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 .
Response to Amendment
This action is in response to applicant’s arguments and amendments filed 5/12/2026, which are in response to USPTO Office Action mailed 4/29/2026. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 103
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, 8, 11, 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over RAGHAVAN (US PGPUB No. 2024/0354830; Pub. Date: Oct. 24, 2024) in view of de Oliviera et al. (US PGPUB No. 2024/0403934; Pub. Date: Dec. 5, 2024), Jiang et al. (US PGPUB No. 2023/0152994; Pub. Date: May 18, 2023), Liu et al. (US PGPUB No. 2023/0230228; Pub. Date: Jul. 20, 2023) and Lawton et al. (US PGPUB No. 2018/0173756; Pub. Date: Jun. 21, 2018).
Regarding independent claim 1,
RAGHAVAN discloses a computer implemented method for managing data, the computer implemented method comprising: training, by a processor set, a large language model with a training dataset, wherein the large language model is trained to understand, generate, and manipulate human language for natural language processing tasks; See Paragraph [0107], (Disclosing a system for identifying complementary objects from images based on an input. The system may generate a refined list of complementary products. Note [0026] wherein the system may employ a large language model (LLM) to determine complementary objects based on a candidate list of complementary objects.) See Paragraph [0060], (A trained ML Model may be turned such as by tuning an ML model for generating natural language that may be trained on text corpuses and may be fine-tuned by further training using additional text as training data samples to inform the intended use of the ML model. Note [0069] wherein transformer 50 is trained on a text corpus. LLMs may be trained on a large unlabeled corpus to ensure that the model is versatile at a variety of language-based tasks including generative tasks such as generating natural language responses to natural language input, i.e. a computer implemented method for managing data, the computer implemented method comprising: training, by a processor set, a large language model with a training dataset, wherein the large language model is trained to understand, generate, and manipulate human language for natural language processing tasks;)
identifying, by the processor set using the large language model, a set of common entities from the common entities associated with the content in input data from the number of data pairs, wherein the set of common entities are directly related to content of output data from the number of data pairs; See Paragraph [0107], (Disclosing a system for identifying complementary objects from images based on an input. The system may generate a refined list of complementary products. Note [0026] wherein the system may employ a large language model (LLM) to determine complementary objects based on a candidate list of complementary objects.) See Paragraph [0091], (Similarity engine 220 may locate matching records using embeddings related to features of a data object based on an input 218 comprising one or more embeddings. Note [0026] wherein the LLM may be used to identify complementary objects such as objects shown in an image based on a candidate list of complementary objects, i.e. identifying, by the processor set using the large language model, a set of common entities from the common entities associated with the content in input data from the number of data pairs, wherein the set of common entities are directly related to content of output data from the number of data pairs;)
generating, by the processor set using the large language model, a list of items for each common entity from the set of common entities, wherein the generation of the list of items for each common entity is performed by making application programming interface (API) calls to the large language model; See Paragraph [0091], (Similarity engine 220 may match, associate, correlate or otherwise determine similarity between embeddings of an input 218 and objects in object database 222, i.e. generating, by the processor set using the large language model, a list of items for each common entity from the set of common entities (e.g. the refined list generated using an LLM as described in [0026]).) See Paragraph [0076], (A computing system may access a remote language model via a software interface such as an API, i.e. wherein the generation of the list of items for each common entity is performed by making application programming interface (API) calls to the large language model;)
combining, by the processor set, the lists of items and output data in the number of data pairs to generate an index for storing in program cache; See Paragraph [0090], (Complementary object engine 300 may receive one or more inputs 218 and generate and provide an output such as a complementary object collection 234. The inputs 218 are provided to a similarity engine 220 for determining and object associated with said inputs 218.) See Paragraph [0100], (Complementary object cache 212 allows application 202 to store complementary object collections 234 for use during a session or any suitable time requested by application 202.) See Paragraph [0107], (The system generates a refined list of complementary products in response to a user input, i.e. combining, by the processor set, the lists of items and output data in the number of data pairs to generate an index for storing in program cache (e.g. cache 212 stores the refined list of complementary products output by the LLM wherein the complementary products are determined based on inputs).)
