DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The claim objections regarding to claim 3, 9, and 15 is withdrawn.
The claim rejection related to 35 USC § 112 regarding to claim 1, 7 and 13 is withdrawn.
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 1-2, 7-8, 13-14, 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (Aggarwal) US 20210326393 A1 and Rajkumar et al. (Rajkumar) US 20200311616 A1 in view of Zhou et al. (Zhou) US 20200286112 A1
In regard to claim 1, Aggarwal disclose A computer implemented method for improving a similarity search of an industrial component model, comprising: ([0009]-[0012] [0023]-[0024] optimizing similarity search of a multi-modal graph that includes objects)
obtaining a set of industrial component models, each having associated attributes and a similarity embedding that is an embedding of at least a portion of said associated attributes; ([0024]-[0035] [0042] Fig. 1, 2 obtaining a graph of the existing objects (images, document, etc.) in a dataset which has nodes and connections and tags, a dataset with objects having metadata and tag embeddings, further similar objects labeled may be connected by an edge to a tag node)
receiving a similarity request using a first industrial component model as an input, an output of said similarity request being a first subset of industrial component models selected from the set of industrial component models based on a comparison between similarity embeddings and a first similarity embedding of the first input industrial component model; ([0033]-[0040][0042]-[0046] search similar objects request, search a dataset for objects similar to query object by comparing query embedding of the objects to the embeddings for the objects in the dataset, and output one or more similar objects)
receiving a second subset of industrial component models selected by a user from said first subset of industrial component models based on an interchangeability criteria of the first industrial component model with any industrial component model of said second subset of industrial component models; ([0024] [0035] [0042]-[0049][0051] claims 1-4, choose a similarity search based on user intent, such as search query to selectively identifying connections to the graph identifying objects pair that are sufficiently similar to each other, based on a rule, two objects are sufficiently similar to be connected is a matter of design based on empirical evidence, which is a design choice, the combination search can be used to further search based on a rule identified. Note: please further define the rule using functional description language to help move forward the prosecution, call to discuss if necessary)
But Aggarwal fail to disclose “adding said unique hash code as a similarity data to the associated attributes of each industrial component model of said second subset of industrial component models; and updating the similarity embeddings of the industrial component models of the set of industrial component models based on the said similarity data.”
Rajkumar disclose adding said unique hash code as a similarity data to the associated attributes of each industrial component model of said second subset of industrial component models; ([0025]-[0037] [0082]-[0089][0233]-[0236] adding the embedding based on the similarity of the object classification corresponding to the object classification label to the each robot in the plurality of robots (models))
updating the similarity embeddings of the industrial component models of the set of industrial component models based on the said similarity data. ([0020] [0037]-[0039] [0084][0085] [0108]-[0116] [0160] [0242]-[0247] [0284] re-generating new embeddings of the models based on the generated embeddings)
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 Rajkumar’s method of evaluating robot learning using ML into Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because Rajkumar’s method of evaluating robot learning using ML would help to provide more method to evaluate the quality of the embeddings to Aggarwal’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 evaluating the quality of the embeddings based on similarity would help to facilitate object similarity identification and therefore improve user experience using the system.
But Aggarwal and Rajkumar fail to disclose “generating a unique hash code distinct from the first similarity embedding and based on said second subset of industrial component models;”
Zhou disclose generating a unique hash code distinct from the first similarity embedding and based on said second subset of industrial component models; (Fig. 2, 3, [0029][0030] [0045]-[0059] [0064]-[0066] [0077]-[0086] [0091]-[0092] generate unique heterogeneous hash code different from other embeddings separate out the user behavior data and contextual information and based on the data portion associated with the specific type of contextual information of the models)
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 Zhou’s hashing based user modeling into Rajkumar and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because Zhou’s hashing based user modeling would help to generate more hashing based embedding to Rajkumar and Aggarwal’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 generating more hashing based embedding would help to facilitate object similarity identification and therefore improve user experience using the system.
