DETAILED ACTION
This communication is in response to the application filed 6/19/2024. Claims 1, 4-7, 10-13, 16-18 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 .
Response to Arguments
Applicant's arguments filed 4/9/2026 have been fully considered but they are not persuasive. Regarding argument 1 on page 7, the independent claims 1, 7, 13 contains if statements. As cited below, 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 statements above are not satisfied, the determining and performing steps need not be carried out in order for the claimed method to be performed.
In response to Applicant's argument 2 on pages 8-9 that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., it is necessary to perform a full-table scan to screen records satisfying the filter condition, and then perform retrieval within these records. The time consumed by the full-table scan is long, and it also has a high proportion in the whole retrieval process, which influences the retrieval efficiency) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. "The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claims above for the convenience of the Applicants. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claims, typically other passages and figures will apply as well.
In addition, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Pollard discloses at para. 47-48: the vector database is specifically optimized to perform nearest neighbor searches on stored embeddings responsive to a submitted query vector. If there is a match to an existing embedding/centroid, at 306 yes, the identifier associated with the existing embedding/centroid is returned and process; para. 59-60: the vector database and/or vector index will return a number of matches (candidate vectors and/or candidate vector indexes) that are similar to the target embedding (embedded search term/input query/target vector).
In relating to the newly amended limitations “which comprises: one bit configured to represent a data state indicating data is present or absent; and a dimension that
is the same as the quantity of data to be scanned”, please see the new combination of references cited below.
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, 4-7, 10-13, 16-18 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, 4-6 fall within the statutory category of a process. Claims 10-12 fall within the statutory category of an apparatus or system. Claims 13, 16-18 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
if the filter condition further comprises…scanning the target vector index…, performing query in the target vector indexes…; acquiring the target vector…”.
When analyzing the claimed method as a whole, the PTAB determined that giving the claim its BRI, "[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 statements above are not satisfied, the determining and scanning or subsequent 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…, scanning…” 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 “A computer-implemented”, “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 the use of physical aids 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 “A computer-implemented”, “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 4-6 and similar claims 10-12, 16-18 add further limitations which are also directed to an abstract idea. The claims recite steps of “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 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, 4-7, 10-13, 16-18 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, 4-5, 7, 10-11, 13, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pollard (US 20250158818) in view of Anderson (US 9449057) and further in view of Bestgen et al. (US 20090313210).
As per claims 1, 7, 13, Pollard (US 20250158818) teaches
a computer-implemented vector retrieval method for a vector data
base storing data of text, speech, image, or video in a vector form, comprising: (para. 3: 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; para. 31-32: use vector indexes alone or in combination with the vector database. Vector databases are a specialized database designed to efficiently store and query vector data (e.g., embeddings); para. 74-75: the activity monitor is configured to identify a voice conference, capture video, images, and/or audio for processing by the identification functions)
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. 47-48: the vector database is specifically optimized to perform nearest neighbor searches on stored embeddings responsive to a submitted query vector. If there is a match to an
existing embedding/centroid, at 306 yes, the identifier associated with the existing embedding/centroid is returned and process; para. 59-60: the vector database and/or vector index will return a number of matches (candidate vectors and/or candidate vector indexes) that are similar to the target embedding (embedded search term/input query/target vector); para. 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.31-32: Vector databases are a specialized database designed to efficiently store and query vector data (e.g., embeddings); para. 48, 51: provide a fast query option where the population of enrolled entities is large (e.g., >40, >50, > 100, > 150, etc.); para. 61-62);
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 (para. 8: generate a target encrypted feature vector from an input of plaintext identifying information, (e.g., during enrollment or prediction) instantiate at least one vector database or vector index configured to process a query on a representation of the target encrypted feature vector and to identify similar representations of respective encrypted feature vectors where present, retrieve an identifier based on the output of the similar lower dimension representations of the respective encrypted feature vectors, and return the identifier; para. 54-57: decrypt a target embedding, decrypt a first embedding or centroid from the local store, compare target embedding to the stored embedding, if a true match is determined, return the PUID associated with the matched embedding in storage, if no match is determined, continue the decrypt and compare operations for each embedding in the local store, if no match is determined for any stored embedding, communicate the target embedding in encrypted form to a remote or cloud-based prediction service; para. 61-62: the original stored embeddings for each of the multiple identities can be retrieved from the database, and compared directly to the target embedding to determine which match is the best/correct (e.g., in terms of distance
evaluation, cosine evaluation, etc.). The verification requires that the stored embedding
be within a threshold distance from the target embedding (e.g., Euclidean distance <0.8 or unknown) to verify a match).
