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 .
DETAILED ACTION
This office action is in response to communication filed on 9/9/2024.
Claims 1-18 are presented for examination.
Claim Rejections - 35 USC § 101
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.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-18, under Step 2A claims 1-18 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites a method for automatically identifying and creating a product catalog, comprising:
Storing.. a plurality of identifiers of radiofrequency (RF) tags,
Receiving.. read data containing a subset of the identifiers detected by a RF identification (RFID) reader; for each of the plurality of identifiers:
generating… a feature vector by combining the read data with contextual data corresponding to the identifier; and
executing.. a reinforcement learning module using the feature vector to select an action predictive of whether the corresponding RF tag is present in the facility;
updating… the stored indicators according to the selected actions; and for each identifier in the subset detected by the RFID reader, applying.. a reward to the reinforcement learning module based on a comparison of the indicator and the updated indicator corresponding to the identifier.
These limitations recite analyzing data, making predictions updating information using mathematical model. Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the mental processes and mathematical concepts, including evaluating input data, applying a learning algorithm and updating stored values based on predicted outcomes, grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements such as RFID tags, RFID reader and reinforcing learning model, feature vector.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). The claimed reinforcement learning module is used only as a tool to perform the abstract ideaof predicting whether an item is present based on data. Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-3, 5, 7-9, recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1. The claims merely specify particular actions, apply positive or negative rewards, enumerate types of contextual data or specify deriving features from historical data. These limitations represent routine data manipulation and result-based functional language and do no add any unconventional technical features sufficient to transform the abstract idea into patent eligible subject matter. Furthermore, claim 4 recites scaling a reward based on elapsed time, in other words adjusting a reward value based on time, which is a known mathematical weighting technique. Claim 6 recites determining whether an item has been returned to the facility which amounts to using additional information to influence a decision outcome which is still part of the abstract idea of evaluating data and making predictions. As such, claims 2-9 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-9 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. This is because claims 2-9 rely on at least similar additional elements as recited in claim 1. That is, the reinforced learning limitations are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Lastly, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, under step 2B, claims 2-9 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 1-9 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 10-18 (system), recite at least substantially similar concepts and elements as recited in claims 1-9 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Furthermore, the mere recitation of generic computing components such as computing device, memory do not remedy the deficiencies because similar logic applied under Step 2A (prong 2) and Step 2B is applicable. As such, claims 10-18 are rejected under at least similar rationale.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Shyamkumar et al. (U.S. Patent No. 9,959,494), in view of Thomas et al. (U.S. Patent Publication No. 2020/0097808).
Regarding claims 1, 10, Shyamkumar teaches:
storing a plurality of identifiers of radiofrequency (RF) tags, (local memory 457 stores data read from tags, or data to be written to tags, such as Electronic Product Codes (EPCs), Tag Identifiers (TIDs) and other data, Col.6 ln 51-59),
for each identifier, an indicator of whether the corresponding RF tag is present in a facility (a represents whether a particular tag is absent from a particular zone, where a=1 indicates tag absence and a=0 indicates tag presence. Further suppose that e represents a tag read event for that particular tag, where e=1 indicates that the tag was read and e=0 indicates that the tag was not read, Col.13 ln 34-45)
receiving read data containing a subset of the identifiers detected by a RF identification (RFID) reader; (the tag tracking system may update one or more counts associated with the presence detection process. For example, the tag tracking system may update a total number of attempted reads or trials (e.g., “N” as described above), a total number of successful trials (e.g., “K” as described above), and/or a number of consecutive failed trials (e.g., “M” as described above), Col.15 ln 50-63).
