Weiping Weiping Peng, 48775 Noe St, San Francisco, CA 94114

2011011, May 19, 2011

Nov 16, 2009

12/619510

Fang Fang - Fremont CA, US
Steven Hartman - Hermosa Beach CA, US
Tim Kaiser - Campbell CA, US
Alex Wu - San Francisco CA, US
Weiping Peng - San Jose CA, US

G06Q 30/00

705 1449

An online audience is targeted for advertisement delivery in an advertising exchange system. The method is performed in an advertising exchange system and may include processing a request to delivery at least one message over a network. An audience segment may be stored for a first entity. The audience segment may identify a population of visitors to one or more network locations of the first entity that form a target-group of members based on activity of the visitors at the first entity. The system may retrieve the audience segment for a second entity and the system may deliver a message from the advertising exchange system in response to the request to the target-group of members on behalf of the second entity by using the audience segment to target the target-group of members.

2022024, Aug 4, 2022

Feb 16, 2022

17/651307

- San Francisco CA, US
Steven Tamm - San Francisco CA, US
Orjan N. Kjellberg - Walnut Creek CA, US
Arvind Krishnan - San Francisco CA, US
Benjamin Snyder - Royal Oak MI, US
Luke A. Ball - Berkeley CA, US
Shannon Hale - San Francisco CA, US
Khanh Kim P. Nguyen - Berkeley CA, US
William Gradin - San Francisco CA, US
Anna Bonner Mieritz - Moss Beach CA, US
Beril Guvendik Maples - Oakland CA, US
Gregg Johnson - Orinda CA, US
Andrew Waite - Novato CA, US
Evan Michael Moses - San Francisco CA, US
Weiping Peng - San Jose CA, US
Scott Peter Perket - San Francisco CA, US

G06F 40/143
H04W 4/21
H04W 4/60
H04L 67/10
G06Q 50/00
H04L 67/00
G06F 40/106
G06F 40/14
G06F 3/0481
G06F 3/0482
G06Q 10/00
G06F 3/04842
G06Q 30/00
H04L 67/50

Disclosed are methods, apparatus, systems, and computer readable storage media for interacting with one or more data objects in an online social network. A user interface includes a publisher with a custom action. The custom action is defined in accordance with custom action instructions provided by a first entity via an application programming interface (API). Such custom action instructions may declaratively or programmatically define the data object, the interaction to be performed with the data object, and other attributes associated with the custom action. A user may request to interact with the data object via selection of the custom action by a second entity. Information may be submitted through the publisher to interact with the data object to update a database system associated with the data object. A feed item may be presented for inclusion in the information feed based on the update.

2021031, Oct 14, 2021

Mar 5, 2021

17/249589

- San Francisco CA, US
Steven Tamm - San Francisco CA, US
Orjan N. Kjellberg - Walnut Creek CA, US
Arvind Krishnan - San Francisco CA, US
Benjamin Snyder - Royal Oak MI, US
Luke A. Ball - Berkeley CA, US
Shannon Hale - San Francisco CA, US
Khanh Kim P. Nguyen - Berkeley CA, US
William Gradin - San Francisco CA, US
Anna Bonner Mieritz - Moss Beach CA, US
Beril Guvendik Maples - Oakland CA, US
Gregg Johnson - Orinda CA, US
Andrew Waite - Novato CA, US
Evan Michael Moses - San Francisco CA, US
Weiping Peng - San Jose CA, US
Scott Peter Perket - San Francisco CA, US

G06F 40/143
H04W 4/21
H04W 4/60
H04L 29/08
G06Q 50/00
G06F 40/106
G06F 40/14
G06F 3/0481
G06F 3/0482
G06Q 10/00
G06F 3/0484
G06Q 30/00

Disclosed are methods, apparatus, systems, and computer readable storage media for interacting with one or more data objects in an online social network. A user interface includes a publisher with a custom action. The custom action is defined in accordance with custom action instructions provided by a first entity via an application programming interface (API). Such custom action instructions may declaratively or programmatically define the data object, the interaction to be performed with the data object, and other attributes associated with the custom action. A user may request to interact with the data object via selection of the custom action by a second entity. Information may be submitted through the publisher to interact with the data object to update a database system associated with the data object. A feed item may be presented for inclusion in the information feed based on the update.