RAGHAVAN does not disclose the step of receiving, by a processor set, a number of data pairs, wherein each data pair in the number of data pairs comprises an input data and an output data that is semantically associated to the input data,
annotating, by the processor set using a large language model, content of input data from the number of data pairs based on common entities associated with items in content of input data from the number of data pairs,
de Oliveira discloses the step of receiving, by a processor set, a number of data pairs, wherein each data pair in the number of data pairs comprises an input data and an output data that is semantically associated to the input data, See FIG. 2 & Paragraph [0038], (Disclosing a system for predicting whether a candidate item is contextually compatible with a reference item. FIG. 2 illustrates a process for determining whether a candidate item is contextually compatible with a reference item comprising step 202 of identifying at least one candidate item as being relevant to at least one reference item, i.e. receiving, by a processor set, a number of data pairs (e.g. the candidate item(s) and reference item(s)).) See Paragraph [0047], (Items are compared based on relationship features describing a measure of similarity or relevance of a candidate item to a reference item based on contextual relationships such as topics, i.e., wherein each data pair in the number of data pairs comprises an input data and an output data that is semantically associated to the input data;)
annotating, by the processor set using a large language model, content of input data from the number of data pairs based on common entities associated with items in content of input data from the number of data pairs, See FIG. 2 & Paragraph [0042], (The method of FIG. 2 comprises step 206 of creating a reference item embedding and candidate item embedding using embedding layer 314.) See Paragraph [0045], (The system comprises an embedding layer 314 which utilizes a language model to create embeddings for both the reference item(s) and candidate item(s) which is used to compare relationship feature(s) indicating a relationship between the reference item(s) and candidate item(s), i.e. annotating, by the processor set using a large language model (e.g. by creating embeddings reflecting features of items), content of input data from the number of data pairs based on common entities associated with items in content of input data from the number of data pairs;)
RAGHAVAN and de Oliveira are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN to include the method of predicting candidate items most similar to an input as disclosed by de Oliveria. Paragraph [0083] of de Oliveira discloses that the active sampling strategy for active learning may be deployed to improve the ability of a context-sensitive classifier 118 to perform difficult prediction tasks including eliminating controversial recommendations by selecting the most influential items within sensitive categories.
RAGHAVAN-de Oliveira does not disclose the step wherein the input data and the output data in each data pair have at least one of an open set to closed set relationship and an close set to close set relationship;
Jiang discloses the step wherein the input data and the output data in each data pair have at least one of an open set to closed set relationship and an close set to close set relationship; See Paragraph [0086], (Disclosing a system for memory mapping to enhance data cube performance. The system may generate a relationship table for specifying relationships among attributes that describe a same dimension at different levels of granularity. A relationship table 320 may represent an index between two attributes wherein the index can be in the form of a 1-to-M mapping, where one value for a first attribute (e.g., country) is mapped to M different values for a second attribute (e.g., states within a country). In some cases, when present in the data set, the relationship table 320 can indicate many-to-many relationships, where there may be multiple mappings between sets of values for different attributes, i.e. wherein the input data and the output data in each data pair have at least one of an open set to closed set relationship and an close set to close set relationship;)
RAGHAVAN, de Oliveira and Jiang are analogous art because they are in the same field of endeavor, data storage and retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira to include the method of generating indexes for data attributes as disclosed by Jiang. Paragraph [0035] of Jiang discloses that the system may allow high performance with lower amounts of physical resources, resulting in cost savings without compromising performance when compared to full in-memory implementations. This allows for effective utilization of physical memory and allows servers to be self-healing while simplifying governance.
RAGHAVAN-de Oliveira-Jiang does not disclose the step wherein the annotation generates a table comprising items in the items that are classified under columns that represent each common entity;
Liu discloses the step wherein the annotation generates a table comprising items in the items that are classified under columns that represent each common entity; See Paragraphs [0028] & [0033], (Disclosing a system for processing medical images using reconstructed images generated using a machine learning based reconstruction network. FIG. 2 illustrates a process of generating one or more reconstructed images by extracting informative features from input images 202 and using an embedding tables where common features are embedded in N of K dimensional one-hot vector e. A codebook is applied as a vector quantization of hidden variables of the reconstruction network 210 for learned common features of in-distribution data, i.e. wherein the annotation generates a table comprising items in the items that are classified under columns that represent each common entity (e.g. the embedding table comprising common features).)