Shyamkumar substantially teaches the claimed invention, however does not teach for each of the plurality of identifiers:
generating a feature vector by combining the read data with contextual data corresponding to the identifier; and
(ii) executing a reinforcement learning module using the feature vector to select an action predictive of whether the corresponding RF tag is present in the facility; updating the stored indicators according to the selected actions;
and for each identifier in the subset detected by the RFID reader, applying a reward to the reinforcement learning module based on a comparison of the indicator and the updated indicator corresponding to the identifier,
However, Thomas teaches:
i) feature extraction 249 may include image recognition functions to obtain quantitative information from images. Feature extraction 249 may include text analytics for obtaining quantitative data from text files. The extracted quantitative data is stored in feature vectors 222 as context data 220. A feature vector 222 is a data structure that stores context information in a predetermined format that is understood by the agent 210, [21],
ii) an action 111 can then be selected based on the output at the output nodes. Reinforced learning system 100 trains the agent 110 by altering the weights between the nodes in the neural network in order to maximize the rewards 113 achieved based on the actions 111 taken. For example, the agent 110 can be exposed to an environment 120 of training data. The agent 110 can make randomized decisions on which actions 111 to take, can make observations 121 regarding the results of the action 111, and determine rewards 113 resulting from the action 111. The agent 110 employs an error calculation to determine the differences between the achieved rewards 113 and the optimal reward 113. The agent 110 can also use observations 121 to determine effects of actions 111 on the environment 120. The agent 110 can then update the weights between the nodes in the neural network based on the observations 121 and achieved rewards 113 relative to the possible rewards 113. The agent 110 can then continue to take more actions 111, receive more rewards 113, and continue to adjust weights. As training continues, the agent 110 progressively discounts random actions 111, and progressively emphasizes selection of actions 111 based on past rewards 113, [16]..The deep neural network 315 is trained using the state variables as input and the value function calculated using immediate reward and delayed reward, [42]).
Shyamkumar teaches RFID identifiers, intermittent RFID real data and per-tag presence indicators that are updated over time and Thomas teaches feature vector generation, reinforcement learning based action selection and reward application based on comparison. It would have been obvious to one with ordinary skill in the art before the effective filing date, to combine these teachings to arrive at the claimed method wherein reinforcement learning is applied to improve RFID presence determination by selecting actions predictive of presence and updating presence indicators based on reward feedback.
Regarding claims 2, 11, Shyamkumar teaches the action is selected from the group consisting of: retaining a current value of the indicator; setting the indicator to indicate that the RF tag is present in the facility; and setting the indicator to indicate that the RF tag is absent from the facility, (Fig. 14, tab absent, tag present, binary presence, Fig. 12 shows determine tag presence indicator).
Regarding claims 3, 12, Shyamkumar teaches when the selected action predicts that the RF tag is present in the facility, and the stored indicator indicates that the RF tag is present in the facility, applying a positive, (correct presence determination see Fig. 15 which shows generating precision and recall by comparing computed indicators with reference data. Shyamkumar does not teach rewards, however, Thomas teaches positive rewards for correct predictions, [42].
Regarding claims 4, 13, Shyamkumar does not explicitly teach applying the positive reward includes: determining an initial reward value; and scaling the initial reward value according to a period of time elapsed since the receipt of previous read data containing the identifier. However, Thomas teaches s discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor (reward scaling), see abstract.
Regarding claims 5, 14, Shyamkumar teaches applying the reward includes: when the selected action predicts that the RF tag is present in the facility, and the stored indicator indicates that the RF tag is absent from the facility, applying a negative reward, (Fig. 15 shows computing precision/recall (incorrect presence calls).
Regarding claims 6, 15, Shyamkumar teaches: prior to applying the negative reward, determining that an item associated with the RF tag has not been returned to the facility, (the tag observability may be a tag presence probability function or a tag absence probability function, which represent the probability that a particular tag is present or absent, respectively, as a function of a particular variable, Col.16 ln 1-18).
Regarding claims 7, 16, Shyamkumar teaches contextual data includes at least one of: the stored indicator corresponding to the identifier, a location from the read data associated with the identifier, previous read data containing the identifier, (see Fig. 9-11, zones and transitions, Fig. 12 for historical trials).
Regarding claims 8, 17, Shyamkumar teaches generating the feature vector includes: determining whether the identifier is contained in the read data, (the trial that includes an identifier specific to the particular tag. A trial may be considered failed or unsuccessful if during the trial the tag tracking system does not receive any messages that include the identifier, Col.15 ln 42-50.
Regarding claims 9, 18, Shyamkumar teaches generating the feature vector includes at least one of: determining a number of times the identifier has appeared in previous read data; determining a period of time elapsed since the identifier was contained in the previous read data; determining a location associated with the identifier in the previous read data; or identifying, in the previous read data, locations of at least one item related to an item associated with the RF tag, (if the tag tracking system arrives at step 1204 from step 1202, in which a successful trial occurred (i.e., an identifier was received from a particular tag), then the tag tracking system may increment the total number of attempted trials and the total number of successful trials to account for the initial tag detection at step 1202, Col.15 ln 42-60).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MILENA RACIC whose telephone number is (571)270-5933. The examiner can normally be reached M-F 7:30am-4pm EST.
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/MILENA RACIC/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627