DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is responsive to the following communication: Original claims filed 06/22/23. This action is made non-final.
3. Claims 1-20 are pending in the case. Claims 8-14 are elected in this case. Claims 8 is an independent claim among the elected claims.
Applicant's election with traverse of 8-14 in the reply filed on 3/30/26 is acknowledged. The traversal is on the ground(s) that the search of the inventive concepts cannot be done in a reasonable manner. This is not found persuasive because the inventive concepts are distinct and the breadth of search would not be reasonable.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 101
4. 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.
Claim 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 8 is a method type claim. Therefore, claims 8-14 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 8, “A method for retrieving information from an object memory, the method comprising: receiving an input object comprising one or more of an embedding or text, wherein the input object is generated by a machine-learning model; retrieving a plurality of stored semantic objects, from the object memory, based on the input object, wherein the object memory is a graph comprising a plurality of nodes, and wherein each node of the plurality of nodes corresponds to a respective stored semantic object of the plurality of stored semantic objects; determining, based on the plurality of semantic objects and the input object, a result; and providing the result as an output.”
Under Step 2A prong 1, “determining, based on the plurality of semantic objects and the input object, a result” discloses an abstract idea -mental process.
Under Step 2A Prong 2, the additional element “receiving an input object comprising one or more of an embedding or text, wherein the input object is generated by a machine-learning model” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g). The additional element “retrieving a plurality of stored semantic objects, from the object memory, based on the input object, wherein the object memory is a graph comprising a plurality of nodes, and wherein each node of the plurality of nodes corresponds to a respective stored semantic object of the plurality of stored semantic objects” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g). The additional element “providing the result as an output” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “receiving an input object comprising one or more of an embedding or text, wherein the input object is generated by a machine-learning model” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) The additional element “retrieving a plurality of stored semantic objects, from the object memory, based on the input object, wherein the object memory is a graph comprising a plurality of nodes, and wherein each node of the plurality of nodes corresponds to a respective stored semantic object of the plurality of stored semantic objects” can be categorized as well understood, routine and conventional activity of “storing and retrieving information in memory” and therefore does not provide significantly more. MPEP 2106.05(d)(iv). The additional element “providing the result as an output” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii).
Regarding claim 9, “wherein each semantic object of the plurality of semantic objects is associated with source data corresponding to the respective content data, wherein the source data comprises one or more of audio files, text files, or image files, and wherein the determining a result comprises: locating the source data; and determining the result based on the input object and the source data..”
Under Step 2A Prong 2, the additional element “wherein each semantic object of the plurality of semantic objects is associated with source data corresponding to the respective content data, wherein the source data comprises one or more of audio files, text files, or image files, and wherein the determining a result comprises: locating the source data; and determining the result based on the input object and the source data..”” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “wherein each semantic object of the plurality of semantic objects is associated with source data corresponding to the respective content data, wherein the source data comprises one or more of audio files, text files, or image files, and wherein the determining a result comprises: locating the source data; and determining the result based on the input object and the source data..” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Regarding claim 10, “wherein the plurality of stored semantic objects each correspond to a different type of content data.”
Under Step 2A Prong 2, the additional element “wherein the plurality of stored semantic objects each correspond to a different type of content data” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “wherein the plurality of stored semantic objects each correspond to a different type of content data” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Regarding claim 11, “determining a respective similarity between the input object and each stored semantic object of the plurality of stored semantic objects; determining an ordered ranking of the one or more similarities or that one or more of the similarities are less than a predetermined threshold; retrieving a subset of stored semantic objects from the plurality of stored semantic objects with similarities to the input object that are less than the predetermined threshold or based on the ordered ranking, thereby retrieving semantic objects that are determined to be related to the input object; and determining the result based on the subset of stored semantic objects and the input object.”
Under Step 2A Prong 2, the additional element “determining a respective similarity between the input object and each stored semantic object of the plurality of stored semantic objects; determining an ordered ranking of the one or more similarities or that one or more of the similarities are less than a predetermined threshold; retrieving a subset of stored semantic objects from the plurality of stored semantic objects with similarities to the input object that are less than the predetermined threshold or based on the ordered ranking, thereby retrieving semantic objects that are determined to be related to the input object; and determining the result based on the subset of stored semantic objects and the input object” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “determining a respective similarity between the input object and each stored semantic object of the plurality of stored semantic objects; determining an ordered ranking of the one or more similarities or that one or more of the similarities are less than a predetermined threshold; retrieving a subset of stored semantic objects from the plurality of stored semantic objects with similarities to the input object that are less than the predetermined threshold or based on the ordered ranking, thereby retrieving semantic objects that are determined to be related to the input object; and determining the result based on the subset of stored semantic objects and the input object” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Regarding claim 12, “wherein the result comprises a plurality of results, wherein one or more of the plurality of results have a respective confidence score and wherein at least one result of the plurality of results is provided as the output, based on the confidence scores.”