In regard to claim 2, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to clam 1, the rejection is incorporated herein.
Aggarwal disclose wherein the similarity embeddings are embedded by vectorization of the industrial component model attributes, and by embedding of resulting vectorized data. ([0028][0035]-[0045] [0063]-[0065] embedding by converting the node into feature vectors and produce output by embedding for the query object)
In regard to claim 19, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to claim 1,
But Aggarwal and Zhou fail to disclose “wherein each industrial component model defines at least one of a mechanical part and an electronic part.”
Rajkumar disclose wherein each industrial component model defines at least one of a mechanical part and an electronic part. ([0004]-[0007] [0073]-[0079] component model include robots which each has an arm with actuator to grasp or other move objects)
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 Rajkumar’s method of evaluating robot learning using ML into Zhou and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because Rajkumar’s method of evaluating robot learning using ML would help to provide more method to evaluate the quality of the embeddings to Zhou and Aggarwal’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 evaluating the quality of the embeddings based on similarity would help to facilitate object similarity identification and therefore improve user experience using the system.
In regard to claims 7-8, 20, claims 7-8, 20 are medium claims corresponding to the method claims 1-2, 19 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 19.
In regard to claims 13-14, 21, claims 13-14, 21 are system claims corresponding to the method claims 1-2, 19 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 19.
Claims 3-6, 9-12, 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (Aggarwal) US 20210326393 A1 and Rajkumar et al. (Rajkumar) US 20200311616 A1, Zhou et al. (Zhou) US 20200286112 A1 as applied to claim 1, further in view of DeFelice et al. (DeFelice) US 2019/0236139 A1
In regard to claim 3, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to claim 2, the rejection is incorporated herein.
Aggarwal disclose wherein the embedding is performed by a context sensitive autoencoder. ([0025]-[0032] [0036] [0042]-[0046][0061]-[0063] [0071]-[0074] encode the connections of the query objects to tags in its resulting embeddings) wherein the input of the context sensitive autoencoder is said vectorized data and constitutes the input of the encoder, and the output of the encoder constitutes the similarity embeddings, ([0006]-[0008][0035]-[0037] [0042]-[0046][0061]-[0064] [0071]-[0074] input the vectored data to the layer does the encoding and output with encoded documents-tag connections) the input of the decoder is the similarity embeddings, ([0035]-[0037] [0042]-[0046][0061]-[0064] [0071]-[0074] claim 1 the encoded documents-tag connections of the objects are inputted to the next layer of NN)
But Aggarwal and Rajkumar, Zhou fail to explicitly disclose “the context sensitive autoencoder comprising an encoder and a decoder which are both neural networks, and the output of the decoder is a term frequency-inverse document frequency of said vectorized data, and said encoder and decoder are tuned such that the term frequency-inverse document frequency of said vectorized data best approximates said vectorized data”
DeFelice disclose the context sensitive autoencoder comprising the encoder and a decoder which are both neural networks, ([0007][0008] [0046][0087] with an encoder and a decoder which are NNs) and the output of the decoder is a term frequency-inverse document frequency of said vectorized data, and said encoder and decoder are tuned such that the term frequency-inverse document frequency of said vectorized data best approximates said vectorized data. ([0065]-[0067][0095]-[0100] TF-IDF of the data are outputted and encoder and decoder are adjusted to generate the output matching the input)
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 DeFelice’s output generation into Zhou, Rajkumar and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because DeFelice’s method of output generation using encoder and decoder would help to provide more NN components to Zhou, Rajkumar and Aggarwal’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 output generation using encoder and decoder would help to provide more intuitive ways using machine learning and facilitate similarity identification at the output generation and therefore improve user experience using the system.
In regard to claim 4, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to claim 2, the rejection is incorporated herein.
But Aggarwal and Rajkumar, Zhou fail to explicitly disclose “wherein the embedding is performed by performing a principal component analysis on a concatenation of an L1- normalization of the attributes with a chosen weight multiplied by the L1-normalization of the similarity data.”