While de Oliveira discloses generating embeddings for relationship features, the embeddings are not described as being arranged in a tabular format. The method of Liu is described as utilizing an embedding table comprising common features between an input and stored data.
RAGHAVAN, de Oliveira, Jiang and Liu are analogous art because they are in the same field of endeavor, data validation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira-Jiang to include the method of managing an embedding table of common features for assessing validity of data as disclosed by Liu. Paragraph [0018] of Liu discloses that the process of excluding cases determined to be out-of-distribution significantly improved the performance of the pre-trained lesion detection model. One of ordinary skill in the art would recognize that a detection model may be trained to exclude dissimilar data of any subject.
RAGHAVAN-de Oliveira-Jiang-Liu does not disclose the step of receiving, by the processor set, a new input comprising content similar to content of input data from the number of data pairs;
and returning, by the processor set, a new output for the new input by searching the index stored in program cache without making additional application programming interface (API) calls to the large language model.
Lawton discloses the step of receiving, by the processor set, a new input comprising content similar to content of input data from the number of data pairs; See FIG. 1B & Paragraph [0036], (Disclosing a system for preparing and using a cache of social media post data. FIG. 1B illustrates the continuation of the method of FIG. 1A comprising step 114 of receiving one or more additional query terms, followed by step 116 of searching a cache index using the additional query terms to retrieve a matching index item, i.e. receiving, by the processor set, a new input comprising content similar to content of input data from the number of data pairs;)
and returning, by the processor set, a new output for the new input by searching the index stored in program cache without making additional application programming interface (API) calls to the large language model. See FIG. 1B & Paragraph [0039], (FIG. 1B illustrates the method comprising step 118 of displaying the results of the search, including matching records retrieved from the cache index.) See Paragraph [0105], (The process of querying the index to retrieve individual social media posts reduces API calls to the main database and reduces workflow, i.e. returning, by the processor set, a new output for the new input by searching the index stored in program cache without making additional application programming interface (API) calls to the large language model.)
RAGHAVAN, de Oliveira, Jiang, Liu and Lawton are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira-Jiang-Liu to include the method of retrieving information from a cache index as disclosed by Lawton. Paragraph [0105] of Lawton discloses that the system may retrieve relevant records from an index in order to reduce API calls to the main database which represents an improvement by reducing workflow required to provide requested data to a user.
Regarding dependent claim 4,
As discussed above with claim 1, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton discloses all of the limitations.
de Oliveira further discloses the step wherein output data matches a portion of content in input data in each data pair from the number of data pairs. See Paragraphs [0047] & [0050], (The method labels pairs of items as contextually compatible or non-compatible. The system may determine that a candidate item and reference item have compatible topics based on analysis of a relationship feature indicating a relationship between said items, i.e. wherein output data matches a portion of content in input data in each data pair from the number of data pairs.)
Regarding independent claim 8,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 11,
The claim is analogous to the subject matter of dependent claim 4 directed to a computer system and is rejected under similar rationale.
Regarding independent claim 15,
The claim is analogous to the subject matter of independent claim 1 directed to a computer readable medium and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 4 directed to a computer readable medium and is rejected under similar rationale.
Claim(s) 2, 9, 16 and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over de RAGHAVAN in view of de Oliveira, Jiang, Liu and Lawton as applied to claim 1 above, and further in view of SAKAMOTO et al. (US PGPUB No. 2015/0006539; Pub. Date: Jan. 1, 2015).
Regarding dependent claim 2,
As discussed above with claim 1, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton discloses all of the limitations.
RAGHAVAN-de Oliveira-Jiang-Liu-Lawton does not disclose the step of modifying, by the processor set, each list of items for each common entity based on metadata information associated with the number of data pairs.