Under Step 2A Prong 2, the additional element “wherein the result comprises a plurality of results, wherein one or more of the plurality of results have a respective confidence score and wherein at least one result of the plurality of results is provided as the output, based on the confidence scores can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “wherein the result comprises a plurality of results, wherein one or more of the plurality of results have a respective confidence score and wherein at least one result of the plurality of results is provided as the output, based on the confidence scores can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Regarding claim 13, “further comprising, prior to receiving the input object: receiving user-input; and generating the input object based on the user-input..”
Under Step 2A Prong 2, the additional element “further comprising, prior to receiving the input object: receiving user-input; and generating the input object based on the user-input” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “further comprising, prior to receiving the input object: receiving user-input; and generating the input object based on the user-input” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Regarding claim 14, “wherein at least one node of the plurality of nodes comprises metadata corresponding to source data of content data to which the at least one node corresponds.”
Under Step 2A Prong 2, the additional element “wherein at least one node of the plurality of nodes comprises metadata corresponding to source data of content data to which the at least one node corresponds” can be categorized as insignificant extra solution activity of mere data gathering and therefore does not integrate into a practical application. MPEP 2106.05(g).
Under Step 2B, the additional element “wherein at least one node of the plurality of nodes comprises metadata corresponding to source data of content data to which the at least one node corresponds” can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii) .
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Coutinho (US 20240202449) in view of Du (US 20220262151).
Regarding claim 8, Coutinho discloses a method for retrieving information from an object memory, the method comprising:
receiving an input object comprising one or more of an embedding or text (see FIG. 2A wherein the input is received from a user),
retrieving a plurality of stored semantic objects, from the object memory, based on the input object (see FIG. 2A wherein the input is searched against a database of semantically parsed language),
determining, based on the plurality of semantic objects and the input object, a result (FIG. 2A, provide the executed action and result to the user);
and providing the result as an output (see FIG. 2A, provide the result to the user)
Coutinho does not disclose wherein the input object is generated by a machine-learning model and wherein the object memory is a graph comprising a plurality of nodes, and wherein each node of the plurality of nodes corresponds to a respective stored semantic object of the plurality of stored semantic objects.
However, Du discloses wherein at least in FIG. 8, the attention mechanism-based neural network model creates the semantic information for the input object. Further, Du discloses wherein the determining a target recognition result from the plurality of recognition results based on the feature information of the image and the semantic information of the plurality of recognition results includes: using the feature information of the image and the semantic information of the recognition results as an input of an attention mechanism-based neural network model, and outputting a target label by using an attention mechanism-based neural network algorithm, where the target label indicates a probability that each recognition result is the target recognition result; and determining the target recognition result of the to-be-recognized text from the plurality of recognition results based on a similarity between the target label and each preset label, where each preset label corresponds to a recognition result obtained by using a recognition method (see paragraph 0014).
The combination of Coutinho and Du would have resulted in the semantic searching/ranking of Coutinho to further utilize the machine learning teachings of searching semantic information and finding relevant information. One would have been motivated to have combined the teachings because a user in Coutinho is already involved in the same endeavor to find relevant results and further using Coutinho would have allowed for a more efficient use to find similarity between semantic inputs. As such, the rationale to combine the teachings would have been that the resulting invention would have been predictable to one of ordinary skill in the art.
Regarding claim 9, Coutinho discloses wherein each semantic object of the plurality of semantic objects is associated with source data corresponding to the respective content data, wherein the source data comprises one or more of audio files, text files, or image files, and wherein the determining a result (aid input comprising a string of text that represents a user intent; performing natural language processing on the input to generate an embedding that corresponds to a semantic representation of the string of text; based on the generated embedding, identifying from a group of actions an action associated with the user intent, where each action in the group of actions is associated with a pre-determined embedding in a group of pre-determined embeddings; executing the identified action, where the executed action returns a result associated with the user intent; and providing the result to the user, see paragraph 0004) comprises:
locating the source data (To obtain the result 130 that is most relevant to the user's intent, the predictor 105 queries one of the multiple storages 135 (equivalently referred to as datastores) via the service 110. The service 110 is flexible enough to allow multiple storages 135 to be used as data sources to fetch and find the most relevant result 130. The result 130 is then returned to the predictor 105 which then returns to the user 120, see paragraph 0030); and
determining the result based on the input object and the source data (To obtain the result 130 that is most relevant to the user's intent, the predictor 105 queries one of the multiple storages 135 (equivalently referred to as datastores) via the service 110. The service 110 is flexible enough to allow multiple storages 135 to be used as data sources to fetch and find the most relevant result 130. The result 130 is then returned to the predictor 105 which then returns to the user 120, see paragraph 0030).