DeFelice disclose wherein the embedding is performed by performing a principal component analysis on a concatenation of an L1- normalization of the attributes with a chosen weight multiplied by the L1-normalization of the similarity data. ([0057]-[0066] [0090]- [0092] normalization of the attributes by linking (vectors) with weight multiplied)
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 DeFelice’s output generation into Zhou and Rajkumar and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because DeFelice’s method of output generation using encoder and decoder would help to provide more NN components to Zhou, Rajkumar and Aggarwal’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 output generation using encoder and decoder would help to provide more intuitive ways using machine learning and facilitate similarity identification at the output generation and therefore improve user experience using the system.
In regard to claim 5, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to claim 2, the rejection is incorporated herein.
But Aggarwal and Rajkumar, Zhou fail to explicitly disclose “wherein the vectorization is performed by a doc2vec vectorization of text attributes, and by a vectorization of the similarity data which includes adding a column for each unique hash code, and, for each industrial component model, filling this column with 1 if the industrial component model is associated with this unique hash code, and 0 otherwise.”
DeFelice disclose wherein the vectorization is performed by a doc2vec vectorization of text attributes, and by a vectorization of the similarity data which includes adding a column for each unique hash code, and, for each industrial component model, filling this column with 1 if the industrial component model is associated with this unique hash code, and 0 otherwise. ([0057]-[0066] [0087]-[0092] Fig. 5c, perform document to vector conversion with column with identifiers (names) and row with 0 and 1 representing relevant or not related to the tag)
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 DeFelice’s output generation into Zhou and Rajkumar and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because DeFelice’s method of vectorization of the attributes using encoder and decoder would help to provide more NN components to Zhou and Rajkumar and Aggarwal’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 vectorization of the attributes using encoder and decoder would help to provide more intuitive ways using machine learning and facilitate similarity identification at the output generation and therefore improve user experience using the system.
In regard to claim 6, Aggarwal and Rajkumar, Zhou disclose The computer implemented method for improving the similarity search of the industrial component model according to claim 2, the rejection is incorporated herein.
But Aggarwal and Rajkumar, Zhou fail to explicitly disclose “wherein the vectorization is performed by applying a Bidirectional Encoder Representations from Transformers technique to the industrial component models.”
DeFelice disclose wherein the vectorization is performed by applying a Bidirectional Encoder Representations from Transformers technique to the industrial component models. (Fig. 2, [0047][0048] [0055] [0064] [0087]-[0092] statements are transformed to a bag of words representations by applying softmax transformation, encoder with forward/backward RNN encoder)
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 DeFelice’s output generation into Zhou and Rajkumar and Aggarwal’s invention as they are related to the same field endeavor of similarity information generation. The motivation to combine these arts, as proposed above, at least because DeFelice’s method of output generation using encoder and decoder would help to provide more NN components to Zhou and Rajkumar and Aggarwal’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 output generation using encoder and decoder would help to provide more intuitive ways using machine learning and facilitate similarity identification at the output generation and therefore improve user experience using the system.
In regard to claims 9-12, claims 9-12 are medium claims corresponding to the method claims 3-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3-6.
In regard to claims 15-18, claims 15-18 are system claims corresponding to the method claims 3-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3-6.
Response to Arguments
Applicant’s arguments with respect to claims 1-18 filed on 10/30/2025 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20180196796 A1 2018-07-12 Wu
SYSTEMS AND METHODS FOR A MULTIPLE TOPIC CHAT BOT
Wu disclose systems and methods for multiple topic automated chatting are provided. The systems and method provide multiple topic automated (or artificial intelligence) chatting by analyzing user inputs in a conversation to determine a plurality topics, to determine and score features related to the determined topics and different users, and to create a knowledge graph of the determined topics. Based on these determinations, the systems and methods may determine if a reply should be provided and then predict a reply… see abstract.
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 XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143