial place names, wherein distance between the vectors in the vector space corresponds to semantic similarity among the colloquial place names”;
A person can mentally or using a pen and paper transform, using a word-embedding algorithm, already preprocessed information into a vector space comprising a plurality of fixed-dimension real-valued vectors, and the person can mentally or using a pen and paper determine that distances between the vectors in the vector space corresponds to semantic similarity.
The limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations, as admitted by the applicant in the declaration filed on 03/16/26.
“embedding, using the word-embedding algorithm, the geographic location information into a geographic location information vector corresponding to the geographic location”;
A person can mentally or using a pen and paper embed, using a word-embedding algorithm, a geographic location information into a vector that corresponds to the geographic location.
The limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations, as admitted by the applicant in the declaration filed on 03/16/26.
"identifying social media data corresponding to the geographic location information by determining that the social media data is geotagged with the one or more colloquial place names corresponding to the one or more colloquial place name vectors that are most similar to the geographic location information vector, wherein the determination is based on the mapping of each colloquial place name to a corresponding colloquial place name vector”.
A person can mentally or using a pen and paper identify social media data corresponding to a geographic location information by mentally or using a pen and paper determining that the social media data is geotagged with one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to a geographic location information vector, wherein the determination is based on the person mentally or using a pen and paper mapping each colloquial place name to a corresponding colloquial place name vector.
The limitation also recites a concept that falls into the “mathematical concepts” group of abstract idea since it involves numerical computations, as admitted by the applicant in the declaration filed on 03/16/26.
The limitations, as recited above in claim 1 and similarly recited in claim 9, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper and/or using a mathematical calculation, but for recitation of generic computer components.
Therefore, the limitations fall within the “Mental Processes” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 1 and similarly independent claim 9 recite the limitations:
“receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts and; a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space”, which is a step of storing data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving geographic location information corresponding to a geographic location wherein the geographic location information comprises a colloquial place name or a non- colloquial geographic location”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“querying the relational database with the geographic location information vector”, which is a step of querying for data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving, in response to the query, one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to the geographic location information vector”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “at one or more processors of a data-processing device”, “a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts”, “a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, “using a word-embedding algorithm”, and “in a relational database”, in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a system…comprising one or more processors and memory storing one or more programs that when executed by the one or more processors cause the one or more processors to”, a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts”, “a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place name”, “using a word-embedding algorithm” and “in a relational database” in the steps in claim 9 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1 and 9 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 1 and similarly independent claim 9 recite the limitations:
“receiving a plurality of colloquial place names associated with a plurality of social media user accounts wherein the plurality of colloquial place names were preprocessed based on at least one of: a bloom filter configured to identify and remove duplicate colloquial place names associated with each social media user account of the plurality of social media user accounts and; a collocation-detection algorithm configured to identify multi-word phrases forming colloquial place names”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“storing, in a relational database, the plurality of colloquial place name vectors and a mapping of each colloquial place name to a corresponding colloquial place name vector in the vector space”, which is a step of storing data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“receiving geographic location information corresponding to a geographic location wherein the geographic location information comprises a colloquial place name or a non- colloquial geographic location”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“querying the relational database with the geographic location information vector”, which is a step of querying for data, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“receiving, in response to the query, one or more colloquial place names corresponding to one or more colloquial place name vectors that are most similar to the geographic location information vector”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 4 and similarly dependent claim 12 recites additional limitations, such as:
“updating the relational database based on the geographic location information and the one of more colloquial place name vectors that are most similar to the geographic location information vector”, which is a step of updating or storing data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering and manipulation, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)) and electronic recordkeeping (MPEP 2106.05(d)(II)(v)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 and similarly dependent claim 16 recite additional limitations, such as:
“wherein the word-embedding algorithm comprises one of word2vec, GloVe, or FastText”, which amounts to no more than mere instructions to apply an exception using generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (MPEP 2106.05(f)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 4, 8, 12, and 16 are also directed to an abstract idea without significantly more and are not patent eligible.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REZWANUL MAHMOOD whose telephone number is (571)272-5625. The examiner can normally be reached M-F 9-5:30.
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/R.M/Examiner, Art Unit 2159 /MARC S SOMERS/Primary Examiner, Art Unit 2159
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