DETAILED ACTION
This communication is in response to the application filed 6/19/2024. Claims 1-18 are pending in the application.
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 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-9, 11-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-6 fall within the statutory category of a process. Claims 7-12 fall within the statutory category of an apparatus or system. Claims 13-18 fall within the statutory category of an article of manufacture.
Step 2A, Prong One: the claim recites a Judicial Exception.
Claim 1 recites “acquiring a plurality of candidate vector indexes generated in advance, the plurality of candidate vector indexes being generated based on a plurality of candidate field values comprised in a target field of original data;
acquiring a query vector and a filter condition, the filter condition being used for indicating a condition required to be satisfied by a target vector corresponding to the query vector” are mental processes as mental evaluation or judgement of converting unstructured data, e.g., text, speech, etc. into numerical arrays/vectors that in a BRI, said vectors map semantic meaning or features into a high dimensional space, where similar data points are positioned closer together, allows for similarity search, classification and/or analysis, placing words with similar meanings close together which can be performed in a human mind. The human mind can perform the conceptual equivalent of vector database searches and vector indexing. The mind can associate concepts, find similarities based on context or with the aid of pen and paper. Therefore, the steps fall within the mental processes and mathematical concepts groupings of abstract ideas.
Claim 1 further recites
“if the filter condition comprises a target field value required to be satisfied by the target field,
determining a target vector index corresponding to the target field value in the plurality of candidate vector indexes; and
performing query in the target vector index based on the query vector to obtain the target vector”.
When analyzing the claimed method as a whole, the PTAB determined that giving the claim its broadest reasonable interpretation, "[i]f the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed" (quotation omitted). Schulhauser at 10. Thus, as the contingent limitation/if statement above is not satisfied, the determining and performing steps need not be carried out in order for the claimed method to be performed. In addition, under prong 1, the limitations: “if the filter condition…, determining…, performing…” are mental processes, e.g., if a store sells laptops, check said store’s catalog/index to find said products. Querying/searching using one or more conditions is something that humans have routinely done, in the mind or with the aid of pen and paper.
The limitations “An electronic device, comprising: at least one processor; and a memory connected with the at least one processor communicatively; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a vector retrieval method”, “A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a computer to perform a vector retrieval method” are nothing more than “apply it” (the abstract idea) on a computer. The fact of using the ‘vector index’ – a search mechanism is performed using “a processor” and “a memory” does not make it not mentally performable. And thus, the steps fall within the mental processes and mathematical concepts groupings of abstract ideas. See MPEP § 2106.04(a)(2)(III). Per MPEP 2106.05(f), this is the quintessential “apply it” consideration and does not provide integration into a practical application or significantly more.
Even if performing this mentally/manually is time consuming, “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible”. (Citing Alice, 573 U.S. at 224 ("use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept)).
Each claimed step can be performed in the human mind, with or without the use of a physical aid such as pen and paper, and thus the steps fall within the mental processes grouping and mathematical concepts, thus, claim 1 recites an abstract idea. See MPEP § 2106.04(a)(2)(III). Independent claims 7 and 13 recite limitations of commensurate scope. For the reasons stated above for claim 1, claims 7 and 13 also recite mental processes and mathematical concepts groupings of abstract ideas.
Step 2A, Prong Two: exception is not integrated into a practical application.
The steps of acquiring/obtaining generated vector indexes, a query vector, a filter condition and determine a target vector index/location/map to obtain a target vector if the condition met is something that humans have routinely done, in the mind or with the aid of pen and paper. Thus, said steps would render the claim eligible. Nothing provides integration into a practical application.
Step 2B: “Inventive Concept” or “Significantly More”
The claim recites generic computer components “An electronic device, comprising: at least one processor; and a memory connected with the at least one processor communicatively; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a vector retrieval method”, “A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a computer to perform a vector retrieval method” (in claims 7 and 13) performing generic computing functions or generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f). Therefore, the recited generic computing functions or components do not provide significantly more and the claim as a whole does not change the conclusion. Accordingly, the claimed limitations recited above are abstract ideas under mental processes
and the claims 1, 7 and 13 are ineligible.