SAKAMOTO discloses the step of modifying, by the processor set, each list of items for each common entity based on metadata information associated with the number of data pairs. See Paragraph [0053], (Disclosing a content recommendation system comprising attribute value storage for storing one or more attribute values for each content. Content recommendation system 10 applies two overlapping filters to recommend content to a user comprising song recommendations, wherein the first and second filters are based on first and second metadata.) See Paragraph [0074], (Second filter 23 receives a list of music IDs and determines a degree of similarity between each feature vector for each music ID and a preference vector from a request reception unit 21, i.e. modifying, by the processor set, each list of items for each common entity based on metadata information associated with the number of data pairs.)
RAGHAVAN, de Oliveira, Jiang, Liu, Lawton and SAKAMOTO are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira-Jiang-Liu-Lawton to include the method of filtering content lists as disclosed by SAKAMOTO. Paragraph [0084] of SAKAMOTO discloses that the system may generate curated lists of content using layered filtering based on metadata as described in [0053], which allows the system to provide lists of content having specific attributes.
Regarding dependent claim 9,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer readable medium and is rejected under similar rationale.
Regarding dependent claim 21,
As discussed above with claim 21, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton-SAKAMOTO discloses all of the limitations.
De Oliveira further discloses the step wherein modification of each list of items comprises filtering each list of items by deleting items that are inconsistent with the metadata information associated with the number of data pairs. See Paragraph [0035], (Context-sensitive classifier 118 may filter out candidate items having an incompatible, sensitive contextual relationship with the reference item, i.e. wherein modification of each list of items comprises filtering each list of items by deleting items that are inconsistent with the metadata information associated with the number of data pairs (e.g. the incompatible contextual relationship represents an inconsistency that causes the system to filter out a candidate item ).)
Regarding dependent claim 22,
The claim is analogous to the subject matter of dependent claim 21 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 23,
The claim is analogous to the subject matter of dependent claim 21 directed to a computer readable medium and is rejected under similar rationale.
Claim(s) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over RAGHAVAN in view of de Oliveira, Jiang, Liu and Lawton as applied to claim 1 above, and further in view of Krauthgamer et al. (US PGPUB No. 2009/0113309; Pub. Date: Apr. 30, 2009).
Regarding dependent claim 3,
As discussed above with claim 1, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton discloses all of the limitations.
RAGHAVAN further discloses the step of storing, by the processor set, the selected list with output data in the number of data pairs in the program cache. See Paragraph [0100], (Complementary object cache 212 allows application 202 to store complementary object collections 234 for use during a session or any suitable time requested by application 202.) See paragraph [0107], (The system generates a refined list of complementary products in response to a user input, i.e. storing, by the processor set, the selected list with output data in the number of data pairs in the program cache.).)
The examiner notes that while RAGHAVAN discloses storing object collections 234 in a complementary object cache 212, RAGHAVAN does not disclose selecting a list with a smallest size.
RAGHAVAN-de Oliveira-Jiang-Liu-Lawton does not disclose the step of selecting, by the processor set, a list with smallest size from the lists of items;
Krauthgamer discloses the step of selecting, by the processor set, a list with smallest size from the lists of items; See FIG. 2 & Paragraph [0082], (Disclosing a system for intersecting a group of lists as part of a search process. FIG. 2 illustrates the method of intersecting a group of lists comprising step 212 of selecting a top list comprising a smallest list of a group of items, i.e. selecting, by the processor set, a list with smallest size from the lists of items;)
RAGHAVAN, de Oliveira, Jiang, Liu, Lawton and Krauthgamer are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira-Jiang-Liu-Lawton to include the method of selecting a smallest list for processing as disclosed by Krauthgamer. Paragraph [0016] of Krauthgamer discloses that the use of smaller lists as part of performing a search method allows a system to form smaller intersections early in a binary search tree by intersecting smaller lists.
Regarding dependent claim 10,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer readable medium and is rejected under similar rationale.
Claim(s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over de RAGHAVAN in view of de Oliveira, Jiang, Liu and Lawton as applied to claim 1 above, and further in view of McCormick (US Patent No.: 8,195,712; Date of Patent: Jun. 5, 2012).
Regarding dependent claim 6,
As discussed above with claim 1, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton discloses all of the limitations.
