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 .
Claims 1-20, filed June 25, 2025, are examined on the merits.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scian et al. (Scian hereafter, US 20080222118 A1) in view of Voskarides et al. (Voskarides hereafter, Query Resolution for Conversational Search with Limited Supervision, 2020).
Claim 1, Scian discloses an apparatus comprising one or more processors and one or more storage devices storing instructions that are operable ([0097], e.g. mobile device 2100), when executed by the one or more processors, to cause the one or more processors to:
receive a service message object via a communication channel of a plurality of communication channels, wherein the service message object defines a feature dataset associated with a service request for an application framework (Abstract, e.g. receiving contact data responsive to the query over the communication network);
query a knowledge base system based on the feature dataset ([0032], e.g. provides queries to and receives data from a contact database 204);
receive a knowledge base data structure from the knowledge base system in response to the query ([0032], e.g. provides queries to and receives data from a contact database 204)…and initiate a resolution action for the service request based on the resolution data object ([0040], e.g. When a contact resolution object 212 is resolved to a particular contact, then a communication application receives the corresponding communication contact data for that contact. For example, if a contact query comprises a search for contacts with the last name "Smith" and the received contact data 214 comprises contact data for three separate contacts with the last name "Smith"--Andy Smith, Betty Smith, and Charlie Smith--then the contact resolution object 212 may be resolved to any one of these three separate contacts. If the contact resolution object 212 is resolved to the first contact, e.g., Andy Smith, then a communication application will receive the corresponding communication contact data for Andy Smith).
However, Scian does not disclose input the knowledge base data structure to a machine learning model trained for reading comprehension to generate a resolution data object associated with a service resolution for the service request.
Voskarides discloses input the knowledge base data structure to a machine learning model trained for reading comprehension to generate a resolution data object associated with a service resolution for the service request (page 4, column 2, e.g. BERT encoder. BERT first represents the input terms with Word-Piece embeddings using a 30K vocabulary. After applying multiple transformer blocks, BERT outputs an encoding for each term. We refer the interested reader to the original paper for a detailed description of BERT [13]).
Claim 2, Scian as modified discloses the machine learning model is a Bidirectional Encoder Representations from Transformers (BERT) language model (Voskarides, page 2, column 1, e.g. QuReTeC (Query Resolution by Term Classification), a query resolution model based on bidirectional transformers [43] – more specifically BERT [13]).
Claim 3, Scian the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
transmit a resolution message object associated with the resolution data object to a user device via the communication channel (Scian, [0085], e.g. the user may then provide an affirmative answer to the query notification 492 to open the e-mail message, resolve the contact resolution object, and send the e-mail message).
Claim 4, Scian the service message object comprises unstructured service message data, and wherein the one or more storage devices store instructions that are operable when executed by the one or more processors, to further cause the one or more processors to:
transform the unstructured service message data into the feature dataset associated with the service request (Scian, [0101], e.g. Analog-to-digital conversion of the received signal allows the DSP to perform more complex communication functions, such as demodulation and decoding. In a similar manner, signals to be transmitted to the network 2119 are processed (e.g., modulated and encoded) by the DSP 2120 and are then provided to the transmitter 2114 for digital to analog conversion, frequency up conversion, filtering, amplification and transmission to the communication network 2119 (or networks) via the antenna 2118).
Claim 5, Scian as modified discloses wherein the knowledge base system comprises a searchable database associated with resolution information, and wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
query the searchable database based on the feature dataset (Scian, [0070], e.g. Upon issuing the search query to the external contact database, the contact resolution object 420 is created and displayed as an entry in the address book. As shown in FIG. 10, the contact resolution object 420 comprises a bracketed string "[Searching: Karen].").
Claim 6, Scian as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
modify one or more parameters of the machine learning model based on the service message object and the resolution action (Voskarides, page 6, column 2, e.g. We index the TREC CAsT collections using Anserini with stopword removal and stemming.10 In the initial retrieval step (section 3.2.1) we retrieve the top 1000 passages using QL with Dirichlet smoothing (we set μ = 2500). We use the default value for the fusion parameter k = 60 [8] in Eq. (1). In the reranking step (section 3.2.2) we use a PyTorch implementation of BERT for retrieval [25].We use the bert-base-uncased pretrained BERT model).
Claim 7, Scian as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
input the service message object to a generative artificial intelligence model to generate resolution information for the service request; and initiate the resolution action for the service request based on the resolution data object and the resolution information (Voskarides, page 6, column 1, e.g. initializes the input embeddings with pretrained GloVe embeddings.8 Given q1:i−1 and qi , it outputs qˆi . It was optimized on the QuAC gold standard resolutions).
Claim 8, Scian as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: input the knowledge base data structure to a generative artificial intelligence model to generate resolution information for the service request; and initiate the resolution action for the service request based on the resolution data object and the resolution information (Voskarides, page 6, column 1, e.g. initializes the input embeddings with pretrained GloVe embeddings.8 Given q1:i−1 and qi , it outputs qˆi . It was optimized on the QuAC gold standard resolutions).
Claim 9, Scian as modified discloses the communication channel corresponds to an email communication channel, a network portal interface communication channel, a user interface widget communication channel, a chat communication channel, or an application programming interface (API) communication channel (Scian, [0098], e.g. PIM may, for example, be operable to organize and manage data items, such as e-mail, calendar events, voice mails, appointments, and task items. The PIM application may also be operable to send and receive data items via the wireless network 2119).
Claims 10-20, Scian as modified discloses a computer implemented method and computer readable medium comprising the same steps as cited above. Therefore, claims 10-20 are rejected for the same citations and rationale as above.
PERTINENT PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tidewell et al. (US 2017/0322959 A1) resolution events are events 350 that are added to event data store 165 when an incident is resolved and/or a conclusion is reached regarding an investigation. A resolution event is similar to a search event in that it may include all of the field values searched in a particular query as well as a time period searched. In one embodiment, the main difference between a resolution event and a search event is that a resolution event is associated with a resolution object and a search object that contributed to resolution of an investigation. Resolution events are indexed in field type tables based on the field values and/or field types that were searched and the time period searched. Accordingly, responses 440 may include resolution events for previous searches that were made for a similar time period and similar field values. Each resolution event may include a reference to an associated search object 454 and/or a reference to an associated resolution object 456. Resolution events and resolution objects 456 are described in greater detail below ([0094]).
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
7/23/2026
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