Claims 2-6 and similar claims 8-12, 14-18 add further limitations which are also directed to an abstract idea. The claims recite steps of “if the filter condition further comprises a condition required to be satisfied by another field where the target vector is located, acquiring a candidate vector matched with the condition required to be satisfied by the other field based on the target vector index; and acquiring the target vector based on similarity between the candidate vector and the query vector”;
“scanning the target vector index based on the condition required to be satisfied by the other field, so as to construct a bitmap index; and performing query in the target vector index based on the bitmap index to obtain the candidate vector”,
“generating one candidate vector index based on the original data corresponding to each candidate field value; or generating one candidate vector index based on the original data corresponding to the plurality of candidate field values”,
“in response to an insert instruction of a new vector, determining a current field value corresponding to the new vector among the plurality of candidate field values, and inserting the new vector into the candidate vector index corresponding to the current field value”,
“if the filter condition does not comprise the target field value required to be satisfied by the target field, performing query in each candidate vector index based on the query vector to obtain a plurality of query results corresponding to the plurality of candidate vector indexes; and merging the plurality of query results to obtain the target vector”.
Said claims can be performed using human mental evaluation or judgement, and fall into the abstract idea of mental processes and mathematical concepts groupings of abstract ideas, similar to the independent claims. Querying/searching using one or more conditions is something that humans have routinely done, in the mind or with the aid of pen and paper. The human mind can perform the conceptual equivalent of vector database searches and vector indexing. The mind can associate concepts, find similarities based on context or with the aid of pen and paper. Therefore, the steps fall within the mental processes and mathematical concepts groupings of abstract ideas. The claims do not impose meaningful limitations on the judicial exception, thus, claims 1-18 are directed to an abstract idea and are not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-2, 4-8, 10-14, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pollard (US 20250158818) in view of Xie et al. (US 20240078234).
As per claims 1, 7, 13, Pollard (US 20250158818) teaches
a vector retrieval method, comprising: acquiring a plurality of candidate vector indexes generated in advance, the plurality of candidate vector indexes being generated based on a plurality of candidate field values comprised in a target field of original data (para. 3: vector indexes can be used to speed queries executed on the vector database; para. 26, 85: establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements; fig. 1: encrypted feature vectors enroll in the vector database/indexes, query the vector database/indexes; fig. 3: item 304: search on existing data);
acquiring a query vector and a filter condition, the filter condition being used for indicating a condition required to be satisfied by a target vector corresponding to the query vector (para. 18: a query on the vector database of encoded identifiers
can return results from multiple entities or multiple matching identifiers; para. 26: employ a vector database and vector indexes that are optimized to store vector data and may include metadata information about each embedding/vector. The vector database can include an interface (e.g., an application programming interface (API)) that abstracts the database operation into traditional database functions (e.g., add, delete, modify, etc.); para. 51: provide a fast query option where the population of enrolled entities is large (e.g., >40, >50, > 100, > 150, etc.));
if the filter condition comprises a target field value required to be satisfied by the target field, determining a target vector index corresponding to the target field value in the plurality of candidate vector indexes; and performing query in the target vector index based on the query vector to obtain the target vector (col. 3: searching or query execution can be run against the stored centroid values and/or can be executed with vector indexes; para. 27: separate databases can be used: one to store the original embeddings used to enroll an entity, and the vector database and/or vector index used to perform matching operations; para. 18: vector databases can be used to store and retrieve the homomorphic encrypted feature vectors. The data itself can be optimized for storing and retrieving, and further vector indexes used for executing more efficient queries against the stored homomorphic encrypted feature vectors. As discussed, vector databases and vector indexes are optimized to manage
vector data. These optimizations can yield architectures that emphasize computational efficiency over precision. In some settings, a query on the vector database of encoded identifiers can return results from multiple entities or multiple matching identifiers); para. 31, 47: the vector database is specifically optimized to perform nearest neighbor searches on stored embeddings responsive to a submitted query vector (e.g., generated from a new embedding)).