2021014, May 20, 2021

Apr 28, 2020

15/929364

- San Francisco CA, US
Weiping Peng - San Francisco CA, US
Na Cheng - Dublin CA, US
Feifei Jiang - San Mateo CA, US
Jacob Nathaniel Huffman - Oakland CA, US
Nandini Suresh Kumar - San Francisco CA, US
Khoa Le - San Francisco CA, US
Christopher Larry - San Francisco CA, US

salesforce.com, Inc. - San Francisco CA

G06F 16/31
H04L 12/58
G06F 16/34
G06F 16/35

Computing systems, database systems, and related methods are provided for recommending values for fields of database objects and dynamically updating a recommended value for a field of a database record in response to updated auxiliary data associated with the database record. One method involves obtaining associated conversational data, segmenting the conversational data, converting each respective segment of conversational data into a numerical representation, generating a combined numerical representation of the conversational data based on the sequence of numerical representations using an aggregation model, generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and autopopulating the field of the case database object with the recommended value.

2021015, May 20, 2021

Nov 16, 2020

17/099083

- San Francisco CA, US
Weiping Peng - San Francisco CA, US
Na Cheng - San Francisco CA, US
Rick Lindstrom - San Francisco CA, US
Zachary Alexander - Snoqualmie WA, US

Salesforce.com, Inc. - San Francisco CA

G06F 40/289
G06F 16/33
G06F 16/31
G06F 16/332

DICTT

2020009, Mar 26, 2020

Sep 21, 2018

16/138662

- San Francisco CA, US
Jayesh Govindarajan - Palo Alto CA, US
Peter White - San Francisco CA, US
Weiping Peng - San Francisco CA, US
Colleen Smith - Mill Valley CA, US
Vishal Shah - Redmond CA, US
Jacob Nathaniel Huffman - Oakland CA, US
Alejandro Gabriel Perez Rodriguez - Redmond WA, US
Edgar Gerardo Velasco - San Francisco CA, US
Na Cheng - Dublin CA, US

G06F 17/27
G06N 3/08
G06N 3/04

A data processing system analyzes a corpus of conversation data received at an interactive conversation service to train a response recommendation model. The response recommendation model generates response vectors based on custom responses and using the trained model and generates a context vector based on received input at the interactive conversation service. The context vector is compared to the set of response vectors to identify a set of recommended responses, which are recommended to an agent conversing with a user using the interactive conversation service.

2020009, Mar 26, 2020

Sep 24, 2018

16/140443

- San Francisco CA, US
Pingping XIU - San Francisco CA, US
Weiping PENG - San Francisco CA, US
Edgar Gerardo VELASCO - San Francisco CA, US
Anjan GOSWAMI - San Francisco CA, US

G06F 17/30

A method is provided for providing a final result set to a user. In some embodiments, the method includes receiving from the user an input question directed to an organization belonging to a particular category. The method includes applying a plurality of rules to the input question, at least one rule being assigned a weight dependent on the particular category to which the organization belongs. The method further includes extracting, based on applying the plurality of rules, multiple collections of keywords and generating a plurality of search queries. Each search query includes a different collection of keywords. The method also includes submitting the plurality of search queries to a database and in response, receiving multiple result sets from the database. The method further includes in response to the input question, providing a final result including a subset of documents included in the multiple result sets to the user.

2020009, Mar 26, 2020

Sep 24, 2018

16/139386

- San Francisco CA, US
Sitaram ASUR - Santa Clara CA, US
Anjan GOSWAMI - Dublin CA, US
Ziwei CHEN - Stockholm, SE
Na CHENG - Dublin CA, US
Suhas SATISH - Fremont CA, US
Jacob Nathaniel HUFFMAN - Oakland CA, US
Peter Francis WHITE - San Francisco CA, US
WeiPing PENG - San Francisco CA, US
Aditya SAKHUJA - Belmont CA, US
Jayesh GOVINDARAJAN - Palo Alto CA, US
Edgar Gerardo VELASCO - San Francisco CA, US

G06F 17/30
G06N 5/00
H04L 29/08

A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.