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 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-9, 10-18, 19-20 are directed to a method, system and computer program product of identifying candidates based on distance function. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 10 and 19 these claims recite
receiving a request for a candidate pair comprising a first entity and a second entity; generating a filtered candidate pool comprising a first number of candidates, the filtered candidate pool comprising a subset of an initial candidate pool comprising a second number of candidates larger than the first number of candidates; selecting a learned distance function from a plurality of distance functions, wherein at least one distance function was predetermined prior to receiving the request and at least one distance function is generated in response to receiving the request; determining a distance measure for each candidate in the filtered candidate pool using the learned distance function; and returning, responsive to receiving the request, a response comprising a top K candidates having a lowest distance measure of the determined distance measures.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0023]-[0031], Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 10, 19 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 10 and 19: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claimed solution is not necessarily rooted in computer technology in order to overcome a problem (DDR v. Hotels.com). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites “receiving…”, “generating…”, “selecting…”, “determining…”, “returning…, etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 11, 20
Claim 2, 11, 20 merely recite other additional elements that selecting a learned distance function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 12
Claim 3, 12 merely recite other additional elements that source and destination embedding space and the learned distance function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 13
Claim 4, 13 merely recite other additional elements that source and destination embedding space and the learned distance function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 14
Claim 5, 14 merely recite other additional elements that define inter-embedding space distance measure which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 15
Claim 6, 15 merely recite other additional elements that source and destination embedding space and the learned distance function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7-8, 16-17
Claim 7-8, 16-17 merely recite other additional elements that selecting model based on the learned distance model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9, 18
Claim 9 merely recite other additional elements that generating the filtered candidate pool which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims 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.
Claims 1-6, 9-15, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (Cheng) US 20240037153 in view of Chang et al. (Chang) US 2023/0092969
In regard to claim 1, Cheng disclose A method comprising: ([0003])
receiving a request for a candidate pair comprising a first entity and a second entity; ([0045]-[0047] [0025]-[0037] the recruiter provide a variety of filtering criteria in a filter section to request for candidate recommendations qualified for the requisitions or related to the recruiters, a candidate pair include a candidate and a recruiter, or a candidate and a requisition, etc. note: there are many possibilities, please further define to help move forward the prosecution,)
generating a filtered candidate pool comprising a first number of candidates, the filtered candidate pool comprising a subset of an initial candidate pool comprising a second number of candidates larger than the first number of candidates; ([0025]-[0037] [0045]-[0048] generate a refined the pool of candidates, the refined pool of candidates are from the initial pool of candidates and can be personalized with subset of candidates)
and at least one distance function is generated in response to receiving the request; ([0040]-[0047] based on the recruiter request, the threshold affinity associated with a threshold distance between the recruiter embedding and a candidate embedding is generated)
determining a distance measure for each candidate in the filtered candidate pool using the learned distance function; ([0007]-[0009] [0025]-[0030] [0039]-[0047] determine an affinity (distance) for each candidate in the refined pool of candidates) and
returning, responsive to receiving the request, a response comprising a top K candidates having a lowest distance measure of the determined distance measures. ([0007]-[0009] [0025]-[0037] [0039]-[0047] candidates that satisfy a threshold ranking can be recommended to the recruiter based on the recruiter request, return the top ranked candidates having the closest in proximity of the affinity distance.)
But Cheng fail to explicitly disclose “selecting a learned distance function from a plurality of distance functions, wherein at least one distance function was predetermined prior to receiving the request;”
Chang disclose selecting a learned distance function from a plurality of distance functions, wherein at least one distance function was predetermined prior to receiving the request; ([0059]-[0069] [0086] [0087] selecting a distance function from distance functions and at least one distance is determined before the request at the training stage of the ML model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 2, Cheng and Chang disclose The method of claim 1,
But Cheng fail to explicitly disclose “wherein selecting a learned distance function from a plurality of distance functions comprises determining a source embedding space for the first entity and a destination embedding space for the second entity.”
Cheng wherein selecting a learned distance function from a plurality of distance functions comprises determining a source embedding space for the first entity and a destination embedding space for the second entity ([0059]-[0069] [0086] [0087] selecting a distance function from distance functions and selecting a embedding space for the data item and a embedding space for the data point in the cluster)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 3, Cheng and Chang disclose The method of claim 2,
But Cheng fail to explicitly disclose “wherein, when the source embedding space and the destination embedding space are a same embedding space, the learned distance function comprises an intra-embedding space distance measure that is predetermined prior to receiving the request.”
Chang disclose wherein, when the source embedding space and the destination embedding space are a same embedding space, the learned distance function comprises an intra-embedding space distance measure that is predetermined prior to receiving the request. ([0059]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are intra-cluster distance for the embedding and the intra-cluster distance for the embedding is determined before the request at the training stage of the ML model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 4, Cheng and Chang disclose The method of claim 2,
But Cheng fail to explicitly disclose “wherein, when the source embedding space and the destination embedding space are different embedding spaces having a known interaction function, the learned distance function comprises an inter-embedding space distance measure that is predetermined prior to receiving the request.”