Even if Pollard does not explicitly teach performing query in the target vector index based on the query vector to obtain the target vector,
Xie teaches at para. 47: a vector database: a database for storing, retrieving and analyzing vectors, which can be used to provide a service for retrieving a picture using a picture, such as face retrieval, human retrieval, vehicle retrieval, and the like; para. 79: it can be understood that segment is the minimum storage unit of the vector database, and a plurality of Segments are stored as the fourth level node in the corresponding third level node Query Node. After Growing Segment is written to the upper storage capacity limit, Growing Segment is sealed, and some vector indexes are created for Sealed Segment; para. 93: the server merges the plurality of second result sets described above in the Shard Leader based on the first level query task to obtain a target result set…then performs layer aggregation on all the obtained result sets to obtain the target result set, thereby completing query processing.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pollard and the combining/merging of query results to obtain the target vector of Xie in order to obtain more relevant and accurate search results since combining vector/semantic search with text/lexical search allow more context aware retrieval of data.
As per claims 2, 8, 14, Pollard teaches
wherein the performing query in the target vector index based on the query vector to obtain the target vector comprises: if the filter condition further comprises a condition required to be satisfied by another field where the target vector is located, acquiring a candidate vector matched with the condition required to be satisfied by the other field based on the target vector index; acquiring the target vector based on similarity between the candidate vector and the query vector (para. 31-32: vector databases are a specialized database designed to efficiently store and query vector data (e.g., embeddings). Vector databases are configured for optimized storage and querying capabilities for embeddings, addressing the limitations of traditional scalar based databases and can even improve over standalone vector indexes. Similarity measures, such as Cosine Similarity or Euclidean Distance, can be used in comparing vectors and identifying relevant results for queries; para. 48: identify any matches between an incoming embedding and a stored embedding and/or centroid (e.g., via the vector database and similarity matching or approximate nearest neighbor searches, among other options used in vector databases and/or vector indexes); para. 58: the best match can be returned, where the best match is determined based on
distance or closest similarity to the target embedding).
As per claims 4,10, 16, Pollard teaches
generating one candidate vector index based on the original data corresponding to each candidate field value; or generating one candidate vector index based on the original data corresponding to the plurality of candidate field values ().
As per claim 5, 11, 17, Pollard teaches
in response to an insert instruction of a new vector, determining a current field value corresponding to the new vector among the plurality of candidate field values, and inserting the new vector into the candidate vector index corresponding to the current field value (para. 48: if the new embedding is not already enrolled, at 306 no, then process 300 continues at 310 with an operation to insert the communicated embedding into the vector database (e.g., Vertex AI matching engine/vector database)).
As per claims 6, 12, 18, Pollard teaches
if the filter condition does not comprise the target field value required to be satisfied by the target field, performing query in each candidate vector index based on the query vector to obtain a plurality of query results corresponding to the plurality of candidate vector indexes; merging the plurality of query results to obtain the target vector (para. 3-5: the encoded embeddings are stored in a vector database during enrollment, retrieved by querying the vector database, and used to establish a match to an identifier, identity, and/or entity. Searching or query execution can be run against the stored centroid values and/or can be executed with vector indexes; para. 8, 30-31: query output can return similar embeddings/query results associated with multiple identities; para. 55-56: once an embedding has been generated, process 400 can execute a prediction or attempt to match the target
embedding to a stored embedding; para. 60-61: the vector database and/or vector indexes are configured to return a threshold number of embeddings (or centroid values) that are similar to the target embedding. The threshold number of embeddings that are returned is a tunable parameter, and can be varied based on the number of embeddings that are stored in the database. Thus, the query results are matched embeddings).