RAGHAVAN-de Oliveira-Jiang-Liu-Lawton does not disclose the step wherein input data and output data in the number of data pairs has closed set to closed set relationship.
McCormick discloses the step wherein input data and output data in the number of data pairs has closed set to closed set relationship. See FIG. 2 & Col. 25, lines 35-41, (Disclosing a system for processing a lattice dataset with a partial order of concepts wherein data elements belong to exactly one associated concept. The system may construct a lattice of data elements for an asserted concept which comprises a process of pairing data attributes. An example is included wherein a lattice is constructed for data relating to cities and states wherein all pairs of cities and states are included where the city uniquely determines the state.)
Paragraph [0054] of Applicant's Specification defines a "close set relationship" as follows: "close set to close set relationship refers to the situation where both input data and output data include fixed values and cannot be changed for representing the same context. For example, input data in data pairs 222 can be states and output data in data pairs 222 can be countries. In this example, both states and countries have fixed values where each state and country can only represent a single geographical area."
The method of McCormick describes "generating pairs of cities and states where the city uniquely determines the state" in Col. 25, lines 35-41 which describes similar data attributes (e.g. cities and states) having similar relationships (e.g. pairs where the city uniquely determines the state).
RAGHAVAN, de Oliveira, Jiang, Liu, Lawton and McCormick are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of de RAGHAVAN-de Oliveira-Jiang-Liu-Lawton to include the method of pairing attributes based on concepts and relationships between attributes as disclosed by McCormick. Col. 7, lines 58-67 of McCormick discloses that the use of lattice representations allows the system to accommodate data in a variety of representations such that one can impose different models on data simultaneously and subsequently pose queries using a particular instance of the lattice data model and obtain a representation of the initial data that is consistent with the particular instance.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer readable medium and is rejected under similar rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over de RAGHAVAN in view of de Oliveira, Jiang, Liu and Lawton as applied to claim 1 above, and further in view of Sullivan (US Patent No.: 11,513,968; Date of Patent; Nov. 29, 2022).
Regarding dependent claim 6,
As discussed above with claim 1, RAGHAVAN-de Oliveira-Jiang-Liu-Lawton discloses all of the limitations.
RAGHAVAN-de Oliveira-Jiang-Liu-Lawton does not disclose the step wherein the lists of items and output data in the number of data pairs are stored in the program cache as key-value pairs.
Sullivan discloses the step wherein the lists of items and output data in the number of data pairs are stored in the program cache as key-value pairs. See FIG. 5C & Col. 8, lines 30-44, (Disclosing a system for maintaining and utilizing a unified cache memory. FIG. 5C illustrates a system comprising coupled cache 150 including a set of coupled cache key-value pairs indicating relationship between keys and values in the coupled keys, i.e. wherein the lists of items and output data in the number of data pairs are stored in the program cache as key-value pairs.)
RAGHAVAN, de Oliveira, Jiang, Liu, Lawton and Sullivan are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of RAGHAVAN-de Oliveira-Jiang-Liu-Lawton to include the method of managing a key-value cache as disclosed by Sullivan. Col. 4, lines 55-64 of Sullivan disclose that the method of managing a unified cache memory may improve the speed of applications, reducing instances of stale data in a cache and allows the system to efficiently utilize data stored in said cache.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 7 directed to a computer system and is rejected under similar rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1, 8 and 15 under 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments modify the scope of the claimed invention and therefore necessitated the new grounds of rejection presented in this Office Action.
Applicant’s remarks and amendments with regard to the rejection of claims 1, 8 and 15 under 35 USC 101 have been considered and are persuasive. The corresponding rejection has been withdrawn.
Applicant’s claims contain at least the following limitations which represent an improvement in the field of computer techniques:
and returning, by the processor set, a new output for the new input by searching the index stored in program cache without making additional application programming interface (API) calls to the large language model.
The claimed invention is directed to an improvement in data retrieval technology by reducing the total number of API calls required to generate an output responsive to a user query. Therefore, claims 1, 8 and 15 are eligible under 35 USC 101.
Applicant’s cancellation of claims 5, 12 and 19 is acknowledged, the corresponding rejections have been withdrawn.
Conclusion
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 Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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