Regarding claim 10, Coutinho discloses wherein the plurality of stored semantic objects each correspond to a different type of content data (For example, if the query refers to ID, the system may need to disambiguate between different forms of ID. If the query refers to a city name, the system may need to disambiguate between different cities in different states with the same name, see paragraph 0060).
Regarding claim 11, Coutinho discloses further comprising :
determining a respective similarity between the input object and each stored semantic object of the plurality of stored semantic objects ([0075] For example, in some embodiments, a similarity score threshold may be predetermined, and for a fact to be identified as a potential match, a similarity score may be calculated between the encoded utterance and each available fact (e.g., facts stored in an index). If the similarity score for a particular fact exceeds the threshold, then that particular fact is identified as a potential match for the utterance. If the similarity score does not exceed the threshold, then that particular fact is not identified as a potential match, see paragraph 0075);
determining an ordered ranking of the one or more similarities or that one or more of the similarities are less than a predetermined threshold (the process 250 ranks the facts that were identified as potential matches to the utterance during the semantic search on the encoded utterance. If there is only a single matching fact, the process 250 may omit operation 261 in some embodiments. The top-ranking fact is selected as the correct fact in reply to the utterance. The process 250 then continues to 262, which is described below, as described in reference to FIG. 2C, see paragraph 0077);
retrieving a subset of stored semantic objects from the plurality of stored semantic objects with similarities to the input object that are less than the predetermined threshold or based on the ordered ranking, thereby retrieving semantic objects that are determined to be related to the input object (the process 250 ranks the facts that were identified as potential matches to the utterance during the semantic search on the encoded utterance. If there is only a single matching fact, the process 250 may omit operation 261 in some embodiments. The top-ranking fact is selected as the correct fact in reply to the utterance. The process 250 then continues to 262, which is described below, as described in reference to FIG. 2C, paragraph 0077); and
determining the result based on the subset of stored semantic objects and the input object (the process 250 determines whether the utterance is a third-person query that refers to a different person than the user who issued the utterance. If the process 250 determines that the utterance refers to a third person, then the process 250 continues to 266, which is described below. If the process 250 determines that the utterance does not refer to a third person (e.g., that the utterance refers to the user), then the process 250 continues to 270, which is described below, paragraph 0080).
Regarding claim 12, Coutinho discloses wherein the result comprises a plurality of results, wherein one or more of the plurality of results have a respective confidence score, and wherein at least one result of the plurality of results is provided as the output, based on the confidence scores (For example, in some embodiments, a similarity score threshold may be predetermined, and for a fact to be identified as a potential match, a similarity score may be calculated between the encoded utterance and each available fact (e.g., facts stored in an index). If the similarity score for a particular fact exceeds the threshold, then that particular fact is identified as a potential match for the utterance. If the similarity score does not exceed the threshold, then that particular fact is not identified as a potential match, see paragraph 0075).
Regarding claim 13, Coutinho discloses further comprising, prior to receiving the input object:
receiving user-input (see FIG. 2A, receiving an input); and
generating the input object based on the user-input (generating an action based on the processing that is made from it, FIG. 2A, and also encoding utterance and performing semantic search as seen in FIG. 2B, see also FIG. 5A).
Regarding claim 14, Coutinho discloses wherein at least one node of the plurality of nodes comprises metadata corresponding to source data of content data to which the at least one node corresponds (the input 125 may also include additional information or metadata associated with the user 120. This additional information or metadata may include, but is not limited to, a first-person identifier associated with the user 120, and a role attribute associated with the user 120. For example, the string of text may refer to multiple entities, such as persons, documents, dates, job positions, places and companies. These entities can express different aspects of the fact being asked, like the subject, a modifier, or even the fact itself. In some embodiments, the predictor 105 receives the input 125 from the user 120 in the form of a data structure. For example, the data structure may be a JavaScript Object Notation (JSON) structure. The string of text may be stored as a field within the JSON structure, for example, with additional metadata and information being stored in other fields. In some embodiments, the handler 150 packages the input 125 into a request payload 152 (e.g., in JSON format, see paragraphs 0040-0041)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID E CHOI whose telephone number is (571)270-3780. The examiner can normally be reached on M-F: 7-2, 7-10 (PST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bechtold, Michelle T. can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID E CHOI/Primary Examiner, Art Unit 2148
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