Even if Pollard does not explicitly teach the limitation merging the plurality of query results to obtain the target vector,
Xie teaches at para. 47: a vector database: a database for storing, retrieving and analyzing vectors, which can be used to provide a service for retrieving a picture using a picture, such as face retrieval, human retrieval, vehicle retrieval, and the like; para. 93: the server merges the plurality of second result sets described above in the Shard Leader based on the first level query task to obtain a target result set…then performs layer aggregation on all the obtained result sets to obtain the target result set, thereby completing query processing.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pollard and the combining/merging of query results to obtain the target vector of Xie in order to obtain more relevant and accurate search results since combining vector/semantic search with text/lexical search allow more context aware retrieval of data.
Claim(s) 3, 9, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pollard (US 20250158818) in view of Xie et al. (US 20240078234) and further in view of Bestgen et al. (US 20090313210).
As per claims 3, 9, 15, Pollard teaches
wherein the acquiring a candidate vector matched with the condition required to be satisfied by the other field based on the target vector index comprises: scanning the target vector index based on the condition required to be satisfied by the other field, so as to construct an index; performing query in the target vector index based on the index to obtain the candidate vector (col. 3: searching or query execution can be run against the stored centroid values and/or can be executed with vector indexes; para. 27: the vector database and/or vector index used to perform matching operations; para. 18: vector databases can be used to store and retrieve the homomorphic encrypted feature vectors. The data itself can be optimized for storing and retrieving, and further vector indexes used for executing more efficient queries against the stored homomorphic encrypted feature vectors. As discussed, vector databases and vector indexes are optimized to manage vector data. These optimizations can yield architectures that emphasize computational efficiency over precision. In some settings, a query on the vector database of encoded identifiers can return results from multiple entities or multiple matching identifiers; para. 56-57: at 406 a linear scan can be executed to compare a new target embedding to every embedding in a local store of embeddings. In other examples, the scan can be made against a centroid value. The local store can be in a variety of formats (e.g., relational database, NoSQL database, SQL database, vector database, among other options)).
Pollard and Xie do not teach bitmap index.
Bestgen teaches at para. 34-35: bitmaps are a special kind of index that work well for data such as gender, which has a small number of distinct values, e.g., Male and Female, but many occurrences of those values, which would happen if, for example, you had gender data for each resident in a city. A database engine may use the vector portion of the EVI to build a dynamic bitmap that contains one bit for each row in the table. If the row satisfies a query selection, the bit is set on. If the row does not satisfy the query selection, the bit is set off; para. 60: If the query had asked that the values be returned, the EMI engine would use the result of the bitmap AND or OR processing to determine which rows satisfy the query, and then probe into the rows as seen in flowchart 200 in fig. 11.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pollard, Xie and the bitmap index of Bestgen in order to allow fast bitwise operations and improve data retrieval speed by using compact binary representations (0s and 1s) which eliminate parsing and allows direct memory loading, reduce memory usage, enable faster data transmission compared to text-based formats.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hudetz et al. (US 20240370479) teaches in fig. 13: retrieve a set of candidate document vectors that are semantically similar to the search vector from a document index of contextualized embeddings for the electronic document. Ghoshal et al. (US 20200125575) teaches at para. 95: input texts therefore may be represented as
weighted vectors of concepts, called interpretation vectors. To speed up semantic
interpretation, an inverted index, which maps each word into a list of concepts in which it appears, may be used. The inverted index also may be used to discard insignificant associations between words and concepts, by removing those concepts whose weights for a given word are below a certain threshold. Dong et al. (US 20240020310) teaches at para. 24: outputting a result obtained by combining the target data and the candidate data based on the assigned rank; para. 28: search target data to which the embedded vector similar to the embedded vector of the target data is associated, as candidate data, by using a search index associating the search target data with the embedded vector of the search target data; para. 40-42.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH BLACK whose telephone number is (571)272-4106. The examiner can normally be reached 9AM-5PM EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LINH BLACK/Examiner, Art Unit 2163
3/10/2025
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163