if the filter condition further comprises a condition required to be satisfied by another field where the target vector is located, scanning the target vector index based on the condition required to be satisfied by the other field (para. 3: vector
indexes can be used to speed queries executed on the vector database, and achieve improved computation, reduced query speed. Searching or query execution can be run against the stored centroid values and/or can be executed with vector indexes; para. 51:
provide a fast query option where the population of enrolled entities is large (e.g., >40, >50, > 100, > 150, etc.) In still others combinations of REDIS and classifier networks can be used in conjunction, and availability and efficiency can be used to manage selection between the options);
a dimension that is the same as the quantity of data to be scanned (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; para. 82: in an example, 100 million 512-dimensional vectors are saved in 200 disk files. When index is built for these vectors, the result is 200 additional index files. Metadata solutions can employ OLTP databases to manage these files and associated information (e.g., metadata can include name of the table the file belongs to (table_id), index type of the file (engine_type), file name (file_id), file type (file_type), file size (file_size), number of rows/dimension (row_count) and file creation date (created_on), among other options). Some implementations can include a query scheduler to optimize hardware utilization and improve efficiency);
performing query in the target vector indexes; and acquiring the target vector based on similarity between the candidate vector and the query vector (para. 7-8: query the vector database or vector index to match in a first pass on a respective
centroid value, return at least one similar representation of a plurality of encrypted feature vectors stored in the vector database or vector index, compare the plurality of respective encrypted feature vectors to the prediction encrypted feature vector to determine a best match or verify a match; para. 59-60: the vector database and/or vector index will return a number of matches (candidate vectors and/or candidate vector indexes) that are similar to the target embedding (embedded search term/input query/target vector); para. 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).
Pollard does not explicitly teach, so as to construct a bitmap index which comprises: one bit configured to represent a data state indicating data is present or absent.
Anderson teaches said limitation at col.9:17-30: the data pattern can be a bitmap in which the elements are bits representing states or properties of the fields in records. For example, a data pattern code formulation can include a bitmap having one or more bits set to denote a state or property of a field as follows: 0 corresponds to a field state of “empty” or “absent” and 1 corresponds to a field state of “not empty” and “not absent”. An alternative bitmap can be as follows: 0 corresponds to field state of “empty” (e.g., no value) or “NULL” (e.g., the field has a predetermined “NULL” value such as one or more space characters), 1 corresponds to a field state or property of “absent” (e.g., the field is not present in the record), and 2 can correspond to a field state of “populated” (e.g., the field has a non-NULL value); col. 11:20-29: data formats of the population for the records can be made conditional on the record type. However, in certain other datasets having conditionally mandatory fields (i.e., fields that are mandatory only when certain data conditions are satisfied) there may be an implied heterogeneity with no explicit field holding information about the record type. In such situations, the data pattern code formulation can be used to determine the underlying collection of record formats and to verify the integrity of datasets having conditionally mandatory fields).
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 a bit configured to represent a data state indicating data is present or absent of Anderson in order to effectively monitor if a field is populated or empty which help increase data quality and reliability by identifying if a record is incomplete etc.
Pollard and Anderson do not explicitly teach performing query in the target vector indexes based on the bitmap index to obtain a candidate vector matched with the condition required to be satisfied by the other field based on the target vector index.
Bestgen teaches
performing query in the target vector indexes based on the bitmap index to obtain 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 (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 flow chart 200 in fig. 11. The element retrieved from the value vector is converted from a bitmap representation back to a value by referencing the appropriate map data structure associated with the sub-column (block 218). This process may be repeated for each of the rows matching the query criteria.
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, Anderson and the bitmap index of Bestgen in order to allow fast bitwise operations and improve data retrieval speed by using compact binary representations (Os and 1s) which eliminate parsing and allows direct memory loading, reduce memory usage, enable faster data transmission compared to text-based formats.
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 (para. 3: the encoded embeddings are stored in a vector database during enrollment. Vector indexes are used to speed queries executed on the vector database. Thus, during a search, the system compares the query vector/target vector against the index to narrow down a list of potential matches/candidate vector; para. 7: compare the plurality of respective encrypted feature vectors to the prediction encrypted feature vector (candidate) to determine a best match or verify a match; para. 59-61: the vector database and/or vector index will return a number of matches that are similar to the target embedding. It is realized that while vector databases and/or vector indexes are efficient at identifying and returning query responses, the precision of such approaches is reduced to achieve the efficiency).
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)).
Claim(s) 6, 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pollard (US 20250158818) in view of Anderson (US 9449057) and further in view of Bestgen et al. (US 20090313210) and Xie et al. (US 20240078234).
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).
Pollard, Anderson, Bestgen do 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, Anderson, Bestgen 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 based on the query.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bienert et al. (US 20180121504) teaches at para. 26: a bitmap is created based on a filter condition contained in the query.
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH BLACK whose telephone number is (571)272-4106. The examiner can normally be reached 9AM-5PM EST M-F.
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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.
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/LINH BLACK/Examiner, Art Unit 2163 7/17/2026
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163
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