Chang disclose wherein, when the source embedding space and the destination embedding space are different embedding spaces having a known interaction function, the learned distance function comprises an inter-embedding space distance measure that is predetermined prior to receiving the request. ([0053]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are inter-cluster distance for the embedding and have a equation to calculate the score and the inter-cluster distance for the embedding is determined before the request at the training stage of the ML model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 5, Cheng and Chang disclose The method of claim 4,
But Cheng fail to explicitly disclose “wherein the inter-embedding space distance measure comprises a same interaction function as the known interaction function.”
Chang disclose wherein the inter-embedding space distance measure comprises a same interaction function as the known interaction function. ([0053]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are inter-cluster distance for the embedding and has the same interaction function with the equation to calculate)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 6, Cheng and Chang disclose The method of claim 2,
But Cheng fail to explicitly disclose “wherein, when the source embedding space and the destination embedding space are difference embedding spaces having an unknown interaction function, the learned distance function is determined using one of a single embedding distance function deep learning model and a multiple embedding distance function deep learning model.”
Chang disclose wherein, when the source embedding space and the destination embedding space are difference embedding spaces having an unknown interaction function, the learned distance function is determined using one of a single embedding distance function deep learning model and a multiple embedding distance function deep learning model. ([0049]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are inter-cluster distance for the embedding and the inter-cluster distance for the embedding is selected using one of the ML model deep learning model, classification model, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
In regard to claim 9, Cheng and Chang disclose The method of claim 1,
Cheng disclose wherein generating the filtered candidate pool comprises applying one or more of a rules-based candidate knockout or an approximate nearest neighbor (ANN) search to the initial candidate pool. ([0025]-[0037] [0045]-[0048] generate a refined the pool of candidates by applying rule-based filter from the initial candidate pool)
In regard to claims 10-15, 18, claims 10-18 are system claims corresponding to the method claims 1-6, 9 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-6, 9.
In regard to claims 19-20, claims 19-20 are computer program product claims corresponding to the method claims 1-2 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2.
Claims 7-8, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (Cheng) US 20240037153 and Chang et al. (Chang) US 2023/0092969
as applied to claim 6, further in view of O’Donncha et al. (O’Donncha) US 2024/0112442
In regard to claim 7, Cheng and Chang disclose The method of claim 6,
But Cheng and Chang fail to explicitly disclose “when the source embedding space and the destination embedding space are, respectively, of a single embedding type.”
Chang disclose when the source embedding space and the destination embedding space are, respectively, of a single embedding type. ([0059]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are intra-cluster distance for the embedding)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
But Cheng and Chang fail to explicitly disclose “wherein the single embedding distance function deep learning model is selected to determine the learned distance function model.”
O’Donncha disclose wherein the single embedding distance function deep learning model is selected to determine the learned distance function model. ([0011]-[0013] [0033]-[0048] determine the intra-cluster distance exceed some threshold, select the intra-cluster model. Note: please use functional language to describe the invention to help move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate O’Donncha’s similarity analysis using ML into Chang and Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because O’Donncha’s similarity analysis using ML based on distance would help to provide more classification methods into Chang and Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using distance would help to improve classification and therefore improve user experience using the device.
In regard to claim 8, Cheng and Chang disclose The method of claim 6,
Cheng fail to explicitly disclose “when the source embedding space and the destination embedding space include, respectively, two or more embedding types.”
Chang disclose when the source embedding space and the destination embedding space include, respectively, two or more embedding types. ([0059]-[0069] [0086] [0087] the embedding space for the data item and the embedding space for the data point in the cluster are inter-cluster distance for the embedding)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s incremental ML using embeddings into Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because Chang’s incremental ML using embeddings would help to provide more classification methods into Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using embeddings would help to improve classification and therefore improve user experience using the device.
But Cheng and Chang fail to explicitly disclose “wherein the multiple embedding distance function deep learning model is selected to determine the learned distance function model.”
O’Donncha disclose wherein the multiple embedding distance function deep learning model is selected to determine the learned distance function model. ([0011]-[0013] [0033]-[0048] determine the inter-cluster distance exceed some threshold, select the inter-cluster model. Note: please use functional language to describe the invention to help move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate O’Donncha’s similarity analysis using ML into Chang and Cheng’s invention as they are related to the same field endeavor of ML learning and training. The motivation to combine these arts, as proposed above, at least because O’Donncha’s similarity analysis using ML based on distance would help to provide more classification methods into Chang and Cheng’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing classifications using distance would help to improve classification and therefore improve user experience using the device.
In regard to claims 16-17, claims 16-17 are system claims corresponding to the method claims 7-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 7-8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20210004693 A1 2021-01-07 Joglekar et al.
Real-Time On The Fly Generation Of Feature-Based Label Embeddings Via Machine Learning
Joglekar et al. disclose The present disclosure is directed to systems and methods that include a machine-learned label embedding model that generates feature-based label embeddings for labels in real-time, in furtherance, for example, of selection of labels relative to a particular entity. In particular, one example computing system includes both a machine-learned entity embedding model configured to receive and process entity feature data descriptive of an entity to generate an entity embedding for the entity and a machine-learned label embedding model configured to receive and process first label feature data associated with a first label to generate a first label embedding for the first label… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights