Obviousness

Obviousness Analysis of US10659682 Under 35 U.S.C. § 103

This analysis identifies combinations of prior art references that would render the claims of US10659682 obvious to a person having ordinary skill in the art (PHOSITA). The references are drawn from the "Prior art" section of the patent itself, and all have priority dates preceding the priority date of US10659682 (2012-10-23). The PHOSITA is considered to have ordinary skill in the art of image processing and camera control systems, with knowledge of various sensors, image analysis algorithms, and user feedback mechanisms.

Core Claim Elements of US10659682 (Embodiment 1)

Embodiment 1, a foundational method claim, encompasses:

1. Computing at least one total quality indicator based on at least two quality indicators.
2. These quality indicators are computed from said captured image and its previous image frames.
3. The purpose is to determine whether the photo quality is acceptable.
4. Providing a corresponding detailed photo quality feedback.

The patent's summary also highlights the inventive aspects of dynamically adjusting the weight of quality indicators based on other quality indicators' values, weights, and confidence levels, and the explicit use of a "confidence level" in the quality assessment.

Combination of Prior Art References

The following combination of prior art references would render the core claims of US10659682 obvious:

Primary Combination: US 20130155474 in view of WO 2006040761 / US 20070195174 and US 20090278958.

References and Their Teachings:

1. US 20130155474: This reference describes a system that provides "feedback prior to the capturing of at least one image" to a user of a mobile device. [cite: a] It teaches that "parameter values may be combined into a group threshold or total overall quality score" which must "exceeds a defined threshold value before the image can be captured by the camera." [cite: a] Furthermore, it details providing "detailed information to assist the user in taking a better quality image," including suggestions like holding the camera "steadier" when blurriness is detected due to motion. [cite: a]

   • Contribution to obviousness: Teaches computing a total quality score from multiple parameters, determining acceptability based on a threshold, and providing real-time feedback and suggestions for improvement before image capture.

2. WO 2006040761 / US 20070195174: This reference discloses a system where an interface module "enables to define the scene dynamics of the captured image and attributes of the captured image scene dynamics include image motion speed and motion speed of the subjects". [cite: b] Crucially, it "further enables setting the captured image attributes relative weight for the compution of the total image grade." [cite: b]

   • Contribution to obviousness: Teaches the concept of dynamically adjusting "relative weight" for different "attributes" (quality indicators) based on "scene dynamics" (e.g., motion speed) when computing a "total image grade" (total quality indicator).

3. US 20090278958: This reference teaches that "The scoring of a current base image may be based on scores which have been given to previously captured base images. In such a manner, redundant calculation may be avoided." [cite: c]

   • Contribution to obviousness: Teaches utilizing information from "previously captured base images" (analogous to previous image frames) to assess the quality of a "current base image."

Motivation to Combine

A PHOSITA, aiming to enhance the real-time photo quality assessment and assistance systems exemplified by US 20130155474, would be motivated to incorporate the teachings of WO 2006040761 / US 20070195174 and US 20090278958 for the following reasons:

1. To improve the accuracy and adaptability of the total quality assessment (from WO 2006040761 / US 20070195174): US 20130155474 already combines multiple parameters into an overall quality score. A PHOSITA would recognize that simply summing or using static weights for these parameters, as some prior art did, might not accurately reflect desired quality across all scenarios. WO 2006040761 / US 20070195174 explicitly teaches defining "scene dynamics" and "setting the captured image attributes relative weight" for the total image grade. [cite: b] It would be an obvious design choice for a PHOSITA to dynamically adjust the importance (weight) of various quality indicators based on the context provided by other indicators or scene attributes (e.g., reducing the weight of an aesthetic score if motion blur is severe, as illustrated in US10659682). This leads to a more intelligent and situation-aware quality assessment.

2. To enhance efficiency and robustness by leveraging temporal data (from US 20090278958): In a real-time system, as described in US 20130155474 (providing "feedback prior to capturing"), continuous image frames are processed. US 20090278958 teaches "scoring of a current base image may be based on scores which have been given to previously captured base images." [cite: c] A PHOSITA would be motivated to integrate this teaching to improve the real-time quality assessment by:

   • Smoothing out fluctuations: Using historical data can stabilize quality readings, making the system less susceptible to momentary sensor noise or algorithm errors.
   • Detecting trends or sudden changes: Analyzing previous frames allows the system to identify if quality is improving or deteriorating, or if a significant event (e.g., rapid subject movement) has occurred.
   • Improving computational efficiency: By avoiding redundant calculations as suggested by US 20090278958, the system can operate faster or with less computational load. [cite: c]

3. To address known issues of unreliability and errors with "confidence levels": US10659682 explicitly notes that prior art QI computation was done "without taking into account the possibility of error" and that "all sensors give out errors." It also highlights that "recognition or pattern algorithms have assumptions that can be related to “probability” of the feature been searched." Given these acknowledged limitations in the art, a PHOSITA, when combining the above systems, would be highly motivated to quantify the reliability of each quality indicator. Developing a "confidence level" (as done in US10659682 through statistical means like normal distributions or cross-referencing sensor data) would be an obvious engineering solution to make the system more robust. This confidence level could then be naturally integrated into the dynamic weighting scheme from WO 2006040761 / US 20070195174 to ensure that unreliable quality indicators have a reduced or negligible impact on the total quality score, thereby preventing the device from taking a picture based on faulty readings.

Obviousness of Specific Embodiments

The combination outlined above also renders other specific embodiments described in US10659682 obvious:

• Embodiment 2 (Automatic capturing when criteria are met): US 20130155474 explicitly states that "Parameter values may be combined into a group threshold or total overall quality score... which exceeds a defined threshold value before the image can be captured by the camera." [cite: a] This directly teaches automatic capture based on a quality criterion.
• Embodiment 3 (Suggestions from a pre-stored table): US 20130155474 discusses "test result messages can suggest that the user hold the camera steadier." [cite: a] Implementing such suggestions using a common software structure like a pre-stored table is an obvious design choice for a PHOSITA.
• Embodiment 7 (Using QI and confidence to change lens/sensor parameters): US 20130155474 provides feedback for "adjusting at least one measured parameter." [cite: a] If QIs are already used to adjust parameters (as in US 20130155474), and confidence levels are integrated (as motivated above), then using these confidence levels to refine or gate the parameter adjustments (e.g., only adjusting if confidence is high) would be an obvious engineering refinement for reliability.
• Embodiment 9 (Blur type detection for object movement): WO 2006040761 / US 20070195174 defines "scene dynamics" including "image motion speed and motion speed of the subjects." [cite: b] Combining this teaching with blur detection (as contemplated by US 20130155474's mention of "motion blur") would naturally lead a PHOSITA to distinguish between device-induced blur and subject-induced blur, and provide specific alerts.
• Embodiment 11 (Evaluating video quality): Given that US10659682's core method already processes "previous image frames" in a continuous stream, extending this frame-by-frame quality assessment to evaluate an entire video by aggregating or averaging the total quality indicators over continuous frames is an obvious application of the disclosed system.

Conclusion:
The combination of US 20130155474, WO 2006040761 / US 20070195174, and US 20090278958, individually and collectively, teaches the fundamental components of US10659682's method for real-time picture quality assessment, including combining multiple quality indicators, using previous frame data, dynamically weighting indicators, determining photo acceptability, and providing feedback. A PHOSITA, seeking to create a more accurate, robust, and intelligent image capture assistance system, would have clear motivations to combine these known prior art elements. The explicit use of "confidence levels" for QIs, while detailed in US10659682, arises as an obvious solution for a PHOSITA to address the known problems of sensor errors and algorithmic unreliability acknowledged in the art.